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Integrating Human Population Genetics and Genomics to Elucidate the Etiology of Brain Disorders

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Integrating Human Population Genetics and Genomics to Elucidate the Etiology of Brain Disorders University of Vermont ScholarWorks @ UVM Graduate College Dissertations and Theses Dissertations and Theses 2017 Integrating Human Population Genetics And Genomics To Elucidate The tE iology Of Brain Disorders Arvis Sulovari University of Vermont Follow this and additional works at: https://scholarworks.uvm.edu/graddis Part of the Genetics and Genomics Commons Recommended Citation Sulovari, Arvis, "Integrating Human Population Genetics And Genomics To Elucidate The tE iology Of Brain Disorders" (2017). Graduate College Dissertations and Theses. 781. https://scholarworks.uvm.edu/graddis/781 This Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks @ UVM. It has been accepted for inclusion in Graduate College Dissertations and Theses by an authorized administrator of ScholarWorks @ UVM. For more information, please contact [email protected]. INTEGRATING HUMAN POPULATION GENETICS AND GENOMICS TO ELUCIDATE THE ETIOLOGY OF BRAIN DISORDERS A Dissertation Presented by Arvis Sulovari to The Faculty of the Graduate College of The University of Vermont In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy Specializing in Cellular, Molecular, and Biomedical Sciences October, 2017 Defense Date: June 14, 2017 Dissertation Examination Committee: Dawei Li, Ph.D., Advisor James J. Hudziak, M.D., Chairperson Russell P. Tracy, Ph.D. Jeffrey P. Bond, Ph.D. Cynthia J. Forehand, Ph.D., Dean of Graduate College © Copyright by Arvis Sulovari October 2017 ABSTRACT Brain disorders present a significant burden on affected individuals, their families and society at large. Existing diagnostic tests suffer from a lack of genetic biomarkers, particularly for substance use disorders, such as alcohol dependence (AD). Numerous studies have demonstrated that AD has a genetic heritability of 40-60%. The existing genetics literature of AD has primarily focused on linkage