Twin and Family Risk from Environment and Epigenetics (FREE) Studies Reveal Strong

Environmental and Weaker Genetic Cues That Explain High Heritability of Eosinophilic

Esophagitis

A dissertation submitted to the Graduate School of the University of Cincinnati

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the Division of Epidemiology and Biostatistics

of the Department of Environmental Health

of the College of Medicine

2014

Eileen Steinle Alexander M.S.

B.S.N. University of Cincinnati College of Nursing, June 1980

M.S. University of Cincinnati College of Arts and Sciences Department of Biology, June 1993

with thanks to

Committee Co-Chairs: Paul A Succop, Ph.D. and Lisa J. Martin, Ph.D.

Members: Margaret H. Collins, M.D., Vincent A. Mukkada, M.D., Erin N. Haynes, D.Sc.

Abstract

Background: Eosinophilic esophagitis (EoE) is a chronic antigen-driven allergic inflammatory disease, likely involving the interplay of genetic and environmental factors, yet their respective contributions to heritability are unknown. This work was developed to meet the needs of families affected by eosinophilic esophagitis, who asked, “Will my next child have EoE?”

Objectives: 1) recruit two study groups: a nuclear family group and twin registry and their first degree relations, collect data and samples, create database, design, analyze and fund family-based studies, 2) quantify risk associated with genes and environment on familial clustering of EoE, and

3) explore and direct new lines of EoE and family-based research, including epigenetic mechanisms and environmental factors, for EoE and related immunologic conditions. The long- term objective is to mitigate risk for families with underlying genetic susceptibility by reducing exposure effects.

Methods: Family history was obtained from a hospital-based cohort of 914 EoE probands,

(n=2192 first-degree “Nuclear-Family” relatives) and the new international registry of monozygotic and dizygotic twins/triplets (n=63 EoE “Twins” probands). Frequencies, recurrence risk ratios (RRRs), heritability and twin concordance were estimated. Environmental exposures were preliminarily examined. DNA collected from twins was analyzed using the Illumina

450Human Methylation array.

Results: Analysis of the Nuclear-Family–based cohort revealed the rate of EoE, in first-degree relatives of a proband, was 1.8% (unadjusted), 2.3% (sex-adjusted), RRRs ranged from 10-64, depending on family relationship, and were higher in brothers (64.0; p=0.04), fathers (42.9; p=0.004) and males (50.7; p<0.001) compared to sisters, mothers and females, respectively. EoE risk for other siblings was 2.4%. In the Nuclear-Families, combined gene and common

2 environment heritability (hgc ) was 72.0±2.7% (p<0.001). In the Twins, genetic heritability was

14.5±4.0% (p<0.001); common family environment contributed 81.0±4% (p<0.001) to phenotypic variance. Proband-wise concordance in MZ co-twins was 57.9±9.5% compared to

36.4±9.3% in DZ (p=0.11). Greater differences in birth-weight were associated with disease discordance in twin pairs (p=0.01;n=35). Birth season was significantly different in concordant and discordant twin pairs (p=0.03;n=63); specifically, birth in Fall was associated with EoE discordance (p=0.02;n=63). Food allergies (p<0.001;n=97) and penicillin allergies (p=0.17; n=66) were associated with EoE. Breastfeeding (p=0.15;n=59) may reduce risk for EoE.

Epigenetic methylation screen of effect size ≥5% difference between affected and unaffected monozygotic twins revealed 349 sites of interest, including candidate and novel genes.

Conclusions: EoE recurrence risk ratios are increased 10 to 64-fold compared to population prevalence. EoE in relatives is 1.8-2.4%, depending upon relationship and sex. Nuclear-Family heritability appeared to be high (72.0%). However, Twins cohort analysis revealed a powerful role for common environment (81.0%) compared with additive genetic heritability (14.5%). The risk of having a second child with EoE is 2.4%. Common family environment (81.0%) and additive genetic heritability (14.5%) explain familial clustering. Early environmental modification may lessen EoE risks. Importantly, epigenetic methylation associated with EoE suggests novel mechanisms and genes. Familial clustering is largely attributable to common environmental exposures, suggesting that identifying modifiable common family environmental factors, in genetically susceptible individuals and families, particularly in early life, may mitigate risk.

iii

Acknowledgements:

Ever since I nervously gave my first injection at Children’s, I’ve wanted to practice here, in my hometown. Thanks to all who helped my work to become my dream, and my dreams help children.

I thank Dr. Marc Rothenberg and Sean Jameson and the families of the Cincinnati Center for

Eosinophilic Disorders and the Twins with EoE Registry for making me aware of their need for family-based inheritance and risk information to share with families and clinicians. Parents have been so very generous with their time, and sharing their information, in hopes of helping other

EoE families.

Thanks to Dr. Chuck DeBrosse for the serendipitous errant email that sent me down the genomic road.

Special thanks to my committee members, Dr. Paul A. Succop, Dr. Lisa J. Martin, Dr. Margaret

H. Collins, Dr. Vincent A. Mukkada and Dr. Erin N. Haynes for their time and thoughtful guidance. Special thanks to Dr. Succop, who has been an excellent teacher.

I am indebted to Dr. Lisa Martin, who has gone well beyond the duty of mentorship to educate, train and inspire me to be an independent scientist. She must surely have the patience of Job.

Note to Dr. Margaret Collins: everybody says you’re awesome!

Thanks for ongoing support from our Genetics Study Group: Dr. Xue Zhang, Dr. Lili Ding, Hua

He, Valentina Pilipenko, Dr. Brad Kurowski, Dr. Tesfaye Mersha, and, especially, Dr. Leah

Kottyan, who often saves me from re-inventing the wheel.

Importantly, thanks for support from the faculty of the Division of Biostatistics and Epidemiology at Cincinnati Children’s Hospital Medical Center: especially, Drs. Jessica Woo, Heidi Sucharew,

Rick Ittenbach, Jane Khoury, Jareen Meinzen-Derr, Bin Huang and Maurizio Macaluso. I’m

deeply indebted to each of you for the collegial environment, excellent coursework and good natured help with my frequent questions. Thanks to Shannon Hatfield for helping me through the administrative hurdles, even when I panicked. I breathe a sigh of relief when I hit the “10” in “S”

(S comes after R and R stands for research) of the new William Cooper Proctor Research

Pavilion.

This work would not have come to fruition without Alexa Greenler, Tommie Grotjan, Heather

Foote, Mike Eby, and Emily Stucke of the Cincinnati Center for Eosinophilic Disorders. Thank you!

Many thanks for support from Dr. Shuk-mei Ho, Dr. Grace LeMasters and Dr. Dan Woo for great advice and essential financial support from the Center for Environmental Genetics and the

Molecular Epidemiology in Environmental Health National Institutes of Health T32 Fellowship.

Thank you to the members of my Qualifying Exam Committee, Drs. Lisa Martin, M.B. Rao and

Melinda Butsch-Kovasic, who gave generously of their time and expertise so that I might pass that milestone on the path to a new career.

Dean Margaret Hanson of the Graduate School of the University of Cincinnati provided amazing opportunities that increased my understanding of professional development, mentorship and polish.

My gratitude extends to Dr. Karen Conneely, who graciously invited me to study methylation analysis with her at Emory University. Thanks to Dr. Hemant Tiwari, and the Section on

Statistical Genetics at the University of Alabama, Birmingham for their support. Fresh perspectives are so helpful.

Thanks to my new colleagues in Health Services Administration at Xavier University, especially

Dr. Eddie Hooker, for their faith and Ignatian philosophy, cura personalis. Thanks to Drs. Hooker

and Browne for their mentorship and encouragement to teach. Thanks to Dr. Sr. Nancy

Linenkugel for modeling leadership, and for those informative and cheery “Chair-y” emails.

Fond thanks to my longtime colleagues at Deaconess Hospital and the University of Cincinnati:

Sandy Oppelt, Carolyn Fiutem, Marcia Endres, Susan Bennett and the GRRL Genius Club.

Thanks to Nancy Wilson, boss, teacher and risk manager extraordinaire, Dr. Jim Sammarco and

Dr. Tim Ramsey (Organic Chem TA who is now at Novartis, Cambridge MA) for writing winning letters of recommendation way back in 2008. Thanks to Ann Marie Kreft for sparking thoughts of geoclustering Type 1 Diabetes.

With help from Drs. Mark Snyder and Jim Sammarco, who tuned me up and gave me the energy needed to compete and achieve my dreams, I can be of service to our community. Thank you.

Thanks to my pseudo lil’ bro, Dr. Glenn Rinsky, for commiserating and cheering on the path to academic scholarship. Apparently, you won :)

Dear friends, Dr. Lakshmi Sammarco, Shirin Zandvakili and Carol Schwetschenau Wood, Esq., have stayed with me, even when I’ve ignored them or complained too much. We’ll have fun again!

Love to my siblings, the Steinle sisters: Carole, Mary Ann, Nancy, Elizabeth, Joyce and Jo ann, who’ve looked after me my whole life, and our mother, Alberta Helen Schloemer Steinle.

Most importantly, my family, Richard Marshall Alexander, our daughters, Katherine Alyssa,

Jennifer Marshall (Jamie), Sara Lauren and my canine supporters, Sophie, Nana, Dakota

Sioux, Heidi, Brisco and Jodie have been there every step of the way, in vivo or spirit. Jodie teaches all of us the joy and charm of living with some genetic imperfection. They fed me, tolerated my whining, forgave my impatience when I was tired and taught me so much about love and commitment. They each deserve an honorary degree. Jodie would probably eat it.

vii

Special thanks for financial support from:

Campaign Urging Research for Eosinophilic Diseases (CURED);

Food Allergy Research and Education (FARE);

Buckeye Foundation;

2014 University of Cincinnati Research Council Graduate Student Fellowship;

2014 Frank C. Woodside, Dinsmore & Shohl Fellowship/Cincinnati Children’s Hospital Div.

Biostatistics and Epidemiology;

National Institute of General Medical Sciences (NIGMS) 2013 Bursary Award R25GM093044;

NIH 1R25GM093044-01 UAB Section on Statistical Genetics 2013;

NIH 8 UL1-TR000077-04 Center for Clinical and Translational Science and Training, CCTST,

CTSA, NCATS 2012 Just in Time; CCTST REDCap UL1-RR026314-01 NCRR/NIH;

NIEHS P30-ES006096 Center for Environmental Genetics 2011 New Investigator Scholar and

2012 Principal Investigator Mentee/Mentor;

NIH T32-ES10957 2011-2013 Molecular Epidemiology in Children’s Environmental Health

Predoctoral Fellowship in, “the key areas of statistical genetics, epidemiology and molecular genetics.”

viii

Table of contents

Abstract ...... ii

Acknowledgements ...... v

Table of Tables and Figures ...... ix

Chapter 1: Introduction, Specific Aims and Significance ...... 1

Chapter 2: Published Abstract: Sex of Affected Parent is Associated with Familial Risk of EoE .... 5

Chapter 3: Aims 1 and 2: Research Manuscript accepted July 3, 2014 ...... 13

Twin and Family Studies Reveal Strong Environmental and Weaker Genetic Cues

Explaining Heritability of Eosinophilic Esophagitis ...... 13

Introduction ...... 19

Methods ...... 21

Results ...... 26

Discussion ...... 30

Tables ...... 35

Figures ...... 42

Online Supplement ...... 48

Chapter 4: Biases, Limitations and Challenges, unabridged ...... 44

Chapter 5: Future Directions ...... 46

Chapter 6: Conclusions ...... 55

References ...... 60

Appendices:

A: Pilot: Sex and Related Conditions Are Associated With Eosinophilic Esophagitis ...... 70

B: Project Management Documents ...... 82

B1: Nuclear Family Study Design and Project Management Plan ...... 82

B2: Twin Study Design and Project Management Plan ...... 86

B3: Analysis Plan, abridged ...... 91

B4: Aim 1: Recruitment, Data and Sample Collection, REDCap data collection forms...... 101

C: Related Grants and Published Abstracts, Manuscripts ...... 117

C1: Center for Environmental Genetics (CEG) 2012 PI Mentee/Mentor ...... 117

C1a: Published Abstracts ...... 137

C1ai: Twin Shared Environment Increases The Risk of Eosinophilic Esophagitis in Families ...... 137

C1aii: Histology Scoring System (HSS) is Superior to Peak Eosinophil Count (PEC) to Identify

Treated vs Untreated Eosinophilic Esophagitis (EoE) Patients ...... 141

C2a: Letters of Support and Award ...... 142

C3: Letter of Acceptance, J. Allergy & Clinical Immunology ...... 155

D: Comparative Heritability Estimation ...... 156

D1: Mplus Twin SEM Model ...... 162

E: Preliminary Environmental Exposure Associations Determine Domains ...... 163

F: Manuscript in Preparation: Methylation Differences in Discordant Monozygotic Twins.... 173

G: UC Reliance review ...... 206

H: CCHMC Human Subjects Documentation ...... 207

References, Appendices ...... 223

Table of Tables and Figures:

Table 2.1 ...... 9

Figure 2.1...... 10

Table 3.1 ...... 30

Table 3.2 ...... 31

Table 3.3 ...... 32

Table 3.4 ...... 33

Figure 3.1...... 39

Figure 3.2...... 40

Figure 3.3...... 41

Figure 3.4...... 42

Figure 5.1...... 55

Figure 5.2...... 56

Figure A1 ...... 79

Figure A2 ...... 80

Figure A3 ...... 81

Figure B4.1 ...... 114

Figure B4.2 ...... 115

Figure B4.3 ...... 116

xii

Table D1 ...... 158

Table E1 ...... 166

Table E2 ...... 168

Table E3 ...... 170

Table E1 ...... 166

Table E2 ...... 168

Table E3 ...... 170

Table F1 ...... 184

Table F2 ...... 185

Table F3 ...... 194

Table F4 ...... 197

Figure F5 ...... 198

Figure F1 ...... 199

Figure F2 ...... 200

Figure F3 ...... 201

Figure F4 ...... 202

Table F5 ...... 203

Table F6 ...... 204

xiii

Chapter 1: Introduction, Specific Aims and Significance

INTRODUCTION

Eosinophilic esophagitis (EoE) is a debilitating, chronic food and swallowed antigen driven allergic inflammatory disease. Although the prevalence of EoE has increased in both adult(1) and pediatric populations,(2) treatment options are very limited.(3)Further, ~70% of those affected by EoE are male,(2,

4, 5) suggesting sex-specific genomic and epigenomic mechanisms. This project is motivated by parents of children treated at the Cincinnati Center for Eosinophilic Disorders (CCED) who asked, “Will my next child will have EoE?”

EoE is due in part to an underlying genetic susceptiblity, given strong family clustering,(6, 7) high sibling recurrence risk(6) and single nucleotide variants associated with EoE.(8-10) However, associated odds ratios are small, and both persistent and transient dysregulation of gene expression in esophageal epithelium(11, 12) suggests the interaction of genes with environment. Further, our new Twins Registry estimates the concordance rate of EoE in MZ twins at 58%, much lower than expected, and dizygotic

(DZ), or fraternal, twins’ concordance at 36%, much higher than expected. Taken together, these results suggest that environmental factors also contribute to EoE risk in genetically susceptible individuals an families. The additive genetic heritability and relative contributions of genes and environmental factors to

EoE have not been quantified.

Indeed, recent environmental survey data have identified early life and environmental factors associated with increased risk,(13-18) including antibiotic exposure in the first year of life.(19) One such mechanism of action for environmental factors is epigenetic variability.(20, 21) Epigenetic modification such as cytosine methylation of CpG sites can result in altered gene expression that may lead to the development of EoE. However, no studies have addressed whether epigenetic changes are associated with

EoE. 1

Although EoE likely involves genes, epigenetics, and environmental factors, their relative contributions and underlying mechanisms are unknown. Our long term goal is to reduce the rate, and therefore, the risk, of EoE. As a first step, we seek to understand the enrichment of EoE and related conditions in families with an EoE proband. Twin studies are designed to disentangle and quantify the effects of shared genes from common household environment.(22-24) A study of allergic disease in twins calculated total serum

IgE heritability at 61%.(24) Further, a twin study of peanut allergy(25) calculated high heritability of

81.6% by path analysis, with concordance reduced by 89.4% in DZ twins, consistent with a predominantly genetic mode of inheritance. In contrast to peanut allergy, I propose that EoE has a complex non-Mendelian mode of inheritance, mediated by both an underlying genetic susceptibility and exposure to environmental factors.

Study Objectives: I will recruit and develop a new Twins Registry cohort. Using this unique resource, I will provide novel evidence that EoE has a complex mode of inheritance, using both nuclear family and twin designs to differentiate and quantify the effects of shared genes from common household environment.(22, 23) I will show that early life factors are associated with increased risk for developing

EoE. Screening methylation chip array studies of discordant twins will identify epigenetic modifications that may contribute to the underlying etiology of EoE for future confirmatory studies. Although the prevalence of EoE has increased,(1, 2, 4, 26) prevention and management are very limited.(3)

Identification of these mechanisms and exposures will give clinicians and genetically susceptible families tools to mitigate their risk of EoE. The central purpose of this study is to quantify the contributions of genetic and environmental factors to EoE.

Hypotheses: EoE has a complex mode of inheritance, with familial enrichment from both genetic and shared common familial environmental effects. Twin models differentiate additive genetic heritability separately from common family environment. Further, environmental factors potentiate risk and result in 2

sustained methylation differences between discordant twins. Thus the EoE-related methylome signature in genetically identical pairs will differ between affected and unaffected individuals. To test these hypotheses, a cross-sectional study of CCED nuclear families and a twin study design using our newly developed international EoE Twins registry will address the following specific aims:

Specific Aims:

Specific Aim 1: Develop a CCED Nuclear Family group and Twins with EoE Registry: develop a nuclear family group at CCED; recruit an international registry of twins, one of whom has EoE for family-based studies.

Specific Aim 2: Quantify the separate contributions of genes and environmental factors on the frequency and variability of EoE: compare the frequency of EoE in nuclear families and twins to expected rates.

Specific Aim 3: Explore and direct new lines of EoE and family-based research, including epigenetic mechanisms and environmental factors, for EoE and related immunologic conditions

Significance/relevance to environmental health: EoE phenotypic variation is an unquantified complex interaction of genes and environment. Defining risk by sex and family relationship is a crucial first step to understanding EoE. Importantly, parents ask clinicians if their next child will have EoE. In addition to heritable genetic components, our preliminary data suggest a strong environmental component in the pathogenesis of EoE. We will open a new area of preventive research, focused on modifiable environmental factors and the unique methylation pattern in individuals with EoE using family-based quantitative designs, new epigenetic assays and computationally intense tools.

Dissertation Objectives: It didn’t have to be this difficult. Adult graduate students are consumers of professional education in an increasingly free market. Evidence-based leadership and education tools are well-supported in the literature. Briefly, andragogy, vis-a-vis pedagogy, is a problem solving approach to adult learning. According to Knowles, adults are internally motivated and self-directed, bring life experiences and knowledge, are goal-oriented, relevancy-oriented, practical, and like to be respected

(http://www.qotfc.edu.au/resource/?page=65375 http://www.medscape.com/viewarticle/547417_2). Therefore, the central purposes of this dissertation are

1) the partial fulfillment of University requirements for the degree of Doctor of Philosophy to provide

“documentation of this research,” as required in the Graduate School Handbook, 2) acquisition of technical and professional skills needed to support a career as an independent scientist and educator in

2014, including grantsmanship and publication, with supporting documentation in the appendices.

Chapter 2: Sex of Parent is Associated with Familial Risk of EoE

INTRODUCTION

Eosinophilic esophagitis (EoE) is a debilitating, chronic food and swallowed antigen driven allergic inflammatory disease. Although the prevalence of EoE has increased in both adult(1) and pediatric populations,(2) treatment options are very limited.(3, 27) Further, ~70% of those affected by EoE are male,(2, 4, 5) suggesting sex-specific genomic and epigenomic mechanisms. EoE prevalence is 5.5 per

10,000(1, 4, 26, 28, 29) EoE patients have co-occurring atopy, asthma and other gastrointestinal disorders.(30, 31) Older pediatric patients present with food impaction and dysphagia.(30) Asthma, eczema, allergic rhinitis, urticaria and food allergies share inflammatory features that may result in a progressive “atopic march.” (32-36) This is important because, as an epidemiology student naive to the study of immunologically mediated disorders, it is necessary to explore biological plausibility prior to study design to 1) identify possible confounders, 2) consider inclusion criteria that maximizes either sensitivity, specificity, or both, and 3) consider proximal and distal causes of disease for future study designs, as described in Aim 3. For example, do genes and environmental nodes that turn on the “atopic march” precede nodes that determine respiratory versus gastrointestinal diseases, and finally, what are the specific susceptibilities and exposures that determine risk of EoE?

The natural history and variability of related conditions in EoE families (Figure 2.1) is not well characterized. I sought to identify co-morbid conditions that define the EoE phenotype and quantify the risk of developing EoE and related conditions in first degree relatives of affected individuals, stratified by sex. Recurrence risk of both EoE and related gastrointestinal and allergic conditions has not been reported. Related conditions are described in Appendix A.

METHODS

Subject Identification and Eligibility

Previsit parent questionnaire with physician confirmation was conducted for family relations and their comorbid conditions, i.e., allergic rhinitis, asthma, eczema, food allergies, urticaria, EoE, other eosinophilic gastrointestinal disorders (EGID), food impaction, esophageal dilation. Affected or nonaffected status was recorded for gastrointestinal (GI) and allergic conditions. GI conditions included

EoE, non-esophageal eosinophilic GI disease (EGID), food impaction and esophageal dilation.

Eosinophilic gastritis, eosinophilic enteritis and eosinophilic colitis were combined to the category

“EGID” by the CCED physician. Confirmed data are recorded in EPIC. Only first degree relations were included, i.e., parents and siblings for our pediatric proband population, who do not yet have children of their own. Sex was available for proband patients and inferred for relations: mother, father, sister, brother.

Exclusion criteria: Proband patients for whom confirmed family history was not available in EPIC.

Statistical Analysis

Exact at p≤0.05. Stratified analysis of co-morbid conditions was by sex and relation. Data were analyzed using SAS 9.2. Recurrence risk ratios (RRR) were calculated as (number affected/total)/prevalence.

RESULTS

Proband patients (n=1059) with EoE were identified at Cincinnati Children’s from our research database;

306 had family history in the medical record. This sample had 1.77 siblings per family compared to the

Ohio mean of 1.87 and the US mean of 1.86. Pedigrees were constructed for 306 families of proband patients. First degree relatives affected with EoE included 3.3% of fathers, 0.4% of mothers, 3.4% of brothers, 2.4% of sisters and 2.9% of siblings overall (Table 2.1). All are significantly increased compared to population prevalence. Related conditions are reported in Appendix A.

DISCUSSION

EoE is a Substantial Disease Burden in Families

Overall, siblings have ~3% risk of EoE. All first degree relatives of EoE probands have a significantly increased frequency of EoE compared to population prevalence. Fathers have the highest rates of EoE and food impaction compared to mothers and other first degree relations. Parents and siblings show distinctly different patterns of comorbid conditions. Siblings report more asthma, eczema and EGID. Half of all first degree relatives of EoE probands have allergic rhinitis, regardless of sex or age. Parents report more food impaction and dilation than siblings.

Further, ~70% of those affected by EoE are male,(2, 4, 5) suggesting sex-specific genomic and epigenomic mechanisms. Reduced rates of EoE in mothers further suggests sex-specific mechanisms, including a possible protective effect from estrogen. Differences, by sex of the parent but not by sex of siblings, need to be confirmed, as this implies specific and testable mechanisms of complex inheritance, such as hormonal mediation and imprinting Studies of sex-based inheritance patterns and family-based quantification of shared environment are warranted.

In summary, clinicians should be aware of a modestly increased risk (~3%) of EoE in siblings of an EoE proband. Symptomatic family members of EoE patients should be evaluated with a high index of suspicion for EoE. Co-morbid variability and relatively rare disorders are associated with complex mechanisms of inheritance.

Table 2.1. Frequency of EoE by sex and relation for first degree relations.

U n Af aff Percen fecte ec te Relations d d t p value Parents 8 430 1.83% Fathers 7 203 3.33% 0.03 ns Mothers 1 227 0.44% Siblings 7 229 2.97% Brothers 4 109 3.54% ns Sisters 3 120 2.44% All 15 659 2.23% Males 11 312 3.41% ns (0.06) Females 4 347 1.14%

Fathers report significantly more EoE than mothers (p = 0.03). The average risk for siblings is ~3%. ns = not significant at p ≤ 0.05. Percent is affected/total x 100.

Figure 2.1. Co-morbid variability in pedigree of EoE research family.

Chapter 3: Twin and Family Studies Reveal Strong Environmental and Weaker Genetic Cues Explaining Heritability of Eosinophilic Esophagitis

Research Manuscript accepted by the Journal of Allergy and Clinical Immunology July 3, 2014, letter Appendix C.

Author: Eileen S. Alexander, MS,a,b,c

Co-authors: Lisa J. Martin, PhD,a,b Margaret H. Collins, MD,a,b Leah Kottyan, PhD,b Heidi Sucharew,

PhD,b Hua He, MS,b Vincent A. Mukkada, MD,a,b Paul A. Succop, PhD,a J. Pablo Abonia, MD,a,b Heather

Foote,b Michael D. Eby, BS,b Tommie M. Grotjan, BS,b Alexandria J. Greenler, BS,b Evan S. Dellon,

MD, MPH,d Jeffrey G. Demain, MD,e Glenn T. Furuta, MD,f Larry E. Gurian, MD, AGAF,g John B.

Harley, MD, PhD,a,b,h Russell J. Hopp, DO,i Ajay Kaul, MD,a,b Kari C. Nadeau, MD, PhD,j,k Richard J.

Noel, MD, PhD,l,m, Philip E. Putnam, MD,a,b Karl F. von Tiehl, MD,n Marc E. Rothenberg, MD, PhDa,b

Affiliations: aUniversity of Cincinnati College of Medicine, Departments of Environmental Health, Pediatrics,

Pathology and Laboratory Medicine, Cincinnati, OH bCincinnati Children's Hospital Medical Center, Divisions of Biostatistics and Epidemiology; Human

Genetics; Pathology; Rheumatology, Center for Autoimmune Genomics and Etiology; Gastroenterology,

Hepatology and Nutrition; Allergy and Immunology, Cincinnati Center for Eosinophilic Disorders,

Cincinnati, OH cXavier University, Health Services Administration, Cincinnati, OH dUniversity of North Carolina School of Medicine, Division of Gastroenterology and Hepatology, Center for Esophageal Diseases and Swallowing, Chapel Hill, NC eDirector, Allergy, Asthma and Immunology Center of Alaska, Anchorage, AK fChildren's Hospital Colorado, Digestive Health Institute, Gastrointestinal Eosinophilic Diseases Program,

University of Colorado School of Medicine, Aurora, CO

gFerrell Duncan Clinic and CoxHealth, Springfield, MO hU.S. Department of Veterans Affairs Medical Center, Cincinnati, OH iDepartment of Pediatrics, Division of Allergy and Immunology, Creighton University, Omaha, NE jStanford Medical School, Stanford, CA kStanford Medical Center and Lucille Packard Children's Hospital, Division of Allergy and Immunology,

Stanford, CA lChildren's Hospital of Wisconsin, Milwaukee, WI mMedical College of Wisconsin, Milwaukee, WI nBowTie Allergy Specialists, Huntington Memorial Hospital, Pasadena, CA

Correspondence: Marc E. Rothenberg, MD, PhD, Cincinnati Children's Hospital Medical Center,

Division of Allergy and Immunology, MLC 7028, 3333 Burnet Avenue, Cincinnati, OH 45229.

E-mail: [email protected]

Supported in part by the: Frank C. Woodside, Dinsmore & Shohl Fellowship through Cincinnati

Children’s Hospital Division of Biostatistics and Epidemiology; National Institutes of Health grants T32-

ES10957 Molecular Epidemiology in Children’s Environmental Health Fellowship 2011-2013; NIEHS

P30-ES006096 Center for Environmental Genetics New Investigator Scholar and PI Mentee/Mentor; NIH

8 UL1-TR000077-04 Center for Clinical and Translational Science and Training, CTSA, NCATS Just in

Time; CCTST REDCap UL1-RR026314-01 NCRR/NIH; 1R25GM093044-01 UAB Section on Statistical

Genetics; NIH-1K24DK100303 (GTF); University of Cincinnati Research Council; Campaign Urging

Research for Eosinophilic Diseases (CURED); Food Allergy Research and Education; Buckeye

Foundation. This work was completed in partial fulfillment of the Doctor of Philosophy degree in

Epidemiology in the Department of Environmental Health, Division of Epidemiology and Biostatistics,

University of Cincinnati College of Medicine.

Abstract

Objective: To quantify risk associated with genes and environment on familial clustering of EoE.

Methods: Family history was obtained from a hospital-based cohort of 914 EoE probands, (n=2192 first- degree “Nuclear-Family” relatives) and the new international registry of monozygotic and dizygotic twins/triplets (n=63 EoE “Twins” probands). Frequencies, recurrence risk ratios (RRRs), heritability and twin concordance were estimated. Environmental exposures were preliminarily examined.

Results: Analysis of the Nuclear-Family–based cohort revealed that the rate of EoE, in first-degree relatives of a proband, was 1.8% (unadjusted) and 2.3% (sex-adjusted). RRRs ranged from 10-64, depending on the family relationship, and were higher in brothers (64.0; p=0.04), fathers (42.9; p=0.004) and males (50.7; p<0.001) compared to sisters, mothers and females, respectively. Risk of EoE for other

2 siblings was 2.4%. In the Nuclear-Families, combined gene and common environment heritability (hgc ) was 72.0±2.7% (p<0.001). In the Twins cohort, genetic heritability was 14.5±4.0% (p<0.001), and common family environment contributed 81.0±4% (p<0.001) to phenotypic variance. Proband-wise concordance in MZ co-twins was 57.9±9.5% compared to 36.4±9.3% in DZ (p=0.11). Greater birth- weight difference between twins (p=0.01), breastfeeding (p=0.15) and Fall birth season (p=0.02) were associated with twin discordance in disease status.

Conclusions: EoE recurrence risk ratios are increased 10-64-fold compared with the general population.

EoE in relatives is 1.8-2.4%, depending upon relationship and sex. Nuclear-Family heritability appeared to be high (72.0%). However, Twins cohort analysis revealed a powerful role for common environment

(81.0%) compared with additive genetic heritability (14.5%).

Clinical Implications: The risk of having a second child with EoE is 2.4%. Common family environment

(81.0%) and additive genetic heritability (14.5%) explain familial clustering. Early environmental modification may lessen EoE risks.

Capsule Summary: Familial clustering is largely attributable to common environmental exposures, suggesting that identifying modifiable common family environmental factors, in genetically susceptible individuals and families, particularly in early life, may mitigate risk.

Key Words: eosinophilia; medical genetics; twins; immune system diseases; heritability; gene- environment interaction; drug hypersensitivity; gastrointestinal diseases; skin diseases

Abbreviations: Eosinophilic esophagitis (EoE), recurrence risk ratio (RRR), narrow-sense additive

2 2 genetic heritability (hag ), combined additive genetic and common environment heritability (hgc ),

Cincinnati Center for Eosinophilic Disorders (CCED), Cincinnati Children’s Hospital Medical Center

(CCHMC), monozygotic (MZ), dizygotic (DZ), eosinophilic gastrointestinal disease (EGID), esophagogastroduodenoscopy (EGD), gastroesophageal reflux disease (GERD), variance components model (VCM)

INTRODUCTION

Eosinophilic esophagitis (EoE) is a debilitating, chronic allergic inflammatory disease of the esophagus triggered by food and ingested antigen sensitization followed by T helper type 2 (Th2) cell adaptive immune responses. Although EoE prevalence has increased in both adult(1, 4, 26, 28) and pediatric(2, 37) populations, strategies for prevention, management and risk mitigation are limited.(3) Research on underlying biologic processes has resulted in new opportunities for treatment, yet risk factors for EoE remain unclear.

One mechanism for high EoE risk is genetic variation. Indeed, Blanchard, et al., estimated an 80-fold increase in sibling recurrence risk, compared to population prevalence, suggesting a strong genetic component.(6) The importance of genetic variants is supported by both candidate gene and genome-wide association studies.(8) Genetic variants in CAPN14, TSLP, TSLPR, CCL26, and FLG have been associated with EoE.(9, 10, 38) However, these variants explain only a small portion of EoE cases, leaving a large portion of the variation unexplained.

There is also substantial evidence that environmental factors influence EoE risk. First and foremost, EoE is an allergic condition responsive to allergen exposure via respiratory, gastrointestinal or cutaneous routes.(17, 39-41) For example, EoE is induced in murine models via respiratory exposure of Aspergillus fumigatus antigens,(17) and molds, including Aspergillus and Penicillium, are associated with eosinophilic asthma.(42) Recently, early environmental exposures, such as antibiotic exposure in the first year of life,(19) have been implicated. Indeed, birth season, climate, seasonality(13, 14, 18, 43, 44) and

Helicobacter pylori exposure(16, 45) modify disease susceptibility. Further, epigenetic regulation(46, 47) may play a role in altered expression(11, 12, 48) associated with EoE. Despite these intriguing findings, the relative roles of genetic and environmental factors in EoE risk are unclear.

The purpose of this study was to estimate the contributions of genes and environment to EoE risk in susceptible families. To accomplish this objective, we used a cohort of nuclear families at the Cincinnati

Center for Eosinophilic Disorders (CCED) at Cincinnati Children’s Hospital Medical Center (CCHMC) and established a new cohort with histologically confirmed EoE in at least one twin/triplet. We estimated

EoE risk as 1.8% in first-degree relatives of probands and 2.4% in siblings of probands. Combined

2 additive genetic and common environment heritability (hgc ) in nuclear families was 72.0±2.7%

(p<0.001). Twin analysis allowed separation of effects of shared genes from common household environment.(49, 50) The majority of phenotypic variance was accounted for by common family

2 environment (81.0%), whereas additive genetic heritability (hag ) accounted for 14.5±4.0%.

Environmental factors of interest include food allergies (p<0.001), twin birth-weight difference (p=0.01),

Fall birth season (p=0.02), penicillin allergy in each twin (p=0.17) and breastfeeding (p=0.15). Therefore, actionable environmental alterations may mitigate risk in susceptible families.

METHODS

To quantify EoE risk due to genes and environment in familial clustering, a retrospective cross-sectional study was conducted using the Nuclear-Family cohort derived from the CCED database and the newly created EoE Twins Registry. The study was performed with CCHMC IRB approval and review by the

University of Cincinnati IRB. Participants or their parent/guardians provided written consent. Children over the age of eleven years provided written assent.

The CCED database was used for the period of August 1, 2008 to April 30, 2013 to identify patients and collect basic demographics, clinical testing and family history. Probands were identified by their CCED physician. Additional history of related medical conditions for first-degree relatives was obtained by parent-report or self-report, using pre-visit questionnaire with subsequent physician confirmation, available in CCHMC’s electronic medical record. Family medical conditions included EoE and other eosinophilic gastrointestinal (GI) diseases (EGID), including eosinophilic gastritis, eosinophilic enteritis and eosinophilic colitis. CCED probands missing physician-confirmed family history were excluded.

Among the 1366 CCED patients seen during this time period, 914 (69%) were included.

Established in 2008, the EoE Twins Registry is an international twin/triplet cohort for EoE and related eosinophilic conditions and was created for this CCHMC study. Recruitment is from physicians specializing in allergy and gastroenterology, centers specializing in EoE, patient and parent EoE interest foundations and twin social networking groups. Initial screening of potential participants was by self/parent report of EoE and EGID. EoE Twins are from the continental United States (n=57), Alaska

(n=2) and Australia (n=4). Information for Twins ˂18 years of age was provided by parent report.

Inclusion and Exclusion Criteria

Eligible participants/parents were asked for reported diagnosis (EoE, other GI conditions, or unaffected).

For all participants that reported EoE, esophagogastroduodenoscopy (EGD) pathology report at diagnosis was reviewed. Pathology slides were requested for all participants with esophageal eosinophils and reviewed by a single pathologist at the CCED (MHC) for the area (0.3 mm2) of greatest intraepithelial eosinophil density. Peak counts were generated (100% of Nuclear-Family; 96% of Twins) to confirm ≥15 eosinophils per high-power field (hpf) at 400X magnification. Slides were requested from an endoscopy performed while the participant was receiving therapy with proton pump inhibitors (PPI) but had not received therapy specifically for EoE, such as steroids and/or diet elimination, as recommended in the

EoE consensus guidelines.(3) PPI administration prior to a positive endoscopy was confirmed in 52% of

Nuclear-Family probands for whom data were available (55%). Affected Twins diagnostic dates ranged from 2001-2012, with 93% diagnosed prior to publication of the current guidelines recommending PPI screening prior to diagnostic endoscopy. Participants with known causes of peripheral blood eosinophilia were excluded. Individuals with reported EoE without confirmatory pathology reports were excluded.

Registry data included demographics (race, ethnicity, sex, age), birth information (gestational age, use of fertility treatments, birth order, birth-weight, birth-length), medical history and family medical history for each family member. Twins were requested to provide a saliva sample for DNA collection; OrageneTM kit

(DNA Genotek, Kanata, Ontario, Canada) was used according to manufacturer’s instructions, with sponges added for children unable to expectorate, typically ≤5 years of age, and prepITTM L2P manual

DNA purification protocol.

Zygosity

Three tools determined zygosity of same-sex twins as monozygotic (MZ) or dizygotic (DZ): 1) genotyping, 2) pea pod questionnaire(51) and 3) parent report. To genetically determine zygosity, we estimated the proportion of identity-by-descent (IBD) sharing between each pair of genotyped individuals and compared it to the proportion expected based on genealogical information.(52) The percentage of 18

identical markers was determined from 94544 high-quality, polymorphic markers, among 196524 variants genotyped by Immunochip(53) (Illumina, San Diego, CA). MZ pairs have identical markers at more than

99% of loci with observed IBD sharing of 0.99-1.0. Analysis was limited to same-sex pairs (n=48) with paired DNA samples available (n=40). For same-sex pairs without paired DNA samples, pea pod questionnaire determined zygosity. Pea pod questionnaire is a validated survey designed to determine how alike twins are based on who can tell them apart34, with 96% accuracy relative to genotyping.(54)

Genetic zygosity results were used as the determinant when available.

Data Management

Study data were collected and managed using REDCap electronic data capture tools hosted at

CCHMC.(55)

Environmental Screening

Because EoE often has an early onset, we focused on perinatal exposures, such as prenatal vitamins, gestational age, breastfeeding and birth-weight, length and order. Birth seasons included winter (northern hemisphere, December 1-March 20), spring (March 21-May 31), summer (June 1-September 20) and autumn (September 21-November 30). Participants from Australia were coded for southern hemisphere birth seasons. Environmental data included food and medication allergies. Data for parent/self-reported factors were obtained from the EGID database for Nuclear-Families and by telephone interview for Twins and their nuclear families. Penicillin, amoxicillin and cephalosporins were grouped together for analysis.

Statistical Analysis

Demographic data and EoE risk estimates were analyzed using JMP Genomics 6.0 (SAS Institute, Cary,

NC). Reported p-values are two-tailed with significance at p≤0.05, unless otherwise specified; exact values at p≥0.001 or p<0.001, were confirmed by permutation test for zero cells.

Demographic characteristics were described using mean ± standard deviation (SD) for normally distributed continuous traits, median and interquartile range for non-normally distributed continuous traits and frequency for discrete traits. Comparability of subgroups was tested using non-parametric Wilcoxon rank sum, parametric t-tests or Chi-square, as appropriate.

Recurrence Risk Ratios and Concordance Estimates

Recurrence risk ratios (RRR) were calculated as (number affected/total)/prevalence, with the point estimate for prevalence set at 5.5 per 10,000.(4, 26, 28) Given the male preponderance of EoE, sex- adjusted frequencies and RRR were calculated; prevalence was set at 8.1 for males and 2.8 for females, on the basis of the 74% male proband frequency in the Nuclear-Family cohort. RRR estimates were

compared using a goodness of fit test ( ). Proband-wise concordance, which provides an estimate for agreement of disease state between twins while accounting for ascertainment, was calculated as

2C/(2C+D),(56) where C is the number of concordant pairs and D is the number of discordant pairs.

Heritability Analyses

To estimate the proportion of variation attributable to genes (heritability) we used variance components analysis for nuclear families and structural equation modeling for twins. Because genes and common environment are not able to be separated in nuclear families, we denoted this heritability as

2 combined gene-environment (hgc ). Details are specified in an Online Supplement.

EoE and Environment

EoE risk associated with individual early environmental exposures, such as parent/self-report of penicillin allergy, was analyzed. Concordance and early life environmental exposures were analyzed for paired covariates, such as age. EoE and non-EoE groups were assumed to be independent; correlation between

the twin sets was ignored due to small sample size. Non-parametric Wilcoxon rank sum, parametric t-tests or Chi-square were used, as appropriate.

RESULTS

Description of Nuclear-Family and Twin Cohorts

Of the 6108 individuals in the 1366 nuclear families screened at the CCED, 914 probands had family history available (69%). After excluding grandparents (n=2391) and twin families (n=31), the Nuclear-

Family cohort comprised 914 probands and 2192 first-degree relatives (n=3106) (Figure 3.1). Twin recruiting strategies identified 91 interested families, of whom 63 met study inclusion criteria and 73% provided family environmental history. For same-sex pairs, twin zygosity was ascertained with parent report, pea pod questionnaire and genotyping. Of the 40 pairs with both parent report and genotyping, there was 82.5% agreement. Of the 40 pairs with both pea pod and DNA zygosity, there was 95.0% agreement. One same-sex pair had parent report of zygosity only. Importantly, recruitment of twin pairs was random with respect to zygosity and concordance, and age by concordance was not significantly different for MZ vs. DZ pairs (p=0.96). There were no significant differences between MZ and DZ twins with respect to race or ethnicity, but MZ twins were more likely to be male (p<0.001) and older (p=0.006;

Table 4.1). There were no significant differences between the Nuclear-Family and Twin cohorts with respect to sex, race, ethnicity or age. The median ages of Nuclear-Family (range 1.0-64.0 years) and Twin

(range 3.0-51.8 years) cohort probands were 12.3-13.2 years with interquartile ranges of approximately

7.7 to 19.1 years of age. Interestingly, both cohorts were 73-74% male, 87-94% white and 94% non-

Hispanic.

Frequency, Recurrence and Concordance of EoE

To characterize familial clustering of EoE, we first calculated EoE frequency in first-degree relatives of probands. Overall, 1.8% of first-degree relatives had EoE (Table 3.2). Given the higher rate of EoE in males, we examined sex-adjusted frequency, which increased to 2.3%. The risk of having another child with EoE was 2.4% in the Nuclear-Family cohort. Fathers (2.4%; p=0.004) and brothers (3.5%; p<0.04) had EoE at significantly higher rates compared to mothers (0.6%) and sisters (1.3%), respectively. EoE

frequency in both MZ (41.0%) and DZ (22.0%) twins was significantly higher than in siblings (Figure

3.2). Surprisingly, EoE frequency in DZ twins was increased compared to non-twin siblings from the

Nuclear-Family cohort (p<0.001, Figure 3.2).

Compared to the general population, the risk of EoE for first-degree relatives from the Nuclear-Family cohort (n=2192) was increased; RRR (RRR=λR) was highest in brothers (64.0; p=0.04) and fathers (42.9; p=0.004), compared to sisters (24.0) and mothers (9.9), respectively. Males had higher RRR compared to females (50.7 vs. 14.7; p<0.001) (Table 3.2). Sibling RRR compared to parent RRR (44.2 vs. 25.8; p=0.09; Table 3.2) was not significantly higher. Sex-stratified RRRs implicated greatly increased risk for sisters (adjλR=45.5), mothers (adjλR=19.1), and females (adjλR=28.2).

Proband-wise concordance in MZ co-twins was 57.9±9.5% compared to 36.4±9.3% in DZ twins.

Although these concordances were not significantly different from each other (p=0.11), the higher rates of

EoE in MZ compared to DZ are supportive of genetic patterning.

Familial Patterning Supports Non-Mendelian and Complex Mode of Inheritance

Examining familial patterning in more detail, information can be gained about the likely mode of inheritance (Figure 3.3). Traditional Mendelian inheritance includes dominant, recessive, and X-linked patterns. In dominant inheritance, transmission between an affected parent and a child is ~50%; however, in the Nuclear-Family cohort, 98% of probands have unaffected parents. Autosomal recessive inheritance often has children with unaffected parents, but ~25% of probands’ siblings would also be affected.

Overall EoE frequency in affected siblings is 2.4%, much less than expected in an autosomal recessive disorder. Only 1.9% of families had at least one additional EoE affected sibling. Lastly, male predominance of EoE creates suspicion for X-linked inheritance. However, parent-to-child transmission was observed from both mothers and fathers, and father-to-son transmission is not supportive of X-linked inheritance. Thus, it is reasonable to deduce that EoE has a complex mode of inheritance. 23

Contribution of Genes and Environment to Familial Clustering

To quantify the affect of genes and environment, we used both the Nuclear-Family and Twin cohorts. In

2 the Nuclear-Family cohort, combined gene-environment “heritability” (hgc ) was estimated at 72%

(p<0.001; SE=0.027) of the total phenotypic variance, suggesting a strong affect from genetics. Parallel

2 analyses in twins estimated combined AE “heritability” (hgc ) at 99.5% (p<0.001). However, the model that separates genetic heritability and common environment (ACE Goodness of fit p=0.56) fit the data better than either the model with genetics (AE Goodness of fit p<0.001) or common environment (CE

Goodness of fit p=0.006) (Table 3.3), suggesting that EoE risk resulted from both genetic and shared environmental factors. Importantly, the heritability (estimate 14.5±4%; p<0.001; Figure 3.4A) changed greatly by analysis of twins, when accounting for a common environment component. The reduction in heritability is attributable to the large proportion of variation explained by common environment

(estimate 81.0±4.0%; p<0.001; Figure 3.4A). Thus, heritability estimates are markedly inflated when common environment is not accounted for (Figure 3.4B).

Evidence for Shared Environmental Effects

Given increased EoE rates in DZ twins compared to non-twin siblings, we tested environmental factors that may be shared between twin pairs but not necessarily between siblings. Although sample size was limited, greater differences in birth-weight were associated with disease discordance in twin pairs

(p=0.01; n=35; Table 3.4). Birth season was significantly different in concordant and discordant twin pairs (p=0.03; n=63); specifically, birth in Fall was associated with EoE discordance (p=0.02; n=63).

Food allergies (p<0.001; n=97) were associated with EoE, and penicillin allergies (p=0.17; n=66) and breastfeeding (p=0.15; n=59) may influence risk for EoE.

DISCUSSION

Previous studies reported familial clustering of EoE,(6, 7, 57-61) suggesting that clustering is attributable to genetics. Indeed, our large cohort of Nuclear-Families demonstrated that family members are at increased EoE risk compared to the general population and that inheritance is complex and not

Mendelian. The Nuclear-Family–based design yielded an inflated heritability (proportion of variation explained by genes) estimate. However, our Twins’ heritability estimates suggest that familial clustering is due in large part to common, or shared, family environment rather than genetics. We demonstrated that environmental factors, such as food and penicillin allergies, and greater difference in birth-weight, may affect EoE risk, whereas Fall birth season and breastfeeding may reduce risk, supporting further exploration of early life factors. Thus, we propose that disease susceptibility in genetically pre-disposed families may be potentiated by early life environment. Notably, colonization by immune-shaping commensal microbiota, in the gut and also in the esophagus,(62-65) could be a key determinant of environmental risk.

First-degree Relatives of Probands have a Higher Rate of EoE than the General Population

In the 1.9% of families in the Nuclear-Family cohort that had at least one additional child with EoE, 2.4% of probands’ siblings also had EoE. This is a 44-fold increase over the general population prevalence and consistent with the previously published high rate.(6) Compared to other allergic diseases, such as asthma with sibling RRR between 1.25 and 2.25,(66) the sibling RRR of EoE is much higher. We also found EoE enrichment in all first-degree relatives of probands, with fathers and brothers being particularly at risk.

EoE is likely underestimated in pediatric subgroups. In the Nuclear-Family cohort, the relatively low risk of having at least one additional child who also has EoE (1.9%; Figure 3.3) is not supportive of an autosomal recessive inheritance proportion indicative of carrier parents. Conversely, relatively low parent-to-child transmission (2.0%), observed for both mothers and fathers, does not support autosomal dominant inheritance. Father-to-son transmission refutes traditional Mendelian X-linked inheritance.

Therefore, these data collectively support EoE having a non-Mendelian, or complex, pattern of inheritance involving numerous genetic and environmental factors.

Family Studies Reveal Genetic Susceptibility

Enrichment in first-degree relatives, in our study and others, suggests a genetic component,(7, 57) and, indeed, Nuclear-Family heritability was estimated at 72%. A strong genetic basis for EoE is further supported by candidate and genome-wide association studies that identified risk variants,(8-10) as well as

EoE-specific gene expression profiles.(38) However, estimating heritability from nuclear families has limited interpretation, as genes and family environment cannot be distinguished.(67, 68) Specifically, similar environmental exposures and risk within the common family environment mimic genetic inheritance patterns and confound heritability. Thus, high heritability estimates in nuclear family study designs may be explained in part by common environment, in addition to genetic susceptibility.

Twin, or extended family, study designs disentangle the effects of genes from common environment.(22,

2 69, 69) Indeed, the heritability estimate from the reduced AE model (hgc ; which ignores common environment) was inflated (99.5%). This high value is not unexpected as twin models often produce inflated estimates(70) due to ascertainment bias. However, by including common environment in the full model, heritability is estimated at 14.5%, with common environment accounting for 81.0% of the variation. The importance of common environment is further supported by our finding that DZ twins are enriched for EoE compared to non-twin siblings. Thus, using the traditional nuclear family approach, the proportion of variation expected to be explained by genetic factors is dramatically overestimated. This overestimation is a problem because these heritability-based estimates are often used as a metric for the amount of variation expected to be explained by single-nucleotide polymorphisms in traditional genetic association studies. The failure of single-nucleotide polymorphisms to account for this variation has been termed “missing” heritability,(71-74) and “phantom” heritability is speculated to be the result of genetic

interactions.(69) Our results show that the amount of variation attributed to genetic factors is overestimated due to failure to account for common family environment.

Early Life Exposures Likely Contribute

Our results suggest that early life exposures likely contribute to EoE risk. High concordance of EoE for

DZ twins compared to non-twin siblings is unexpected because both non-twin siblings and DZ twins share on average 50% of their genome; thus, the inflation of EoE rates in DZ twins is likely not due to genetic factors. Concomitant timing of exposures during specific windows of critical early development may play an important role in EoE pathogenesis.(75-78) Preliminary family environmental data suggest that factors in early life, such as birth season, breastfeeding, and penicillin allergy, which implies previous antibiotic use, are likely to be important given that these factors are associated with twin concordance for

EoE. Indeed, antibiotic use during infancy has recently been identified as a risk factor for EoE.(42) Prior studies and our data substantiate the importance of early life exposures, such as antibiotics,(79-81) specifically penicillins and cephalosporins(82) that alter gut colonization, likely reflecting the role of the metagenome and early microbiota and helminth colonization in priming the developing immune system.(62-65) Parent/self-report of penicillin-like allergies in twins differentiates concordant and discordant pairs. Further, young children ingest food, water, juice, airborne particles, soil, and dust exposure doses many times higher compared to adults,(83) presenting an opportunity for identification of novel environmental risk factors that alter expression at an early age. An environmental affect on EoE risk is plausible given the dynamic nature of the EoE transcriptome, which varies with allergen exposure

(e.g. diet).(11, 38) Our breastfeeding data suggest a protective effect against EoE, consistent with current recommendations.(84) Although birth-weight differences between twins and birth season may affect outcomes, they are less modifiable. These data should be interpreted with caution given small sample size of the Twin cohort and their first-degree relations.

In summary, we have demonstrated that EoE clusters in families and much of the clustering can be attributed to common family environment. These results are clinically important because our EoE families report considerable concern about EoE risk when planning their family. Evidence-based risk assessment data show that, overall, the risk is modest (2.4%), but does seem to be increased by the presence of affected parents and offspring. Much of this familial clustering is attributable to environmental factors, suggesting that for individuals with a family history of EoE, identification of early life factors will be essential to reduce risk. We propose that early life exposures prime genetically susceptible individuals for the development of EoE, highlighting the need to rigorously identify salient genetic and environmental risk mechanisms. Thus, future prospective clinical studies will facilitate translation of these findings to actionable recommendations.

Acknowledgments

We thank our families and their physicians. We gratefully acknowledge the contributions of our clinical, laboratory and research staff at the Cincinnati Center for Eosinophilic Disorders and Center for

Autoimmune Genomics and Etiology at Cincinnati Children’s Hospital Medical Center and thank Shawna

Hottinger for editorial assistance.

Tables and Figures

Table 3.1. Demographics of EoE Nuclear-Family and Twin Cohorts

Nuclear- Twin Family

All MZ DZ

All Families (n) 914 63 28 35

Male Sex (%) 74.0 73.4 92.9* 58.3*

White 86.7 93.7 100.0 88.6

Black 3.9 0 0 0 Race (%) Asian 0.7 0 0 0

AI/AN 0.3 0 0 0

Other 8.4 6.4 0 11.4

Non- 94.2 93.7 96.4 91.4 Ethnicity (%) Hispanic

Hispanic 1.9 3.2 3.6 2.9

Missing 3.9 3.2 0 5.71

Age (years, median) 12.3 13.2 15.8** 10.2**

(IQR) (7.7-17.2) (8.1-19.1) (8.3-32.0) (7.9-16.7)

Range 1-64 3.0-51.8 6.2-51.8 3.0-34.9

AI/AN, American Indian or Alaska Native; DZ, dizygotic; IQR, interquartile range; MZ, monozygotic;

MZ>DZ male sex (*p<0.001). MZ>DZ age (**p=0.006). All others: not significantly different by X2,

Fisher’s Exact, or Wilcoxon nonparametric test.

Table 3.2. Frequency and Recurrence Risk Ratios (λR) in EoE Nuclear-Family Cohort First-degree

Relatives

First-degree Frequency p-value Sex-adjusted RRR Sex-stratified

Relative (%) Frequency RRR

(%)

All 1.8 32.5 ---

Males 2.8* 50.7* 34.3

Females 0.8 <0.001 2.3 14.7 28.2

Parents 1.4 25.8 ---

Fathers 2.4* 42.9* 29.0

Mothers 0.6 0.004 1.9 9.9 19.1

Siblings 2.4 44.2 ---

Brothers 3.5* 64.0* 43.2

Sisters 1.3 0.04 2.9 24.0 45.5

RRR, recurrence risk ratio (frequency/prevalence); Prevalence at 5.5/10,000; Sex-adjusted prevalence for males is 8.2 and 2.8 for females on the basis of the 74% male proband frequency. Unadjusted *p<0.05 by

2 X df=1.

Table 3.3. Nested ACE Twin Models to Estimate Heritability

Twin Pair Parameter Estimates Model Fit Intraclass

Correlation

Model MZ DZ ag2 c2 e2 χ² p-value

(df)

ACE 0.955 0.883 0.145 0.810 0.045 2.04 0.56

(3)

CE 0.940 0.940 --- 0.94 0.060 14.64 0.006

(4)

AE 0.995 0.498 0.995 --- 0.005 489.92 <0.001

(4)

A (ag) additive genetic; C (c) common environmental exposures; E (e) error due to unique environmental exposures df, degrees of freedom; DZ, dizygotic; MZ, monozygotic; Non-significant p-value for χ² indicates superior fit of the model to the data.

Table 3.4. Preliminary Screen of Environmental and Co-morbid Risk Factors in the Twin Cohort

A. Twin Pair

Discordant Concordant p- OR CI n Frequency (% or Frequency (% 95 Exposures for Pairs value mean±SD) or mean±SD) (maximum n=63)

Current Age (years) 63 16.3±11.3 16.0±11.8 0.96 1.0 ---

Gestational Age (weeks) 43 35.0±3.4 35.0±2.2 0.58 ------

Pre-term Birth (≥33.5 43 75.76 80.0 1.00 1.3 0.2-7.3 weeks)

Term Birth (≥35 weeks) 43 50.0 69.7 0.25 0.4 0.1-1.8

Twin Birth-Weight 35 335.7±273.0 145.6±133.7 0.01 ------

Difference (grams)

Birth Season 63 Fall 43.2% Fall 10.5% 0.03 ------

Adjusted for Hemisphere Winter 13.6% Winter 31.6%

Spring 18.2% Spring 10.5%

Summer 25.0% Summer 47.4%

Birth Season Fall 63 43.2% 10.5% 0.02 0.2 0.03-0.8

Fertility Treatments 47 45.7 33.3% 0.52 0.6 0.2-2.3

Fertility Treatment 20 sparse data 0.43 ------

(by type)

Chorion/Amnion 32 sparse data 0.56 ------

Number

Prenatal Vitamins 44 93.9% 100% 0.99 ------

Birth Order (twins only) 45 47.1% 54.6% 0.67 1.4 0.3-5.3

Penicillin Allergy in 44 21.2% 36.4% 0.42 2.1 0.5-9.4

Family

B. Individual Twin

Individual Twin n Not EoE EoE p-value OR CI95

Exposures

(maximum n=128)

Breastfeeding 59 90.0% 65.3% 0.15 0.2 0.02-1.8

Birth Order 91 47.1% 50.1% 0.72 1.2 0.5-2.7

(second, twins only)

Birth-weight (grams) 80 2400±663 2358±532 0.77 ------

Birth-length (inches) 38 19.2±1.4 18.6in±1.1 0.24 ------

Allergies, environmental 97 64.7% 76.2% 0.23 1.7 0.7-4.3

Allergies, Spring 69 90.9% 83.0% 0.48 0.49 0.09-2.5

Allergies, Summer 13 66.7% 80.0% 1.00 2.0 0.1-34.8

Allergies, Fall 68 86.3% 87.0% 1.00 1.1 0.2-4.7

Allergies, Winter 66 59.1% 61.4% 0.86 1.1 0.4-3.1

Allergies, year round 66 61.9% 64.4% 0.84 1.1 0.4-3.3

Food Allergies 97 23.5% 81.0% <0.001 13.8 5.0-38.0

Penicillin Allergy 66 0.0%* 100.0% 0.17 ------

th CI95, 95 percentile for confidence interval; OR, odds ratio; *confirmed by permutation test.

Environmental risk exposures for individual twins/triplets (n=128) by EoE-affected status; twin pairs

(n=63) by disease concordance for EoE. Pearson correlation or Fisher’s Exact Test was used for discrete variables; Student t-test for continuous variables.

Figure 3.1. Recruitment Algorithms and Case Identification for Nuclear-Family and Twin Cohorts

A. Nuclear-Family Cohort. B. Twin Cohort.

A. Nuclear-Family cohort from the Cincinnati Center for Eosinophilic Disorders; B. EoE Twins

International Registry cohort. EGD, esophagogastroduodenescopy; EoE, eosinophilic esophagitis; Not

EoE, unaffected by eosinophilic esophagitis; MZ, monozygotic; DZ, dizygotic.

Figure 3.2. Rates of EoE in Twin Cohort and Nuclear-Family Cohort Sibling Non-probands

Frequency of EoE in dizygotic (DZ) non-proband co-twins (n=36), non-proband Nuclear-Family siblings of proband (n=782) compared to population prevalence by X²df=1. MZ, monozygotic.

Figure 3.3. Summary Pedigrees Support a Complex Mode of EoE Inheritance. A. Nuclear Family

Cohort. B. Twin Cohort (Monozygotic). C. Twin Cohort (Dizygotic)

Diamond shape represents both brothers and sisters whose number range by “Number of probands’ siblings.” Frequency (%) is the percent of families with that summary pedigree as a percent of all families in panels A, B, and C. In the large Nuclear-Family cohort, families with unaffected parents and at least one additional brother or sister with EoE comprise 1.9%.

Figure 3.4 A: Twin Cohort ACE Model More Accurately Estimates Heritability by Separating

Common Environment. B. Twin Cohort ACE Heritability Model Estimates Compared to Twin

Cohort AE and Nuclear-Family AE Cohort Estimates

A. “ACE” latent class path analysis estimates (point prevalence estimate at 5.5/10,000) represent a generalized model across all twins and all families. By convention, latent variables are represented as ovals and measured variables as squares; MZ, monozygotic; DZ, dizygotic. B. Twin cohort ACE path analysis (black) separates common family environment, estimating heritability at 14.5±4% (p<0.001) with superior model fit (p=0.56). As expected, using the same data and model but excluding common family environment (dark gray) inflates heritability to 99.5%. Similarly, Nuclear-Family cohort (light 37

gray) inflates heritability estimate to 72±2.7% (p<0.001;liability threshold model); A, additive genetic variance (heritability); C, common, shared household, environmental variance; E, unique environment

“error” variance.

Figure 3.1. Recruitment Algorithms and Case Identification for Nuclear-Family and Twin Cohorts

A. Nuclear-Family Cohort

B. Twin Cohort

Figure 3.2. Rates of EoE in Twin Cohort and Nuclear-Family Cohort Sibling Non-probands

Figure 3.3. Summary Pedigrees Support a Complex Mode of Inheritance

A. Nuclear Family Cohort

B. Twin Cohort (Monozygotic)

C. Twin Cohort (Dizygotic)

Figure 3.4A: Twin Cohort ACE Model More Accurately Estimates Heritability by Separating

Common Environment

B. Twin Cohort ACE Heritability Model Estimates Compared to Twin Cohort AE and Nuclear-Family

AE Cohort Estimates

Online Supplement:

Heritability Analyses

Heritability calculations are detailed in the Online Supplement. In the Nuclear-Family cohort, heritability was modeled with liability thresholds and variance components modeling using sequential oligogenic linkage analysis routines (SOLAR; Texas Biomedical Research Institute, San Antonio, TX).(85) Briefly, this approach decomposes the trait’s phenotypic variance into additive genetic variance and residual effects operationalized as follows:

2 2 Ω = 2Φσag + Iσε

Ω is the covariance between a pair of relatives and captures phenotypic variance (V), Φ is the kinship

2 2 coefficient matrix, σag is the additive genetic variance, I is the identity matrix and σε is the residual

2 2 variance due to stochastic error (“noise”) and unique environment. In this model, only ag and ε are estimated with the matrices defined a priori. To account for ascertainment, the sample mean was set to population prevalence (5.5/10,000).(26) To assess significance of the genetic variance component, twice the difference between the log-likelihoods of this model and one without the genetic component was

2 2 2 computed and compared to a distribution. Heritability was defined as σag /(σag + σε ). However, given

2 the nuclear family design, this heritability estimate is denoted as hgc to account for the fact that common family environment (C) cannot be separated from additive genetic effects.

To appropriately account for twin relationships, structural equations modeling was applied(50, 69, 86, 87) using Mplus (Mplus: Muthén & Muthén, Los Angeles, CA). Briefly, these models examine the covariance within and between twins. Importantly, jointly estimating effects in MZ and DZ twins, variation can be portioned into genetic and environmental components (AE model; additive genetic (A)

and unique environment/error (E)). However, because all twin pairs share common environmental exposures, variation can further be partitioned into additive genetic (A), common environment (C) and unique environment/error (E) using the ACE model. Using the terminology of variance components analysis, the ACE model can be operationalized as follows:

2 2 2 Ω = 2Φσag + CσC + Iσε

2 C is a matrix used to derive the variance explained by common family environment (σC ). Model constraints included intra-family environmental correlation (C) at 1.0 for both types of twins and genetic component (A) correlation at 1.0 for MZ twins and 0.50 for DZ twins. Ascertainment was corrected by incorporating the point estimate of prevalence of 5.5 per 10,000 in the population.(4, 26, 28) As with variance components modeling, significance of effects was determined by comparing the log-likelihoods of nested models for all three combinations of ACE, AE and CE models to determine the best-fitting, data-driven model. Non-significant p-values indicate better model fit to the data. Heritability can be measured from both the AE and ACE models. However, heritability from the AE model does not separate

2 additive genetic effects from shared family environment; it is designated as hgc . Heritability from the

2 ACE model uniquely separates additive genetic effects and thus is designated hag .

Chapter 4: Biases, Limitations and Challenges

In this study, a case registry was used. For the nuclear family studies, all cases were seen at the CCED.

For twins, the majority of twins were referred to the study by their treating physicians. Thus, this study is clearly a biased sample. Given the rarity of EoE, population-based twin sampling would be prohibitive.

Possible problems with our ascertained sample include inflation of frequency of disease in co-twins and the lack of concordant unaffected twins and their families.

Family-based studies use case status to recruit the families of probands. Although rare diseases, such as

EoE, are not generally considered appropriate for cross-sectional studies due to difficulty identifying cases, EoE is amenable to cross-sectional study due to its early-onset, severe and chronic natural history.

Taken together, this allows case identification of EoE. Non-response for family data is possible, however, enrollment of CCED proband families who have family data available in the electronic medical record is likely random. Although associations can be tested in this observational study, causal inference cannot be made.(88) This study is also subject to recall bias. However, because EoE has a high burden, our parents generously share their time and their family medical information in hopes of helping other EoE families.

Diagnostic data were used, as noted, whenever possible. Although the exposure and outcome are measured simultaneously, and exposure data are retrospective, we gain a snapshot of associations at very little expense, by which we can determine domains for future study and prospective, longitudinal comparison of causality.

Another challenge is that our sample was primarily white. Although this is consistent with reported demographics, it may be influence access to care and diagnostic procedures. Minorities, specifically blacks, American Indian and mixed race children are likely underrepresented in this predominantly white sample. Anecdotally, EoE families are reported to be of higher socioeconomic status.

Twin and closely related family studies may inflate heritability estimates due to common family environment. Estimates may be biased high due to confounding variables on which the twins were likely matched.(69, 89) Further, selection bias is of particular concern in this highly ascertained phenotype.

Importantly, twins were recruited without preference for zygosity or phenotypic concordance. Proband- wise ascertainment bias was addressed using an appropriate correction for the calculation of disease concordance and by estimating the proportion of unaffected pairs. Future population-based samples could also address this limitation.

High density datasets, such as methylation epigenetic data, add complexity to analyses and data management.

Finally, EoE is currently a dichotomous phenotype, limited to analysis by logistic-based methodologies.

Our work with Dr. M. H. Collins (Appendix C) seeks to validate a histologically-based scoring system that can be used with ordered categorical statistical methodologies.

Chapter 5: Future Directions

Early Life Exposures Likely Contribute

Our results suggest that early life exposures likely contribute to EoE risk. High concordance of EoE for

DZ twins compared to non-twin siblings is unexpected because both non-twin siblings and DZ twins share on average 50% of their genome; thus, the inflation of EoE rates in DZ twins is likely not due to genetic factors. Concomitant timing of exposures during specific windows of critical early development may play an important role in EoE pathogenesis (Figure 5.1).(75-78) Preliminary family environmental data suggest that factors in early life, such as birth season, breastfeeding, and penicillin allergy, which implies previous antibiotic use, are likely to be important given that these factors are associated with twin concordance for EoE. Indeed, antibiotic use during infancy has recently been identified as a risk factor for

EoE.(42) Prior studies and our data substantiate the importance of early life exposures, such as antibiotics,(79-81) specifically penicillins and cephalosporins(82) that alter gut colonization, likely reflecting the role of the metagenome and early microbiota and helminth colonization in priming the developing immune system.(62-65) Parent/self-report of penicillin-like allergies in twins differentiates concordant and discordant pairs. Further, young children ingest food, water, juice, airborne particles, soil, and dust exposure doses many times higher compared to adults,(83) presenting an opportunity for identification of novel environmental risk factors that alter expression at an early age. An environmental affect on EoE risk is plausible given the dynamic nature of the EoE transcriptome, which varies with allergen exposure (e.g. diet).(11, 38) Our breastfeeding data suggest a protective effect against EoE, consistent with current recommendations.(84) Although birth-weight differences between twins and birth season may affect outcomes, they are less modifiable. These data should be interpreted with caution given small sample size of the Twin cohort and their first-degree relations.

Medication Allergies and Mold-related Domains of Exposure Deserve Inquiry

We have shown that EoE clusters in families and much of the clustering can be attributed to common family environment. These results are clinically important because our EoE families report considerable 47

concern about EoE risk when planning their family. Evidence-based risk assessment data show that, overall, the risk is modest (2.4%), but does seem to be increased by the presence of affected parents and offspring. Much of this familial clustering is attributable to environmental factors, suggesting that for individuals with a family history of EoE, identification of early life factors will be essential to reduce risk.

We propose that early life exposures prime genetically susceptible individuals for the development of

EoE, highlighting the need to rigorously identify salient genetic and environmental risk mechanisms.

Further, penicillin is a mold derived pharmaceutical. Mold exposures are associated with eosinophilic esophagitis and are used to induce EoE in murine models of disease.(17, 90) EoE, eosinophilic asthma and aspergillosis share the characteristics of eosinophilic inflammation and its sequellae.(91, 92) Our data suggest that penicillin is associated with EoE risk. Further, Penicillium and Aspergillus have been associated with pediatric asthma risk.(93) Aspergillus, and Cladosporium molds have been shown to be more specifically related to the classic Th2 eosinophilic inflammation(94). Indeed, cladosporium exposed dectin1 knock-out mice induce Th2 with a robust airway hyperresponsiveness, characteristic of eosinophilic asthma(95). According to ProMED editor, Larry Madoff M.D.:

“fungal alerts had increased from 1% to 7% between the years 1995 and 2010. HealthMap saw a similar trend in the period 2007 to 2011, with alerts for infectious fungi affecting animals increasing from 0.1% to 0.3% and alerts for infectious fungi affecting plants increasing from 0.1% to 0.2%. National and international trade in products and food can introduce new fungi to vulnerable communities, often with devastating effects.”

(See http://www.nature.com/nature/journal/v484/n7393/full/nature10947.html)

Further, the 1990 southern Ohio plant hardiness zone was 5, consistent with colder winters and broad temperature ranges typical of the entire state. Due to increasing temperatures, this was changed in 2012 by the U.S. Department of Agriculture to zone 6, supporting more southerly plants, such as azaeleas, echinacea and coreopsis, now found routinely in this region. Hardly a day goes by that the Wall Street

Journal fails to address the business implications of climate change. Indeed, the range of mold-related pathogens has been expanding recently, further supporting increased alerts. The National Allergy Bureau,

associated with the American Academy of Allergy, Asthma and Immunology, collects, tracks and reports mold and pollen at specific testing stations across the U.S. Geoclustering these data with EoE cases may provide insight into mold-related risk of EoE by latitude, longitude and ecological biome. Patterns of food use, including increased consumption of relatively expensive, imported fruits and vegetables out of season, may be associated with anecdotal reports of higher socio-economic strata of EoE families. A recent outbreak of brucellosis, or “Malta fever,” illustrates the connection of common source microbiota, food, family, and generational age to classic epidemiologic studies of infectious diseases, and disease rates that have increased 2.6-fold since 2011:

“The Director of the pediatric infectious disease service in the Galilee medical center, Dr. Daniel Glickman, said the patients, arriving during the last 2 weeks at the hospital, are residents of the Druze villages Yarka and Julis, half of them children who consumed unpasteurized cheese; many of them relatives (father and daughter, cousins) who most probably consumed cheese from a common source.”

(Byline: Achiya Rabbed http://www.ynet.co.il/articles/0,7340,L-4526332,00.html and ProMED Digest, Vol 24, Issue 22 [email protected] )

Finally, other potential environmental factors were explored for their association with EoE risk and negative results are reported herein (Appendix E).

Early life factors domain, including breastfeeding, which (p=0.15; n=59) may have a protective effect on

EoE risk and early infections and treatments, and will require prospective studies to determine temporality and ascertain precise treatments and response.

Medication allergies, as a distinct domain, deserve additional inquiry. This is needed for true IgE mediated allergies, possible related sensitization to functional chemical groups, their associated pharmacogenomic response implications and the myriad of potential microbiota exposures, i.e., indications, for which they were prescribed, whether empirically or by culture and sensitivity analysis.

The relationship to food allergies and food additives warrants further study. Indeed, one father reported an allergy to blue cheese, in the Penicillium family of molds. Food additives, such as citric acid are produced 49

using Penicillium and Aspergillus (Table E.2). The home environment domain, both indoor and outdoor, including specific mold exposures, birth season and temporo-geospatial dose effects for mother, conceptus and newborn may provide unique insight into EoE risk due to common family environment.

Environmental exposures are associated with epigenetic methylation

Because we cannot go back in time and measure the environmental exposures of each subject with and without EoE, we often use markers of environmental exposure as a surrogate in our analysis. One such marker for environmental factors is epigenetic variability.(20, 21) Environmental factors such as smoking(96) have been shown to result in measurable differences in the methylation of peripheral blood cells, diesel exhaust in saliva(21) and folic acid in cord blood.(97) Mechanistically, epigenetic modification such as cytosine methylation of CpG sites can result in altered gene expression that may lead to the the development of EoE.

Epigenetic methylation alters gene expression

In addition, methylation may be associated with altered transcription and cellular function. Specifically, eotaxin-3 transcription is enhanced by promoter associated hypomethylation. (46)Human twin studies have not addressed the hypothesis that epigenetic changes are associated with EoE.

Gene discovery algorithm needed for epigenetic methylation studies

High density datasets, such as methylation epigenetic data, add complexity to analyses. Newly available bioinformatics approaches provide options for interpretation of complex patterns in very large datasets.

Bioinformatics approaches have been developed to assist investigators to synthesize and perform quality control, permutation tests, fixed and mixed models as well as graphics.(98) I have determined a preliminary pipeline for prioritizing sites for confirmation by pyrosequencing (Appendix F: Methods).

Gene discovery pipelines that increase efficacy and cost effectiveness of biological confirmation research are needed to assess large sample size genomic data. 50

EoE diagnostic panel genes have differential methylation in EoE discordant MZ Twins

Using high-dimensional genomic research methodologies to explore potential distal causes, such as epigenetic mechanisms and environmental factors, of EoE and related immunologic conditions, phase 1 screen of effect size difference in discordant MZ pairs identified 349 CpG sites with altered methylation that warrant further study (Appendix F: Results Table F1). As outlined in Appendix F, I will confirm loci that differ significantly between 11 discordant MZ twin pairs, using a comparative control group of

MZ pairs who are concordant for EoE to screen out loci that are discordant for methylation in pairs that are concordant for EoE, thus reducing potential false positive associations. Although it would be ideal to have a replication set of discordant MZ twin pairs, there are no such cohorts currently available to us.

Therefore, I will compare findings to discordant DZ twin pairs .

A comparison of the methylation difference between paired discordant twins resulted in 349 sites with an effect size of 5% or greater and 86 sites >6% (Table F2). Comparison of autosomal sites of CpG methylation to published EoE expression data and diagnostic panel identified the H19/SCUBE2 (Table

F3), as well as LRRC26 regions (Table F4). High site-specific methylation at STK38L (13.9%), TBX1

(11.2%, 7.2%), SLC38A10 (9.4%), WNT6 (9.2%) will be interrogated. Genes in networks predicted to be relevant to EoE risk include CD40LG, ITGB2, RUNX1, GHR, KCNQ10T1, SGK1, ELANE, WNT5. In addition, TBX1 and WAS were also highly predictive. Further, SGK1 was identified in the transcriptomes of both EoE(48) and eosinophilic gastritis (EG) and has high differential methylation (7.1%; p- value=0.06).

Methylation in regions MUC4, UBD, TSPAN12, WDR/TSLP, GRK5, SLC25a24, ANO1, CITED2 that were associated with altered esophageal expression did not show promising results.

Because of the male preponderance of subjects with EoE, differential methylation of loci on the sex chromosomes will be explored. For these studies we compared discordant X male-male and X female- female pairs. Indeed, a preliminary analysis of the X yielded 10 sites ≥5% effect size in the discordant

MZ cohort.

Immunoglobulin Superfamily CpG Methylation on X Chromosome:

The mechanisms of male predominance in EoE have not been explored. Sites of interest such as

CD40LGT and SNORD61on Xq25, adjacent to an immunoglobulin superfamily, warrant further study

(cg02936290; RNA). (http://omim.org/entry/300137 ; http://useast.ensembl.org/Homo_sapiens/Gene/Summary?g=ENSG00000206979;r=X:135961358-

135961430;t=ENST00000384252)

Two adjacent sites (cg24428913, cg00078867) at Xp11.4-p11.21encoding Wiskott-Aldrich protein family associate with Cdc42, a regulator of actin cytoskeleton involved in antigen attachment, expressed exclusively in hemopoietic cells and associated with rare X-linked immune dysfunction. http://www.ncbi.nlm.nih.gov/gene?cmd=Retrieve&dopt=full_report&list_uids=7454

Given the male predominance of EoE, adenoma cases showed male predominance of a specific mutation at methylated CpG site (cg02895192; ARHGAP4;ARHGAP6 http://omim.org/entry/300014 ).

In addition to methylation, histone modifications that alternately expose or occlude DNA to transcription factors have been identified that suggest additional epigenetic mechanisms (cg16806953 http://omim.org/entry/300269 ).

Early Genetic Annotation Supports an Environmental Role

Previously, structural equations modeling showed that EoE has a complex mode of inheritance, with estimates of 14.5% genetic heritability and 81% of phenotypic variance due to common environment. 52

Genetically identical MZ twins allow the study of environmental factors that alter gene expression.(99) In this study, sites with sustained methylation differences between identical, or monozygotic (MZ), twins who have EoE and their unaffected co-twin provide novel evidence that methylation contributes to the underlying etiology of EoE.

Wen et al., recently published a 96-gene EoE diagnostic panel (EDP) used to differentiate EoE patients from controls and from patients with gastroesophageal reflux disease.(48) This panel includes

H19/SCUBE2 on chromosome 11p15.3 which was first associated with EoE by Blanchard et al. in

2011.(11) CpG island (cg01977486) near the H19 promoter were identified with >4% hypomethylation in affected twins (p=4.2e-5). Strikingly, H19 (located at 11p15.3) is a non-coding RNA known to be imprinted (Figure F2), or methylated, resulting in altered gene expression. Maternal expression of H19 and paternal expression of IGF2 are imprinted by a paternal-specific region upstream. http://www.ncbi.nlm.nih.gov/gene/283120. Further, large deletions in this region are associated with

Beckwith-Wiedemann syndrome. http://www.ncbi.nlm.nih.gov/clinvar?term=H19 Methylation restricts gene expression and may behave like a deletion.

Both the EDP(48) and the EG transcriptome(100) recently characterized by Caldwell et al., includes

SGK1, at 6q23, known to have alternate transcripts, including a glucocorticoid-regulated serine/threonine protein kinase protective under conditions of cellular stress.(101) http://www.ncbi.nlm.nih.gov/gene?cmd=Retrieve&dopt=full_report&list_uids=6446 Glucocorticoid- regulated genes, such as FKBP51, have previously been associated with EoE.(102)

Epigenetic and Early Life Environmental Differences in Twins Drive New Research

From conception, epigenetic differences arise that may result in phenotypically different manifestations of complex diseases in genetically identical twins. Differences in maternal nutrition have been demonstrated to affect expression with differential effects by sex.(103) We have found promising sites of altered

methylation in MZ twins that will be further investigated in this study. Future prospective studies will address temporal exposure issues and the effect of age on twin methylation patterns. Comparative tissue

DNA methylation studies will be undertaken as the Twins’ biobank expands. Prospective studies could study miscarriage rate in female probands. Importantly, altered patterns of X-linked methylation may underlie the male preponderance of EoE. Future studies will address contribution of sex to the differences identified at these loci.

This screening methylation chip array study of discordant twins importantly has identified EoE esophageal transcriptome candidate genes and novel methylation sites as possible mechanisms of dyregulated expression. Further insights into the novel hypothesis that altered patterns of methylation contributes to the underlying etiology of EoE may identifiy new therapeutic targets giving clinicians and genetically susceptible families tools needed to mitigate risk of EoE and its sequelae.

Prospective cohort studies are needed to quantify and distinguish temporality of specific environmental risk factors. Comparing methylation in EoE and asthma probands may differentiate altered sites of methylation associated with generalized atopic pathways from pathways unique to asthma or EoE. Larger and extended family studies are needed to identify rare genetic variants associated with EoE, perhaps targeting the extended family of our confirmed MZ triplets to compare those three individuals with identical genotypes to their relatives by genotyping and environmental exposures. Importantly, the

Family-based Risk from Epigenetics and Environment (FREE) studies seek to identify genes associated with EoE for which methylation alters gene expression, thus increasing EoE risk (Figure F1). Further studies that address male predominance are needed. Although results are promising (Appendix F Table

2), the methodologies to address X linked inheritance require further development.

Figure 5.1 Methylation screening algorithm

Phase 1: Effect size difference screen > 5% Novel MZ autosomal chromosome sites for EoE methylation = 251884

EXCLUDED MZ = 251535

Phase 2: Prioritize biological pathways via predictive model MZ = 349;

EXCLUDED Failed α threshold MZ = Phase 3: Hypothesis testing: Hyp 1 = α = 0.05 Phase 1 MZ = 85; Phase 1 & 2 = Hyp 2 = MZ discordant difference > MZ concordant difference Hyp 3 = MZ discordant difference, direction vs “WT control” %Me, if site is known H1: Failed α threshold MZ = EXCLUDED H2: Failed α threshold MZ = H3: Failed α threshold MZ =

Epigenetic methylation of Epigenetic methylation of Epigenetic methylation of candidate esophageal novel autosomal sites = novel X and Y chromosome expression gene sites = sites see Figure

Figure 5.2: Planned Research Development for Family-based Risk from Epigenetics and

Environment (FREE) due to Methylation (Me) and sexually dimorphic effects associated with the X Chromosome.

EoE Planned Research Development: X/Y analysis of sexually dimorphic Family‐based Risk from Epigenetics and Environment (FREE) complex genetic due to Methylation (Me) and sexually dimorphic effects on X diseases

2013‐Anticipated publication:2017 EoE FREE_X chrom Discovery Arm Pyroseq. Confirmation See Table

Anticipated publication: 2015‐16 EoE FREE Gene Discovery Arm See Table

Anticipated publication: 2015‐16 EoE FREE Discovery support of Candidate genes from esophageal expresssion See Table 2012‐Anticipated publication: 2015 post Pyroseq Confirmation EoE Epigenetic Methylation: 3 FREE_Me study arms: Candidate genes, Discovery autosomal and X chromosome 2010‐2014 Published from dissertation: FbR, Twin Registry, heritability, early life environment

HSS Collins: Histology Scale development for EoE as a continuous outcome EoE studies: Twins development; Hamilton County prevalence follow‐up; Blanchard: Analysis/Review EoE esophageal expression

Mentors: UC: Succop; CCHMC: Rothenberg, Martin, Collins, Macaluso Funding: UC MECEH T32NIH 2011‐13; CEG 2011,2012; CCTST 2012 Year 2010 2011 2012 2013 2014 2015 2016 2017 Current Embargo 8/2014………………………………………………………………….8/2016

Chapter 6: Conclusions and Significance to Environmental Health

The prevalence of food allergies is increasing, perhaps in response to alterations in food chemistry

(preparation, shelf life, processing), genetic modification, ambient aerochemistry or exposures to both infectious diseases and nonpathogens. EoE is a chronic food allergy. Previous studies by Blanchard and

Rothenberg, et al., have demonstrated that EoE has a strong genetic component. However, if EoE were primarily a genetic disorder, we would expect that DZ twins would have about half the rate of disease compared to MZ twins. Our data show that the concordance rate in MZ and DZ twins are not significantly different. Peanut allergy, by comparison, has a very wide gap between MZ and DZ concordance, implying a greater importance of genes over environment and twin timing. This implies that environmental exposures and the simultaneous timing of twin exposures play large roles in EoE in genetically susceptible individuals. As such, the phenotypic variation is a complex interaction of genes and environment. The 81% contribution of environmental factors to EoE risk strongly suggests that exposures that cause the gut to become sensitized, allergic and chronically diseased are modifiable. This work is significant because identification of unique methylation profiles and environmental factors associated with of EoE could yield novel prevention strategies. Indeed, Dr. Marshall Plaut, MD, chief of the Allergic mechanisms Section at the NIAID, stated recently, “Food allergy is considered a major public health problem, and is one for which we don’t have any recognized ways to prevent or treat the disease.”(104) To address this problem, we will continue our studies of epigenetic alterations signature of affected twins to identify potential mechanisms of gene-environment interactions. In addition, we propose prospective studies that will support long term public health strategies for EoE. These long term health strategies could include primary prevention by modification of environmental sensitizing agents, secondary prevention by clinically induced therapeutic tolerance of environmental antigens (e.g., immunologically mediated sublingual or injectable treatments for antigens) and tertiary prevention of sequelae.

Innovation

Until recently, EoE research has been focused on dietary triggers and the effects of eliminating those triggers. The genetic variants and gene expression profiles recently identified make this project possible by identifying candidate regions for epigenetic interrogation. However, other than diet, the effects of environment have not been studied or suggested by the research designs. The recent rise in both MZ and

DZ twin births, increased from about 1% to a current rate of 1 in 30 births(105-108) creates a new

“natural resource.” Our preliminary data on twin concordance of EoE suggests that environment and timing of exposure may play relatively large roles in the development of EoE. This project is innovative because it proposes to open a new area of preventive research, by focusing on modifiable environmental factors and the identification of a unique methylation pattern in individuals with

EoE.

Immediate Impact for Families

When families affected by eosinophilic esophagitis, ask, “Will my next child have EoE?” clinicians can state with confidence, “The risk of having a second child with EoE is about two-and-a-half percent.” Our unique twin-based designs have allowed me to quantify the respective contributions of genetic and environmental factors to the additive genetic heritability of EoE. The identification of environmental risk factors associated with this chronic antigen-driven allergic inflammatory disease, as well as domains for further study, allow us to compare models of risk. Clinicians should also be aware of modestly increased risk of EoE in siblings of an EoE proband. Symptomatic family members of EoE patients should be evaluated with a high index of suspicion for EoE.

In summary, we have transformed EoE research. In addition to genetics, we now know that environmental conditions and early life factors influence risk. Early analysis supports further study of altered methylation sites, such as H19/SCUBE2 and others, as mechanisms of EoE risk. Co-morbid variability and relatively rare disorders are associated with complex mechanisms of inheritance due to the interplay 58

of underlying genetic susceptibility and interacting environmental risk factors (Appendix F, Figure F5) that determine the overall threshold for disease. Further, although genetic drift is a slow mechanism occurring in evolutionary time, “epigenetic drift” occurs relatively rapidly. Thus, future prospective clinical studies will facilitate translation of these findings to actionable recommendations for clinicians and EoE families. Taken together, these data open new directions for EoE research and risk mitigation.

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Appendix A: Summer 2010 Pilot Study, unabridged:

Sex and Related Conditions Are Associated With EoE

INTRODUCTION

This pilot study made me eligible to apply for the NIH T32 predoctoral fellowship in Molecular Epidemiology of

Children’s Environmental Health, which requires work with a genetics group, such as the Rothenberg lab. In addition, pilot data also made an entry level NIH subaward through the Center for Environmental Genetics (CEG) possible. This grant was used to purchase hardware and software needed for planned genetic analyses. Recipients of the New

Investigator Scholar award then enter a limited pool of applicants to be mentored in the skills needed for an independent research career as a principal investigator. This is important because junior research faculty members are required to generate 80% of their salary within three years to retain their position. Therefore, one cannot wait until after doctoral study to acquire communication skills, such as grantsmanship, manuscript publication and oral presentation, required to thrive in the current marketplace. The work that follows would not be possible without generous financial support, mentorship and inspiration from:

Campaign Urging Research for Eosinophilic Diseases (CURED);

Food Allergy Research and Education (FARE);

Buckeye Foundation;

NIEHS P30-ES006096 Center for Environmental Genetics 2011 New Investigator Scholar and

2012 Principal Investigator Mentee/Mentor;

NIH T32-ES10957 2011-2013 Molecular Epidemiology in Children’s Environmental Health Predoctoral Fellowship in,

“the key areas of statistical genetics, epidemiology and molecular genetics.”

NIH 8 UL1-TR000077-04 Center for Clinical and Translational Science and Training, CCTST, CTSA, NCATS 2012 Just in Time; CCTST REDCap UL1-RR026314-01 NCRR/NIH.

These grants, project management tools and resulting recruitment, data and sample collection complete Aim 1. Project management documents were needed to design, plan, recruit study participants, collect and manage large scale data and

DNA samples and plan statistical and laboratory analysis, including outsourcing of genotyping for zygosity determination

and epigenetic methylation studies. These are detailed in Appendix B. The complete CEG Mentee/Mentor grant application is in Appendix C.

Aims 1 and 2 are described in the research manuscript accepted for publication July 3, 2014 by the Journal of Allergy and

Clinical Immunology, in Chapter 2. Background studies of comparative heritability are detailed in Appendix D. The unabridged environmental factors, including negative results, are detailed in Appendix E. These results open new areas of research in EoE.

Further, the exploration of new areas of EoE research, such as epigenetic methylation in twins, suggested by pilot study and stated in Aim 3 are described in Chapter 4, Future Directions and detailed in Appendix F. Required Human Subjects documentation from both UC and CCHMC are in Appendices G and H. If you are reading this, thank you, I hope it is helpful.

Sex of Affected Parent and Related Conditions Are Associated with Familial Risk of Eosinophilic Esophagitis

E. S. Alexander1,2, L. J. Martin3,1, J. P. Abonia2,1, H. Foote2, M. D. Eby2, M. E. Rothenberg2,1

1University of Cincinnati College of Medicine, Cincinnati, OH, 2Division of Allergy and Immunology, Cincinnati

Children's Hospital Medical Center, Cincinnati, OH, 3Division of Biostatistics and Epidemiology, Cincinnati Children's

Hospital Medical Center, Cincinnati, OH.

This abstract for preliminary study, was accepted for platform presentation at American Academy of Allergy, Asthma and

Immunology, March 18-22, 2011.

Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. J Allergy Clin Immunol 2011:127(2) AB217.

Abstract:

RATIONALE: Eosinophilic esophagitis (EE) occurs more frequently in families with atopy. Our objective is to quantify the risk of developing EE in first degree relatives of affected individuals.

METHODS: Previsit parent questionnaire with MD confirmation was conducted for family relations and comorbid conditions, i.e., allergic rhinitis, asthma, eczema, food allergies, urticaria, EE, other eosinophilic gastrointestinal disorders

(EGID), food impaction, esophageal dilation. Pedigrees for 306 families were constructed using a database with complete family information for 29%. Data were analyzed with X²df=1 at p<=0.05. Risk ratios were calculated as

(#affected/total)/prevalence.

RESULTS: Our sample had 1.77 siblings per family compared to the Ohio mean of 1.87 and the US mean of 1.86.

Pedigrees were constructed for 306 families of probands. First degree relatives affected with EE included 3.3% of fathers,

0.4% of mothers, 3.4% of brothers and 2.4% of sisters. The risk ratios (RR) were 53, 33, 60, and 8 for siblings, parents, fathers and mothers, respectively. EGID (p=0.017), food impaction (p=0.001) and esophageal dilation (p=0.002) were significantly more common in parents. EE (p= 0.024) and food impaction (p=0.039) were reported more frequently by fathers than mothers. Asthma (p=0.001) and eczema (p=0.0001) were more common in siblings of probands than parents.

CONCLUSIONS: In conclusion, we have defined specific risk ratios of EE in first degree relatives with a range of 8-60 depending upon relationship; fathers were more commonly affected with EE compared with mothers or siblings.

Furthermore, parents and siblings show distinctly different patterns of comorbid conditions. A further study of sex-based inheritance patterns is warranted.

INTRODUCTION

Eosinophilic esophagitis (EoE) is a debilitating, chronic food and swallowed antigen driven allergic inflammatory disease.

Although the prevalence of EoE has increased in both adult(1) and pediatric populations,(2) treatment options are very limited.(3, 4) Further, ~70% of those affected by EoE are male,(2, 5, 6) suggesting sex-specific genomic and epigenomic mechanisms. EoE prevalence is 5.5 per 10,000(1, 5, 7-9) EoE patients have co-occurring atopy, asthma and other gastrointestinal disorders.(10, 11) Older pediatric patients present with food impaction and dysphagia.(10) This is important because, as an epidemiology student naive to the study of immunologically mediated disorders, it is necessary to explore biological plausibility prior to study design to 1) identify possible confounders, 2) consider inclusion criteria that maximizes either sensitivity, specificity, or both, and 3) consider proximal and distal causes of disease for future study designs, as described in Aim 3. For example, do genes and environmental nodes that turn on the “atopic march” precede nodes that determine respiratory versus gastrointestinal diseases, and finally, what are the specific susceptibilities and exposures that determine risk of EoE?

Asthma affects 12% of U.S. white children and 18% of U.S. black children. Although the trend of increasing prevalence appears to have reached a plateau in the U.S., severity and prevalence in low and middle income countries is increasing worldwide.(11-13)Asthma, eczema, allergic rhinitis, urticaria and food allergies share inflammatory features that may result in a progressive “atopic march.” (14-18) Asthma sibling recurrence risk ratio (RRR) is commonly reported to be ~2.

However, to our knowledge, peer-reviewed source data are not available. Sibling RRR for genetic polymorphisms associated with asthma have been reported at ~2(19) and asthma prevalence in U.S children is ~12-13 %.(20)

The natural history and variability of related conditions in EoE families (Chapter 2, Figure 2.1) is not well characterized.

I sought to identify co-morbid conditions that define the EoE phenotype and quantify the risk of developing EoE and related conditions in first degree relatives of affected individuals, stratified by sex. Recurrence risk of both EoE and related gastrointestinal and allergic conditions has not been reported.

METHODS

Subject Identification and Eligibility

A retrospective, cross sectional study was conducted for the period August 2008 to July 2010 to identify proband patients with documented family history. Probands confirmed by histology (15eos/400xhpf). Previsit parent questionnaire with physician confirmation was conducted for family relations and their comorbid conditions, i.e., allergic rhinitis, asthma, eczema, food allergies, urticaria, EoE, other eosinophilic gastrointestinal disorders (EGID), food impaction, esophageal dilation. Affected or nonaffected status was recorded for gastrointestinal (GI) and allergic conditions. GI conditions included EoE, non-esophageal eosinophilic GI disease (EGID), food impaction and esophageal dilation. Eosinophilic gastritis, eosinophilic enteritis and eosinophilic colitis were combined to the category “EGID” by the CCED physician.

Confirmed data are recorded in EPIC. Only first degree relations were included, i.e., parents and siblings for our pediatric proband population, who do not yet have children of their own. Sex was available for proband patients and inferred for relations: mother, father, sister, brother.

Exclusion criteria: Proband patients for whom confirmed family history was not available in EPIC.

Statistical Analysis

Pedigrees were constructed to identify related pairs, using PEDSYS and SOLAR software, from a database with first degree relation information for 29%. Data were analyzed with Chi-square and Fisher’s Exact at p≤0.05. Stratified analysis of co-morbid conditions was by sex and relation. Data were analyzed using SAS 9.2. Recurrence risk ratios (RRR) were calculated as (number affected/total)/prevalence.

RESULTS

Proband patients (n=1059) with EoE were identified at Cincinnati Children’s from our research database; 306 had family history in the medical record. This sample had 1.77 siblings per family compared to the Ohio mean of 1.87 and the US mean of 1.86. Pedigrees were constructed for 306 families of proband patients. First degree relatives affected with EoE included 3.3% of fathers, 0.4% of mothers, 3.4% of brothers, 2.4% of sisters and 2.9% of siblings overall (Chapter 2,

Table 2.1). All are significantly increased compared to population prevalence.

EoE (p= 0.024) and food impaction (p=0.039) were reported more frequently by fathers than mothers (Figure A1). Food impaction (p=0.001) and esophageal dilation (p=0.002) were significantly more common in parents compared to siblings

(Figure A2). Half of all first degree relatives have allergic rhinitis (Figure A3). EGID (p=0.017), asthma (p=0.001) and eczema (p=0.0001) were more common in siblings of probands compared to parents. There were no significant differences in allergic conditions reported for fathers vs. mothers.

Frequency of asthma in siblings of an EoE proband is 35.0%, and RRR at 2.9, with pediatric prevalence set at 13.2%.(20)

Frequency of eczema in siblings of an EoE proband is 30.1%, and RRR at 3.0, with pediatric prevalence set at 10%.(21)

DISCUSSION

EoE is a Substantial Disease Burden in Families

Siblings report more asthma, eczema and EGID. Half of all first degree relatives of EoE probands have allergic rhinitis, regardless of sex or age. Parents report more food impaction and dilation than siblings.

Further, ~70% of those affected by EoE are male,(2, 5, 6) suggesting sex-specific genomic and epigenomic mechanisms.

Reduced rates of EoE in mothers further suggests sex-specific mechanisms, including a possible protective effect from estrogen. Differences, by sex of the parent but not by sex of siblings, need to be confirmed, as this implies specific and testable mechanisms of complex inheritance, such as hormonal mediation and imprinting Studies of sex-based inheritance patterns and family-based quantification of shared environment are warranted.

Signs and Symptoms Vary by Age

Food impaction (p=0.001) and esophageal dilation (p=0.002) were significantly more common in parents compared to siblings, suggesting undiagnosed EoE, and are consistent with changes in clinical disease recognition and diagnosis over time. Self-report of food impaction and dilation further suggest symptom progression and structural changes, such as strictures. However, EGID (p=0.017) is more common in siblings, compared to parents. This may be due to improved diagnostic criteria and new diagnostic codes. However, younger generations may also be exposed to new environmental factors that were not present when their parents experienced the same developmental windows. Further, epigenetic changes are consistent with relatively rapid increases in disease prevalence.

Asthma in Siblings of an EoE Proband is 3-fold Higher than Pediatric Asthma Prevalence

Asthma (p=0.001) and eczema (p=0.0001) were more common in siblings of probands compared to parents. Importantly, asthma in the siblings of an EoE proband is 35%, and ~3 times higher compared to pediatric asthma prevalence alone.

These data are consistent with recent reports by Spergel et al. for the co-occurrence of EoE and asthma in children. Half of

all first degree relatives in EoE families have allergic rhinitis, consistent with underlying atopy in EoE families. Familial clustering of EoE and related allergic conditions further suggests underlying genetic susceptibility.

In summary, clinicians should be aware of a modestly increased risk (~3%) of EoE in siblings of an EoE proband.

Symptomatic family members of EoE patients should be evaluated with a high index of suspicion for EoE. Co-morbid variability and relatively rare disorders are associated with complex mechanisms of inheritance.

Future directions: Sex is a potential confounder in EoE studies and will be adjusted or stratified as sample size allows.

Prospective cohort studies are needed to distinguish temporality of specific risk factors. Comparing methylation in EoE probands and probands with related conditions may differentiate altered sites of methylation associated with generalized atopic pathways from pathways unique to asthma or EoE. Larger and extended family studies are needed to identify rare genetic variants associated with EoE. Twin studies of monozygotic (MZ) pairs discordant for EoE and pairs concordant for EoE will differentiate epigenetic methylation sites associated with EoE risk versus family specific patterns not associated with EoE.

Figure A1. Patterns of related clinical conditions in parents and siblings of EoE proband patients are different.

Percent of first degree affected relations is reported, Chi² at p≤0.05. GI conditions by sex of parent: Fathers report more

EoE and food impaction than mothers.

Figure A2. Patterns of related clinical conditions in parents and siblings of EoE proband patients are different.

Percent of first degree affected relations is reported, Chi² at p≤0.05. GI conditions by relation: siblings report more EGID than parents. Parents report more food impaction and dilation than siblings.

Figure A3. Patterns of related clinical conditions in parents and siblings of EoE proband patients are different.

Percent of first degree affected relations is reported, Chi² at p≤0.05. Allergic conditions by relation: siblings report more asthma and eczema than parents. There is a high rate (~50%) of allergic rhinitis in all first degree relatives.

Appendix B: Project Management Documents

B1 Nuclear Families Study Design Proposal and Project Management

Familial Risk Study

Pilot: Summer 2010

Replication: funded CEG NIS May, 2011 (dated 11 May 2011, received 27 May 2011)

Background:

Subjective/Anecdotal: EoE is more common in families than in the general population.

Objective/Published: One published study estimated the sibling recurrence risk ratio of EoE at 80 (Blanchard, Wang, Rothenberg, 2006).

Assessment/So What? No studies have confirmed this estimate or estimated the risk in other relations.

Plan/Purpose: To study EoE and related allergic conditions in the allergic family

Hypothesis: The recurrence risk ratio in first degree relations of EoE proband patients will be higher than the general population prevalence.

Aims:

1. Estimate frequency of EoE, by sex and relation in Fa, Mo, bro, sis, males, females a. Confidence intervals for a single proportion tested against the binomial probability distribution (binomial because the proportions are small) b. Test difference in EoE and related conditions by sex and relation using Chi^2 and Fisher’s Exact (n<=5) for dichotomous data, with Wald confidence intervals for two proportions 2. Estimate Recurrence Risk Ratio of EoE, by sex and relation in Fa, Mo, bro, sis, males, females 3. Clarify association of related clinical conditions in first degree relations of probands with EoE a. Descriptive statistics of the group i. Age ii. Sex iii. Relation iv. EoE Clinical diagnosis (pathology report or parent report) vs eos >=15/hpf v. Estimate frequency of Comorbid conditions 1. Asthma 2. Allergic Rhinitis 3. Eczema 4. Food Allergy 5. Urticaria 6. EGID 7. Impaction 8. Dilation b. Test difference in EoE and related conditions by sex and relation using Chi^2 and Fisher’s Exact (n<=5) for dichotomous data, with Wald confidence intervals for two proportions

TEAM ROLES:

1.Concept/ Senior author: Marc

Content experts:

EoE, Allergy, MolGen: Marc, Pablo

Statistical/Epi Genetics: Lisa

2.Funding/Budget/Grants:

Database Management: Mike/CCHMC A&I

Data Specialist: Heather/CCHMC A&I

Hardware,Software: MECEH NIEHS T32-ES10957

CEG NIS NIEHS P30-ES006096

CCHMC DBE & DHG

Poster printing: DHC and IES posters/ CCHMC A&I, CCED

IES poster/CCHMC Div Human Genetics

Publication cost: CEG NIS NIEHS P30-ES006096

AAAAI: 2011 Marc/CCED; 2012 Grace/CEG

DISCLOSURES:

MECEH NIEHS T32-ES10957

CEG NIS NIEHS P30-ES006096

CCED: CURED, Food Allergy Initiative, NIAID, NIDDK, Buckeye Foundation

3.Study Design: L,E

4.Data Analysis Plan (Methods, Power, Results): L,E

5.Data Management: Pablo, Mike, Lisa, Eileen, Heather

Regulatory Compliance: Bridget

REDCap Set up: Lisa (owner), Eileen, Heather

Recruitment, Consenting, Data collection, entry: retrospective: Eileen, Heather, Alexa, Angie, Jess

Data Sources: EPIC/patient MR and EGID research databases, participant interview

Sample collection for epigenetic/methylation study

6. Statistical Support: Lisa Martin PhD, Paul Succop PhD

7. Grant products: Abstracts, Manuscripts, Additional Grants from this work

First author responsibility: Eileen

Editing responsibility: Marc, Lisa, Pablo

PUBLICATIONS

Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. J Allergy Clin Immunol 2011:127(2) AB217 (abstract).

PRESENTATIONS Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of affected parent is associated with familial risk of eosinophilic esophagitis. Poster presentation at the International Eosinophil Society Biennial Meeting, June 21-25, 2011, Quebec City, Quebec, Canada. Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. Poster presentation at the Postdoctoral Recruitment Symposium, Cincinnati Children’s Hospital Medical Center, Cincinnati OH, April 29, 2011, Cincinnati, OH. Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. Oral presentation at American Academy of Allergy, Asthma and Immunology 2011 Annual Meeting, March 18-22, 2011, San Francisco, CA. Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. Poster presentation at the Digestive Health Center Retreat, Cincinnati Children’s Hospital Medical Center, Cincinnati OH, March 8, 2011, Cincinnati, OH. Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. Oral presentation at The University of Cincinnati College of Medicine, Department of Environmental Health, Division of Epidemiology and Biostatistics Seminar Series, February 17, 2011, Cincinnati, OH.

AWARDS Young Investigator Award at the International Eosinophil Society Biennial Meeting, June 21-25, 2011, Quebec City, Quebec, Canada.

8.Acknowledgements:

Care of Participants: retrospective

Histology: Collins

Cincinnati Center for Eosinophilic Disorders

Cincinnati Children’s Division of Allergy and Immunology

Cincinnati Children’s Division of Biostatistics and Epidemiology

Cincinnati Children’s Division of Human Genetics

CURED, Food Allergy Initiative, Buckeye Foundation

University of Cincinnati College of Medicine Department of Environmental Health, Division of Epidemiology & Biostatistics

B2: Twins Study Design Proposal and Project Management

Design Twin Registry and Studies

Why do this: EoE is typically an early onset, life-long and debilitating food allergy of unknown mechanisms. EoE and related conditions are clustered in families. Parents want to know if their next child will have EoE.

Project description: 1. Describe genetic vs. environmental risk of EoE in MZ and DZ twins 2. Epigenetic/methylation study 3. Environmental questionnaire

TEAM ROLES:

1. Concept/ Senior author: Marc

Content experts: EoE, Allergy, MolGen: Marc; Allergy and EGID database: Pablo Gastroenterology: Vince Statistical Genetics/ Study Design: Lisa Structural Equations Modeling: Paul, Heidi Environmental questionnaire design: Epigenetics/methylation: Shuk-Mei Ho, Hong Ji ImmunoChip Zygosity: Leah

2. Regulatory Compliance, Recruitment, Retention: CRC function

2008-mid 2010: Annette? 2010-November 2011: Bridget Buckmeier-Butz, Supervisor; Hong Phamm, Alexa Greenler November 2011-January 2012: Bridget Buckmeier-Butz, Supervisor January 2012-November 2012: Sean Jameson, Supervisor; Jessica King, CRC II November 2012-November 2013: Jessica King, CRC II November 2013-present: Tommie Grotjan, CCRC IV, Jonathan Kuhl, CRC II

3. Study Design: Eileen, Lisa, Marc MZ and DZ twin study

4. Funding/Budget/Grants: Eileen, Lisa, Marc

Database Management: REDCap/CCTST, no charge: Eileen, administrator; EGID and CCHMC EMR: Mike Data Specialist: Heather/CCHMC A&I and CEG NIS NIEHS P30-ES006096 Hardware,Software: MECEH NIEHS T32-ES10957 CEG NIS NIEHS P30-ES006096 CCHMC DBE & DHG Poster printing: DHC and IES posters/ CCHMC A&I, CCED IES poster/CCHMC Division of Human Genetics AAAAI: 2011 Marc/CCED; 2012 Grace/CEG Publication cost:

DISCLOSURES: MECEH NIEHS T32-ES10957 CEG NIS NIEHS P30-ES006096 2011, 2012 A&I, CCED: CURED, Food Allergy Initiative, NIAID, NIDDK, Buckeye Foundation? CCTST JIT 2012

5.Data Analysis Plan (Methods, Power, Results): Eileen, Lisa

frequencies, recurrence risk, Chi-square, t-tests, paired analyses, SEM/h2 Statistical and Graphic Support: Lisa Martin PhD, Paul Succop PhD, Eileen Alexander MS

6.Data Management: Pablo, Mike, Lisa, Eileen, Heather

REDCap Set up: Lisa (owner), Eileen (administrator), Heather Data Sources: EPIC/patient MR and EGID research databases, participant interview, pathology reports, slides

7.Data Collection and entry: shared function, as below, CRC coordinates

Histology: esophageal tissue eosinophil protocol, assessment, writing and editing: Margaret H. Collins MD

Recruitment, Consenting/MRR, Slide acquisition, Data collection, entry, medical records: Pablo, Heather, Alexa, Angie, Tommie, Jess CRCs: 2008 - mid 2010: Annette? 2010-November 2011: Bridget Buckmeier-Butz, Supervisor; Hong Phamm, Alexa Greenler November 2011-January 2012: Bridget Buckmeier-Butz, Supervisor January 2012-November 2012: Sean Jamison, Supervisor; Jessica King, CRC II November 2012-present: Jessica King, CRC II

Sample collection & processing for zygosity testing, epigenetic/methylation study: Alexa, Jess, Katie, and Leah Kottyan, CAGE. Leah also provided ImmunoChip processing, data, technical references and editing. Katie also provided technical references and SOPs.

8.Samples: shared function, as below, CRC coordinates

Collection, work with Mike to assign sample number and enter in EGID and REDCap databases, transfer to our lab and to internal/external core facilities for analyses: Alexa, Tommie, Katherine Kemme, Jessica King

Laboratory Analyses, Methodology references/SOPs, QC, processing, storage: Rothenberg Lab: Katherine Kemme; Emily Stucke, Supervisor CAGE: Leah Kottyan, PhD ImmunoChip processing, methodology, analysis of zygosity and drafting of the manuscript Ho Core: Xiang Zhang, PhD and Miral Patel: Qubit and gel QC, Illumina Human Methylation analysis Ji Core: Hong Ji. PhD and Ashley Ulm: Pyrosequencing methodology and analysis

9. Communication: ??? How do other teams communicate and keep track of their “to-do” list? Maybe we can re-define structure, function, roles, reports vs emails, timeliness, cost, documentation, accountability? Team is not accustomed to timed agenda, written minutes, accountability

10. Process Management and Improvement Tools: Team is not accustomed to the use of evidence-based leadership tools NEED: organizational chart with delineation of both line accountability and functional team responsibilities

11. Acknowledgements/Authorship, as appropriate:

Care of Participants: retrospective Histology: Margaret Collins

12. Grant products: Abstracts, Manuscripts, Grants from this work

First author responsibility: Eileen Primary Editing responsibility: Marc, Lisa, Margaret, Leah

RESEARCH SUPPORT

 2014 University of Cincinnati Research Council Graduate Student Fellowship (competitive).

 Frank C. Woodside III, MD, Dinsmore & Shohl Fellowship via the Cincinnati Children’s Hospital Medical Center Division of Biostatistics and Epidemiology, $12,500.00 (competitive 2014).

 Interact for Health Scholarship for the study of Structural Equations Modeling, Interact for Health Foundation, Cincinnati, OH, 2014.

 National Institute of General Medical Sciences (NIGMS) 2013 Bursary Award under Grant No. R25GM093044. (competitive 10/31/2013) for the study of methylation analysis with K. Conneely, PhD, Emory University, Atlanta, GA.

 NIH 1R25GM093044-01 University of Alabama Birmingham Statistical Genetics Program, $2650.00 in July, 2013 (competitive).

 NIH 8 UL1-TR000077-04 Center for Clinical and Translational Science and Training, CCTST, CTSA, NCATS 2012 Just in Time Grant, Epigenetic Methylation and Risk of Eosinophilic Esophagitis in Twins, $7500.00, 2012 with LJ Martin PhD.

 NIEHS P30-ES006096-20 Director’s Matching Funds Grant from the Center for Environmental Genetics $3520.00 in 2012 (G100121-6261205000-1-1009619)  Research support for lab materials from the Molecular Epidemiology in Children’s Environmental Health, NIH T32 ES10957, $2000.00 in 2012.

 Educational Grant to the UCLA Statistical Genetics summer program from the Molecular Epidemiology in Children’s Environmental Health Travel Grant, NIH T32 ES10957, 2012.

 NIEHS P30-ES006096-20 Mentee PI-Mentor PI Grant from the Center for Environmental Genetics; $21,000.00 in 2012 (competitive); (G100121-6261205000-1-1009620-I/O S10801) http://www.eh.uc.edu/ceg/project_2012.asp

 Travel Grant to the American Academy of Allergy, Asthma and Immunology Annual Meeting from the Center for Environmental Genetics, NIEHS P30-ES006096, 2012.

 Travel Grant to the International Eosinophil Society from the Molecular Epidemiology in Children’s Environmental Health, NIH T32 ES10957, $2375.00 in 2012.

 NCRR/NIH UL1-RR026314-01 Center for Clinical and Translational Science and Training, REDCap data management.

 Molecular Epidemiology in Children’s Environmental Health predoctoral Fellowship (tuition and stipend), NIH T32 ES10957, GK LeMasters, R Deka, K Dietrich, PIs, (competitive), $35,422.00 in 2011, $35,422.00 in 2012, $35,422.00 in 2013 ($13,822.00 tuition).

 Full University Graduate Scholarship, University of Cincinnati, College of Medicine, Department of Environmental Health, Epidemiology and Biostatistics Division, ~$30,000.00 in Sept 2008, 2009.

PEER REVIEWED PUBLICATIONS

Submitted to Journal of Allergy and Clinical Immunology (JIF 12.1) on May 16, 2014, accepted after minor revisions on July 3, 2014. Title: Twin and Family Studies Reveal Strong Environmental and Weaker Genetic Cues Explaining Heritability of Eosinophilic Esophagitis Author: Eileen S. Alexander, MS,a,b,c Co-authors: Lisa J. Martin, PhD,a,b Margaret H. Collins, MD,a,b Leah Kottyan, PhD,b Heidi Sucharew, PhD,b Hua He, MS,b Vincent A. Mukkada, MD,a,b Paul A. Succop, PhD,a J. Pablo Abonia, MD,a,b Heather Foote,b Michael D. Eby, BS,b Tommie M. Grotjan, BS,b Alexandria J. Greenler, BS,b Evan S. Dellon, MD, MPH,d Jeffrey G. Demain, MD,e Glenn T. Furuta, MD,f Larry E. Gurian, MD, AGAF,g John B. Harley, MD, PhD,a,b,h Russell J. Hopp, DO,i Ajay Kaul, MD,a,b Kari C. Nadeau, MD, PhD,j,k Richard J. Noel, MD, PhD,l,m, Philip E. Putnam, MD,a,b Karl F. von Tiehl, MD,n Marc E. Rothenberg, MD, PhDa,b

Blanchard C, Stucke EM, Rodriguez-Jimenez B, Burwinkel K, Collins MH, Ahrens A, Alexander ES, Buckmeier-Butz BK, Jameson SC, Kaul A, Franciosi JP, Kushner JP, Putnam PE, Abonia JP, Rothenberg ME. A striking local esophageal cytokine expression profile in eosinophilic esophagitis. J Allergy Clin Immunol 2011;127:208-217e7.

ABSTRACTS PUBLISHED IN PEER REVIEWED JOURNALS

Alexander ES, Martin LJ, Abonia JP, Collins MH, Succop PA, Greenler AJ, Dellon ES,. Demain JG, Franciosi JP, Furuta GT, Gurian LE, Hopp RJ, Kaul A, Nadeau K, Noel RJ, Putnam PE, von Tiehl KF, Eby MD, Foote H, Ellison AC, Rothenberg ME. Twin Shared Environment Increases The Risk of Eosinophilic Esophagitis in Families J Allergy Clin Immunol 2012:129(2) ABp245#926.

Collins MH, Martin LJ, Alexander ES, Pentiuk S, Ellison AC, Putnam PE, Franciosi JP, Abonia JP, Rothenberg ME. Histology Scoring System (HSS) is Superior to Peak Eosinophil Count (PEC) to Identify Treated vs Untreated Eosinophilic Esophagitis (EoE) Patients J Allergy Clin Immunol 2012:129(2) AB#363.

Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. J Allergy Clin Immunol 2011:127(2) AB217.

ORAL PRESENTATIONS (invited)

Alexander ES. A TALE of TWO Core facilITIES: Resources for studying Family-based Risk from Epigenetics, Environment (FREE) and heritability at The University of Cincinnati College of Medicine, Department of Environmental Health, Division of Epidemiology and Biostatistics Seminar Series, February 20, 2014, Cincinnati, OH.

Alexander ES. Environmental and Genetic Contributions to the Risk of Eosinophilic Esophagitis by Analysis of Families and Twins at the Center for Environmental Genetics Symposium, May 21, 2013, Kehoe Auditorium, Kettering Laboratory, University of Cincinnati College of Medicine, Cincinnati, OH.

Alexander ES. Family Risk in Eosinophilic Esophagitis: Twin and Family Genetics Studies at The Division of Biostatistics and Epidemiology Research in Progress Seminar Series, February 13, 2013, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH.

Collins MH and Alexander ES. Histology Scoring System for esophageal biopsies at The University of Cincinnati College of Medicine, Department of Environmental Health, Division of Epidemiology and Biostatistics Seminar Series, September 6, 2012, Cincinnati, OH.

Alexander ES. Twin Shared Environment Increases The Risk of Eosinophilic Esophagitis at the Center for Environmental Genetics Symposium, June 26, 2012, Kehoe Auditorium, Kettering Laboratory, Cincinnati, OH.

Alexander ES. Twin Shared Environment Increases The Risk of Eosinophilic Esophagitis in Families at The University of Cincinnati College of Medicine, Department of Environmental Health, Division of Epidemiology and Biostatistics Seminar Series, February 2, 2012, Cincinnati, OH.

Required Citations:

Study data were collected and managed using REDCap electronic data capture tools hosted at [YOUR INSTITUTION].1 REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing: 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources.

Paul A. Harris, Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, Jose G. Conde, Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. 2009 Apr;42(2):377-81.

Appendix B3

Analysis Report, abridged Research Report for Family Risk of EE Data Analysis Plan- updated 2014. CCHMC IRB: Study: EGID Study

Study #: 2008-0098 01-3-15 Study Type: Study Application

Principal Investigator: Marc Rothenberg Prepared By: Annette Ahrens

Most Recent Review Type: Full IRB Review Initial Approval Date: 4/10/2007

Current Approval Date: 2/6/2012 Initial Review Type: Full IRB Review

Current Approval Letter: View Initial Approval Letter:

Expiration Date: 2/5/2013 Risk Level: Minimal Risk

Subject COI Last Name First Name Department Role Date Added Interaction Status

Biostatistics & Alexander Eileen Direct Statistician 5/18/10 Current

Epidemiology

Collins Margaret Research Pathology Indirect Sub-investigator 2/13/08 Current

Database

Eby Michael Allergy & Immunology Indirect Other 9/4/09 Current Administator

Foote Heather Adolescent Medicine Indirect Sub-investigator 11/13/09 Current

Grotjan Tommie Allergy & Immunology Direct Coordinator 9/4/09 Current

Jameson Sean Allergy Direct Sub-investigator 2/13/08 Current

Kemme Katherine Allergy & Immunology Indirect Sub-investigator 2/22/11 Current

King Jessica Allergy & Immunology Direct Coordinator 11/23/11 Current

Kottyan Leah Rheumatology Indirect Sub-investigator 3/28/11 Current

Martin Lisa Human Genetics Indirect Sub-investigator 10/28/08 Current

Study: EOSINOPHILS AND INFLAMMATION, AN EXPANDED STUDY

Study #: 2008-0090 04-12-8 Study Type: Study Application

Principal Investigator: Marc Rothenberg Prepared By: Margaret Palazzolo

Most Recent Review Type: Full IRB Review Initial Approval Date: 3/13/2007

Current Approval Date: 1/23/2012 Initial Review Type: Full IRB Review

Current Approval Letter: View Initial Approval Letter:

Expiration Date: 1/22/2013 Risk Level: Minimal Risk

Statisticians: Lisa J. Martin PhD, Eileen Alexander MS

1. Study Aims

EoE is a complex genetic disorder whose risk factors include both underlying genetic variants as well as environmental exposures. However, how both genes and environment act together to determine EoE risk is unclear. The objective of this study is to determine the roles of genetic and environmental variation in EoE. We will do this using a combination of traditional family based analyses examining risk for first degree relations in families of EoE index cases and twin studies.

This combination approach will allow us to begin to disentangle the relative contribution of genetics vs shared environment. Based on a genetic model, we hypothesize that concordance will be higher in MZ than DZ twins and siblings.

2. Study Design

Two retrospective cohorts were designed to examine familial risk of EoE. Using reported family history in the clinical record of EoE probands at our center, we report the frequency and recurrence risk in first degree relations (CCED cohort).

MZ and DZ twins (proband with EoE) were enrolled worldwide (Twin cohort). In probands and co-twins, EoE was ascertained by pathology report or slide confirmation of an esophageal biopsy with ≥15 intraepithelial eosinophils per

high power field. EoE absence was ascertained by negative symptoms or biopsy. Frequencies, recurrence risk ratios

(RRR), proband-wise concordance and heritability were calculated.

3. Analysis Population

Over the past two years, the Twin Team has recruited a cross-sectional, worldwide cohort. Twin subjects, one of whom has EoE, provided family data, pathology reports confirming EoE, Oragene saliva DNA samples and histology slides, when possible. For this study 60 confirmed EoE twin probands and their co-twins are included. This cohort includes one set of discordant MZ triplets and one set of concordant DZ triplets.

In the CCED cohort, proband patients with family history data were identified for the period August 1, 2008 to December

31, 2011. EoE probands were defined by clinico-pathologic criteria, including 15 or more eosinophils per high powered field (400x) in the esophageal biopsy and assessment by their Cincinnati Center for Eosinophilic Disorders (CCED) physician. History of related medical conditions for first degree relations was by parent or self report. GI conditions included EoE, non-esophageal eosinophilic GI disease (EGID), food impaction and esophageal dilation. Eosinophilic gastritis, eosinophilic enteritis and eosinophilic colitis were combined to the category “EGID” by the CCED physician.

Sex was available for proband patients and inferred for relations: mother, father, sister, brother. CCED proband patients for whom confirmed family history was not available in EPIC were excluded. Among the 1171 proband patients seen at the CCED during this time period, 682 had family medical history available (58%). Race was coded as white, black,

Asian, Native American, mixed, other and unknown.

4. Variables (Available from the Twin Registry – Database)

Demographic data

A. Family, by parent report

a. adoption

b. age adoption

c. sex

d. race

e. ethnicity

B. Twins

a. proband, by earlier date of diagnostic EGD, if possible

b. birthdate

c. contact data to derive season and warm/cold with birthdate

C. Participant identifiers

a. Study ID

b. EGID per Mike: IDNO, EGID Family ID

c. Rothenberg Lab: Mike verifies identifiers and supplies Subject ID, tissue type, collection date prior to

sample pull

d. REDCap Twin database with participant entry by CRC and subset confirmed by CRC as consented for

analysis 9/26/2012, can be contacted for samples, lost to follow-up

Sample data

A. Quality Control for Oragene saliva DNA subset

a. Nucleic acid concentration ng/uL

b. Volume uL available

c. A230

d. A260

e. A280

f. Collection date

g. SampID (barcode)

B. ImmunoChip Zygosity data

a. Zygosity determination (probability allele sharing by descent) by 196,524 SNPs

C. ADD Methylation Data, when available

Zygosity

A. Parent report

B. Pea pod Questionnaire, continuous score 3 questions, validated questionnaire

C. Immunochip, as listed above under sample data

Histopathology measures for affected twins (one pair may need to be removed if CRC is unable to obtain the pathology slides on twin 2, as they are self-report affected but pathology report does not have counts)

A. Peak eosinophil count on pathology report from another institution

a. proximal

b. mid

c. distal

d. site unknown

B. Peak eosinophil count on pathology report from CCHMC

a. proximal

b. mid

c. distal

d. site unknown

C. Peak eosinophil count on research slides reviewed by Dr Collins at CCED

a. proximal

b. mid

c. distal

d. site unknown

D. EoE derived

E. ADD HSSe data when available

Medical and Surgical History for twins

D. Parent report

a. Zygosity

b. Diagnosis other eosinophilic disorders

c. age symptoms

d. event or trigger (sparse data)

e. EGD number

f. PPI history

g. Other EoE related medication data sparse

E. Surgical reports

a. pH probe and result (sparse data)

b. PPI prior to EGD and length of time (sparse data))

c. EGD date

d. EGD number

e. Colonoscopy and related data sparse

f. Other surgical history sparse

Parent or Self-reported Symptoms and co-morbid conditions for twins, and first degree relations (children of twins have sparse data)

A. Eczema

B. Psoriasis

C. Nausea

D. Vomiting

E. Diarrhea

F. Abdominal Pain

G. Difficulty Swallowing

H. Food stuck

I. Food Impaction

J. Emergent Food Removal

K. Dilation

L. Heartburn

M. Crohn’s

N. Age of symptoms

O. Suspected undiagnosed EoE

P. MD diagnosed EoE

Q. MD diagnosed EG

R. MD diagnosed EC

S. MD diagnosed HES

T. MD diagnosed Asthma

U. MD diagnosed Barrett’s esophagus

V. MD diagnosed esophageal cancer

Parent or Self-reported Medical data for twins only

A. By individual

a. mitochondrial disorder

Parent or Self-reported Environmental data

A. By twin pair

a. Location (derive latitude, longitude), ADD prior locations

b. DOB (derive age, season of birth, warm/cold)

c. Fertility treatment, yes/no and Mother: oral, injectable, IVF

i. ADD Father: oral, wash, egg harvest by Mother

d. Amnion and chorion number categorical (Sparse)

e. Gestational age

f. Prenatal vitamin use and length of use categorical

g. Breast feeding yes/no

B. By individual

a. Breast feeding yes/no: ADD full, partial, number of months, exclusivity, solid food introduction

b. Birth weight (derive weight difference between twins)

c. Birth length (derive length difference between twins)

d. Birth order

C. By twins and all first degree relations: mother, father, siblings; children (sparse data)

a. Allergies, environmental

b. Allergies, spring

c. Allergies, summer

d. Allergies, fall

e. Allergies, winter

f. Allergies, year round

g. Allergies, food

h. Allergies, medication, list any (derive by type)

i. Anaphylaxis

D. ADD Aim 2 Environmental Questionnaire and validation data when available

5. Variables (Available from the CCED EGID and CCHMC EPIC databases)

Demographic data

A. Family, by parent report

a. birthdate proband

b. relation name (derive younger/older)

c. sex of proband; sex of relation inferred from mother, father, brother, sister relationship

d. race

e. ethnicity

f. family size

B. Cross reference identifiers

a. EGID per Mike

b. EPIC per Mike

Parent report, MD confirmed medical history of EoE and related co-morbid conditions

A. EoE

B. EG

C. Food Impaction

D. Esophageal dilation

E. Food allergy

F. Allergic rhinitis

G. Asthma

H. Eczema

I. Urticaria

J. Seen by Dr JPA (yes/no)

BOLD available in both cohorts, note that criteria are not identical

5. Analysis Plan

Data were analyzed using JMP 5.1 (SAS Institute, Cary, NC). All reported p-values are two-tailed with significance at p≤0.05, unless otherwise specified. To compare groups, X²df=1 or Fisher’s Exact test were used, unless otherwise specified.

Patient study characteristics: Demographic characteristics of the sample population and subgroups, by analyses, were described using means±standard deviations for continuous traits and frequencies for discrete traits. Comparability of subgroups was tested using X², Fisher’s Exact test and logistic regression. The associations of covariates were assessed using parametric t-tests for normally distributed variables and Wilcoxon non-parametric tests for non-normally distributed variables, as appropriate.

For EoE and related conditions, confidence intervals, Chi-square or Fisher’s Exact were calculated by sex and relationship. The binomial distribution was used for confidence interval calculations in the CCED cohort, when specified.

In the subset ascertained by Immunochip (n=19), we estimated the proportion of Identity by decent sharing derived from the genome between each pair of genotyped individuals and compared it with the proportion expected based on the genealogical information. For this analysis we used 196,524 SNPs on the Illumina ImmunoChip. Using the PLINK platform for analysis, we derived the probability π, of sharing two genes identity by decent plus half the probability of sharing one gene identity by decent. ImmunoChip SNPs include a high proportion of linked SNPs and many rare SNPs.

Monozygotic Twins are defined by π of 0.99-1. Dizygotic twins and full siblings are defined by π between 0.45-0.65, half siblings and cousins are defined by π between 0.2-0.45 and unrelated individuals are defined by PI_HAT = <0.2.

Family wise analyses include recurrence risk ratios, twin concordance estimates, and heritability estimates. Risk ratios were calculated as (#affected/total)/prevalence. Prevalence was set at 5.5 per 10,000 (Prasad, GA, 2009).

Collectively, frequencies, RRR, and heritability estimates indicate the pattern of causation. Disorders that aggregate in families, as EoE does, are likely caused by genetic mutations. However, common family environment can mimic genes.

Pedigree patterns suggest dominant, recessive or polygenic modes of inheritance. Furthermore, twin-based models of heritability estimate the relative contribution of genes, family environment and variability unique to the individual. The purpose of estimating genetic and environmental causes of EoE is to direct our research.

Proband-wise concordance was chosen for its ascertainment correction and is calculated as 2C/(2C+D).

As an exploratory aspect we will test to see if concordance or presence of EoE is associated with previously collected environmental factors. These pilot data will inform and direct the focus areas for validation in Aim 2 of the Family Risk of Eosinophilic Esophagitis (FREE) and Environmental Risk study.

6. Indivisual analyses are not presented here

7. Tables and Figures: see Chapters and manuscripts

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B4: Aim 1 Recruitment, Data and Sample Collection

Briefly, The EoE Twins Registry began in 2008 with ~12 pairs of twins, one of whom had physician diagnosed EoE. In mid-2010, I was assigned to design and manage a Twin study resource to assess EoE risk. I began with one Clinical

Research Coordinator (CRC), and quickly requested statistical genetics mentorship from Dr. Lisa Martin and Allergist Dr.

Pablo Abonia. For comparison, I started the nuclear family study to answer the question posed by families at the

Cincinnati Center for Eosinophilic Esophagitis who asked, “Will my next child have EoE?” We sought to recruit, design the study, mine data, collect data and DNA samples and apply for funding to support our aims. The Twin team was formed in late 2010 to meet these aims.

I used an evidence-based project management model to design and execute the registry and study groups (Appendices A1 and A2). Further, based on optimal workgroup size of 6-8, I designated needed roles and content expertise and identified high functioning individuals for the team. High staff turnover and lack of familiarity with the tools of project management, evidence-based leadership and process improvement proved challenging.

Based on early pilot data from the large nuclear families group, I was able to secure both a predoctoral Fellowship in

Molecular Epidemiology in Children’s Environmental Health and also internal funding for additional staff and laboratory analysis. This New Investigator Scholar grant from the Center for Environmental Genetics (CEG), in turn, made me eligible for additional funding from the CEG, Director’s Funds and Center for Clinical and translational Science and

Training (CCTST), to fully fund pilot epigenetic methylation studies with collaborators at the University of Cincinnati

Department of environmental Health and at Cincinnati Children’s Hospital Medical Center. Specific mechanisms of funding were previously acknowledged.

Data management options were explored due to the high-dimensionality of the database, and anticipated increase. The

REDCap system (CCTST) met study needs at no cost to us. An Analysis Report (Appendix A3) was established to establish continuity of internal communication for available variables and results. Further, we anticipated storage needs on the Martin Lab site and future use of the computational cluster.

Despite challenges, we had a 5-fold increase in sample size within three years for a total of 63 pairs fully enrolled (Figure

2.1). Twins were recruited internationally and successfully enrolled from the continental U.S., Alaska and Australia

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(Figure 2.2). Language and consenting issues restrained recruitment in many countries. The relative rarity of EoE constrained access to existing Twin Registries. Paired saliva DNA samples were collected from ~half (Figure 2.3).

Inclusion criteria and DNA sample collection are elaborated in Section 4.

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REDCap Data collection form: Family History was collected for first degree relations, i.e., mother, father, siblings, co-twins and children, when available.

Field Attributes (Field Type, Validation, Field Label # Variable / Field Name Choices, Branching Logic, Calculations, Field Note etc.)

Instrument:Intake 1 study_id Study ID text 2 intakedate intake date text (date_ymd) 3 contact_consent Can the patient be called for history collection? yesno 1Yes 0No 4 sample_consent Can samples be collected from the patient? yesno 1Yes 0No 5 analysis_consent Can the patient be included in analysis? yesno 1Yes 0No 6 lost_fu Lost to Follow-up yesno 1Yes 0No 7 first_name First Name text Name of child 8 last_name Last Name text Name of child 9 dob DOB text (date_ymd) 10 sex Sex radio 0 Female 1Male 11 father__first_name Father First Name text 12 father_last_name Father Last Name text 13 mother_first_name Mother First Name text 14 mother_last_name Mother Last Name text 15 contact Contact radio person to contact about twin study 1 mother 2 father 3twin 4 other 16 other_contact Other contact text Show the field ONLY if: [contact]=4 17 address Address notes 18 street_contact Street address text 19 city_contact City text 20 state_contact State text 21 zip_contact Zip Code text 22 country_contact Country text 23 phone_number_home Home Phone Number text 24 phone_number_cell Cell Phone Number text 25 callnote Calling Notes notes Enter any information regarding good and bad times to call twins 26 email Email text 27 contact2 Contact radio

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person to contact about twin study 1 mother 2 father 3twin 4 other 28 other_contact2 Other contact text Show the field ONLY if: [contact]=4 29 address2 Address notes 30 phone_number_home2 Home Phone Number text 31 phone_number_cell2 Cell Phone Number text 32 callnote2 Calling Notes notes Enter any information regarding good and bad times to call twins 33 email2 Email text 34 proband Proband radio First affected family member that receives medical 1Yes treatment 0No -9 Missing 35 twin_study How did you hear about the twin study? radio 0 Doctor/Nurse 1 Research Staff (CRC) 2 Support group (APFED, CURED) 3 Social Media (Facebook, Twitter) 4 Twin Group 5 CCED website 36 twin_group Have you ever been a member of a twin club? yesno 1Yes 0No 37 twin_recruit Comments text 38 consent_scan consent scan of signature file 39 background_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:ContactLog 40 contact_notes_1 Contact notes 1 notes 41 contact_notes_2 Contact notes 2 notes 42 contact_notes_3 Contact notes 3 notes 43 contactlog_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Baseline 44 mrn MRN text 45 race Race radio 0 White/Caucasian 1 Black/African American 2 Asian 3 American Indian/Alaska Native 4 Native Hawaiian/Pacific Islander 5 Other 6 Mixed race

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7 Indian -9 missing 46 ethnicity Ethnicity radio 0 NonHispanic 1 Hispanic 2 Other -9 Missing 47 sex_of_twin_pairs Sex of twin pairs radio 0MM 1MF 2FF 48 zygosity Zygosity radio zygosity by parent report 0 MZ (Identical) 1 DZ (Fraternal) -9 Missing 49 pea_pod_1 Were your twin children "as alike as two peas in a radio Show the field ONLY if: pod"? 1 1 As alike as two peas in a pod Ask the parent to answer the questions thinking back [sex_of_twin_pairs]=0 or 2 2 Usual sibling similarity [sex_of_twin_pairs]=2 to when the twins were 1 years old. If adult twins, ask them to think back to when they were children. 3 3 Quite different 50 pea_pod_2 Were they mixed up at that age? radio Show the field ONLY if: 1 1 Yes, very often [sex_of_twin_pairs]=0 or 2 2 Now and then [sex_of_twin_pairs]=2 33 Never 51 pea_pod_3 By whom were they mixed up? radio Show the field ONLY if: 1 1 Parents [sex_of_twin_pairs]=0 or 2 2 Relatives or neighbors [sex_of_twin_pairs]=2 3 3 Others 4 4 Nobody 52 pardx Participant's diagnosis by parent report radio 1EoE 2 GI symptoms 3 healthy 4 missing 53 compardx Comments on parent reported dx notes 54 age_symptoms Age EE symptoms started text 55 was_there_an_event_or_trig Was there an event or trigger prior to your notes diagnosis of EoE? 56 egd_ # EGDs text 57 ppi_hx PPI hx text 58 hospital Hospital text 59 physician_gi GI Physician text fill in with physician name 60 physician_allergy Allergy Physician text fill in with physician name 61 demographics_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Pathology 62 earliest_eoe_path_egd_date Earliest EoE path EGD date text The earliest path report with >15 eos 63 earliest_egd EoE Dx EGD Date text earliest EGD with >15 eos that were confirmed at CCHMC

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64 proximal_eos_slides Read by Margaret Collins/Research proximal eos text (integer, Min: 0, Max: 300) count on slides 65 mid_eos_slides Read by Margaret Collins/Research mid eos count text (integer, Min: 0, Max: 300) on slides mid esophagus or location other than proximal and distal 66 distal_eos Read by Margaret Collins/Research distal eos text (integer, Min: 0, Max: 300) count on slides 67 site_unknown_eos Read by Margaret Collins/Research site unknown text eos count 68 othpeos Read by CCHMC pathologist proximal counts on text slides 69 othmeos Read by CCHMC pathologist mid eos counts on text slides 70 othdeos Read by CCHMC pathologist distal eos counts on text slides 71 otherunkeos Read by CCHMC pathologist site unknown eos text count 72 proximal_eos_counts_on_pat Outside Path Report proximal eos counts text (integer, Min: 0, Max: 300) 73 mid_eos_counts_on_path_rep Outside Path Report mid eos counts text (integer, Min: 0, Max: 300) 74 distal_eos_counts_on_path Outside Path Report distal eos counts text (integer, Min: 0, Max: 300) 75 unknown_eos_counts_on_path Outside Path Report site unkown eos count text 76 ph_probe pH/Impediance Probe radio, Required 1yes 0no -9 missing 77 ph_probe_result Result from pH/ Impediance Probe radio, Required Show the field ONLY if: 0 Positive [ph_probe]= "1" 1 Negative -9 missing 78 ppi_egd PPI at Dx EGD radio, Required 1yes 0no -9 missing 79 time_ppi Time on PPI before Dx EGD text Show the field ONLY if: [ppi_egd]=1 80 other_eosinophils Abnormal eosinophils in other GI location(s)? yesno EG, EC, EGE 1Yes 0No 81 eg_path Eosinophilic Gastritis yesno Show the field ONLY if: 1Yes [other_eosinophils] = '1' 0No 82 eg_eos_count Eos count yesno Show the field ONLY if: 1Yes [other_eosinophils] = '1' 0No 83 ec_path Eosinophilic Colitis yesno Show the field ONLY if: 1Yes [other_eosinophils] = '1' 0No 84 ec_eos_count Eos count yesno Show the field ONLY if: 1Yes [other_eosinophils] = '1' 0No 85 ege_path Eosinophilic Gastroenteritis yesno Show the field ONLY if: 1Yes [other_eosinophils] = '1' 0No 86 ege_eos_count Eos count yesno Show the field ONLY if:

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[other_eosinophils] = '1' 1Yes 0No 87 eos_duodenitis Eosinophilic Duodenitis? yesno Show the field ONLY if: 1Yes [other_eosinophils] = '1' 0No 88 ed_eos_count Eos count yesno Show the field ONLY if: 1Yes [other_eosinophils] = '1' 0No 89 location_of_eos_in_gi_trac Location of eos in GI tract text Show the field ONLY if: [other_eosinophils] = '1' 90 eos_count Eos count text (integer, Min: 0, Max: 1000) Show the field ONLY if: [other_eosinophils] = '1' 91 date_of_egd_colonoscopy Date of EGD/Colonoscopy text Show the field ONLY if: [other_eosinophils] = '1' 92 pathology_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Post Ppi Pathology 93 start_ppi date PPI trial start text (date_mdy) 94 stop_ppi date PPI trial stop text (date_mdy) 95 egd_date_post_ppi egd date after ppi trial text (date_mdy) 96 pppi_prox_mhc proximal esophageal eosinophil count Collins text (integer) 97 pppi_mid_mhc mid esophageal eosinophil count Collins text (integer) 98 pppi_distal_mhc distal esophageal eosinophil count Collins text (integer) 99 pppi_unknwn_site_mhc site unknown esophageal eosinophil count Collins text (integer) 100 pppi_prox_cchmc proximal esophageal eosinophil count CCHMC text (integer) 101 pppi_mid_cchmc mid esophageal eosinophil count CCHMC text (integer) 102 pppi_distal_cchmc distal esophageal eosinophil count CCHMC text (integer) 103 pppi_unknwn_site_cchmc site unknown esophageal eosinophil count CCHMC text (integer) 104 pppi_prox_outside pppi proximal outside path report esophageal eos text (integer) count 105 pppi_mid_outside pppi mid outside path report esophageal eos count text (integer) 106 pppi_distal_outside pppi distal outside path report esophageal eos text (integer) count 107 pppi_unknwn_site_outside pppi site unknown outside path report esophageal text (integer) eos count 108 post_ppi_pathology_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Background 109 cchmc_patient CCHMC Patient yesno 1Yes 0No 110 egid_fam_id EGID Family ID text 111 egid_idno EGID IDNO text EGID IDNO from Data Specialist 112 consent_eosinflam_date EosInf Consent Date text (date_ymd) 113 consent_egid_affected_date EGID Affected Consent Date text (date_ymd) 114 consent_egid_unaffected_date EGID Unaffected Consent Date text (date_ymd) 115 telecom_date Date telecomm completed text (date_ymd)

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116 dna_saliva Saliva DNA yesno 1Yes 0No 117 dna_saliva_date Saliva DNA Date text (date_ymd) Date saliva sample collected 118 saliva_sampid1 saliva SampID 1 text saliva sample number 1 from data specialist 119 dna_saliva_date2 Saliva DNA Date 2 text Date saliva sample 2 if collected 120 saliva_sampid_2 saliva SampID 2 text saliva sample number 2 if collected from data specialist 121 dna_blood Blood DNA yesno 1Yes 0No 122 dna_blood_date Blood DNA Date text (date_ymd) Date blood sample collected 123 blood_sampid blood SampID text blood sample number from data specialist 124 tissue_sample Tissue Sample yesno 1Yes 0No 125 tissue_sample_date Tissue Sample Date text (date_ymd) Date tissue sample collected 126 tissue_sampid tissue SampID text tissue sample number from data specialist 127 egid_database EGID Database yesno 1Yes 0No 128 chart_review_date Chart Review Date text (date_ymd) 129 research_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:DNAzygosity 130 dst DST percent SNPs in common text percent SNPs in common 131 pi_hat pi_hat text estimated proportion of genes IBD 132 dna_zygosity DNA zygosity radio saliva DNA immunochip zygosity 0 MZ (Identical) 1 DZ (Fraternal) -9 Missing 133 dnazygosity__complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Birth Info 134 adopted Were the twins adopted? yesno 1Yes 0No 135 age_adopt Age of adoption? text If participant is adopted, capture family hx for adopted family 136 gestational_age How many weeks did you carry the twins? text

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137 birth_order Was the patient born first or second? text 138 how_were_your_twins_in_the How were your twins separated in the womb? radio 1 Dichorionic/Diamniotic (2 sacs, 2 placenta) 2 Monochorionic/Diamniotic (2 sacs, 1 placenta) 3 Monochorionic/Monoamniotic (1 sac, 1 placenta) 139 birth_weight_lbs Birth weight in lbs text 140 birth_weight_oz Birth weight in oz text 141 birth_length_inches Birth length in inches text 142 breast_feeding Did you breast feed? yesno 1Yes 0No 143 did_you_take_prenatal_vita Did you take prenatal vitamins? yesno 1Yes 0No 144 how_long_did_you_take_the Were you able to take the prenatalvitamins the radio Show the field ONLY if: whole pregnancy, sone of the time, or start but 1 Entire pregnancy [did_you_take_prenatal_vita]="1" have to stop? 2 Some of the pregnancy 3 Started taking vitamins but stopped 145 did_you_have_fertility_tre Did you use a fertility treatment to assist in getting yesno pregnant? 1Yes 0No 146 what_fertility_treatment_d Did you use oral, injectable, or IVF? radio Show the field ONLY if: 1Oral [did_you_have_fertility_tre]="1" 2 Injectable 3IVF 147 birth_info_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Medhx 148 environmental_allergies Do you have environmental allergies? yesno 1Yes 0No 149 spring_allergies In the spring? yesno Show the field ONLY if: tree pollen 1Yes [environmental_allergies]="1" 0No 150 fall_allergies In the fall? yesno Show the field ONLY if: weeds/mold 1Yes [environmental_allergies]="1" 0No 151 winter_allergies In the winter? yesno Show the field ONLY if: mold/dust mites/cat/dog/roach 1Yes [environmental_allergies]="1" 0No 152 summer_allergies In the summer? yesno 1Yes 0No 153 year_round_allergens Or year round? yesno Show the field ONLY if: mold/dust mites/cat/dog/roach 1Yes [environmental_allergies]="1" 0No 154 anaphylaxis Have you ever had an anaphylaxisc reaction to text Show the field ONLY if: anything?

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[environmental_allergies]="1" list agent causing reaction 155 drug_allergy Any drug allergies that you know of? text Show the field ONLY if: list drug name [environmental_allergies]="1" 156 food_allergies Any food allergies? yesno 1Yes 0No 157 eczema Eczema yesno itchy, dry, red, bumpy 1Yes 0No 158 psoriesis Psoriesis yesno 1Yes 0No 159 nausea Nausea yesno feeling like throwing up 1Yes 0No 160 vomiting Vomiting yesno throwing up 1Yes 0No 161 diarrhea Diarrhea yesno loose watery poop, often 1Yes 0No 162 abdom_pain Abdominal Pain yesno 1Yes 0No 163 diff_swallow Difficulty Swallowing yesno 1Yes 0No 164 food_stuck Food getting stuck yesno drink a lot of water to wash down, repeatedly swallow 1Yes 0No 165 food_impact Food Impaction yesno food stuck in tube and can't bring up or swallow down 1Yes 0No 166 er Did you go to Dr for removal? yesno Show the field ONLY if: 1Yes [food_impact]="1" 0No 167 esoph_dilation Have you ever had your esophageal Dilated or yesno streched? 1Yes stretching the swallowing tube 0No 168 heartburn Any trouble with heartburn? yesno burning feeling in the chest 1Yes 0No 169 crohns Crohns yesno 1Yes 0No 170 asthma Asthma yesno 1Yes 0No 171 barretts Barrett's Esophagus yesno *adults 1Yes 0No 172 esophag_cancer Esophageal Cancer yesno *adults 1Yes

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0No 173 mito_disorder Mitochondrial Disorder yesno 1Yes 0No 174 medhx_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Medications 175 ppi_med Ever taken a PPI? yesno prevacid, nexium, prilosec, protonix 1Yes 0No 176 flovent_med Ever taken swallowed Flovent? yesno 1Yes 0No 177 plumicort_med Ever taken swallowed Pulmicort yesno 1Yes 0No 178 prednisone Ever taken Prednisone? yesno 1Yes 0No 179 other Other Medications for EE notes 180 medications_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Surghx 181 egd_surg Have you ever had an upper endoscopy? yesno 1Yes 0No 182 egd__surg Do you know about how many upper endoscopies text Show the field ONLY if: you've had? [egd_surg]="1" 183 egd__bx Of those endoscopies about how many showed text Show the field ONLY if: active EoE? [egd_surg]="1" 184 colonoscopy Have you ever had a colonoscopy? yesno 1Yes 0No 185 colon_ Do you know about how many colonoscopies text Show the field ONLY if: you've had? [colonoscopy]="1" 186 colon__bx Of those colonoscopies did any of them show text elevated eosinophils in the colon? 187 f_tube Do you have a feeding tube? radio 0 NG Tube 1 J Tube 2 GJ Tube 3 NJ Tube 4 G Tube 188 sinus_surg Have you ever had sinus surgery? yesno 1Yes 0No

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189 nissen Have you ever had a Nissen? yesno Fundoplication: stomach wrapped around lower end of 1Yes esophagus(swallowing tube) to help the sphincter 0No 190 tef_repair Have you ever had a TEF Repair? yesno Connection between esophagus and trachea 1Yes 0No 191 surghx_complete Complete? dropdown 0 Incomplete 1 Unverified 2 Complete

Instrument:Famhx 192 mother Mother yesno 1Yes 0No 193 dob_m DOB text Show the field ONLY if: [mother]="1" 194 environmental_allergies_m Do you have environmental allergies? yesno Show the field ONLY if: 1Yes [mother]="1" 0No 195 spring_allergies_m In the spring? yesno Show the field ONLY if: tree pollen 1Yes [mother]="1" and 0No [environmental_allergies_m]="1" 196 fall_allergies_m In the fall? yesno Show the field ONLY if: weeds/mold 1Yes [mother]="1" and 0No [environmental_allergies_m]="1" 197 winter_allergies_m In the winter? yesno Show the field ONLY if: mold/dust mites/cat/dog/roach 1Yes [mother]="1" and 0No [environmental_allergies_m]="1" 198 summer_allergies_m In the summer? yesno Show the field ONLY if: 1Yes [mother]="1" and 0No [environmental_allergies_m]="1" 199 year_round_allergens_m Or year round? yesno Show the field ONLY if: mold/dust mites/cat/dog/roach 1Yes [mother]="1" and 0No [environmental_allergies_m]="1" 200 anaphylaxis_m Have you ever had an anaphylaxis reaction to text Show the field ONLY if: anything? [mother]="1" and [environmental_allergies_m]="1" 201 drug_allergy_m Any drug allergies that you know of? text Show the field ONLY if: [mother]="1" and [environmental_allergies_m]="1" 202 food_allergies_m Any food allergies? yesno Show the field ONLY if: 1Yes [mother]="1" 0No 203 eczema_m Eczema yesno Show the field ONLY if: 1Yes [mother]="1" 0No 204 psoriesis_m Psoriasis yesno Show the field ONLY if: 1Yes [mother]="1" 0No

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205 nausea_m Nausea yesno Show the field ONLY if: 1Yes [mother]="1" 0No 206 vomiting_m Vomiting yesno Show the field ONLY if: spitting up 1Yes [mother]="1" 0No 207 diarrhea_m Diarrhea yesno Show the field ONLY if: 1Yes [mother]="1" 0No 208 abdom_pain_m Abdominal Pain yesno Show the field ONLY if: 1Yes [mother]="1" 0No 209 diff_swallow_m Difficulty Swallowing yesno Show the field ONLY if: 1Yes [mother]="1" 0No 210 food_stuck_m Food getting stuck yesno Show the field ONLY if: 1Yes [mother]="1" 0No 211 food_impact_m Food Impaction yesno Show the field ONLY if: 1Yes [mother]="1" 0No 212 er_m Did you go to Dr for removal? yesno Show the field ONLY if: 1Yes [mother]="1" and 0No [food_impact_m]="1" 213 esoph_dilation_m Have you ever had your esophageal Dilated or yesno Show the field ONLY if: stretched? 1Yes [mother]="1" 0No 214 heartburn_m Any trouble with heartburn? yesno Show the field ONLY if: feels like burning 1Yes [mother]="1" 0No 215 crohns_m Crohns yesno Show the field ONLY if: 1Yes [mother]="1" 0No 216 age_symptoms_m Do you know about how old you were when the GI text Show the field ONLY if: symptoms started? [mother]="1" 217 undiagnosedeosinophilic_m Do you think you have an undiagnosed eosinophilic yesno Show the field ONLY if: disorder? 1Yes [mother]="1" 0No 218 ee_m Do you carry a diagnosis of Eosinophilic yesno Show the field ONLY if: Esophagitis? 1Yes [mother]="1" 0No 219 eg_m Do you carry a diagnosis of Eosinophilic Gastritis? yesno Show the field ONLY if: 1Yes [mother]="1" 0No 220 ec_m Do you carry a diagnosis of Eosinophilic Colitis? yesno Show the field ONLY if: 1Yes [mother]="1" 0No 221 hes_m Do you carry a diagnosis of HES? yesno Show the field ONLY if: 1Yes [mother]="1" 0No 222 asthma_m Asthma yesno Show the field ONLY if: 1Yes [mother]="1"

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0No 223 barretts_m Barrett's Esophagus yesno Show the field ONLY if: 1Yes [mother]="1" 0No 224 esophag_cancer_m Esophageal Cancer yesno Show the field ONLY if: 1Yes [mother]="1" 0No

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Figure B4.1: Twin Recruitment and Funding

$ CCTST JIT $ CEG 2012 Mentee PI $ CEG 2011 NIS

# pairs enrolled # pairs interested # families with data

Epigenetic Methylation Samples to Twin Registry REDCap Ho and Ji Cores database Study Team What’s your EQ?

MECEH support 2011, 2012, 2013

ES Alexander2013

115

Figure B4.2: Geocoding of EoE Twins

116

Figure B4.3: EoE Twins Sample Collection Timeline

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Appendix C: Related Grants and Published Abstracts, Manuscripts

C1: Center for Environmental Genetics (CEG) 2012 PI Mentee/Mentor funded 2012: This is representative. Other successful grants are acknowledged on page v..

Do Early Environmental Factors and DNA Methylation Patterns Affect the

Concordance of Eosinophilic Esophagitis in Twins?

PI/Mentee: Eileen S. Alexander MS

Co-Investigator/Mentor: Lisa J. Martin PhD

Co-Investigators (alphabetically):

Margaret H. Collins MD

Hong Ji PhD

Marc E. Rothenberg MD, PhD

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Program Director/Principal Investigator (Last, First, Middle): Alexander, Eileen, S.

PROJECT SUMMARY: Using technical language, briefly describe the research design and rationale for achieving the stated goals. Although the prevalence of food allergies has increased, and outcomes are serious, the strategies for prevention and management of this public health problem are very limited. Our long term goal is to reduce the rate, and therefore, the risk, of EoE in families with enriched epigenetic modifications and environmental exposures by developing a model to identify potential gene*environment interactions. As a first step toward this goal, we will determine if early life environmental factors and patterns of methylation are associated with EoE. We hypothesize that the methylation of EoE related genes in affected twins will be significantly different compared to unaffected twins and we propose to identify a methylome signature for EoE related genetic regions that correlates with prenatal, early life and home exposures. To do this, we will address two specific aims. First, we will determine an EoE methylome signature that correlates with EoE phenotype by comparing methylation patterns in discordant twin pairs. We will use the Illumina Infinium HumanMethylation450 BeadChip to identify new candidate gene regions. Previously identified genes (from expression and genetic studies) such as TSLP, WDR36, DSG1 and CCL11 (eotaxin) are also included in this chip. In addition, we will identify potential epigenetic causes of EoE both in existing and newly identified genes by region-specific CpG analysis using the Targeted sequenom MassArray or pyrosequencing, depending on the proximity of CpG sites in each gene region. We expect that the EoE-related methylome signature will differ between affected and unaffected individuals both between and within pairs. Twins are truly a unique opportunity to control for many potential confounders by, in effect, utilizing a matched case-control study design. In addition, twin studies allow us to disentangle and quantify the relative contributions of genes and environment. However, phenotypic misclassification is always a concern. By applying additional histologic parameters from our pilot study with Dr. Collins, we will quantify phenotype more precisely. Our second aim is to identify environmental factors associated with EoE phenotype to discern environmental differences within families and between individual twins. We will develop an early life factors (ELF) environmental questionnaire for EoE. We expect that specific environmental factors will explain a high proportion of the variability in development of EoE. We will use a combination of existing environmental questionnaires and the research medical history developed by our clinicians to develop a tool specific to EoE, which we will test for inter-rater reliability and validity. Because EoE is typically an early onset disorder, we will focus on prenatal, early life and home environmental factors. We expect that this pilot project will quantify both epigenetic and environmental components of EoE.

RELEVANCE: Using no more than two or three sentences, describe the relevance of this research to public health. The prevalence of food allergies is increasing, perhaps in response to alterations in food chemistry and genetic modification. The phenotypic variation of EoE is a complex interaction of genes and environment and has not yet been quantified. Defining phenotypic risk by methylome signature and environmental exposure status is crucial to understanding EoE, to answer questions posed by our families and clinicians and to reduce risk in susceptible individuals and families.

PERFORMANCE SITE(S) (organization, city, state) Cincinnati Children’s Hospital Medical Center, Divisions of Allergy & Immunology, Biostatistics & Epidemiology, Human Genetics University of Cincinnati, College of Medicine, Department of Environmental Health, Center for environmental Genetics Core Facilities University of Cincinnati, College of Medicine, Department of Environmental Health,, Division of Epidemiology & Biostatistics

KEY PERSONNEL. Use continuation pages as needed to provide the required information in the format shown below. Start with Principal Investigator. List all other key personnel in alphabetical order, last name first.

Name Organization Role on Project Alexander, Eileen S UC PI/Mentee Martin, Lisa J CCHMC Mentor/Co-I

Collins, Margaret H CCHMC Co-I Ji, Hong CCHMC Co-I

119

Rothenberg, Marc E CCHMC Co-I

120

Principal Investigator/Program Director (Last, First, Middle): Alexander, Eileen S

Page Numbers Face Page includes signature of Department Chair, Dr Shuk-Mei Ho ...... 1 Project Summary, Relevance, Performance sites, Key Personnel ...... 2 Table of Contents ...... 3 Detailed Budget ...... 4 Budget Justification ...... 5 Biographical Sketch – Principal Investigator/Program Director ...... 7 Other Biographical Sketches includes other support for all investigators ...... 11 Prior CEG funding and products includes NIS 2011 ...... 22 Research Plan ...... 23 A. Specific Aims ...... 23 B. Research Strategy: Background, Methods, CEG 23 C. Human Subjects (includes consultation with CEG Integrative health Services Core, Dr Susan Pinney) ...... 28 Protection of Human Subjects includes IRB approval ...... 28 Data and Safety Monitoring Plan ...... 28 D. Vertebrate Animals (not applicable) ...... 29 E. Literature Cited ...... 29 F. Long term goals of the research project 31 G. Description of CEG F&S Cores you will be utilizing 31

H. Letters of Support (e.g., Consultants) ...... 31 Appendix (No page numbering necessary for Appendix.) Check if Appendix is Pilot studies abstracts (3) Included

Attachments: Rothenberg biosketch Harley consultant letter for Immunochip analysis Face page scan with Dr Ho’s signature

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Page 3 Form Page 3

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FROM THROUGH

3/01/12 2/28/13 DETAILED BUDGET FOR INITIAL BUDGET PERIOD PERSONNEL (Applicant organization only) Months Devoted to Project DOLLAR AMOUNT REQUESTED (omit cents)

ROLE ON Cal. Acad. Summer INST.BASE SALARY FRINGE NAME PROJECT Mnths Mnths Mnths SALARY REQUESTED BENEFITS TOTAL Principal Alexander, Eileen S 0 0 0 Investigator

Martin, Lisa J Mentor/Co-I 0 0 0

Collins, Margaret H Co-I 0

Ji, Hong Co-I 0

Rothenberg, Marc E Co-I 0

Data Foote, Heather 1.8 $6466. $1843. $8309. Specialist

SUBTOTALS $6466. $1843. $8309.

CONSULTANT COSTS 0 0 EQUIPMENT (Itemize) Software Sigma Plot sublicense ($150 if funding permits)

SUPPLIES (Itemize by category) Oragene kits and mailing provided by Dr Rothenberg’s lab = $0 DNA extraction: $15 per sample * 36 samples = $540 Zygosity Chip: $39.00 * 36=$1404 Illumina Infinium HumanMethylation450 BeadChip:$380 * 36=$13,680 $15,624 TRAVEL 0 PATIENT CARE COSTS INPATIENT 0 OUTPATIENT 0 ALTERATIONS AND RENOVATIONS (Itemize by category) 0 OTHER EXPENSES (Itemize by category) Pathology Slide Scan @ $10 ea less DHC discount = $6.50 x 36 samples = $234 Zygosity ascertainment: $ no additional charge Illumina Infinium HumanMethylation450 BeadChip analysis: $ no additional charge Primer design and PCR: $3500 A: Targeted Sequenom MassArray Analysis: ($ 1920/384 plate with amplified DNA) Or B: Region Specific validation analysis (Pyroseq): $2200 for 8 regions (higher option figure used) $5934.

TOTAL DIRECT COSTS FOR INITIAL BUDGET PERIOD (Item 10, Face Page) $ $29867.00

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Page 4 Form Page 4 JUSTIFICATION. In the proposed project, “The Association of Epigenetic Methylation Patterns and Concordance of Eosinophilic Esophagitis in Twins,” our central hypothesis is that the methylation of EoE related genes in affected twins will be significantly higher compared to unaffected twins both between and within pairs. Utilizing the resources from the CCHMC Division of Allergy & Immunology, the Center for Environmental genetics Core facilities, and our newly created twin registry, we are uniquely positioned to address this hypothesis.

PERSONNEL

Eileen S. Alexander, MS (10.8 calendar months = 90% effort-no cost) is the PI/Mentee and will oversee and coordinate all aspects for the project, with support from Dr. Martin. She requests no salary support. She is a student in the Molecular Epidemiology in Children’s Environmental Health predoctoral fellowship. As hospital epidemiologist at a community hospital, she oversaw the Infection Control program and JCAHO hospital accreditation for that function, including data mining, descriptive statistics and clinical rounds for infectious disease. She will work closely with Ms. Foote.

Lisa J. Martin, PhD (1.2 calendar months = 10% effort – no cost) is the mentor and co-investigator for the proposal and will be responsible for routine oversight of the project. She requests no salary support. She is a genetic epidemiologist with over 11 years’ experience with statistical genetic analyses. Dr. Martin will oversee all aspects of the project and participate directly in data preparation. She will mentor Ms. Alexander.

Margaret H. Collins, MD (0.24 calendar months = 2% effort-no cost) is the pathologist with the Cincinnati Center for Eosinophilic Disorders and will evaluate the research biopsy slides for the twin study. She requests no salary support. She will use her expertise in the evaluation of biopsies for eosinophilic esophagitis to apply her newly developed Histology Scoring System to clarify the EoE phenotype in this study, such that heritability analyses of genetic and environmental factors may be more accurately and precisely partitioned.

Hong Ji, PhD (0.24 calendar months = 2% effort-no cost) is the director of the Pyrosequencing core Laboratory for Genomic and Epigenomic Research and an assistant professor in the division of asthma research at CCHMC. She requests no salary support. She will work as a co-investigator to assist in the analysis of the genomic methylation data and advise, process and analyze methylation levels of candidate genes. She will perform pyrosequencing analysis, as needed, to measure DNA methylation levels in genes previously found to be associated with EoE as well as candidate genes identified from microarray analysis.

Marc E. Rothenberg MD, PhD (0.24 calendar months = 2% effort-no cost) is the director of the division of allergy and immunology and the Cincinnati Center for Eosinophilic Disorders as well as a professor of pediatrics at CCHMC. In addition to practicing medicine, he is an internationally known expert in eosinophilic esophagitis, allergic diseases and immunology. His molecular laboratory and research staff will provide clinical research and laboratory support to this project, including CRC (Jessica King) and regulatory duties (Hong Pham), such as recruitment, intake data and sample collection and sample documentation (Michael Eby), processing and storage (Emily Stucke). Dr. Rothenberg will provide conceptual and executive oversight, including, but not limited to, the application of existing genetic and expression knowledge to this project within an immunologic framework. He requests no salary support.

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Page 5 Form Page .

Heather Foote (2.4 calendar months = 20% effort-$11,078) is currently a Data Specialist in the Division of Allergy and Immunology. Her previous expertise as a CRC enables her to fulfill dual roles in this project to collect environmental data from participants using the questionnaire developed as part of Aim 2 and assist the PI in the acquisition, entry and management of data utilizing the REDCap database that was developed for this project during the pilot phase. To complete Aim 2, we are requesting funds for an experienced interviewer with expertise in data mining and management. For three months, Dr Rothenberg has provided resources for Ms Foote to enter our family history data into REDCap, so she is acclimated to the project.

EQUIPMENT We are not requesting funds to purchase any new equipment.

TRAVEL We are not requesting funds for travel

MATERIALS AND SUPPLIES We are requesting funds for the following materials and supplies:

Preparation: DNA extraction: We are requesting $540 for DNA extraction in the Genotyping Core. This is based on 36 samples at $15/sample. Laboratory Analysis, Phase 1: Illumina Infinium HumanMethylation450 BeadChip analysis: We are requesting $13,680 to perform whole genome methylation studies. Analyses will be performed in the CEG Core by the Genomics and Microarray Laboratory (Saikumar Karyala). Laboratory Analysis, Phase 2: Targeted sequenom MassArray option: We are requesting $1920 to perform 2 amplicons/gene for 36 samples and five genes Option A: Analyses will be performed in the CEG Core by the Genomics and Microarray Laboratory (Saikumar Karyala). Pre-pyrosequencing preparation (required for both options): This step includes consultation, assay design and optimization, and PCR amplification of regions of interest (800). We are requesting $3,500. This will allow us to locate and analyze the regions currently known to be associated with EoE and consider new candidate gene regions from our first phase of laboratory analysis. (Option B) Consultation, assay design and optimization $300

PCR amplification and quality control $800

Primer ordering and optimization: $2,400 (this includes biotinylated primers for 8 genes)

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Pyro Sequencing option: We are requesting $2,200 to perform Pyro sequencing for follow-up (Option B) Number of individuals 36

Number of genes 8

Number of sequencing regions (PCR reactions) per gene 3

Number of primer per sequencing regions 3

Number of reaction =36X8X3 = 864

90 samples per plate (controls included)

Number of plates required (864 /90 = 9.6) = 10

Cost per plate $220.

Total cost = 220 x 10 = $2200

Option A or B are dependent upon the results of Phase 1 and the relative spacing of the CpG islands. Each region will be analyzed independently to determine the appropriate methodology. Laboratory Analysis: 36 individuals (18 discordant pairs)

COSORTIUM/CONTRACTUAL COSTS None. We have used REDCap, a compliant data management system available from the Center for Clinical and Translational Science and Training, at no charge.

PRODUCT of CEG NIS grant 2011 ($1530.00):

Data collection from that grant is complete and the analysis is in progress. The manuscript in progress will combine data from the Family Risk Study funded by the CEG in 2011 ($1530.00) and the preliminary concordance and heritability estimates from this Twin Study.

PUBLISHED ABSTRACTS

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Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. J Allergy Clin Immunol 2011:127(2) AB217.

PRESENTATIONS Alexander ES. Twin Shared Environment Increases The Risk of Eosinophilic Esophagitis in Families. Oral presentation at The University of Cincinnati College of Medicine, Department of Environmental Health, Division of Epidemiology and Biostatistics Seminar Series, February 2, 2012, Cincinnati, OH. Alexander ES, Martin LJ, Abonia JP, Collins MH, Succop PA, Greenler AJ, Dellon ES,. Demain JG, Franciosi JP, Furuta GT, Gurian LE, Hopp RJ, Kaul A, Nadeau K, Noel RJ, Putnam PE, von Tiehl KF, Eby MD, Foote H, Ellison AC, Rothenberg ME. Twin Shared Environment Increases The Risk of Eosinophilic Esophagitis in Families Oral presentation at American Academy of Allergy, Asthma and Immunology 2012 Annual Meeting, March 2-6, 2012, Orlando, FL. Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Male sex of affected parent is associated with familial risk of eosinophilic esophagitis. Poster presentation at the International Eosinophil Society, June 21-25, 2011, Quebec City, Quebec, Canada.

BIOSKETCHES sent

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A. Specific Aims People with eosinophilic esophagitis (EoE) have food allergies with chronic inflammation and episodes characterized by esophageal swelling and food impaction that require endoscopic removal of food, often followed by esophageal dilation. EoE is a chronic food allergy of increasing prevalence1. Known causes include food and swallowed aeroallergens. During periods of exacerbation, they may not be able to eat food and require nourishment and treatment with an unpalatable liquid elemental amino acid formula. Immediate effects include characteristic eosinophilic inflammation with ≥15 eosinophils per high power field2,that may result in esophageal food impaction. The natural history of EoE, other causes and long term sequelae are not well elucidated, although it is an early onset disease, with age specific symptoms that may result in long term esophageal fibrosis. Histologic findings, such as basal layer hyperplasia and lamina propria fibrosis may explain the frequent esophageal dilations that are part of EoE treatment and cause scarring of the esophagus. Although the specific antigens that cause EoE are not known, identification of prenatal and early life exposures that sensitize infants and children born in a susceptible family is crucial to primary and secondary prevention of EoE.

Previously, we and others have demonstrated that EoE has a genetic basis. Genome wide association studies of EoE have identified TSLP and WDR to be correlated with the EoE phenotype3,4. Expression studies have implicated TSLP, TSLP receptor, WDR, DSG1 and eotaxin-3 5,6. Further, EoE is enriched in families probands. However, the relative contributions of genes and environment to the development of EoE has not been reported. For the past year, we have been exploring family based approaches to tease apart genetic and environmental influences of EoE and have generated several novel findings. First, fathers of EoE probands have significantly higher frequency and recurrence risk compared to mothers, unlike siblings with similar risk7. Second, we have developed the only twin registry of EoE in the world8. Using this resource we found that although identical, or monozygotic (MZ) twins share 100% of their genes and fraternal, or dizygotic (DZ) share only half, the DZ twins in our study had a surprisingly high concordance rate and were not significantly different from MZ twins. Further, the frequency of EoE in the non-proband DZ twins was ~25% higher than non-twin siblings, suggesting exposures which occurred during the same developmental window for both twins.

Our long term goal is to reduce the rate, and therefore, the risk, of EoE in families with enriched epigenetic modifications and environmental exposures by developing a model to identify potential gene*environment interactions. As a first step toward this goal, we will determine if early life environmental factors and patterns of methylation are associated with EoE using our unique twin resource. We hypothesize that methylation and early life factors will be associated with development of EoE in twins. To test this hypothesis we will complete the following aims.

Specific Aim 1) To determine an EoE methylome signature that correlates with EoE phenotype. We will compare methylation patterns in discordant twin pairs to identify epigenetic patterns associated with EoE. We expect that the EoE-related methylome signature will differ between affected and unaffected individuals.

Specific Aim 2) To identify environmental factors associated with EoE phenotype to discern environmental differences within families and between individual twins. We will develop an early life factors (ELF) environmental questionnaire. We expect that specific environmental factors will explain a high proportion of the variability in EoE.

Upon completion of the project, we expect to show that discordant twin pairs have unique methylation patterns and that early life factors are associated with development of EoE. These findings would be important clinically because while much research has focused on un-alterable factors, such as genetics, environmental factors could be modifiable and thus could be used to reduce risk in individuals susceptible to EoE. In addition, identification of unique methylation profiles elucidate novel pharmacologic targets.

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B. Research Strategy Significance to Environmental Health The prevalence of food allergies is increasing, perhaps in response to alterations in food chemistry (preparation, shelf life, processing), genetic modification, ambient aerochemistry or exposures to both infectious diseases and nonpathogens. EoE is a chronic food allergy. Previous studies (Blanchard, Rothenberg) have demonstrated that EoE has a strong genetic component. However, if EoE were primarily a genetic disorder, we would expect that DZ twins would have about half the rate of disease compared to MZ twins. Our preliminary data show that the concordance rate in MZ and DZ twins are not significantly different. Peanut allergy, by comparison, has a very wide gap between MZ and DZ concordance, implying a greater importance of genes over environment and twin timing9. This implies that environmental exposures and the simultaneous timing of twin exposures plays a relatively large role in EoE in genetically susceptible individuals. As such, the phenotypic variation of is a complex interaction of genes and environment which has not yet been quantified. The contribution of EoE environmental factors strongly suggested by our pilot data means that the exposures that cause the gut to become sensitized, allergic and chronically diseased are modifiable. This work is significant because identification of unique methylation profiles and environmental factors associated with of EoE could yield novel prevention strategies. Indeed, Dr. Marshall Plaut, MD, chief of the Allergic mechanisms Section at the NIAID, stated recently, “Food allergy is considered a major public health problem, and is one for which we don’t have any recognized ways to prevent or treat the disease10. To address this problem, we will collect data that will support long term public health strategies for EoE. These long term health strategies could include primary prevention by modification of environmental sensitizing agents, secondary prevention by clinically induced therapeutic tolerance of environmental antigens (e.g., immunologically mediated sublingual or injectable treatments for antigens) and tertiary prevention of sequelae.

Innovation Until recently, EoE research has been focused on dietary triggers and the effects of eliminating those triggers. The genetic variants and gene expression profiles recently identified make this project possible by identifying candidate regions for epigenetic interrogation. However, other than diet, the effects of environment have not been studied or suggested by the research designs. The recent rise in both MZ and DZ twin births, increased from about 1% to a current rate of 1 in 30 births11-14, creates a new “natural resource.” Our preliminary data on twin concordance of EoE suggests that environment and timing of exposure may play relatively large roles in the development of EoE. This project is innovative because it proposes to open a new area of preventive research, by focusing on modifiable environmental factors and the identification of a unique methylation pattern in individuals with EoE.

Approach Rationale EoE is a complex combination of genetic and environmental causes. To date, the primary research focus has been on identifying genetic factors (variation and expression levels) that are associated with disease. Based on our preliminary findings that both MZ and DZ twins are enriched for EoE compared to siblings, we believe that early life exposures may have an important role in the risk of developing EoE. One potential mechanism for the impact of early life exposures influencing later disease is methylation (refs). Thus, in this proposal we seek to determine if methylation patterns or early life environmental factors are associated with development of EoE. The aims of this proposal will be accomplished using twin pairs, their parents and data collected regarding their first degree relation’s allergy history. Twins are a unique opportunity to quantify the relative contributions of genes and environment by essentially matching the twins on confounders such as age, most aspects of home environment as well as the timing and dose of exposures. Identifying regions of epigenetic dysregulation of twins, and associated home, and specific food exposures as well as early and antenatal factors that differ

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between and within twin pairs will suggest possible gene*environment interactions for further study.

Justification and Feasibility Since EoE was first described in the 1990’s, and defined as a unique disorder in 200715, the prevalence has increased, as have the public health concerns associated with food allergies. Cincinnati Children’s Hospital currently sees approximately 30 EoE patients per week. Genetic variants have recently been associated with EoE, such as TSLP, WDR36, DSG1 and CCL11 (eotaxin) and CRLF2 (refS) which allow us to begin interrogation of known candidate gene regions for dysregulated patterns of methylation.

The Cincinnati Center for Eosinophilic Disorders staff will continue to actively recruit, consent and collect data and Oragene saliva DNA samples from twins worldwide. Twins are recruited through websites associated with twins, social media and special interest groups related to EoE. They are also recruited through their allergists, gastrointestinal physicians and professional conferences. At this time we have 73 twin pairs recruited, 58 of whom have completed our basic EoE profile and confirmation process, and 19 of whom have paired saliva samples. Currently, we are extending the medical history to include first degree relations in the twin families. Using this resource we found that although identical, or monozygotic (MZ) twins share 100% of their genes and fraternal, or dizygotic (DZ) share only half, the DZ twins in our study had a surprisingly high concordance rate and were not significantly different from MZ twins. Further, the frequency of EoE in the non-proband DZ twins was ~25% higher than non-twin siblings, although they both share about half of their genome, suggesting exposures which occurred

Specific Aim 1. To determine an EoE methylome signature that correlates with EoE phenotype during the same developmental window for both twins.

Research Design Research Strategy We will use a twin study design to compare twins affected with EoE to twins unaffected with EoE, i.e., discordant pairs. Twins are truly a unique opportunity to control for many potential confounders by, in effect, utilizing a case-control study design matched on both timing and many exposures. In addition, twin studies allow us to disentangle and quantify the relative contributions of genes and environment. However, phenotypic misclassification is always a concern. By applying additional histologic parameters from our pilot study with Dr. Collins, we will quantify phenotype more precisely16. At this stage, we will compare dizygotic, or fraternal twins, who share, on average, 50% of their genome. We currently have 8 paired saliva DNA samples in dizygotic twins and 6 in monozygotic pairs. We anticipate continued recruitment with data and sample collection over the next six months.

First, we will determine an EoE methylome signature that correlates with EoE phenotype by comparing methylation patterns in discordant twin pairs. We will use the Illumina Infinium HumanMethylation450 BeadChip to identify new candidate gene regions. Previously identified genes such as TSLP, WDR36, DSG1 and CCL11 (eotaxin) which are included in this chip can be used to verify previous findings. Also of interest, the TSLP receptor, located in the pseudoautosomal region of Xp22.33, CRLF2 is not included in this chip, as it is a provisional gene at this time, but will be interrogated during the second phase of methylation confirmation by either mass spectrometry or pyrosequencing. We will validate potential epigenetic causes of EoE both in existing and newly identified genes by region-specific CpG analysis using the

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Targeted sequenom MassArray or pyrosequencing, depending on the proximity of CpG sites in each gene region. Mass spectrometry skips sites in close proximity. This technique has been used successfully in asthma. We expect that the EoE-related methylome signature will differ between affected and unaffected individuals both between and within pairs.

Sample Collection Once consent is obtained, Oragene saliva DNA kits are barcoded and mailed with kit and return mail instructions. For children younger than five years of age and others, as needed, we include the saliva sponge kit that is also made by Oragene. When received, barcode numbers are recorded in the Eosinophilic Gastrointestinal Disorder (EGID) database. Samples are processed and stored in the Rothenberg lab. Illumina requires 500 ng intact DNA.

Laboratory Methods DNA Extraction. Oragene, Oragene sponge kit and PrepIT L2P,(DNAGenotech) will be used to extract DNA according to the manufacturer’s instructions17.

Immunochip zygosity tesing is covered in the section for both aims. Whole genome methylation. Illumina Infinium HumanMethylation450 BeadChip. Genomic genomic DNA to % methylation at all 450,000 sites. TSLP, WDR36, DSG1 and CCL11 (eotaxin) are included in this chip. TSLP receptor, CRLF2 is not included in this chip, as it is a provisional gene at this time18-19.

Pyrosequencing. Individual samples will be bisulfite treated using an EZ DNA methylation, Gold Kit (ZYMO Research) and subject to PCR amplification of top three regions identified from microarray analysis as well as TSLP, WDR36, DSG1 and CCL11 (eotaxin) and the TSLP receptor, CRLF2, located in the pseudoautosomal region of Xp22.33. DNA methylation of individual CpG sites will be measured using pyroMark Q96 MD system (Quigen) and determined using the pyro Q-CpG methylation software (Quigen) in the Pyrosequencing Core Laboratory for Genomic and Epigenetic Research directed by our Co-investigator, Dr. Ji. DNA methylation patterns have been shown to differentially regulate disease phenotype in asthma20-211.

Mass spectrometry. Following PCR amplification, as described above, samples will be analyzed for methylation of CpG sites using the Sequenom MassArray as described22.

Analysis. Prior to the analysis of the methylation data, we will apply quality control procedures by evaluating overall performance of the bisulfite conversion, performance of each probe, and performance of individuals. Given concerns with the Illumina’s analysis platform, we will utilize R programs specifically designed to analyze methylation based data including lumi to calculate a normalized M-value. This statistic has been found to exhibit better behavior than the beta value from Illumina (Personal Communication Xue Zhang PhD, CCHMC Human Genetics)22, We export these M-values into SAS or JMP and will use paired t-tests to determine if loci differ significantly. Pyrosequencing and Mass spectrometry data will be analyzed in SAS or JMP using paired t-tests

Histologic analysis. Significant variability in diagnostic criteria for eosinophilic esophagitis exists, and in a large proportion of studies, criteria are not reported. Because of this lack of a common disease definition, conclusions drawn from the cumulative EoE literature should be viewed with caution. A consensus research-quality standard for diagnosis of eosinophilic esophagitis is

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needed. By applying the new Histology Scoring System (HSS) in development by Dr. Collins at CCHMC, we can determine whether disease severity is associated with differences in methylation profiles16.

Specific Aim 2. To identify environmental factors associated with EoE phenotype to discern environmental differences within families and between individual twins.

Research Design Research Strategy Our research strategy is a twin based (matched) case-control design to identify environmental factors associated with EoE, while controlling for timing of exposure and reducing the effects of genetics by studying first degree relations. By using both concordant and discordant twin pairs, we will maximize power and hypothesize that environmental exposures associated with EoE will be different between discordant pairs and similar between concordant pairs. To minimize bias, prospective data will be included whenever possible, e.g., one week logs of meals prepared outside the home by a commercial enterprise will be collected twice, with updated family history data collected at those contacts. This will allow us to test internal and external validity of our environmental questionnaire by completing a recheck of some questions, checking the internal consistency of questions and allow for inter-rater reliability testing.

Environmental Exposure Assessments Until now, the focus of EoE research is on dietary triggers and the effects of elimination. Demonstrating the correlation of prenatal, early life and home environment as well as the dysregulation of methylation patterns with EoE will open up this new area of research. However, there are no existing environmental exposure assessment tools unique to EoE. We therefore propose a mixed method, using existing tools for prenatal environmental assessment24-27, data from the AAAAI nab outdoor pollen and mold surveillance network, a log of “eating out” combined with telephone interviews to update the medical and family history that we have already collected. Although developing a tool is challenging, it also allows us to test aspects of face validity, utilizing the extensive clinical expertise of the allergists and gastroenterologists at the Cincinnati Center for Eosinophilic Disorders.

Our focus in this project is on prenatal exposures related to allergy, season of birth, latitude, commercially prepared foods, and ambient mold exposure for the Ascomycetes family of molds, which includes Aspergillus, used for induction of tissue eosinophilia in research animals. Aspergillus molds are ubiquitous, grow on carbon-based substrates and include many species in the phylum Ascomycetes, some of which are human and agricultural pathogens. Some produce oncogenic toxins, such as aflatoxin. The Aspergillus genome was published in 2005. Ascomycetes are part of the nab surveillance data.

Prenatal questionnaire We are evaluating validated questionnaires from the ISAAC (asthma), ALEX (endotoxins), PASTURE (farm exposures during pregnancy), and PARSIFAL (farming and lifestyle) studies (refs) that may be adapted for ingested or swallowed food and aeroallergens. We will include birth data related to amnion/placenta, when medical data are available, and birth date/season24-27.

Home environment A one week food log of commercially prepared foods will be collected twice. Ambient mold assessment data from the AAAAI nab outdoor pollen and mold surveillance network will be correlated with methylation patterns. If funded, we will apply to the American Academy of Allergy, Asthma and Immunology to use these data. http://www.aaaai.org/global/nab-pollen-

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counts.aspx Latitude and longitude data will be determined from street address to find the surveillance station closest to the participant.

Analysis. Paired data will be adjusted for possible confounders, such as, but not limited to age and sex and analyzed using logistic regression with odds ratios. Linear models will be used for the continuous outcomes generated by Dr. Collins’s HSS system.

Considerations for both Aims Potential Problems and Alternative Strategies: We are familiar with the challenge of participant contact and cohort retention. By using a mixed method for environmental exposure assessment, we will optimize our opportunity to identify environmental exposures accurately and precisely, while minimizing the time burden to participants. However, we have found that the reduced quality of life that results from EoE makes participants very motivated to share their history.

Zygosity Ascertainment DNA testing is the “gold standard” for zygosity testing, so we have chosen the Immunochip, with 196,200 single SNPs to validate ascertainment by “Pea Pod” questionnaire and parent report. Since misclassification of twin zygosity could seriously bias our data, our twins will be triply ascertained if funding permits. Another advantage of choosing the Immunochip for zygosity ascertainment is that the data can be used to assess population stratification of genetic data , due to 5000 HLA system markers on chromosome 6p21.3 and >3500 ancestral markers. Since our study is recruiting twins worldwide, this aspect will be critical. Additionally, the Immunochip includes SNPs associated with related immune disorders, allowing secondary analyses of the data generated from this study.

Data management Methylation data generated by Aim 1, coupled with Dr Collins’s data using a vastly more precise histology scoring system for EoE, the family data that is already part of our twin registry collection protocol and the Aim 2 environmental exposure data will create vast amounts of data for analysis. We have planned for this need by using our CEG New Investigator Scholar funds from last year to acquire the hardware and software needed to manage and analyze data that results from genetic epidemiology studies. In addition, the Family Risk study funded in 2011 created a “training set” for us to ramp up a large, compliant data stream using the REDCap data management system available from the CCTST at no additional cost.

Sample Size Justification A peanut allergy twin study with 75 pairs recruited and 58 pairs in the analysis, nearly identical to our group at this time, was powered to detect a difference in concordant and discordant pairs. Based on our preliminary studies, we calculated that ~300 MZ and ~700 DZ twin pairs would be required to detect a difference of 0.05 based on a concordance difference of only 7%. Because the epigenetic modifications related to EoE are unknown, and there is no pilot data available, we will do a post hoc power calculation prior to future studies with the MZ twin cohort. We have chosen this strategy for two reasons. First, paired samples are more difficult in MZ pairs because many are older, often living further apart, require separate consents, mailings, and sample collection. In short, their mother is not in charge anymore. Second, MZ twins, who share 100% of their genetic material, are the ideal

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matched comparison to study environmental factors, so we wish to screen for epigenetic modifications and associated environmental factors in our DZ cohort, at a less rigorous α value, as one would for an early drug study, and then use the MZ cohort to confirm our findings in our next study.

Expected Outcomes With completion of the study aims, we expect to identify an EoE-related methylome signature. Further, as part of the study, we will develop an early life factors (ELF) environmental questionnaire, we expect to identify specific environmental factors will explain a high proportion of the variability in EoE. Taken together these expected outcomes will open a new area of preventive research by collecting data that will support long term public health strategies for EoE.

Future Directions Our long term goal is to reduce the rate, and therefore, the risk, of EoE in families with enriched epigenetic modifications and environmental exposures, specifically in the home structure itself, and the family food mileau as well as early life/antenatal factors, by developing a model to identify potential gene*environment interactions. As a next step towards this goal, to test identified effects in the discordant monozygotic, or 100% genetically identical, twins. In this way, we will quantify the overall contribution of environmental as well as suggest specific exposures for further study. Additionally, we propose to complete a larger family based study to validate our findings in a larger cohort of concordant and discordant sibling of probands with EoE. Thus study would leverage the sizable cohort of EoE patients in the CCED and allow us to be optimally powered to continue this line of research.

Timeline We will develop the environmental questionnaire in May-July while continuing recruitment and collecting our current data and saliva samples from the twins (REDCap forms available on request). We will administer the questionnaire and food log in August and repeat the food log in Jan-Feb. Aim 1 whole genome methylation site interrogation will occur as a single batch in October, followed by analysis, primer design, PCR and region analysis Nov-Jan.

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C. Human Subjects.

Very few diseases are currently known to be caused by dysregulation of methylation. While this technology offers promise in the discovery of the cause of disease, and warrants ethical concerns, it is unlikely to generate new sources of concern for these participants because they are unlikely to suffer from these disorders. Genetic data generated is in this project is research data, not intended for the clinical record. Indeed, it is recognized that variants identified using next generation sequencing are prone to errors (Durbin 2010). As such much care must be taken if this data is to be incorporated into clinical care. Further, the biologic impact of identified variants is uncertain and incidental findings may also be present. Yet, there is not a clear plan how these issues should be addressed in exome studies. By the completion of the study, we will have developed a disclosure plan including genetics educational materials to educate providers and a plan to address unanticipated functional variants for unrelated diseases.

Human Subjects Involvement, Characteristics and Design: To date, we have xx twin pairs recruited and xx pairs enrolled. We anticipate that 18 twin pairs will provide saliva DNA samples. Participants are being enrolled worldwide through the CCED. All probands will exhibit esophageal eosinophilia (≥15 eosinophils per 400x high power field) without an alternative known etiology. Parents of eligible patients will be approached by their physician, who cares for these patients to determine their interest in this study or by their voluntary recruitment from social and special interest groups related to their disorder or their “twin-ness.”.

Until a patient’s family consents to participate in the study, only their physician, or an appropriately IRB-approved member of the CCED will have access to individually identifiable private information.

Sources of Material: As part of this project, participants will be asked to provide a saliva sample for DNA and storage for later studies. We will also request access to the patient’s medical record. Parents will be asked to complete questionnaires regarding their medical history and environmental exposures.

Potential Risks: We anticipate no significant risks to the subjects in participating in the proposed study. It is possible that genetic findings (including findings unrelated to EoE) may cause distress. Thus, a genetics professional or genetics trainee under the supervision of a genetics professional will disclose the genetics results.

ADEQUACY OF PROTECTION AGAINST RISK Recruitment and Informed Consent: At the time of consent the participant or parents of the patients will have the purpose of the study explained. Special consideration will be taken to ensure that the parents understand that findings related to their child’s condition as well as unrelated conditions may be identified in their child.

Protection Against Risk: The major risks for participation in this study involve disclosure of information to the parents. Thus, any findings unrelated to EoE will be reviewed by a group of clinical genetics professionals and will decide if incidental findings have sufficient evidence to be reported to patients. These incidental findings will also be validated in a CLIA approved laboratory. POTENTIAL BENEFITS OF THE PROPOSED RESEARCH TO HUMAN SUBJECTS AND OTHERS The proposed studies may benefit the research participants by identifying a cause for EoE. Currently, we do not expect that this knowledge will lead to improved treatment in the short

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term.

IMPORTANCE OF THE KNOWLEDGE TO BE GAINED At present, there is minimal information about the specific causes of EoE. We propose to open a new area of preventive research by collecting data that will support long term public health strategies for EoE, such as primary prevention by modification of environmental sensitizing agents, secondary prevention by clinically induced therapeutic tolerance of environmental antigens (e.g., immunologically mediated sublingual or injectable treatments for antigens) and tertiary prevention of sequelae by identifying environmental factors associated with HSS parameters of long term mucosal damage that require aggressive and targeted therapeutics and the primary outcome metrics for their evaluation.

DATA AND SAFETY MONITORING PLAN N/A

Inclusion of Women and Minorities Inclusion of Women: In general, EoE affects males at a higher rate than females. Thus we expectan increased number of male participants. Inclusion of Minorities: Based on existing data, we expect participants to be predominantly white. We expect equal participation across the racial groups. This is reflected in the Targeted/Planned Enrollment Table.

Inclusion of Children As our objective is to identify environmental and epigenetic causes of EoE, children will be included in this protocol.

D. Vertebrate Animals.

Not applicable.

E. Literature Cited.

1. Noel RJ, Putnam PE, Rothenberg ME. Eosinophilic esophagitis. N Engl J Med 2004;351:940-1. 2. Liacouras CA, Furuta GT, Hirano I, Atkins D, Attwood SE, Bonis PA, Burks AW, Chehade M, Collins MH, Dellon ES, Dohil R, Falk GW, Gonsalves N, Gupta SK, Katzka DA, Lucendo AJ, Markowitz JE, Noel RJ, Odze RD, Putnam PE, Richter JE, Romero Y, Ruchelli E, Sampson HA, Schoepfer A, Shaheen NJ, Sicherer SH, Spechler S, Spergel JM, Straumann A, Wershil BK, Rothenberg ME, Aceves SS. Eosinophilic esophagitis: updated consensus recommendations for children and adults. J Allergy Clin Immunol. 2011 Jul;128(1):3-20.e6; quiz 21-2. Epub 2011 Apr 7. Review. 3. Rothenberg ME, Spergel JM, Sherrill JD, Annaiah K, Martin LJ, Cianferoni A, et al. Common variants at 5q22 associate with pediatric eosinophilic esophagitis. Nat Genet 2010:42:289- 91.

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4. Sherrill JD, Gao PS, Stucke EM, Blanchard C, Collins MH, Putnam PE. Variants of thymic stromal lymphopoietin and its receptor associate with eosinophilic esophagitis. J Allergy Clin Immunol 2010; 126:160-165e3. 5. Blanchard C, Wang N, Rothenberg ME. Eosinophilic esophagitis: pathogenesis, genetics, and therapy. J Allergy Clin Immunol 2006;118:1054-9. 6. Blanchard C, Stucke EM, Rodriguez-Jimenez B, Burwinkel K, Collins MH, Ahrens A, Alexander ES et al. A striking local esophageal cytokine expression profile in eosinophilic esophagitis. J Allergy Clin Immunol 2011; 127: 208-217e7. 7. Alexander ES, Martin LJ, Abonia JP, Foote H, Eby MD, Rothenberg ME. Sex of parent is associated with familial risk of eosinophilic esophagitis. J Allergy Clin Immunol 2011:127(2) AB217. 8. Alexander ES, Martin LJ, Abonia JP, Collins MH, Succop PA, Greenler AJ, Dellon ES,. Demain JG, Franciosi JP, Furuta GT, Gurian LE, Hopp RJ, Kaul A, Nadeau K, Noel RJ, Putnam PE, von Tiehl KF, Eby MD, Foote H, Ellison AC, Rothenberg ME. Twin Shared Environment Increases The Risk of Eosinophilic Esophagitis in Families J Allergy Clin Immunol 2012 (pending). 9. Sicherer SH, Furlong TJ, Maes MH, Desnick RJ, Sampson HA, Gelb BD. Genetics of peanut allergy: A twin study. J Allergy Clin Immunol 2000; 100:53-56. 10. Voelker R, Researchers look to genetic analyses for new options in treating food allergy. JAMA 2010; 304:2001. 11. Aston KI, Peterson CM, Carrell DT. Monozygotic twinning associated with assisted reproductive technologies: a review. Reproduction 2008;136:377–386. 12. Kochanek KD, Kirmeyer SE, Martin JA, Strobino DM, Guyer B. Annual summary of vital statistics: 2009. Pediatrics. 2012;129(2):338-48. 13. Miró F, Vidal E, Balasch J. Increased live birth rate in twin pregnancies resulting from embryo assistance. Obstet Gynecol. 2012;119(1):44-9. 14. Smits J, Monden C. Twinning across the developing world. PLoS ONE. 2011;6(9). 15. Furuta GT, Liacouras CA, Collins MH, Gupta SK, Justinich C, Putnam PE, Bonis P, Hassall E, Straumann A, Rothenberg ME; First International Gastrointestinal Eosinophil Research Symposium (FIGERS) Subcommittees. Eosinophilic esophagitis in children and adults: a systematic review and consensus recommendations for diagnosis and treatment. Gastroenterology 133(4):1342-63, 2007. 16. Collins MH, Martin LJ, Alexander ES, Pentiuk S, Ellison A, Putnam PE, Franciosi JP, Abonia JP, Rothenberg ME. Histology Scoring System (HSS) is Superior to Peak Eosinophil Count (PEC) to Identify Treated vs Untreated Eosinophilic Esophagitis (EoE) Patients . J Allergy Clin Immunol 2012 (abstract,in press). http://www.jacionline.org/webfiles/images/journals/ymai/Tuesday_March_6_2012.pdf 17. www.dnagenotek.com 18. Howard TD, Ho SM, Zhang L, Chen J, Cui W, Slager R, Gray S, Hawkins GA, Medvedovic M, Wagner JD. Epigenetic changes with dietary soy in cynomolgus monkeys. PLoS One. 2011;6(10):e26791. 19. http://www.illumina.com/products/methylation_450_beadchip_kits.ilmn 20. Ho SM. Environmental epigenetics of asthma: an update. J Allergy Clin Immunol . 2010;126(3):453-65 21. Ji H, Ehrlich LI, Seita J, Murakami P, Doi A, Lindau P, et al. Comprehensive methylome map of lineage commitment from haematopoietic progenitors. Nature. 2010;467(7313):338-42. PMCID: 2956609. 22. Tang W, Morey LM, Cheung YY, Birch L, Prins GS, Ho SM. Neonatal exposure to estradiol/bisphenol alters promoter methylation and expression of Nsbp1 and Hpcal1

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genes and transcriptional programs of Dnmt3a/b and Mbd2/4 in the rat prostate gland throughout life. Endocrinology 2012, 153(1):42–55. 23. Personal communication, Xue Zhang PhD, Human Genetics. 24. von Mutius E, Schmid S, The PASTURE project: EU support for the improvement of knowledge about risk factors and preventive factors for atopy in Europe. Allergy 2006;61:407-13. 25. Asher MI, Keil U, Anderson HR, Beasley R, Crane J, Martinez F, et al. International Study of Asthma and Allergies in Childhood (ISAAC): rationale and methods. Eur Resp J 1995;8:483- 91. 26. Riedler J, Braun-Fahrlander C, Eder W, Scheuer M, Waser M, Maisch S et al. Exposure to farming in early life and development of asthma and allergy : a cross sectional survey. Lancet 2001;358:1129-33. 27. Alfven T, Braun-Fahrlander C, Brunekreef B, von Mutius E, Riedler J, Scheleynius A et al. Allergic diseases and atopic sensitization in children related to farming and anthroposophic lifestyle- the PARSIFAL study, Allergy 2006;61:414-21.

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F. Brief description of long-term goals of the research project you plan to develop.

Although allergy related disorders are now commonplace, their impact on quality of life is substantial. Extended families are eager to share their stories. Recent advances allow us to screen recently identified genes known to be associated with EoE for mechanisms, such as epigenetic dysregulation, that are plausibly related to known relationships with EoE, such as male predominance, aspergillus exposure, perhaps from home remodeling, and antenatal epigenetic modification by altered DNA methylation. Since there are no published studies of methylation or twins with EoE, we have a unique opportunity to advance our knowledge of genetic and environmental mechanisms involved in this immunologically mediated disorder. Our long term goal is to reduce the rate, and therefore, the risk, of EoE in families with enriched epigenetic modifications and environmental exposures, specifically in the home structure itself, and the family food mileau as well as early life/antenatal factors, by developing a model to identify potential gene*environment interactions. By utilizing the best available technology and social media to address questions posed by our patients’ families and by giving clinicians the tools to better do their job, including ease of family history data collection for personalized medicine, we stretch the limits of current knowledge in a way that makes sense. We therefore propose to open a new area of preventive research by collecting data that will support long term public health strategies for EoE, such as primary prevention by modification of environmental sensitizing agents, secondary prevention by clinically induced therapeutic tolerance of environmental antigens (e.g., immunologically mediated sublingual or injectable treatments for antigens) and tertiary prevention of sequelae by identifying environmental factors associated with HSS parameters of long term mucosal damage that require aggressive and targeted therapeutics and the primary outcome metrics for their evaluation.

G. Brief description of the proposed CEG Research Cores you will be utilizing.

CEG FACILITIES AND SERVICES (F&S) CORES to be utilized:

Facility Core 1: Integrative Technologies Support Core

Component Heads: Shuk-mei Ho: Genomic and Microarray Laboratory (GML)

Facility Core 2: Integrative Health Sciences: Susan Pinney

Consultation with Dr. Susan Pinney of the CEG Integrative Health Sciences Facility Core (IHSFC) for expertise regarding human subjects research has been completed.

We anticipate initial consultation with the Bioinformatics Core after methylation data are available.

H. Letters of support. A letter of support from Dr John Harley is attached for the Immunochip zygosity testing.

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Appendix C1: Published Abstracts i.

Twin Shared Environment Increases The Risk of Eosinophilic Esophagitis in Families.

E. S. Alexander1,2, L. J. Martin1,3, J. P. Abonia1,4, M. H. Collins1,5, P. A. Succop1, A. J. Greenler4, E. S. Dellon6, J. G. Demain7, J. P. Franciosi1,8, G. T. Furuta9, L. E. Gurian10, R. J. Hopp11, A. Kaul1,8, K. Nadeau12,13, R. J. Noel14,15, P. E. Putnam1,8, K. F. von Tiehl1,4, M. D. Eby4, H. Foote4, A. C. Ellison4, M. E. Rothenberg1,16

1University of Cincinnati College of Medicine, Cincinnati, OH, 2Cincinnati Children's Hospital Medical Center Division of Biostatistics and Epidemiology, Cincinnati, OH, 3Cincinnati Children's Hospital Medical Center Division of Human Genetics, Cincinnati, OH, 4Cincinnati Children's Hospital Medical Center, Division of Allergy & Immunology, Cincinnati, OH, 5Cincinnati Children's Hospital Medical Center, Division of Pathology, Cincinnati, OH, 6North Carolina Department of Medicine, Division of Gastroenterology and Hepatology, Chapel Hill, NC, 7Director, Allergy, Asthma & Immunology Center of Alaska, Anchorage, AK, 8Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology & Nutrition, Cincinnati, OH, 9Children's Hospital Colorado, Division of Gastroenterology, Aurora, CO, 10Cox Health, Springfield, MO, 11Creighton University, Omaha, NE, 12Stanford Medical School, Stanford, CA, 13Stanford Medical Center & Lucille Packard Children's Hospital, Division of Allergy & Immunology, Stanford, CA, 14Children's Hospital of Wisconsin, Milwaukee, WI, 15Medical College of Wisconsin, Milwaukee, WI, 16Cincinnati Children's Hospital Medical Center, Director, Division of Allergy & Immunology, Cincinnati, OH.

Rationale: Eosinophilic esophagitis (EoE) has evidence of genetic and environmental contributions. Family studies support increased risk in EoE patients’ first degree relatives. However, no studies have examined EoE in monozygotic (MZ) and dizygotic (DZ) twins. By comparing concordance between siblings and twins, contributions of shared environment and genetics may be better understood. Methods: A retrospective study was conducted to examine EoE familial risk using reported family history in the clinical record (January2008-July2011). A second retrospective study enrolled MZ and DZ twins (proband with EoE). In twins, EoE was ascertained by pathology report or slide confirmation of an esophageal biopsy (EGD) with ≥15 intraepithelial eosinophils per high power field. EoE absence was ascertained by negative symptoms or EGD. Recurrence risk ratios (RRR) and proband-wise concordance were calculated for family members and twins, respectively. To compare groups, X²df=1 or Fisher’s Exact test were used. Results: In 554 families, 3.3%, 0.7%, 3.8%, and 1.6% of fathers, mothers, brothers and sisters (respectively) had EoE. RRR ranged from 12-70. The twin sample included 24 MZ and 46 DZ pairs. EoE concordance was not significantly different in MZ (45%) versus DZ (33%) twins

(p>0.05), but frequencynon-proband_twins (22.8%) was higher than non-twin siblings (p<0.0001). Conclusions: We confirmed increased EoE recurrence risk in families, supporting genetic

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inheritance. However, EoE concordance is surprisingly similar in MZ and DZ twins. Twins’ markedly higher RRR than non-twin siblings support an unexpected strong effect of environmental factors in EoE etiology and point to the importance of early life events.

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ii. SEX OF AFFECTED PARENT IS ASSOCIATED WITH FAMILIAL EOSINOPHILIC ESOPHAGITIS

Eileen S. Alexander1,3, Lisa J. Martin1,3, J. Pablo Abonia1,2, Paul A. Succop1 , Heather Foote2, Michael D. Eby2, Marc E. Rothenberg1,2

1University of Cincinnati College of Medicine, Cincinnati, OH, USA and

2Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA and

3Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA

Background: Eosinophilic esophagitis (EoE) occurs more frequently in males and in families with allergies and atopy.

Objectives: We will quantify the risk of developing EoE and related conditions in first degree relatives of affected individuals, stratified by sex.

Methods: A retrospective, cross sectional study was conducted for the period August 2008 to July 2010 to identify proband patients with documented family history. Previsit parent questionnaire with MD confirmation was conducted for family relations and their comorbid conditions, i.e., allergic rhinitis, asthma, eczema, food allergies, urticaria, EoE, other eosinophilic gastrointestinal disorders (EGID), food impaction, esophageal dilation. Pedigrees were constructed, using PEDSYS and SOLAR software, from a database with first degree relation information for 29%. Recurrence risk ratios (RRR) were calculated as (#affected/total)/prevalence. Data were analyzed with Chi-square and Fisher’s Exact at p<=0.05.

Results: This sample had 1.77 siblings per family compared to the Ohio mean of 1.87 and the US mean of 1.86. Pedigrees were constructed for 306 families of proband patients. First degree relatives affected with EoE included 3.3% of fathers, 0.4% of mothers, 3.4% of brothers, 2.4% of sisters and 2.9% of siblings overall. Fathers are affected significantly more frequently than mothers (p=0.03).The RRR is 33 and 54, for parents and siblings, 62 and 21 for males and females, and 61, 8, 64, 44 for fathers, mothers, brothers, sisters, respectively. All are significantly increased compared to population prevalence. EGID (p=0.017), food impaction (p=0.001) and esophageal dilation (p=0.002) were significantly more common in parents compared to siblings. EoE (p= 0.024) and food impaction (p=0.039) were reported more frequently by fathers than mothers. Asthma (p=0.001) and eczema (p=0.0001) were more common in siblings of probands compared to parents.

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Conclusions: In conclusion, we have defined specific risk ratios of EoE in first degree relatives with a range of 8-64 depending upon relationship and sex; fathers were more commonly affected with EoE compared with mothers. Parents and siblings show distinctly different patterns of comorbid conditions. However, recurrence risk does not differentiate between genetic and environmental contributions to disease. Further studies of sex-based inheritance patterns and family-based quantification of shared environment are warranted.

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iii.

Histology Scoring System (HSS) is Superior to Peak Eosinophil Count (PEC) to Identify Treated vs Untreated Eosinophilic Esophagitis (EoE) Patients

Margaret H. Collins, M.D.1,2 , Lisa J. Martin,Ph.D.1,2, Eileen S. Alexander, M.S.1,2, Scott Pentiuk, M.D.1,2 , Angela Ellison, B.S.1, Philip E. Putnam, M.D.,1,2 , James P. Franciosi, M.D.1,2, J. Pablo Abonia, M.D.1,2, Marc E Rothenberg, M.D., Ph.D.1,2

1Divisions of Pathology, Human Genetics, Biostatistics and Epidemiology, Gastroenterology, Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 2University of Cincinnati College of Medicine, Cincinnati, OH.

Rationale: PEC ≥15 eosinophils per high power field in esophageal biopsies is an EOE diagnostic criterion. We hypothesize greater discrimination among EoE biopsies occurs using a HSS including additional pathology (eosinophil abscesses/surface layering, epithelial hyperplasia and necrosis/apoptosis, dilated intercellular spaces, lamina propria fibrosis).

Methods: Biopsy slides were scored retrospectively using an 8 point HSS to grade (measure severity) and stage (measure prevalence) histologic characteristics. Univariate data were analyzed using two-tailed Student t-tests at p≤0.05. Wilcoxon 2-sample nonparametric tests were used to compare HSS parameters by treatment status at p≤0.05. Logistic regression models (p≤0.05) with AICc were used to compare goodness of fit for models designed to predict treatment status.

Results: A total of 46 proximal and 42 distal esophageal biopsies from 41 patients were scored. Demographics were 81%male, 100%white, age 10.16±4.53years (range3-18years) without differences between treated (diet and/or topical steroids) (35 endoscopies) vs untreated (11 endoscopies). Using nonparametric tests, PEC in distal (PECD) and proximal (PECP) biopsies, maximum PEC in either site (PECMax), and maximum stage (MSS) and grade (MGS) scores were associated with treatment status (p<0.01). Logistic regression models were significant for PECP and PECMax, and MSS and MGS, but not for PECD. Goodness of fit was superior for both MSS(42.79) and MGS(46.78), compared to PECP and PECMax(50.3,50.4) with MSS having the best fit.

Conclusions: HSS is superior to eosinophil counts to identify EoE biopsies following therapy. HSS more completely evaluates mucosal healing than PEC, and likely forms a better basis for making therapeutic decisions.

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Appendix C: Related Grants, Published Abstracts, Letters

C2: Letters

C2: Letter of Support UAB Statistical Genetics and Genomics Course

C2: Letters of Support

C2: Letters of Award: CEG2011, CEG2012, CCTST, CCHMC, URC

C3: Letter of Acceptance, J. Allergy and Clinical Immunology

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C3: Letter of Acceptance, J. Allergy & Clinical Immunology

Appendix C3: Letter of Acceptance, J. Allergy and Clinical Immunology

From: "[email protected]" Date: Thursday, July 3, 2014 5:54 PM To: Marc Rothenberg Subject: JACI: Decision on your manuscript JACI-D-14-00669R1

FOR ALL SUBSEQUENT CORRESPONDENCE OR QUESTIONS REGARDING THIS MANUSCRIPT, IT IS IMPORTANT THAT YOU CONTACT Gretchen Leech at [email protected]

Re: JACI-D-14-00669R1, Twin and Family Studies Reveal Strong Environmental and Weaker Genetic Cues That Explain High Heritability of Eosinophilic Esophagitis

Dear Dr. Rothenberg:

We are pleased to inform you that your revised manuscript, noted above, has been accepted for publication in The Journal of Allergy and Clinical Immunology. The following items need to be addressed before the manuscript can be transferred to the publisher:

** The JACI examines titles to address possible edits that improve readability and interest. It has been suggested that your current title ("Twin and Family Studies Reveal Strong Environmental and Weaker Genetic Cues That Explain High Heritability of Eosinophilic Esophagitis") could be revised. The current title is overly long. We suggest revising it to "Twin and family studies reveal strong environmental and weaker genetic cues explaining heritability of eosinophilic esophagitis". Please indicate whether this is acceptable or if you would suggest any alternatives or edits.

** Please submit a separate CONFLICT OF INTEREST statement for each author who is listed on the title page, using the form found on the Journal's Elsevier Editorial System homepage. You can download the form directly by going to http://ees.elsevier.com/jaci/img/forms.html. The document(s) can be sent via email to Gretchen Leech at [email protected], or they can be sent to the Editorial Office off-line by fax (303-270-2269).

As soon as the requested items are received, the manuscript will be forwarded to the publisher's team who will send you galley proofs within a few weeks. We thank you for your contribution to our journal.

Sincerely,

Scott H. Sicherer, MD

Associate Editor

The JACI is the #1 most-cited allergy/immunology journal, with an impact factor of 12.047.

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Appendix D: Comparative Heritability Methodologies

INTRODUCTION

In aim 2, I sought to quantify the separate contributions of genes and environmental factors on the frequency and variability of EoE. It was determined that comparing models of EoE risk in nuclear families and twins would satisfy this objective. Specifically, twin models can differentiate additive genetic heritability from common environment. Therefore, a software platform and model methodology with twin analysis capability was needed. The purpose of this exercise is 1) to clarify terminology related to heritability, 2) evaluate software platforms for nuclear families and twins, and 3) compare the available statistical methodologies.

BACKGROUND

2 Heritability of EoE (modeled as hcg ), can be calculated in nuclear families: The proportion of the risk of developing EoE between individuals within a population that is due to differences in genetic markers, gene-environment interactions, gene-gene interactions, and shared environments.

2 Narrow-sense heritability of EoE (modeled hag ), requires twins or multigenerational pedigrees to calculate: The additive genetic portion of the risk of developing EoE between individuals within a population that is due to additive genetic differences.

In nuclear family-based studies, the additive genetic components cannot be separated from the shared or

2 common environmental components. Heritability (hcg ) can be modeled, but common family environment cannot be separated and inflates the estimate.

The advantage of twin studies is that the total variance can be split up into genetic, shared or common environmental, and unique environmental components. This separation of genetic and environmental components allows for more accurate estimation of heritability. Of the DNA that is polymorphic,

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Dizygotic (DZ) twins share around half of their genetic variants while monozygotic (MZ) twins share almost all of their variants. Twins share the vast majority of their environmental exposure history whether they are MZ or DZ. Including common family environment in the narrow-sense heritability

2 model (hag ) gives us a more accurate estimation of the contributions of genetic differences to the variation in EoE risk in our population (14.5%) (Figure 4). In contrast, if we do not separate common

2 family environment in the heritability model (and use the model hcg ), heritability is estimated at over

99% (Figure 4), which can be attributed to differences in genetic markers, gene-environment interactions, gene-gene interactions, and shared environments.

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METHODS AND RESULTS

Table D.1. Comparative Heritability Methods and Results for MZ and DZ Twins with and an EoE Proband

Method Reference Platform Estimate h² Limitations Holzinger’s Tan et al. 2005 (CMZ–CDZ)/(1–CDZ) Used for twins only; Levitan,1988 18.03 Sensitive to prevalence, Emery, 1983 18.00–18.06 Underestimates if rare Tan et al. Tan, 2005 N/A Used for twins when zygosity is unknown Spearman Smith, C. SAS, (2(corr MZ-corrDZ) Used for quantitative traits in Regression 1970,1974 JMP 10% twins, based on defined Neale, MC. Genomics kinship and household 1998 Logistic Sham PC et al. SAS, N/A Used for pop-based data that Regression 1994 JMP includes 0/0 pairs Genomics Sensitive to ascertainment bias VCA: Laird & Lange, SAS,PROC Genetic = 18% Used for Mendelian traits, Mixed 2011 GENMOD Environment = 25% used for nuclear families, model logistic doesn’t partition family genes PROC from family environment MIXED continuous VCA Almasy & SOLAR swings wildly based on doesn’t account for twins, Blangero, many model and prevalence used for nuclear families, 19981 possible specifications very sensitive to prevalence models 0-100 SEM ACE Muthén, L.K. Mplus A narrow 14.5 Used for family and twin data and Muthén, C 81.0 Partitions variance into genes B.O. E & family environment 20042 Later for unique environment and timing Prevalence 5.5/10,000 and 70% male (Prasad et al. 2009) MZ Frequency 32/49 individuals = 65% affected (includes one set of MZ triplets) DZ Frequency 42/67 individuals = 63% affected (includes one set of DZ triplets) MZ 8/24 pairs; Proband-wise concordance 0.50 ±0.11 CI95 (0.29 to 0.71)

1John Blangero, Kenneth Lange, Laura Almasy, Tom Dyer, Harald Göring, Jeff Williams and Charles

2Muthén, L.K. and Muthén, B.O. (1998-2012). Mplus User’s Guide. Seventh Edition.

Los Angeles, CA: Muthén & Muthén. Prescott, C. Behav Gen 34:1; 2004.

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SOLAR uses a liability threshold model to estimate dichotomous outcomes. This assumes that the dichotomous trait is influenced by an underlying continuous variable which is distributed as a normal distribution. When you exceed a threshold you have disease. Using variance component analysis, the heritability of EoE is 97% (p=3.3e-15; SE=0.055) with a non-significant estimate of shared sibling environment at 3.84% (p=0.11; SE=0.55. Sex was not significant (p=0.11). This sample is highly ascertained and thus, an ascertainment correction accounting for proband was applied.

By contrast, a mixed model approach in the Twin cohort, estimated heritability at 18% with a 25% effect from common household, but does not account for effects that mimic genetics, such as timing of exposure. In addition, the mixed model treats the dichotomous outcome as continuous.

To quantify the separate effects of genes and environment, we used both the Nuclear-Family and Twin

2 cohorts. In the Nuclear-Family cohort, combined gene-environment “heritability” (hgc ) was estimated at

72% (p<0.001; SE=0.027) of the total phenotypic variance, suggesting a strong affect from genetics.

2 Parallel analyses in twins estimated heritability (hgc ) at 99.5% (p<0.001). However, the ACE model

(Goodness of fit p=0.56) fit the data better than either the AE (Goodness of fit p<0.001) or CE models

(Goodness of fit p=0.006), suggesting that EoE risk resulted from both genetic and shared environmental factors. Importantly, the heritability (estimate 14.5±4%; p<0.001) changed greatly by analysis of twins, when accounting for a common environment component.

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CONCLUSIONS

The reduction in heritability is attributable to the large proportion of variation explained by common environment (estimate 81.0±4.0%; p<0.001). Thus, heritability estimates are markedly inflated when the study design, statistical analysis does not separate common environment or when the software platform does not accommodate twin data.

Heritability estimates are very population specific. These families are all highly enriched for EoE. This suggests that there are time specific exposures and more general family exposures that contribute to the risk of EoE.

The separate contributions of additive genetic heritability and common, or shared, environmental factors on the frequency and variability of EoE were quantified. I compared EoE risk in nuclear families and twins. Twin, or extended family, study designs disentangle the effects of genes from common environment.(22, 23) Twin study design was chosen to quantify the separate contributions of genetic and environmental variation to EoE heritability. Using the ACE structural equations model and Mplus software platform, additive genetic heritability was calculated at 14.5% with common environment contributing 81% to total phenotypic variance. As expected, the heritability estimate from the reduced AE

2 model (hgc ; which ignores common environment) was inflated (99.5%). This high value is not unexpected as twin models often produce inflated estimates(24) due to ascertainment bias. However, by including common environment in the full model, heritability is estimated at 14.5%, with common environment accounting for 81.0% of the variation. The importance of common environment is further supported by our finding that DZ twins are enriched for EoE compared to non-twin siblings. Thus, using the traditional nuclear family approach, the proportion of variation expected to be explained by genetic factors is dramatically overestimated. This overestimation is a problem because these heritability-based estimates are often used as a metric for the amount of variation expected to be explained by single-

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nucleotide polymorphisms in traditional genetic association studies. The failure of single-nucleotide polymorphisms to account for this variation has been termed “missing” heritability,(25-28) and

“phantom” heritability is speculated to be the result of genetic interactions.(22) Our results show that the amount of variation attributed to genetic factors is overestimated due to failure to account for common family environment in nuclear family data. Therefore, a twin “ACE” structural model was chosen for the analysis plan in aim 2.

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Appendix D.1 Mplus Twin SEM Model

TITLE: this is an example of a two-group twin model for categorical outcomes using parameter constraints DATA: FILE IS: H:\Mplus\TwinSEMcnt0.055.txt; VARIABLE: NAMES ARE zygot y1 y2 weight;

categorical=y1 y2; usevar are y1 y2; grouping=zygot(1=MZ 2=DZ); ! specify the two groups MZ and DZ freqweight=weight;

model: [y1$1 y2$1] (t); y1 with y2 (mzc);

model dz: y1 with y2 (dzc);

model constraint:

new(a c e x y z); a=x*x; c=y*y; e=1-x*x-y*y; z=sqrt(1-x*x-y*y); mzc=x*x+y*y; dzc=0.5*x*x+y*y;

! Uncomment for Model AE ! c=0;

! Uncomment for Model CE ! a=0;

a=0; c=0;

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Appendix E: Preliminary Environmental Exposure Associations Determine Domains

Briefly, the purpose of the preliminary environmental questionnaire was to determine domains for further study in the Twins group. As previously stated, greater differences in birth-weight were associated with disease discordance in twin pairs (p=0.01; n=35; Table 3.1). Birth season was significantly different in concordant and discordant twin pairs (p=0.03; n=63); specifically, birth in Fall was associated with EoE discordance (p=0.02; n=63). Breastfeeding (p=0.15; n=59) may have a protective effect.

Food allergies (p<0.001; n=97) were associated with EoE. Penicillin allergies (p=0.17; n=66) may increase risk for EoE. Importantly, 100% of twins who reported penicillin allergy had confirmed EoE and there were no twins unaffected by EoE who reported penicillin allergy. Due to this zero cell, an additional statistical test was performed. Permutation testing confirmed the inference that penicillin, antibiotics, early life infection or, perhaps, immune deficiency may be associated with EoE. Recently, Jensen et al., reported early life pilot data for environmental exposures with increased risk of EoE in children exposed to antibiotics during their first year of life. (29) Indeed, parents reported a wide range of relatively unusual drug allergies (Tables E.1 and E.2), including associated DRESS drugs,(30, 31) many antibiotics(32) and anti-protozoans. Early childhood antibiotic use, and penicillin specifically, warrants further study, as do their potential indications. Indeed, occult mechanisms of confounding by indication due to genetic background and effect modifiers of biochemical response to viruses may mimic the association between antibiotic use in early life and asthma.(33)

Further, penicillin is a mold derived pharmaceutical. Mold exposures are associated with eosinophilic esophagitis and are used to induce EoE in murine models of disease.(34, 35) EoE, eosinophilic asthma and aspergillosis share the characteristics of eosinophilic inflammation and its sequellae.(36, 37) Our data suggest that penicillin is associated with EoE risk. Further, Penicillium and Aspergillus have been associated with pediatric asthma risk.(38) Aspergillus, and Cladosporium molds have been shown to be more specifically related to the classic Th2 eosinophilic inflammation(39). Indeed, cladosporium exposed

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dectin1 knock-out mice induce Th2 with a robust airway hyperresponsiveness, characteristic of eosinophilic asthma(40). According to ProMED editor, Larry Madoff M.D.:

(See http://www.nature.com/nature/journal/v484/n7393/full/nature10947.html)

The National Allergy Bureau, associated with the American Academy of Allergy, Asthma and

Immunology, collects, tracks and reports mold and pollen at specific testing stations across the U.S.

Geoclustering these data with EoE cases may provide insight into mold-related risk of EoE by latitude, longitude and ecological biome. Patterns of food use, including increased consumption of relatively expensive, imported fruits and vegetables out of season, may be associated with anecdotal reports of higher socio-economic strata of EoE families. A recent outbreak of brucellosis, or “Malta fever,” illustrates the connection of common source microbiota, food, family, and generational age to classic epidemiologic studies of infectious diseases, and disease rates that have increased 2.6-fold since 2011:

(Byline: Achiya Rabbed http://www.ynet.co.il/articles/0,7340,L-4526332,00.html and ProMED Digest, Vol 24, Issue 22 [email protected] )

Finally, other potential environmental factors were explored for their association with EoE risk and negative results are reported herein (Table E.3).

In summary, the early life factors domain, including breastfeeding, which (p=0.15; n=59) may have a protective effect on EoE risk and early infections and treatments, and will require prospective studies to

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determine temporality and ascertain precise treatments and response. Medication allergies, including penicillin and antibiotics, as a distinct domain, deserve additional inquiry for both true IgE mediated allergies, possible related sensitization to functional chemical groups, their associated pharmacogenomic response implications and the myriad of potential microbiota exposures indications for which they were prescribed, either empirically or by culture and sensitivity analysis. The relationship to food allergies and food additives warrants further study. Indeed, one father reported an allergy to blue cheese, in the

Penicillium family of molds. Food additives, such as citric acid are produced using Penicillium and

Aspergillus (Table E.2). The home environment domain, both indoor and outdoor, including specific mold exposures, birth season and temporo-geospatial dose effects for mother, conceptus and newborn may provide unique insight into EoE risk due to common family environment.

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Table E.1: Group Determination of Self/parent Report of Chemical and Pharmaceutical “Allergies”

Medication Relation Abx PC PCN Sulfa azole thi DRES GI s Allergy or 44 families N like azole S e anaphylaxis +der d reported and (indication) by class pro co sib mo fa alinia (protozoa) x x flagyl anaerobes t x x (protozoa) zythromax t x x macrolide erythromycin t x x macrolide biaxin macrolide t x minocycline x x x tetracycline

Sulfa t x x x x

Penicillin t x x x x x x amoxicillin t x x x x augmentin x x x x cephalosporin x x x sulfasalazine x x (UC?) plaqualin (SLE?) x hydralazine x pseudofed x omeprazole x xopenex t albuterol t prevacid t x reglan t x MOM x x ibuprofen x tylenol x aspirin t x demerol x x valium x x

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vicodin t x codeine t x x x

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Table E.2: Number of Potentially Relevant Self/Parent-reported Medication Allergies

Medication Relation Abx P Sulfa azole thi DR- GI S Allergy or 44 families C + der azole ESS E anaphylaxis UPDATED for 45 N +der D TOTAL allergies 38 19 12 17 15 2 4 9 pro co sib mo fa 45 47 45 43 42 Sulfa, Bactrim, 3 1 1 5 1 11 11 11 11 sulfamethoxazole

Alinia ASA, S 1 1 1 1 nitazoxanide minocycline 1 1 1 MACROLIDES: zithromax 1 1 1 3 azithromycin erythromycin 1 1 2 biaxin 1 1 clarithromycin

Flagyl 1 1 2 2 metronidazole PENICILLINS: cephalosporin 1 1 1 Penicillin 4 3 4 3 14 14 P. chrysogenum amoxicillin, 1 1 1 3 3 augmentin blue cheese 1 1 P. roqueforti P. glaucum commercially prep x foods c/ citric, tartaric, gluconic acids & -ases P. & Aspergillus sulfasalazine 5- 1 1 1 1 1 ASA (RA,IBD,UC?) aspirin 1 1 plaquenil 1 (RA,SLE?) hydralazine 1 pseudoephedrine 1 singulair 1

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albuterol,xopenex 1 Nexium 1 1 esomeprazole Prilosec 1 1 1 1 omeprazole Prevacid 1 1 1 1 lansoprazole reglan 1 1 MOM 1 1 ibuprofen 1 tylenol 1 demerol 1 1 valium 1 1 vicodin 1 1 codeine 2 2 2 6 Final60withCREATEDcorrected

# 45 siblings in 32 families that have at least one other child; 12 families do not have any additional children; 16 families do not have family data collected yet

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Table E.3. Preliminary screen of environmental and co-morbid risk factors for each twin.

Individual Environmental Analysis EoE Twins cohort BY affected status Individual twin n Not EoE EoE Test p OR CI95 2-Tail Exposures maximum n=128 Breast feeding 59 90.0% 65.3% Fisher Exact 0.15 0.21 0.02-1.8 df1 Birth Order (2) 91 47.1% 50.1% Pearson 0.72 1.2 0.5-2.7 Birth weight (grams) 80 2400.9 2358 Student’s 0.77 ±532.0 t-test ±662.9 Birth length (inches) 38 19.2±1.4 18.6in±1.1 Student’s 0.19 inches t-test inches Allergies, 97 64.7% 76.2% Pearson 0.23 1.74 0.7-4.3 environmental Allergies, spring 69 90.9% 83.0% Fisher Exact 0.48 0.49 0.09-2.5 df1 Allergies, summer 13 66.7% 80.0% Fisher Exact 1.00 2.00 0.1-34.8 df1 Allergies, fall 68 86.3% 87.0% Fisher Exact 1.00 1.05 0.2-4.7 df1 Allergies, winter 66 59.1% 61.4% Pearson 0.86 1.10 0.4-3.1 Allergies, year round 66 61.9% 64.4% Pearson 0.84 1.12 0.4-3.3 Food allergies 97 23.5% 81.0% Pearson <0.001 13.81 5.0-38.0 Eczema 97 32.4% 52.3% Pearson 0.06 2.30 1.0-5.5 Abdominal pain 96 21.2% 65.1% Pearson <0.001 6.92 2.6-18.5 Difficulty swallowing 96 9.1% 60.3% Fisher Exact <0.001 15.20 4.2-55.2 df1 Food stuck while 96 0% 55.6% Fisher Exact <0.001 * df1 swallowing Food impaction 93 0% 5.0% Fisher Exact 0.55 df1 * PCN allergy each 66 0% 100% Fisher Exact 0.17 df1 twin * Permutation *Permutation test to be done; at this time Odds Ratio undefined due to zero cell

H:\2014manuscript1\FRms2014drafts\Final63withALLderivedANDenvUNABRIDGED3

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Percent: Number exposed AND EoE / all with EoE; Number exposed AND no EoE/all without

EoE

Paired Environmental Analysis EoE Twins cohort BY concordance for EoE Exposures for pairs n Discordant Concordant Test p OR CI95 maximum n=63 frequency (%) frequency 2-Tail continuous: (%) mean±SD continuous: mean±SD Current age (years) 63 16.3±11.3 16.00±11.8 Student’s 0.96 1.0 NA t-test Gestational age (weeks) 43 35.0±3.4 35.0±2.2 Wilcoxon 0.58 Term (≥33.5 weeks) 43 75.76 80.0 Fisher 1.00 1.3 0.2- Exact df1 7.3 Term birth (≥35 weeks) 43 50.0 69.7 Pearson 0.25 0.4 0.1- df3 1.8 Term birth Twin birth weight 35 335.7±273.0 145.6±133.7 t-test 0.01 difference (grams) Wilcoxon 0.06 Season x4 63 fall 43.2% 10.5% Pearson 0.03 adjusted for hemisphere winter 13.6% 31.6% df3 spring 18.2% 10.5% summer 25.0% 47.4% Season fall 63 43.2% 10.5% Fisher 0.02 0.2 0.03- Exact df1 0.8 Fertility treatments 47 45.7 33.3% Fisher 0.52 0.6 0.2- Exact df1 2.3 Fertility treatments 20 sparse data Pearson 0.43 (by type) df2 Chorion/amnion number 32 sparse data Pearson 0.56 Prenatal vitamins 44 93.9% 100% 1.00 0.99 Birth order (2) 45 47.1% 54.6% Pearson 0.67 1.4 0.3- 5.3 Penicillin allergy 36 6.7% 33.3% Fisher 0.12 7.0 0.8- proband Exact df1 64.6 Penicillin allergy either 25 10.5% 50.0% Fisher 0.07 8.5 1.0- twin Exact df1 74.4 missing data:11co-twins Penicillin allergy mother 30 12.5% 16.7% Fisher 1.00 1.4 0.1- Exact df1 16.5 Penicillin allergy father 21 18.8% 20.0% Fisher 1.00 1.1 0.1- Exact df1 13.5 Penicillin allergy sibling 22 12.5% 16.7% Fisher 1.00 1.4 0.1- Exact df1 19.0 Penicillin allergy family 44 21.2% 36.4% Fisher 0.42 2.1 0.5- Exact df1 9.4 H:\2014manuscript1\FRms2014drafts \45family_drug_allergies

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Legend: Environmental risk exposures for individual twins/triplets (n=128) by EoE affected status; twin pairs (n=63) by disease concordance for EoE. Pearson correlation or Fisher’s Exact Test (FE) was used

th for discrete variables; Student t-test for continuous variables; p-value, OR=Odds ratio, CI95=95 percentile Confidence Interval reported.

Chorion/amnion number 1=di/di; 2=diamnionic/monochorionic; 3=mono/mono

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Appendix F: Manuscript in preparation

Epigenetic Methylation Differences in EoE Discordant Monozygotic Twins

INTRODUCTION

Groups studying Eosinoiphilic Esophagitis (EoE) have made significant progress towards an understanding of the genomic mechanisms driving disease etiology and flare. Our group has shown that

EoE is a complex genetic disease with a strong environmental component.(41) Dependence of disease severity on antigen exposure further suggests strong environmental effects. Twin studies have been especially informative in identifying a strong environmental component of EoE risk. Wide gaps in twin concordance are indicative of high genetic heritability, as seen in peanut allergy.(42); however, EoE disease concordance in monozygotic (MZ) twins is only 58%, much lower than expected. Likewise, dizygotic (DZ), or fraternal, twins’ concordance is 36%, and is much higher than expected. The small difference beween MZ and DZ EoE disease concordance are reflected in the relatively low (14.5%) genetic heritability estimate. Taken together, these results suggest that environmental factors contribute to

EoE risk in genetically susceptible individuals. Recent environmental survey data have identified early life and environmental factors associated with increased risk of EoE,(35, 43-47) including antibiotic exposure in the first year of life.(29) Greater birth-weight difference between twins (p=0.01), breastfeeding (p=0.15) and Fall birth season (p=0.02) were associated with twin discordance in disease status.(41) These early life environmental factors warrant further study.

Because we cannot go back in time and measure the environmental exposures of each subject with and without EoE, we often use markers of environmental exposure as a surrogate in our analysis. One such marker for environmental factors is epigenetic variability.(48, 49) Environmental factors such as smoking(50) have been shown to result in measurable differences in the methylation of peripheral blood cells, diesel exhaust in saliva(49) and folic acid in cord blood.(51) Mechanistically, epigenetic modification such as cytosine methylation of CpG sites can result in altered gene expression that may lead

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to the the development of EoE. In addition, methylation may be associated with altered transcription and cellular function. Specifically, eotaxin-3 is a gene which is upregulated in EoE, and its Stranscription is enhanced by promoter associated hypomethylation.(52) Human twin studies have not addressed the hypothesis that epigenetic changes are associated with EoE.

We will gain insight into the hypothesis that methylation contributes to the underlying etiology of EoE.

This is important because although the prevalence of EoE has increased,(1, 2, 5, 7) secondary and tertiary prevention and management strategies are very limited.(4) Indeed, no targeted theraputic is FDA approved for the treatment of EoE.(53) Furthermore, identification of the etiological and pathogenic mechanisms and, ultimately, identification of exposures will give clinicians and genetically susceptible families tools needed to mitigate risk of EoE and its sequelae. As a first step, we will quantify and confirm saliva DNA methylation differences between discordant MZ and DZ twin pairs and compare these to site-specific differences found in concordant pairs. We propose an algorithm using these differences and biologic pathway analysis to prioritize genes for further study. This study will test methylation as a mechanism of action for known candidate genes and identify new genetic regions of interest. Associated epigenetic modifications will be used to construct an ‘EoE methylome’ signature. We expect that the EoE-related methylome signature will differ between affected and unaffected individuals both between and within discordant pairs, and will test this hypothesis by assessing non-related subjects with and without EoE and assessing our ability to cluster subjects by disease status using the EoE methylome. The central purpose of this study is to identify candidate and genome-wide novel sites with sustained methylation differences between monozygotic (MZ) twins who have EoE and their unaffected co-twin.

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METHODS

Study design: A cross-sectional twin paired study compared differential methylation in candidate and novel genes using the EoE Twins Registry.(41) As described, we collected demographic, zygosity and phenotypic information for each subject. Eleven discordant MZ, 8 DZ twins and 7 pairs of comparative concordant controls will be used in the analysis (Table 1). EoE expression and genetic studies have identified CpG islands near candidate loci, such as DSG1, TSLP, WDR36(54-56), and CCL26 (eotaxin)

(54-57) that are included in this custom array.

Specific Testing Considerations:

Comparative Approach Considerations Saliva DNA and collection:

Saliva DNA was chosen due to its proximity to the esophageal tissue study site, and availability for unaffected twins. During sample collection, loss and recollection issues related to volume and quality were identified. Young twins’ difficulty producing an adequate volume of saliva resulted in parent frustration. Also, when lids on the kit were not closed tightly, the preservative and saliva did not mix, compromising sample quality. Issues were improved by adding the manufacturer’s sponge kit for younger children and by improving written and verbal instructions for parents, including phone instruction during collection, as needed. Therefore, a pilot quality control study confirmed the use of salivary DNA as a cost effective alternative for pediatric participants and family studies. Further, intrauterine growth differences in MZ twins did not alter saliva DNA methylation beyond that expected from technical variation(58) and environmental exposure to diesel exhaust has been associated with hypermethylation in

DNA from saliva.(49)

Phase 1 Laboratory Analysis using the Illumina Infinium HumanMethylation450 BeadChip:

Paired Oragene DNA samples from twins were analyzed using the Illumina Infinium

HumanMethylation450 BeadChip analysis by the Genomics and Microarray Laboratory at the University of Cincinnati (Shuk-mei Ho, PhD, Director). Given the lack of previous studies in methylation of EoE

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patients, the Illumina Methylation chip was used to interrogate >485,000 CpG sites across the genome, with both Types I and II assays(59), and evaluate candidate gene regions identified by EoE GWAS and expression studies for evidence of epigenetic methylation differences. It covers 99% of RefSeq genes, with an average of 17 CpG sites per gene region distributed across the promoter, 5’UTR, first exon, gene body and 3’UTR. It also covers 96% of CpG islands, with additional coverage in island shores and the regions flanking them. Illumina’s Infinium assay measures DNA methylation using quantitative genotyping of bisulfite-converted genomic DNA.

Sample Preprocessing:

Genomic DNA samples (500ng ) were bisulfite converted by Zymo’s EZ-96 DNA Methylation Kit

(#D5004) using recommended conditions for Infinium assays (16 cycles of 950C for 30 sec., 500C for 60 min) at the Genomics and Microarray lab at University of Cincinnati Medical Center. DNA was prepared using Illumina HumanMethylation450 BeadChip kit (#WG-314-1002). Bisulfite converted

DNA was denatured, isothermally amplified, fragmented and hybridized to Infinium

HumanMethylation450 BeadChips. The BeadChips were washed, stained and scanned on Illumina’s iSCAN.

Statistical Analysis:

Data preprocessing:

Quality of the array was assessed(60) using sample-independent and dependent internal control probes included on the array for staining, extension, hybridization, specificity and bisulfite conversion. All samples had >98% CpG sites detected at p=0.01 level, and ~95% bisulfite conversion rate (Figure 1).

The signal intensities were then background-adjusted and normalized using GenomeStudio Methylation

Module (v1.8 User Guide, Illumina), and used to calculate the beta values as

=/(++100):

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, β , ,

After quality control, there were 53 samples for further analysis. Specific sites with lower performance were removed from each analysis. Site specific exclusion criteria were: 1) CpG sites that were not detected in >95% of the samples at p=0.01 level; 2) CpG sites with one or more <5 bead number; and 3)

CpG sites with SNPs present nearby (>10bp or ≤ 10bp from query site).

Like blood, saliva contains a mixture of cell types. Standard practice of the analysis of blood methylation includes a deconvolution step in which the expected methylation level of each cell type is used to adjust each sample for differences in cellular composition of the original sample. There are no reference values for cell type composition in saliva. To our knowledge, there are no reports that the cellularity of saliva between patients with EoE and controls are markedly different. Therefore, we will not include this adjustment in our analysis.

Phase 2 Algorithm:

Methylation (beta value) differences were calculated by site(61) comparing affected probands to their unaffected twin, by zygosity status. Using 92 genes identified by the EoE diagnostic panel for esophageal tissue as a training set, and349 sites of differential methylation ≥5%, in 230 genes, biological pathways associated with differential methylation were prioritized using ToppGene (toppgene.cchmc.org). An algorithm was developed to prioritize genes for confirmation by pyrosequencing (Figure 2). We will test three hypotheses: 1) Paired t-test of the difference in percent methylation of affected – unaffected was used for MZ and DZ discordant pairs at α=0.05, 2) the difference between discordant pairs is greater than the difference between concordant pairs for sites of biological interest, and 3) the direction and effect size of methylation for known sites. We will preferentially include large regions with many CpG sites, if there is evidence of differential methylation.

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Phase 3 Laboratory Analysis: I will confirm potential epigenetic modifications associated with EoE identified by the Illumina array by region-specific CpG analysis by pyrosequencing, an independent method. Briefly, pyrosequencing is a quantitative sequencing method that measures the intensity of light emitted by pyrophosphate conversion during complementary nucleotide binding with ~5% lower detection limit.(62) Because of the different chemistry used in this analysis, pyrosequencing will also be used to explore sites not represented on the Illumina chip array, such as loci near TSLPR; TSLP was associated with a male subgroup of patients with EoE.(55) TSLPR resides in the pseudoautosomal region of chromosomes X and Y, and was implicated in one of our groups published candidate SNP study.(55)

Loci that are not included on the Illumina array that are near genes in the EoE transcriptome panel or associated with EoE(63) will be also be compared using this secondary method.

Phase 3 Statistical Analysis: For regions that pass hypothesis testing, the Illumina Methylation/Gene

Expression Comparison tool will be employed utilizing esophageal expression data.

Replicate: Although it would be ideal to have a replication set of discordant MZ twin pairs, there are no such cohorts currently available to us. Therefore, I will compare findings to discordant DZ twin pairs

Phase 1 n=8; Phase 2 n=10).

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RESULTS

OrageneTM DNA samples for paired analysis of methylation in Phase 1 were processed on five chips, in a continuous run. Quality control analysis was performed at the sample level. Samples all had >98% CpG sites detected at p=0.01 level, and ~95% bisulfite conversion rate. Quality control data for a total of 26 twin pairs (samples=53, including one MZ discordant triplet) resulted in 53 samples for further analysis

(Figure 1).

A comparison of the methylation difference between paired discordant twins resulted in 349 sites with an effect size of 5% or greater (Table 2) and 86 sites >6% (Table 3). Comparison of autosomal sites of CpG methylation to published EoE expression data and diagnostic panel identified the H19/SCUBE2 (Table

4), as well as LRRC26 regions (Table 5). High site-specific methylation at STK38L (13.9%), TBX1

(11.2%, 7.2%), SLC38A10 (9.4%), WNT6 (9.2%) will be interrogated. ToppGene analysis of the EDP

(63)genes used as a training set for networks predicted to be relevant to EoE risk include, in predictive order, CD40LG/RBMX/SNORD61 region, ITGB2, RUNX1, GHR, KCNQ10T1/H19 region, SGK1,

ELANE, WNT5, GNAS, SPI1. CpG sites in TBX1, WNT6, ARGAP4, STKL38L, LRRC26 and WAS were predicted to be in pathways rated at 21, 27, 68, 70, 72 and 102 of 231 genes, respectively. Further,

SGK1 was identified in the transcriptomes of both EoE(63) and eosinophilic gastritis (EG) and has high differential methylation (7.1%; p-value=0.06).

Methylation in regions MUC4, UBD, TSPAN12, WDR/TSLP, GRK5, SLC25a24, ANO1, CITED2 that were associated with altered esophageal expression did not show promising results.

MZ cohort, including CD40LGT, RBMX, SNORD61, WAS and BCOR.

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Immunoglobulin Superfamily CpG Methylation on X Chromosome:

The mechanisms of male predominance in EoE have not been explored. Sites of interest such as

CD40LGT and SNORD61on Xq25, adjacent to an immunoglobulin superfamily, warrant further study

(cg02936290; RNA). (http://omim.org/entry/300137 ; http://useast.ensembl.org/Homo_sapiens/Gene/Summary?g=ENSG00000206979;r=X:135961358-

135961430;t=ENST00000384252)

Given the male predominance of EoE, adenoma cases showed male predominance of a specific mutation at methylated CpG site (cg02895192; ARHGAP4;ARHGAP6 http://omim.org/entry/300014 ).

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DISCUSSION

Previously, structural equations modeling showed that EoE has a complex mode of inheritance, with estimates of 14.5% genetic heritability and 81% of phenotypic variance due to common environment.

Genetically identical MZ twins allow the study of environmental factors that alter gene expression.(64) In this study, sites with sustained methylation differences between identical, or monozygotic (MZ), twins who have EoE and their unaffected co-twin provide novel evidence that methylation contributes to the underlying etiology of EoE.

Early Genetic Annotation is Promising

Wen et al., recently published a 96-gene EoE diagnostic panel (EDP) used to differentiate EoE patients from controls and from patients with gastroesophageal reflux disease.(63) This panel includes

H19/SCUBE2 on chromosome 11p15.3 which was first associated with EoE by Blanchard et al. in

2011.(65) CpG island (cg01977486) near the H19 promoter were identified with >4% hypomethylation in affected twins (p=4.2e-5). Strikingly, H19 (located at 11p15.3) is a non-coding RNA known to be imprinted (Figure 3), or methylated, resulting in altered gene expression. Maternal expression of H19 and paternal expression of IGF2 are imprinted by a paternal-specific region upstream. http://www.ncbi.nlm.nih.gov/gene/283120. Further, large deletions in this region are associated with

Beckwith-Wiedemann syndrome. http://www.ncbi.nlm.nih.gov/clinvar?term=H19 Methylation restricts gene expression and may behave like a deletion. KCNQ10T1 at 11p15.5 is expressed as a parent of origin effect with two large clusters of genes regulated by independent imprinting control regions (ICR).

Also on X, BCOR includes a large differentially methylated region associated with germ center formation, apoptosis, zinc finger interactions and histone deacetylases, with a pseudogene on the Y chromosome. http://www.ncbi.nlm.nih.gov The Wiskott-Aldrich syndrome (WAS) region includes increased Cdc42 binding prior to WAS protein binding and associated with severe congenital neutropenia.

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http://ghr.nlm.nih.gov/gene/WAS Although counterintuitive, flux and depletion in the system could result in a counter mechanism by which autoimmunity results.

Collagen-related phenotypes have been anecdotally reported with EoE. Indeed, ELANE and GNAS are associated with collagen IV, elastin and collagen V, actin cytoskeleton binding, and thus, antigen recognition, as well as hydrolysis of granulocyte lysosomes. http://www.ncbi.nlm.nih.gov/gene

Both the EDP(63) and the EG transcriptome(66) recently characterized by Caldwell et al., includes

SGK1, at 6q23, known to have alternate transcripts, including a glucocorticoid-regulated serine/threonine protein kinase protective under conditions of cellular stress.(67) http://www.ncbi.nlm.nih.gov/gene?cmd=Retrieve&dopt=full_report&list_uids=6446 Glucocorticoid- regulated genes, such as FKBP51, have previously been associated with EoE.(68)

Future directions:

Epigenetic and Early Life Environmental Differences in Twins Drive New Research

From conception, epigenetic differences arise that may result in phenotypically different manifestations of complex diseases in genetically identical twins (Figure F4). Differences in maternal nutrition have been demonstrated to affect expression with differential effects by sex.(69) We have found promising sites of altered methylation in MZ twins that will be further investigated in this study. We propose a model in which primary and secondary exposures interact with altered activation pathways differentially in males with underlying genetic susceptibility to EOE (Figure F5). Future prospective studies will address temporal exposure issues and the effect of age on twin methylation patterns. Comparative tissue DNA methylation studies will be undertaken as the Twins’ biobank expands. Prospective studies could study miscarriage rate in female probands. Importantly, altered patterns of X-linked methylation may underlie the male preponderance of EoE. Future studies will address contribution of sex to the differences identified at these loci (Figure F6).

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To date, screening methylation chip array studies of discordant twins importantly have identified EoE esophageal transcriptome candidate genes and novel methylation sites as a possible mechanisms of dyregulated expression. Further insights into the novel hypothesis that altered patterns of methylation contributes to the underlying etiology of EoE may identifiy new therapeutic targets giving clinicians and genetically susceptible families tools needed to mitigate risk of EoE and its sequelae.

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Table F1. Paired Saliva DNA Samples for Methylation Analyses, Phases 1 and 2

Current MZ twin paired samples DZ twin OrageneTM paired saliva DNA samples

discordant concordant for discordant for EoE EoE: for EoE comparative controls Illumina450 11 7 8

Illumina450 data for 26 twin pairs (samples=53, including one MZ discordant triplet). These are OrageneTM DNA samples for paired analysis of methylation in Phase 1. Samples were processed on five chips, in a continuous run, in July 2013.

187

Table F2. CpG sites (n=349) with ≥5% difference methylation (Me) = affected % Me –unaffected %

TargetID diff p CH MAPINFO DMR ENH REG UCSC NAME value R cg00030047 ‐0.055 0.106 1 6,268,790 N_Shore TRUE Prom RNF207 cg02084087 ‐0.058 0.135 1 6,526,049 Island TNFRSF25 cg14418802 ‐0.051 0.228 1 9,299,979 DMR Cell_sp H6PD cg20700740 ‐0.05 0.31 1 9,339,683 TRUE cg02396224 ‐0.063 0.057 1 10,698,794 Island CASZ1 cg13875012 ‐0.062 0.162 1 11,710,610 Island Unclas FBXO2 cg01554529 ‐0.057 0.235 1 11,722,935 N_Shore FBXO6 cg01567509 ‐0.054 0.282 1 15,104,223 KIAA1026 cg12911732 ‐0.067 0.265 1 50,744,322 TRUE cg10512745 ‐0.067 0.113 1 50,884,480 Island DMR DMRTA2 cg03989480 ‐0.055 0.146 1 50,885,352 Island DMR DMRTA2 cg16732616 ‐0.071 0.076 1 50,886,782 Island DMR DMRTA2 cg01066472 ‐0.064 0.015 1 75,591,029 Island cg20431191 ‐0.054 0.258 1 86,043,795 S_Shore Unclas DDAH1 cg03423077 ‐0.053 0.321 1 87,599,262 S_Shore LOC339524 cg17811845 0.0615 0.009 1 89,358,043 S_Shore CDMR GTF2B cg19413693 ‐0.056 0.293 1 108442921 TRUE VAV3 cg14221700 ‐0.056 0.005 1 111153602 S_Shelf cg19255477 ‐0.072 0.098 1 151810586 Island LOC100132111; C2CD4D cg15015892 ‐0.066 0.09 1 151810887 Island C2CD4D; LOC100132111 cg05021743 ‐0.057 0.106 1 151810893 Island C2CD4D; LOC100132111 cg04296699 ‐0.058 0.105 1 151810904 Island C2CD4D; LOC100132111 cg11576590 ‐0.068 0.178 1 152011357 S_Shelf cg06188545 ‐0.071 0.138 1 156863643 Island PEAR1 cg20746702 ‐0.05 0.023 1 160864761 cg09755589 ‐0.053 0.225 1 166459325 Prom cg10861751 ‐0.053 0.247 1 192544716 Prom RGS1 cg10889502 ‐0.053 0.001 1 200398670 TRUE cg25407979 ‐0.055 0.345 1 204256846 TRUE PLEKHA6 cg21160472 0.0511 0.022 1 212782112 Island Prom ATF3 cg23128584 ‐0.05 0.336 10 323,649 DIP2C cg18692070 ‐0.055 0.172 10 3,466,852 cg05587400 ‐0.053 0.182 10 5,817,943 TRUE Prom GDI2 cg09684112 ‐0.053 0.29 10 13,701,447 S_Shore Unclas FRMD4A cg03174507 ‐0.051 0.122 10 21,789,582 Island RDMR Cell_specific cg04707519 ‐0.051 0.181 10 21,799,314 Island TRUE Cell_specific

188

cg12786570 ‐0.05 0.239 10 30,316,432 Cell_spe KIAA1462 cg09509909 0.0531 0.241 10 93,672,748 S_Shelf Cell_specific cg00487187 ‐0.053 0.221 10 94,451,625 Island RDMR Prom HHEX cg12691572 ‐0.059 0.273 10 114574959 VTI1A cg26518861 ‐0.055 0.207 10 122708898 Island Cell_specific cg06966660 ‐0.053 0.171 10 123923066 Island TRUE TACC2 cg07451524 ‐0.053 0.14 10 123923518 Island TRUE TACC2 cg09580822 ‐0.054 0.184 10 130834003 Cell_specific cg21350697 ‐0.051 0.297 10 134149366 S_Shelf LRRC27 cg11546385 ‐0.052 0.206 11 269,375 DMR TRUE Prom cg22136363 ‐0.051 0.297 11 1,911,511 LSP1 cg05608541 ‐0.053 0.254 11 2,321,770 C11orf21; TSPAN32 cg26104781 ‐0.054 0.008 11 2,721,383 Island Prom KCNQ1OT1 cg07595203 ‐0.054 0.004 11 2,721,480 Island Prom KCNQ1OT1 cg05290058 ‐0.05 0.015 11 2,890,551 Island KCNQ1DN cg01089001 ‐0.054 0.321 11 11,610,742 TRUE GALNTL4 cg12071328 0.0608 0.008 11 20,690,930 Island NELL1 cg03565868 ‐0.055 0.192 11 47,400,146 S_Shore PromCS SPI1 cg24259560 0.063 0.259 11 50,226,789 N_Shore cg15698795 ‐0.053 0.14 11 67,177,103 Island TRUE Prom TBC1D10C cg02874908 ‐0.054 0.12 11 67,205,113 S_Shore Prom PTPRCAP cg23478547 ‐0.052 0.23 11 69,259,265 S_Shore TRUE Unclassi cg16182447 ‐0.052 0.258 11 69,264,617 DMR TRUE Cell_specific cg17542408 ‐0.068 0.214 11 78,673,100 Island TRUE ODZ4 cg09754341 0.0502 0.135 11 120294781 TRUE ARHGEF12 cg16777618 0.0614 0.346 11 128694184 Island cg19369955 ‐0.052 0.38 12 2,030,178 cg20927656 0.0725 0.001 12 7,863,229 TRUE DPPA3 cg00610577 ‐0.056 0.292 12 12,008,666 TRUE ETV6 cg07360028 0.0563 0.096 12 13,364,332 TRUE EMP1 cg19611616 0.1388 0.091 12 27,397,833 S_Shore Prom STK38L cg12353452 ‐0.059 0.22 12 51,717,865 Prom BIN2 cg18610205 ‐0.057 0.196 12 51,717,960 Prom BIN2 cg06380123 ‐0.055 0.229 12 51,717,978 Prom BIN2 cg00240653 ‐0.052 0.387 12 55,378,411 Prom KIAA0748 cg04850731 ‐0.05 0.194 12 57,618,943 Island Cell_spe NXPH4 cg08425810 0.0515 0.122 12 58,132,558 Island AGAP2 cg09067021 ‐0.057 0.081 12 123637556 S_Shore TRUE Cell_specific cg11834730 ‐0.056 0.098 12 124942248 S_Shore Prom NCOR2 cg14245548 0.0518 0.321 12 130604221 cg04355697 ‐0.05 0.326 13 20,731,771 N_Shelf GJA3 cg19023589 ‐0.055 0.299 13 114182159 N_Shelf TMCO3 cg09842118 ‐0.05 0.277 14 21,359,737 RNASE3 cg06765217 ‐0.053 0.195 14 38,091,644 Island TRUE

189

cg26260369 0.0573 0.009 14 68,141,723 Island Prom VTI1B cg18500714 0.0708 0.02 14 100706288 Island Prom YY1 cg05398700 ‐0.055 0.353 14 102677141 N_Shelf TRUE WDR20 cg21845957 0.0583 0.003 14 103988428 Island CKB cg00988056 ‐0.055 0.226 14 106374554 cg12309653 ‐0.057 0.219 15 37,170,454 N_Shelf TRUE LOC145845 cg15902390 ‐0.051 0.172 15 37,387,438 Island DMR MEIS2 cg02958515 0.055 0.198 15 39,650,295 TRUE cg18760360 0.0588 0.247 15 60,953,215 TRUE RORA cg03455316 ‐0.053 0.069 15 62,516,405 Island Cell_specific cg19580937 ‐0.055 0.224 15 70,740,429 DMR Cell_specific cg03278514 ‐0.056 0.344 15 70,779,346 TRUE Unclassi cg26266708 ‐0.059 0.173 15 76,630,962 Island CDMR ISL2 cg05310486 ‐0.059 0.167 15 90,724,460 N_Shelf cg12299554 ‐0.052 0.326 15 94,840,953 MCTP2 cg10453419 ‐0.052 0.228 15 101991560 PCSK6 cg09866569 ‐0.052 0.299 16 2,737,340 Unclassi KCTD5 cg06195379 ‐0.052 0.278 16 3,124,591 cg27094376 ‐0.057 0.114 16 3,639,688 S_Shore BTBD12 cg16702014 ‐0.069 0.123 16 16,168,399 TRUE ABCC1 cg26828017 ‐0.056 0.33 16 22,409,023 TRUE cg25341726 ‐0.05 0.245 16 28,518,331 IL27 cg05769344 ‐0.055 0.104 16 28,996,358 Cell_spe LAT cg05798125 ‐0.056 0.155 16 28,996,362 Cell_spe LAT cg08347500 ‐0.056 0.175 16 54,316,049 Island CDMR cg07737292 ‐0.062 0.243 16 56,892,460 Cell_spe MIR138‐2 cg09000178 0.0503 0.07 16 67,063,319 Island CBFB cg09451235 ‐0.058 0.143 16 67,433,458 Island Unclassi ZDHHC1 cg24654547 0.0516 0.102 16 68,057,165 Island Prom DUS2L;DD X28 cg07786668 0.0663 0.017 16 73,092,391 Island Unclassi ZFHX3 cg00614832 0.0542 0.004 16 73,092,394 Island Unclassi ZFHX3 cg03669394 ‐0.051 0.275 16 75,685,254 S_Shelf TERF2IP cg06465011 ‐0.052 0.252 16 84,860,871 TRUE CRISPLD2 cg01574513 ‐0.059 0.181 16 85,981,720 Island Prom cg04887172 ‐0.061 0.183 16 89,041,793 CBFA2T3 cg27202913 ‐0.055 0.091 16 89,258,862 Island Cell_spe CDH15 cg18317439 ‐0.052 0.221 17 643,637 FAM57A cg00911794 ‐0.055 0.16 17 1,962,132 Island Unclassi HIC1 cg19447962 ‐0.064 0.241 17 17,628,656 S_Shore RDMR RAI1 cg01050010 ‐0.067 0.114 17 31,149,877 Island Unclassi MYO1D cg21401740 0.0587 0.021 17 35,763,755 N_Shelf ACACA cg11512009 0.0509 0.001 17 38,220,694 S_Shore RDMR THRA cg06506560 ‐0.053 0.061 17 38,474,741 Island Prom RARA cg18026225 ‐0.055 0.098 17 43,198,423 Island PLCD3

190

cg08278108 ‐0.052 0.138 17 48,042,917 Island CDMR Cell_specific cg25534244 ‐0.051 0.187 17 53,341,098 N_Shore DMR Cell_spe HLF cg07665510 ‐0.063 0.106 17 55,952,063 TRUE Unclassi CUEDC1 cg20961045 ‐0.059 0.099 17 55,952,128 TRUE Unclassi CUEDC1 cg11151395 ‐0.05 0.272 17 56,355,299 Island MPO cg26112797 ‐0.059 0.294 17 56,409,011 TRUE Prom MIR142 cg02678768 0.0887 0.14 17 74,002,944 N_Shore Unclassi EVPL cg19430537 ‐0.056 0.226 17 74,118,361 Island TRUE Prom cg27410601 ‐0.051 0.225 17 76,121,564 Unclassi TMC6 cg11153071 ‐0.065 0.213 17 78,748,077 RPTOR cg08224920 ‐0.056 0.164 17 79,259,283 Island TRUE Prom SLC38A10 cg16863795 ‐0.094 0.083 17 79,259,536 Island TRUE Prom SLC38A10 cg18048655 ‐0.057 0.288 17 79,453,575 N_Shore cg08574915 ‐0.058 0.24 17 79,924,772 TRUE Unclassi cg02116768 ‐0.059 0.257 17 80,545,322 N_Shore FOXK2 cg02927747 ‐0.05 0.198 18 60,381,593 N_Shore PHLPP1 cg11984636 ‐0.051 0.313 18 74,845,706 S_Shore MBP cg11977716 ‐0.055 0.193 18 77,284,742 Island NFATC1 cg15488009 ‐0.055 0.11 19 681,445 Island FSTL3 cg04382396 ‐0.054 0.222 19 852,311 N_Shore ELANE cg08269974 ‐0.053 0.136 19 853,054 Island Cell_spe ELANE cg23057220 0.0708 0.165 19 1,356,315 S_Shore RDMR TRUE MUM1 cg01608030 0.0539 0.002 19 1,605,680 Island Prom UQCR cg20583073 ‐0.051 0.126 19 3,178,759 Island Prom S1PR4 cg20695297 ‐0.056 0.133 19 3,178,844 Island Prom S1PR4 cg13456960 ‐0.055 0.253 19 5,139,479 N_Shore Cell_spe KDM4B cg21986966 ‐0.054 0.224 19 6,481,951 PromCel DENND1C cg17218495 0.0556 0.032 19 11,071,743 Island Prom SMARCA4 cg12019614 ‐0.056 0.113 19 11,353,996 Island TRUE Unclassi DOCK6 cg11550234 ‐0.05 0.309 19 14,550,997 N_Shore Prom PKN1 cg04172000 ‐0.052 0.061 19 16,771,088 Island Prom TMEM38A ; C19orf42 cg25383503 ‐0.051 0.222 19 18,714,552 N_Shore DMR Cell_spe CRLF1 cg09314196 0.0532 0.035 19 22,816,508 N_Shore DMR ZNF492 cg05017628 ‐0.05 0.016 19 48,698,632 Island cg15028160 ‐0.057 0.024 19 49,622,717 Island Prom PPFIA3; C19orf73 cg04210100 0.055 0.025 2 9,614,471 Island IAH1 cg15006298 ‐0.054 0.076 2 10,217,164 N_Shelf Unclassi CYS1 cg17742416 ‐0.05 0.18 2 25,499,619 N_Shore TRUE Prom DNMT3A cg14189391 ‐0.056 0.343 2 25,527,347 TRUE DNMT3A cg08570472 ‐0.058 0.097 2 26,408,040 Island FAM59B cg14036868 0.0711 0.006 2 38,604,442 Island Prom ATL2 cg12302982 ‐0.052 0.007 2 39,471,028 Island Cell_specific

191

cg04175739 ‐0.062 0.037 2 47,748,042 Island DMR TRUE KCNK12 cg07938743 ‐0.052 0.232 2 63,283,939 Island OTX1 cg10122865 ‐0.062 0.29 2 63,284,132 Island TRUE OTX1 cg11536474 ‐0.065 0.143 2 63,286,049 Island DMR TRUE Unclassi cg00720159 ‐0.055 0.31 2 65,431,864 cg11877270 0.0512 0.007 2 65,658,583 Island SPRED2 cg11220663 0.0656 0.021 2 70,994,863 Island DMR Cell_spe ADD2 cg05287321 0.0721 0.001 2 114415472 cg27405400 ‐0.054 0.277 2 127839539 TRUE BIN1 cg10004780 ‐0.053 0.145 2 131722307 S_Shore DMR Cell_spe ARHGEF4 cg05624376 ‐0.051 0.281 2 169939876 DHRS9 cg08809260 0.0568 0.038 2 177054140 Island Cell_spe HOXD1 cg17100158 ‐0.057 0.298 2 180307728 ZNF385B cg22704520 0.0545 0.03 2 200820451 Island Prom C2orf60 cg06862374 ‐0.092 0.126 2 219736549 Island CDMR TRUE Cell_spe WNT6 cg22587479 ‐0.052 0.238 2 219738226 Island DMR Cell_spe WNT6 cg25242471 ‐0.053 0.15 2 219738732 Island Cell_spe WNT6 cg01727145 ‐0.056 0.135 2 220313422 Island Unclassi SPEG cg17674726 ‐0.053 0.12 2 231743193 ITM2C cg20002901 ‐0.081 0.006 2 238777656 RAMP1 cg01879591 ‐0.056 0.246 2 242954430 S_Shore Cell_specific cg17794299 ‐0.061 0.007 20 623,187 Island Cell_specific cg05857996 ‐0.057 0.07 20 2,675,418 S_Shore RDMR TRUE EBF4 cg18455653 0.0511 0.018 20 17,662,865 Island Prom RRBP1 cg11162385 0.0577 0.006 20 25,604,740 Island Prom NANP cg03904042 ‐0.061 0.008 20 32,255,491 Island NECAB3; C20orf134 cg13403462 ‐0.058 0.007 20 32,256,071 S_Shore Prom NECAB3; C20orf134 cg11597277 ‐0.06 0.306 20 52,492,248 SUMO1P1 cg25858160 ‐0.052 0.347 20 55,499,436 N_Shore RDMR cg01817393 ‐0.06 0.01 20 57,427,642 N_Shore TRUE Unclassi GNAS cg21988465 ‐0.052 0.014 20 57,429,277 Island Unclassi GNAS cg00267746 ‐0.06 0.001 20 57,463,984 Island Prom GNAS cg06047881 ‐0.053 0.004 20 57,465,132 Island GNAS cg14236389 ‐0.055 0.095 20 58,631,038 PromCS C20orf197 cg12349676 ‐0.064 0.019 21 34,350,934 Island TRUE cg19521832 0.0531 0.089 21 35,831,996 Island DMR KCNE1 cg08443845 ‐0.054 0.308 21 36,421,955 TRUE Prom RUNX1 cg09556952 ‐0.056 0.054 21 38,119,946 Island SIM2 cg23286646 ‐0.081 0.027 21 38,120,350 Island SIM2 cg01853561 ‐0.074 0.016 21 38,120,466 Island SIM2 cg23896164 ‐0.063 0.032 21 38,120,518 Island SIM2 cg24167037 ‐0.057 0.167 21 46,340,776 TRUE Prom ITGB2 cg24200083 ‐0.054 0.002 22 19,279,245 Island Prom CLTCL1

192

cg23787873 ‐0.059 0.123 22 19,710,909 Island Cell_spe GP1BB;SEPT5 cg01697719 ‐0.054 0.304 22 19,754,125 Island TRUE TBX1 cg08382235 ‐0.072 0.149 22 19,754,251 Island TRUE TBX1 cg04999026 ‐0.112 0.073 22 19,754,815 Island TRUE TBX1 cg27098470 ‐0.061 0.157 22 42,828,411 NFAM1 cg07264666 ‐0.061 0.155 22 42,828,415 NFAM1 cg18036763 0.0687 0.022 22 45,404,910 Island DMR PHF21B cg22413388 ‐0.08 0.109 22 46,367,617 Island DMR WNT7B cg13458651 ‐0.055 0.103 22 46,367,703 Island DMR WNT7B cg05861567 ‐0.053 0.282 22 50,523,686 CDMR TRUE PromCel MLC1 cg03422583 ‐0.068 0.058 22 50,630,972 N_Shore CDMR Prom TRABD cg18034501 ‐0.05 0.041 3 4,043,162 TRUE cg00668519 ‐0.054 0.27 3 11,597,941 VGLL4; ATG7 cg25357825 ‐0.052 0.227 3 11,697,138 TRUE VGLL4 cg18562578 ‐0.056 0.203 3 55,517,853 Island RDMR WNT5A cg25724751 ‐0.055 7E‐04 3 59,690,178 TRUE cg18542842 ‐0.056 0.153 3 63,857,914 TRUE ATXN7 cg12678686 ‐0.055 0.326 3 127327369 S_Shelf MCM2 cg13029635 0.0612 0.017 3 150096716 TRUE cg09652652 ‐0.066 0.115 3 194408845 Island DMR FAM43A cg00352417 ‐0.057 0.278 3 194408901 Island FAM43A cg18095675 ‐0.052 0.284 3 197272311 BDH1 cg15569052 ‐0.059 0.248 4 2,814,122 TRUE Prom SH3BP2 cg18645906 ‐0.063 0.002 4 15,704,599 N_Shore DMR Cell_spe BST1 cg23437337 ‐0.051 0.125 4 54,239,663 N_Shelf cg18670770 ‐0.06 0.065 4 121991672 N_Shore C4orf31 cg19780352 ‐0.054 0.058 4 169798931 N_Shore PALLD cg05477457 ‐0.051 0.255 4 169799308 Island Cell_spe PALLD cg10380328 ‐0.064 0.258 4 188953126 cg17774559 ‐0.061 0.093 5 1,879,698 Island IRX4 cg18096251 0.0684 0.058 5 2,205,553 cg04514047 ‐0.056 0.259 5 10,562,456 N_Shore cg08522087 0.0573 0.027 5 14,871,910 N_Shore Cell_spe ANKH cg24382521 ‐0.057 0.011 5 40,908,228 C7 cg18304305 ‐0.057 0.205 5 42,720,521 DMR GHR cg26293423 ‐0.054 0.2 5 71,613,149 N_Shelf MRPS27 cg04759220 0.053 0.003 5 78,532,560 Island JMY cg21097090 0.0578 0.008 5 118693764 S_Shelf TRUE Cell_spe TNFAIP8 cg21036194 0.0674 0.249 5 121742044 SNCAIP cg24792289 ‐0.053 0.196 5 134825895 Island RDMR cg05652757 ‐0.062 0.016 5 139227606 Island NRG2 cg22060611 ‐0.066 0.027 5 139227610 Island DMR NRG2 cg10468961 ‐0.069 0.03 5 139227979 Island NRG2 cg01278596 ‐0.083 0.015 5 139228062 Island DMR NRG2

193

cg15992535 ‐0.092 0.03 5 139228150 Island DMR NRG2 cg05852276 ‐0.064 0.129 5 140579470 N_Shore RDMR PCDHB11 cg12153755 ‐0.054 0.261 5 141062571 Unclassi ARAP3 cg02098752 ‐0.056 0.085 5 176024005 Island Unclassi GPRIN1 cg27035514 0.0534 0.003 5 177539998 N_Shore N4BP3 cg04003615 ‐0.052 0.158 5 179486382 TRUE RNF130 cg17014647 ‐0.051 0.036 6 16,431,306 TRUE ATXN1 cg14736087 ‐0.054 0.005 6 24,724,537 S_Shelf cg13045351 0.0502 0.212 6 28,092,048 Prom ZSCAN16 cg20110349 0.0571 0.023 6 28,183,414 N_Shelf Cell_spe LOC222699 cg08827322 0.0529 0.022 6 28,921,485 Cell_specific cg07382347 ‐0.064 0.169 6 30,039,408 Island RNF39 cg13401893 ‐0.07 0.155 6 30,039,432 Island RNF39 cg10568066 ‐0.058 0.219 6 30,039,442 Island RNF39 cg23903723 ‐0.055 0.006 6 30,655,567 S_Shore Prom KIAA1949 cg14437986 ‐0.051 0.203 6 31,691,035 N_Shore C6orf25 cg27579121 0.0587 0.325 6 41,859,617 N_Shelf USP49 cg23687909 ‐0.067 0.001 6 52,991,871 GCM1 cg14547726 ‐0.053 0.182 6 116832794 CellS FAM26E;BET 3L cg06642177 0.0715 0.058 6 134496341 Island SGK1 cg13027965 ‐0.05 0.31 6 144056673 TRUE PHACTR2 cg27001715 0.0555 0.078 6 150329845 S_Shelf cg12603453 ‐0.052 0.198 6 151694679 TRUE GeCS ZBTB2 cg20192387 ‐0.051 0.252 6 166856056 RPS6KA2 cg11082635 ‐0.069 0.147 6 166856074 RPS6KA2 cg27495951 ‐0.051 3E‐04 6 170608479 S_Shelf cg07689396 ‐0.059 0.152 7 633,050 S_Shore PRKAR1B cg25495650 ‐0.05 0.283 7 633,202 S_Shore PRKAR1B cg01410316 ‐0.052 0.492 7 971,767 N_Shore ADAP1 cg09937438 ‐0.056 0.179 7 1,095,005 N_Shelf PrCS C7orf50 cg22963979 ‐0.054 0.201 7 1,858,916 MAD1L1 cg15704521 ‐0.055 0.247 7 2,773,877 N_Shore GNA12 cg16993754 ‐0.057 0.282 7 3,020,282 CARD11 cg02779037 ‐0.066 0.195 7 4,848,683 Island CellS RADIL cg20592700 ‐0.056 0.002 7 5,230,083 Island Prom WIPI2 cg01580340 ‐0.055 0.223 7 16,891,079 Island cg06310816 ‐0.058 0.119 7 42,267,719 Island RDMR TRUE GLI3 cg10673833 ‐0.057 0.164 7 45,018,849 Prom MYO1G cg12903171 ‐0.055 0.018 7 50,850,564 Island PrCS GRB10 cg24977055 ‐0.051 0.008 7 50,850,870 Island GRB10 cg20482143 ‐0.051 0.021 7 64,340,804 cg16454099 ‐0.054 0.026 7 92,818,323 HEPACAM2 cg18194887 ‐0.061 0.012 7 93,629,937 N_Shelf BET1 cg21108767 0.0521 0.012 7 99,933,721 Island Prom PILRB

194

cg17607973 0.0782 0.011 7 100027408 Island Prom MEPCE;ZCWP W1 cg21784396 ‐0.052 0.18 7 127991421 Island CellS PRRT4 cg04553410 0.0529 0.03 7 150864885 Island TRUE GBX1 cg01545109 ‐0.05 0.234 7 151087534 WDR86 cg22528270 ‐0.051 0.344 7 151505116 PRKAG2 cg25707994 0.0605 0.019 7 157129685 Island Prom DNAJB6 cg02046552 ‐0.059 0.212 8 21,914,287 Island EPB49 cg14284618 ‐0.05 0.097 8 38,627,889 TRUE Prom TACC1 cg24312537 ‐0.051 0.366 8 38,831,332 N_Shore HTRA4;PLEKH A2 cg26157756 ‐0.051 0.179 8 61,917,230 TRUE CellSpec cg17662034 0.0559 0.062 8 74,207,518 Island RDH10 cg02160684 ‐0.05 0.226 8 126448033 S_Shelf CDMR TRIB1 cg16231917 ‐0.066 0.132 8 128930166 TRUE Prom PVT1 cg26769700 ‐0.068 0.159 8 140945810 TRAPPC9 cg01062470 0.0503 0.064 9 34,316,383 N_Shore KIF24 cg13464240 ‐0.051 0.193 9 92,099,056 TRUE PromCellSpec cg14341177 0.067 0.344 9 95,475,787 N_Shore BICD2 cg20324199 ‐0.051 0.161 9 96,080,326 Island WNK2 cg22902266 ‐0.054 0.133 9 96,714,313 Island DMR TRUE BARX1 cg14508093 ‐0.054 0.205 9 98,862,825 TRUE cg08300899 0.0603 0.002 9 140064540 Island LRRC26 cg13408086 ‐0.058 0.325 9 140221397 S_Shelf CellSpec EXD3 cg04055739 ‐0.063 0.104 X 11,157,142 Island ARHGAP6 cg07987169 ‐0.057 0.182 X 12,938,504 TLR8;LOC 349408 cg20199120 ‐0.052 0.283 X 13,583,772 N_Shelf cg10978544 ‐0.056 0.158 X 15,339,848 PIGA cg26215003 ‐0.058 0.167 X 20,431,166 CDMR cg16617551 ‐0.057 0.105 X 39,714,465 DMR TRUE Unclassi cg06008640 ‐0.055 0.246 X 39,864,206 N_Shore RDMR cg01110765 ‐0.059 0.232 X 40,016,611 N_Shore RDMR BCOR cg12111783 ‐0.056 0.084 X 40,027,674 Island Cell_spe BCOR cg18457851 ‐0.05 0.167 X 40,104,514 DMR TRUE cg00191052 ‐0.053 0.186 X 46,432,770 N_Shore Unclassi CHST7 cg00965330 ‐0.052 0.26 X 47,861,943 N_Shore ZNF182 cg24428913 ‐0.07 0.047 X 48,541,432 WAS cg00078867 ‐0.065 0.108 X 48,542,398 ProCSp WAS cg12239365 0.054 0.053 X 51,075,790 Island NUDT10 cg27065374 ‐0.056 0.027 X 68,060,181 Island EFNB1 cg16806953 ‐0.074 0.12 X 70,272,769 N_Shore TRUE cg20475304 0.0607 0.008 X 73,513,906 S_Shore NCRNA00 182 cg23079782 ‐0.052 0.264 X 78,200,921 DMR P2RY10

195

cg03440485 0.0545 0.022 X 107179415 Island cg09012264 ‐0.053 0.08 X 118828143 Island SEPT6 cg18091964 ‐0.054 0.154 X 128913980 Prom SASH3 cg27207932 ‐0.057 0.063 X 135730306 CD40LG cg02936290 0.0716 0.056 X 135962199 Island PrCSp RBMX; SNORD61 cg06298190 ‐0.054 0.232 X 138913929 TRUE ATP11C cg15056572 ‐0.055 0.133 X 139006615 MIR505 cg17938879 ‐0.059 0.108 X 148156001 DMR cg13661446 ‐0.067 0.196 X 151093031 MAGEA4 cg19925887 0.0553 0.16 X 151146909 S_Shelf cg02895192 ‐0.073 0.049 X 153190027 N_Shore DMR PrCSp ARHGAP4

196

Table F3. Sites of CpG Methylation with Effect Size >6% difference between unaffected and affected twins, by paired analysis

p MAP ENHA TargetID value ES ch INFO UCSC_REFGENE_NAME NCER REG cg02396224 0.057 0.063 1 10698794 CASZ1 cg13875012 0.162 0.062 1 11710610 FBXO2 Unclass cg12911732 0.265 0.067 1 50744322 TRUE cg10512745 0.113 0.067 1 50884480 DMRTA2 cg16732616 0.076 0.071 1 50886782 DMRTA2 cg01066472 0.015 0.064 1 75591029 cg17811845 0.009 0.062 1 89358043 GTF2B cg19255477 0.098 0.072 1 151810586 LOC100132111;C2CD4D cg15015892 0.09 0.066 1 151810887 C2CD4D;LOC100132111 cg11576590 0.178 0.068 1 152011357 cg06188545 0.138 0.071 1 156863643 PEAR1 cg12071328 0.008 0.061 11 20690930 NELL1;NELL1 cg24259560 0.259 0.063 11 50226789 cg17542408 0.214 0.068 11 78673100 ODZ4 TRUE cg16777618 0.346 0.061 11 128694184 cg20927656 0.001 0.072 12 7863229 DPPA3 TRUE cg19611616 0.091 0.139 12 27397833 STK38L Promoter cg18500714 0.02 0.071 14 100706288 YY1 Promoter ABCC1;ABCC1;ABCC1;ABCC1; cg16702014 0.123 0.069 16 16168399 ABCC1 TRUE cg07737292 0.243 0.062 16 56892460 MIR138-2 Uncl_CTS cg07786668 0.017 0.066 16 73092391 ZFHX3 Unclass cg04887172 0.183 0.061 16 89041793 CBFA2T3 cg19447962 0.241 0.064 17 17628656 RAI1 cg01050010 0.114 0.067 17 31149877 MYO1D Unclass cg07665510 0.106 0.063 17 55952063 CUEDC1 TRUE Unclass cg02678768 0.14 0.089 17 74002944 EVPL Unclass cg11153071 0.213 0.065 17 78748077 RPTOR;RPTOR cg16863795 0.083 0.094 17 79259536 SLC38A10;SLC38A10 TRUE Promoter cg23057220 0.165 0.071 19 1356315 MUM1;MUM1 TRUE cg14036868 0.006 0.071 2 38604442 ATL2;ATL2;ATL2 Promoter cg04175739 0.037 0.062 2 47748042 KCNK12 TRUE cg10122865 0.29 0.062 2 63284132 OTX1 TRUE cg11536474 0.143 0.065 2 63286049 TRUE Unclass cg11220663 0.021 0.066 2 70994863 ADD2;ADD2;ADD2;ADD2;ADD2 Uncl_CTS cg05287321 0.001 0.072 2 114415472 cg06862374 0.126 0.092 2 219736549 WNT6 TRUE Uncl_CTS cg20002901 0.006 0.081 2 238777656 RAMP1

197

cg17794299 0.007 0.061 20 623187 Uncl_CTS cg03904042 0.008 0.061 20 32255491 NECAB3;NECAB3;C20orf134 cg11597277 0.306 0.06 20 52492248 SUMO1P1 cg12349676 0.019 0.064 21 34350934 TRUE cg23286646 0.027 0.081 21 38120350 SIM2 cg01853561 0.016 0.074 21 38120466 SIM2 cg23896164 0.032 0.063 21 38120518 SIM2 cg08382235 0.149 0.072 22 19754251 TBX1;TBX1;TBX1 TRUE cg04999026 0.073 0.112 22 19754815 TBX1;TBX1;TBX1 TRUE cg27098470 0.157 0.061 22 42828411 NFAM1 cg07264666 0.155 0.061 22 42828415 NFAM1 cg18036763 0.022 0.069 22 45404910 PHF21B;PHF21B cg22413388 0.109 0.08 22 46367617 WNT7B cg03422583 0.058 0.068 22 50630972 TRABD Promoter cg13029635 0.017 0.061 3 150096716 TRUE cg09652652 0.115 0.066 3 194408845 FAM43A;FAM43A cg18645906 0.002 0.063 4 15704599 BST1;BST1 Uncl_CTS cg18670770 0.065 0.06 4 121991672 C4orf31 cg10380328 0.258 0.064 4 188953126 cg17774559 0.093 0.061 5 1879698 IRX4 cg18096251 0.058 0.068 5 2205553 cg21036194 0.249 0.067 5 121742044 SNCAIP cg05652757 0.016 0.062 5 139227606 NRG2;NRG2;NRG2;NRG2 cg22060611 0.027 0.066 5 139227610 NRG2;NRG2;NRG2;NRG2 cg10468961 0.03 0.069 5 139227979 NRG2;NRG2;NRG2;NRG2 cg01278596 0.015 0.083 5 139228062 NRG2;NRG2;NRG2;NRG2 cg15992535 0.03 0.092 5 139228150 NRG2;NRG2;NRG2;NRG2 cg05852276 0.129 0.064 5 140579470 PCDHB11 cg07382347 0.169 0.064 6 30039408 RNF39;RNF39 cg13401893 0.155 0.07 6 30039432 RNF39;RNF39 cg23687909 0.001 0.067 6 52991871 GCM1 cg06642177 0.058 0.071 6 134496341 SGK1;SGK1;SGK1;SGK1 cg11082635 0.147 0.069 6 166856074 RPS6KA2;RPS6KA2 cg02779037 0.195 0.066 7 4848683 RADIL Uncl_CTS cg18194887 0.012 0.061 7 93629937 BET1 cg17607973 0.011 0.078 7 100027408 MEPCE;ZCWPW1 Promoter cg25707994 0.019 0.061 7 157129685 DNAJB6;DNAJB6 Promoter cg16231917 0.132 0.066 8 128930166 PVT1 TRUE Promoter cg26769700 0.159 0.068 8 140945810 TRAPPC9;TRAPPC9 cg14341177 0.344 0.067 9 95475787 BICD2;BICD2 cg08300899 0.002 0.06 9 140064540 LRRC26 cg04055739 0.104 0.063 X 11157142 ARHGAP6;ARHGAP6 cg24428913 0.047 0.07 X 48541432 WAS

198

cg00078867 0.108 0.065 X 48542398 WAS Prom_CTS cg16806953 0.12 0.074 X 70272769 TRUE cg20475304 0.008 0.061 X 73513906 NCRNA00182 RBMX;RBMX;RBMX;RBMX; cg02936290 0.056 0.072 X 135962199 SNORD61 Prom_CTS MAGEA4;MAGEA4;MAGEA4; cg13661446 0.196 0.067 X 151093031 MAGEA4 cg02895192 0.049 0.073 X 153190027 ARHGAP4;ARHGAP4 Prom_CTS

199

Table F4. H19/SCUBE2

diff me TargetID p-value difference chrom map region prom cg11492040 0.89983713 0.00402103 11 2016513 N_Shore cg23977670 0.90494543 0.00185173 11 2016848 N_Shore cg04647234 0.68789344 -0.00586637 11 2016903 N_Shore cg11716026 0.5716316 0.01004606 11 2016937 N_Shore cg26808784 0.86862046 0.00399324 11 2017483 Island cg25852472 0.94677221 0.00169532 11 2017664 Island cg15963714 0.85052357 0.00358339 11 2017819 Island cg26857192 0.95138184 0.00249699 11 2017939 Island cg02694715 0.02462058 -0.01692952 11 2019730 Island TRUE cg01977486 4.2064E-05 -0.04030266 11 2019736 Island TRUE cg04088212 0.03296517 -0.02273869 11 2019859 Island TRUE cg01539474 0.03374608 -0.03012969 11 2019862 Island TRUE cg18362496 0.00814085 -0.02474815 11 2019930 S_Shore TRUE cg04817190 0.06746656 -0.03510665 11 2020028 S_Shore TRUE cg06749854 0.03633317 -0.03321802 11 2020030 S_Shore TRUE cg24510613 0.00323482 -0.02668534 11 2020036 S_Shore TRUE cg16675558 0.11400597 -0.01910789 11 2020101 S_Shore RDMR TRUE cg03996735 0.49478071 -0.01089096 11 2020104 S_Shore RDMR TRUE cg18104242 0.23373258 -0.02059539 11 2020118 S_Shore RDMR TRUE cg27300742 0.12871596 -0.0203218 11 2020129 S_Shore RDMR TRUE cg25281616 0.01379822 -0.02306232 11 2020279 S_Shore RDMR TRUE cg01895612 0.01025557 -0.01579307 11 2020286 S_Shore RDMR TRUE cg23476401 0.85407418 0.00220067 11 2020296 S_Shore RDMR TRUE cg06765785 0.41788255 -0.00853562 11 2020391 S_Shore RDMR cg25821896 0.12403902 -0.01308101 11 2020417 S_Shore RDMR cg18454954 0.12247858 -0.00818517 11 2020537 S_Shore RDMR cg25579157 0.01929295 -0.01291557 11 2020549 S_Shore RDMR

200

Table F5. LRRC26 has effect size difference 6%

cg08300899 0.002 0.06 9 140064540 LRRC26

201

Figure F1. Bisulfite conversion and detectable CpG sites are successful

A: Illumina450 data (n=53 samples on x-axis) CpG sites detected at p=0.05 in reference to background are of good quality.

Illumina Genome Studio 2011.1

Methylation Module 1.9.0

B and C. Bisulfite conversion is successful in ≥96% of samples.

B. C.

202

Figure F2. Methylation Screening Algorithm

203

Figure F3. Idealized example of Maternal Imprinting and Paternal Expression

MaternalMaternal Imprintingimprinting

●Paternal mutation/indel

●

204

Figure F4. Environmental Effects of Common Household Exposures on Twins: Epigenetic modifications in children (F3) occur through both parents’ germ cells, and through the maternal grandmother (F1) during maternal gestation. This is because the mother’s (F2) eggs for her future progeny (F3) were formed while she was still in utero. Hence, environmental effects on the maternal grandmother (F1) are relevant. For perspective, a small proportion of today’s pediatric population

(F3) can trace their environmental history to the mid-1950s.

Environmental Effects: Common Household

Father: age at reproduction e.g., smoke, any, days per week, since year Fertility treatment, topical, wash

Other siblings Maternal Grand-Mother F1 SES Common house environment, epigenetic modification Food Mother F2: age at reproduction e.g., smoke, any, days per week, since year Fertility treatment, oral, injectable, egg harvest

Common environment ALL Mother + children = F1 + F2 + F3 Father + children = F2 + F3 Children only = F3 = time recent and younger Twin specific F3 Twin timing F3 Twin unique environment environment increases by age, sex

Random effects within families Fixed effects between families

205

Figure F5. A “Two Hit” Exposure Model Explains the Effect of Underlying Genetic Background on

Tissue Eosinophilia in Eosinophilic Esophagitis. Low primary exposure dose may cross the threshold for disease when combined with low tissue repair function. Conversely, high dose exposure may not penetrate the disease threshold in individuals with high tissue repair function.

Total Susceptibility = genes + 1° and 2° exposure doseoutcome High secondary exposure, bio‐activator of some kind Low repair

High repair Low secondary exposure, bio‐activator of some kind Low repair

Low repair (marker)

High secondary exposure, Disease threshold bio‐activator of some kind High repair count

High repair Low secondary exposure, bio‐activator of some kind Low repair Eosinophil High repair

Low primary exposure High primary exposure PRIMARY ANTIGEN EXPOSURE (dose) ES Alexander2013

206

Figure 6. Future Directions

2013‐Anticipated publication:2017 EoE FREE_X chrom Discovery Arm Pyroseq. Confirmation See Table

Anticipated publication: 2015‐16 EoE FREE Gene Discovery Arm See Table

HSS Collins: Histology Scale development for EoE as a continuous outcome EoE studies: Twins development; Hamilton County prevalence follow‐up; Blanchard: Analysis/Review EoE esophageal expression

207

Protocol, in brief:

Collection protocol: Genomic DNA was collected using Oragene+sponge kit according to standard instructions.

Extraction protocol: DNA was extracted

Label: cytosine 5 Illumina protocol

Hybridization: Bisulphite converted DNA was amplified, fragmented, hybridized to Illumina Human Methylation 450 Beadchip using standard Illumina protocol

on 5 chips: 8795207002, 8795207134, 8795207003, 8795207132, 8795207001

Scan protocol: Arrays were imaged using Beadarray reader using standard Illumina scanner setting

Data processing: Illumina Genome Studio Methylation data were imported into the Illumina Genome Studio software GenomeStudio Data Analysis Software’s Gene Expression Module (GSGX) Version 1.1.0 twin_anno.txt has annotation data but does not specify the build that was used newest is hg38 (Dec 2013)

208

Appendix G: Human Subjects UC Reliance Review

From: Perez, Adrienne Sent: Monday, June 03, 2013 9:12 AM To: Butz, Bridget; [email protected] Cc: Gardner, Anthony (gardneay) ([email protected]); Gibson, Lyda (Jean); Zimmerly, John Subject: RE: Reliance Review for CCHMC IRB # 2008-0090

It looks like ePAS might still be having problems with notifications. UC completed their reliance review of 5/7/2013. According to the reliance activity no notification was sent to the study team. It looks like we have an ePAS glitch. I suggest using the screen capture as documentation for the study record.

Adrienne

Adrienne S. Perez, MA, CIP Human Protections Analyst Institutional Review Board Cincinnati Children's Hospital Medical Center 3333 Burnet Avenue, MLC 5020 Cincinnati, OH 45229-3026

513-636-8039 IRB Office

513-636-0914 Direct 513-636-3959 Fax [email protected]

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Appendix H: Human Subjects CCHMC IRB Staff, Documents Lists and Protocol

Study: EOSINOPHILS AND INFLAMMATION, AN EXPANDED STUDY

Study #: 2008-0090 04-12-8 Study Type: Study Application

Principal Investigator:Marc Rothenberg Prepared By: Margaret Palazzolo

Most Recent Review Type: Full IRB Review Initial Approval Date: 3/13/2007

Current Approval Date: 1/13/2014 Initial Review Type: Full IRB Review

Current Approval Letter: View Initial Approval Letter:

Expiration Date: 1/12/2015 Risk Level: Minimal Risk Study Department:

CURRENT CONSENT FORMS:

Name Version

Eosinophils and Inflammation Consent 0.20

FormV22.0_ clean.doc

Histo Amendm Continuing Reportable Sta Docume Change Reviewer

ry ents Reviews Events ff nts Log Notes

Current Approved Staff

Principal Investigator: Marc Rothenberg

Current approved study team members:

Last First Subject Date COI Department Role Name Name Interaction Added Status

Allergy & Sub- Abonia J. Pablo Direct 2/6/2008 Current

Immunology investigator

210

Biostatistics & Alexander Eileen Direct Statistician 5/21/2010 Current

Epidemiology

Sub-

Collins Margaret Pathology Indirect 2/6/2008 Current investigator

Allergy & Database Eby Michael Indirect Other 9/4/2009 Current

Immunology Administator

Allergy & Grotjan Tommie Direct Coordinator 9/4/2009 Current

Immunology

Center for Autoimmune Sub- Harley John Indirect 3/28/2011 Current Genomics and investigator

Etiology

Center for Autoimmune Sub- Kottyan Leah Indirect 3/28/2011 Current Genomics and investigator

Etiology

Sub-

Martin Lisa Human Genetics Indirect 10/28/2008 Current investigator

Sub-

Mukkada Vincent Gastroenterology Direct 3/13/2013 Current investigator

Appendix T Online Environmental Questionnaire Screen Shots | History 1/27/2014 3:51 PM 0.02

Appendix A - Phone Script Sample Collection TC | History 2/24/2014 1:08 PM 0.05

Appendix C - Consent Letter | History 12/18/2012 1:41 PM 0.02

Appendix D - thank you letter | History 12/18/2012 1:41 PM 0.02

Appendix E- Recruitment Letter | History 12/18/2012 1:41 PM 0.02

Appendix F - Genetic Questionnaire | History 12/18/2012 1:42 PM 0.02

Appendix G - Family tree questionnaire | History 1/13/2014 2:16 PM 0.03

Appendix K - Saliva Instructions | History 1/13/2014 2:16 PM 0.03

Appendix L - OG-250 Saliva Kit User Instructions | History 12/3/2012 9:29 AM 0.01

Appendix M - Oragene-RNA RE-100 User Instructions | History 12/3/2012 9:30 AM 0.01

211

Appendix N - CS1 Saliva Sponge Collection | History 12/3/2012 9:31 AM 0.01

Appendix O- Medical Release | History 12/18/2012 1:42 PM 0.02

Appendix Q - e-consent e-mail notification | History 1/13/2014 4:32 PM 0.03

Appendix R-Telephone Script for Re-consent | History 6/28/2013 1:29 PM 0.02

Appendix S Email | History 6/28/2013 1:26 PM 0.03

Appendix U-Questionnaire Link | History 1/13/2014 2:03 PM 0.01

Appendix V-Telephone Script- Environmental Questionnaire | History 1/13/2014 2:04 PM 0.01

Appendix W-Thank you letter-Environmental questionnaire | History 1/13/2014 2:04 PM 0.01

Appendix X -Reminder email for Environmental Questionnaire | History 1/13/2014 2:05 PM 0.01

Appendix Y Saliva Kit User Instructions | History 1/13/2014 2:13 PM 0.01

Twin Flyer | History 3/20/2013 12:27 PM 0.01

212

CINCINNATI CHILDREN’S HOSPITAL MEDICAL CENTER CONSENT TO PARTICIPATE IN A RESEARCH STUDY

STUDY TITLE: EOSINOPHILS AND INFLAMMATION, AN EXPANDED STUDY

SPONSOR NAME: Marc E. Rothenberg, M.D., Ph.D. Division of Allergy & Immunology

INVESTIGATOR INFORMATION:

Marc E. Rothenberg, M.D., Ph.D. (513) 636-4200

Principal Investigator Name Telephone Number 24 hr Emergency Contact

Parent/Guardian Name:______Date of Birth______/_____/____

Subject Name: ______Date of Birth: _____/_____/____

Throughout this document, references to “You” may stand for either the research study subject or for the parents or legal guardians of the research study subject if the subject is under 18 years of age or otherwise unable to legally give informed consent to participate in the research study. The signature(s) at the end will clarify whether the research study subject is signing this consent form on their own behalf or via a legal guardian or legal personal representative.

INTRODUCTION:

You have been asked to participate in a research study. Before agreeing to participate in this study, it is important that you read and understand the following explanation. It describes, in words that can be understood by a lay person, the purpose, procedures, benefits, risks and discomforts of the study and the precautions that will be taken. It also describes the alternatives available and the right to withdraw from the study at any time. No guarantee or assurance can be made as to the results of the study. Also, participation in the research study is completely voluntary. Refusal to participate will involve no penalty or loss of benefits to which you are otherwise entitled. You may withdraw from the study at any time without penalty.

213

WHY IS THIS RESEARCH BEING DONE?

The purpose of this research study is to learn more about both the underlying immune system responses in eosinophilic disorders and the genetic factors that may predispose individuals to developing eosinophilic diseases. More specifically, the purpose of these studies is to: 1) Measure the function of purified blood immune cells such as neutrophils, lymphocytes and eosinophils, by examining their natural responses, responses to different stimuli, such as food and airborne allergens, and some of the proteins and/or toxins that they may make such as eosinophil major basic protein, eosinophil derived neurotoxin, and hormones like IL5.

2) To isolate and study some of the proteins, RNA, and DNA (the material contained in genes) from the blood such as the level of eosinophil attraction proteins (eotaxins) and eosinophil growth factors (such as interleukin 5).

3) To manipulate blood cells, buccal cells, or any cells from the gastrointestinal tract. to become cells that can become any other type of cell such as organs and tissues. The researchers will NOT try to create sperm or egg cells. These new types of cells are called Induced Pluripotent Stem Cells.

4) To study important cells and cell products in the tissues of the gastrointestinal tract, including lymphocytes and eotaxins.

5) To isolate and study RNA and DNA and other substances including proteins and cells in the tissues of the gastrointestinal tract so that we can examine the function of genes and cells in the body by using DNA expression chips and cell culture.

6) To gather clinical information about patients with eosinophilic or allergic diseases, including their medical and family history.

7) To gather quality of life information and symptom severity about patients with eosinophilic disorders. The data gathered from these questionnaires will be correlated with biomarkers that are associated with Eosinophilic Esophagitis

8) Some samples collected from the colon may be examined to look at bacteria levels and types of bacteria and their genetic content present in the colon. This data will be used to determine if bacteria are associated with inflammatory gastrointestinal disorders, in particular Eosinophilic Colitis.

9) To measure the levels of proteins and other factors in the skin and relate them to the patients’ disease and allergy history.

10) To measure and study RNA, eosinophil levels, and other factors in saliva samples and/or oral samples of participants

11) To develop a scoring system to measure the severity of the histologic changes in the biopsy of the gastro-intestinal (GI) tract.

WHY HAVE YOU BEEN ASKED TO TAKE PART IN THIS RESEARCH STUDY?

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You are being asked to take part in this research study because you have been diagnosed with an eosinophilic disorder, you are a normal, healthy individual, or you are the caretaker of a person participating in this study. Samples collected from normal, healthy volunteers will be used to compare with samples taken from participants who have an eosinophilic disorder. If you are a caretaker of a person participating in this study, your responses to questionnaires will be used to compare to your child’s responses.

HOW LONG WILL YOU BE IN THE RESEARCH STUDY?

You will be in the research study until the end of the study. This consent, unless you choose to withdraw it, shall remain in effect until the end of the study.

The researcher may decide to take you off this research study at any time.

WHO IS CONDUCTING THE RESEARCH STUDY?

This study is sponsored by the division of Allergy and Immunology at Cincinnati Children’s Hospital Medical Center. This study is directed by Marc E. Rothenberg, the principle investigator at Cincinnati Children’s Hospital Medical Center.

HOW MANY PEOPLE WILL TAKE PART IN THE RESEARCH STUDY?

About 2500+ people will take part in this study at Cincinnati Children’s Hospital Medical Center.

WHAT IS INVOLVED IN THE RESEARCH STUDY?

Blood cells and other blood components may be purified from your/your child’s blood. This blood may be drawn from a vein in your arm, hand, or foot. The risks involved in this procedure are similar to those associated with having you/your child’s blood drawn in the past, and will therefore pose the same risks as for any blood test. The quantity to be obtained will vary depending on which study you qualify for and wish to participate in. The maximum amount of blood drawn will not exceed 3.75 milliliters for each kilogram of weight, which equals about one and a half tablespoons for every 15 pounds of weight. For example, for a person weighing 45 pounds, this equals about 5 tablespoons. You will not be allowed to donate more then once in a 4 week period of time. In patients 18 years of age and over, the maximum donation allowed will be 250 milliliters (8.5 ounces) in a 4 week period of time. For studies involving RNA, a relatively small amount of blood (1 to 4 teaspoons) will be used for subsequent RNA isolation. Sometimes, if blood has already been drawn for clinical reasons, and there is some extra that is not used, we may ask your permission to use it for the study.

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Biopsy specimens, from an endoscopy and/or another procedure, may be obtained from you. This procedure is part of your routine clinical care and the specimen will be collected at that time. The number of extra specimens collected will not exceed 3 biopsies in each normally surveyed region of the gastrointestinal tract. By checking the box near your signature, you give permission for your biopsy sample, including its RNA or DNA (genetic material) to be analyzed. Sometimes if tissue has already been obtained by your doctor for clinical reasons, and there is some extra that is not used, we may ask your permission to use it for the study. If you are a patient at a hospital or institution other than Cincinnati Children’s Hospital Medical Center, you may be asked to send previously obtained slides and medical records to 3333 Burnet Avenue Cincinnati, OH 45229-3039 MLC # 2010.

Samples of your skin may also be collected. This is done by a method called tape stripping. Adhesive- coated discs will be placed on the back of your hand, leg or arm for 5 to 10 seconds. Even pressure will be applied to the discs. Then, the discs will be removed using forceps. The discs will be reapplied at least 10 times.

You may volunteer to provide samples of saliva or oral rinses/saline swishes for this study. The samples will be obtained in the clinic. You may be given 10 milliliters, approximately 2 teaspoons, of saline (a salt water solution). You will be asked to rinse your mouth with the saline for about 30 seconds and then spit the saline rinse into a container. You may be asked to provide a sample of saliva in a container. This may be done in the clinic, or at your home. Also, your throat may be swabbed with a cotton swab, similar to a strep test. A plastic spoon, or swab, free of any sharp edge/end, may be used to scrape the inner cheek 5 times on each side to collect cells. Also, you may be asked to exhale into acontainer and keep breathing as usual for about 10 minutes.

Information about your case may be collected and compiled into a confidential file. You may be asked to answer a questionnaire regarding your medical history and current medical conditions. This information, including your samples, will also be stored for possible testing at a later date. Some research data and/or samples that are collected may be sent to other hospitals, institutions, and testing companies for additional analysis. Data and/or samples will have nothing attached that could identify you as a participant. None of your private health information will be associated with the sample (i.e name, and, DOB). Data and/or samples will only be sent to hospitals, institutions, or testing companies that are working together with Dr. Marc Rothenberg. Participants may be asked to fill out questionnaires regarding quality of life and symptom severity. If the participant is five years of age or older they will be asked to fill out part of the questionnaire as well as the parent or legal guardian. If the participant is four years of age or younger the parent or legal guardian will be answering all the questions in the questionnaires. Your identity will be kept confidential so that the patient information used for the study cannot be traced to you. The information collected may be used in studies requiring analysis of patient information.

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If you are the parent or caretaker of a participant, the only study activity in which you’ll be involved is the completion of questionnaires about you and your child.

WHAT ARE THE RISKS AND DISCOMFORTS OF THE RESEARCH STUDY?

The blood drawing procedure is similar to blood drawing you have had done in the past and will pose the same risks as for any blood test. The blood drawing procedure is a simple venipuncture (a needle inserted into a vein) conducted by a trained phlebotomist or physician. The most common complication is bruising at the site of the blood test. Because this blood sample is not essential for your health, there will not be multiple attempts (needle sticks) to obtain the sample. If larger samples of blood are obtained (60-250 ml), you will not be permitted to “re-volunteer” for the study for 4 weeks. This amount of blood drawn and the frequency of repeat sampling should not cause anemia (low numbers of red blood cells).

The obtaining of biopsies in a gastrointestinal endoscopy is a well-established procedure with a very low rate of complications. The most recent study indicates a serious complication such as bleeding or perforation (hole formation) rate of 1 case out of 2046 cases. The most severe gastrointestinal complication is perforation, but this is generally resolves on its own and poses no life-threatening risk.

The method of collection skin samples by tape-stripping or adhesive skin sampling disc is non-invasive. The risk of this widely-used procedure is minimal. The skin sampling discs are adhesive medical tapes. Mild discomfort during the removal of the adhesive discs may occur. Minor skin irritation (swelling, redness/soreness) may occur locally at the point of contact.

There is no foreseeable risk associated with the oral rinse/saline swish study procedure. You may experience an unpleasant or salty taste in your mouth during the procedure or if the saline is accidentally swallowed.

There may be unknown or unforeseen risks associated with study participation.

WHAT ARE THE RISKS OF STOPPING YOUR CURRENT TREATMENT?

Your current treatment will not be altered or adjusted to take part in this study. You should not stop or alter dosages of medications on your own.

ARE THERE DIRECT BENEFITS TO TAKING PART IN THE RESEARCH STUDY?

If you agree to take part in this research study, there is not a direct medical benefit for you. The information learned from this research study may benefit other patients with eosinophilic disease in the future

WHAT OTHER CHOICES ARE THERE?

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Your participation is completely voluntary. Your clinical care will not be affected If you choose not to participate.

HOW WILL INFORMATION ABOUT YOU BE KEPT PRIVATE AND CONFIDENTIAL?

Every effort will be made to maintain the confidentiality of your medical and research information (“Protected Health Information” or “PHI”), consisting of the following:

- Name - Age - Date of Birth - Sex - Race - Ethnicity - Address - Phone Number - Medical Record Number - Any other unique identifying number, characteristic or code

Protected Health Information is defined as health information, whether verbal or recorded in any form (such as on a piece of paper or entered in a computer), that identifies you as an individual or offers a reasonable basis to believe that the information could be used to identify you.

By signing this consent form you are giving permission for representatives of the Cincinnati Children’s Hospital Medical Center (“CCHMC”), the Investigator and CCHMC employees involved with the research study including the Institutional Review Board and the Office for Research Compliance, and any sponsoring company or their appointed agent as well as CCHMC study staff and physicians, to be allowed to inspect sections of your medical and research records related to this study.

For individuals that participate in the environmental questionnaire, study investigators at University of North Carolina will be allowed to inspect sections of your medical and research records related to this study.

The information from the research study may be published; however, you will not be identified in such publication. The publication will not contain information about you that would enable someone to determine your identity as a research participant without your authorization.

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Cincinnati Children’s Hospital Medical Center and/or the Investigator will take the following precautionary measures to protect your privacy and confidentiality of your research and/or medical records: All personal information and samples obtained for this study will be kept strictly confidential. Information and samples will be given a code number and will not be identified by name.

A copy of this consent form will be included in your medical research record.

You will be registered in the Cincinnati Children’s Hospital Medical Center’s computer system as a research subject which may be beneficial for future clinical care.

USE AND DISCLOSURE OF YOUR PROTECTED HEALTH INFORMATION

The Protected Health Information described in the section above will be used /disclosed for the purpose of research by CCHMC to the other persons or entities identified above.

“Use” of an individual’s health information is defined as the sharing, examination or analysis (break down) of the information that is collected and maintained for the length of the research study.

“Disclosure” of an individual’s health information is defined as the release, transfer, providing access to, or to reveal in any other manner, the information outside the persons or entity holding the information as described in the section “How Will Information About You Be Kept Private And Confidential” in this consent form.

Once your Protected Health Information is disclosed, the information may be subject to re-disclosure and may no longer be protected by the federal privacy regulations.

This authorization includes the use and/or disclosure of information concerning HIV testing or the treatment of AIDS or AIDS-related conditions, drug or alcohol abuse, drug-related conditions, alcoholism, and/or psychiatric/psychological conditions to the entities listed in this authorization in the event that your medical record contains such information.

AVAILABILITY OF INFORMATION?

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Information obtained from your tissue sample or any other results will not be made directly available to you. However, if there is a research finding (such as a genetic abnormality) that affects your health or clinical care, the Investigator may share this information with your doctor in order to determine the best care for you. For information regarding this study, including the research and the research subject’s rights, you may contact the Investigator of the study, Marc E. Rothenberg, at Cincinnati Children’s Hospital Medical Center at (513) 636-4200, or via mail at 3333 Burnet Avenue, ML 7028, Cincinnati, Ohio, 45229.

WHAT ARE YOUR COSTS TO BE IN THIS STUDY?

There are no costs associated with your participation in this study.

WILL YOU BE PAID TO PARTICIPATE IN THIS RESEARCH STUDY?

Participants who have a blood draw for research purposes only (ie do not have a line or other method of drawing blood already in place), outside of an endoscopy or colonoscopy procedure, will be reimbursed $10.00.

Participant who complete the environmental online questionnaire will receive a $20.00 incentive in the form of cash or gift card after the completion of the form.

Tissues or body fluids obtained in this research may result in the development of a product that could be patented or licensed. There are no plans to provide financial compensation to you should this occur.

WHAT COMPENSATION IS AVAILABLE IN CASE OF INJURY?

If you believe that you have been injured as a result of participation in biomedical or behavioral research you are to contact Dr. Marc Rothenberg at (513) 636-4200 or the Director of Social Services (513) 636- 4711 to discuss your concerns. Cincinnati Children's Hospital Medical Center follows a policy of making all decisions concerning compensation and/or medical treatment for physical injuries occurring during or caused by participation in biomedical or behavioral research on an individual basis.

WHAT ARE YOUR RIGHTS AS A PARTICIPANT?

Your participation in this study is completely voluntary. You may choose either to take part or not to take part in this research study. Your decision whether or not to participate will not result in any penalty or loss of benefits to you and the standard medical care for your condition will remain available to you.

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If you decide to take part in the research study, you are free to withdraw your consent and discontinue participation in this research study at any time. Leaving the study will not result in any penalty or loss of benefits to you.

You may revoke (choose to withdraw) this Authorization as provided under the Health Insurance Portability and Accountability Act of 1996 (HIPAA”) at any time after you have signed it by providing a written statement that you wish to withdraw this Authorization. Your withdrawal of this Authorization will be effective immediately and your Protected Health Information can no longer be used/disclosed for research purposes by CCHMC and the other persons or entities that are identified in the “Use or Disclosure of Your Protected Health Information” section of this consent, except to the extent that CCHMC and/or the other persons or entities identified above have already taken action in reliance upon your consent. In addition, your Protected Health Information may continue to be used/disclosed to preserve the integrity of this research study.

The investigators will tell you about significant new findings developed during the course of the research and new information that may affect your health, welfare, or willingness to stay in this study.

If you are a CCHMC employee, refusal to participate or withdrawal from the study will not jeopardize and of your opportunities, rights, or benefits.

If you have questions about the study, you will have a chance to talk to one of the study staff or your regular doctor. Do not sign this form unless you have had the chance to ask questions and have received satisfactory answers.

Nothing in this consent form waives any legal rights you may have nor does it release the investigator, the sponsor, the institution, or its agents from liability for negligence.

For further information about your rights, please see CCHMC Notice of Privacy Practices. A copy of the CCHMC Notice of Privacy Practices may be obtained from any patient registration area or online at www.cincinnatichildrens.org. (From the internet page select in the following order: About Us, Corporate Information, HIPAA). You may also contact our Privacy Officer at (513) 636-4707 to obtain a copy.

ABILITY TO CONDITION TREATMENT ON PARTICIPATION IN THIS STUDY

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You have a right to refuse to sign this consent to use/disclose your Protected Health Information for research purposes.

If you refuse to sign this consent, your rights concerning treatment, payment for services, enrollment in a health plan or eligibility for benefits will not be affected.

WHO DO YOU CALL IF YOU HAVE QUESTIONS OR PROBLEMS?

For questions about this research study or to report a research-related injury, you can contact the researcher, Marc E. Rothenberg, M.D., Ph.D., at (513) 636-4200. Researchers are available to answer any questions you may have about the research at any time.

If you have general questions about your rights as a research participant in this research study, you can call the Cincinnati Children’s Hospital Medical Center Institutional Review Board at (513) 636-8039.

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SIGNATURES:

I have read the information given above. The investigator or his/her designee have personally discussed with me the research study and have answered my questions. I am aware that, like in any research, the investigators cannot always predict what may happen or possibly go wrong. I have been given sufficient time to consider if I (or my child) should participate in this study. I hereby consent for myself (or my child) to take part in this study as a research study subject.

Yes □ No □ I consent to a collection of blood from myself/my child. This blood may be used to study immune cells and their by-products and other proteins.

Yes □ No □ I consent to the research use of blood that has already been drawn for clinical uses from me/my child and that will otherwise be discarded. This blood may be used to study immune cells and their by-products and other proteins.

Yes □ No □ I consent to a collection of blood from myself/my child to be used in studies involving examination of genetic materials called RNA and DNA.

Yes □ No □ I consent to the research use of blood that has already been drawn for clinical uses from me/my child and that will otherwise be discarded. This blood may be used in studies involving examination of genetic materials called RNA and DNA.

Yes □ No □ Biopsy specimens may be obtained from myself/my child for evaluation of genetic material called RNA and DNA. I understand that this procedure is part of my/my child’s routine clinical care and that the specimens will be collected only at that time.

Yes □ No □ I consent to the research use of biopsy specimens that have already been obtained for clinical purposes from me/my child. These samples may be used for evaluation of genetic material called RNA and DNA or for evaluation of my medical history.

Yes □ No □ I consent to the collection of skin samples from me/my child. These skin samples may be used to study the proteins and other factors in the skin. Yes □ No □ I consent to the collection of saliva or oral (rinse/saline swish, cheek scraping/swab, breathing into jar, throat swab) samples from me/my child. These samples may be used to study DNA, RNA, eosinophil levels or other factors.

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Yes □ No □ I consent to the collection of samples from myself/my child to be used in studies involving the generation of stem cells.

Yes □ No □ I consent to have my slides reviewed for research to develop a scoring system to measure the severity of the histologic changes in the biopsies of the gastrointestinal (GI) tract.

Yes □ No □ I consent to have information about my/my child’s case collected and compiled into a confidential file. The information collected may be used in retrospective and or prospective studies requiring analysis of patient clinical information for eosinophilic disorders.

Yes □ No□ I consent to the participation of the questionnaires about quality of life and symptom severity. My/my child’s identity will be kept confidential and cannot be traced to my/my child’s identity.

Yes □ No□ I consent to the participation of the environmental questionnaires regarding medical and family history.

Yes □ No □ If samples are collected, but not needed for the current research projects, I consent that my samples may be stored and used for future eosinophilic research.

Yes □ No □ May we contact you in the future regarding this study or to inform you of additional studies that may be related to this disease that we are studying?

Yes □ No □ I consent that this from this point forward, until I provide notice that I have changed my mind, this Informed Consent may serve as the consent for samples to be collected in the future. I understand that I will ALWAYS be contacted by a member of the CCED team prior to additional samples being taken. I will be able to withdraw my consent at any time.

______Date: ______

Subject's signature indicating consent or assent

______Date: ______

Parent/Legal Guardian (Signature Indicating Parental Permission)

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______Date: ______

Investigator/ specific individual who has been designated to obtain consent (Signature)

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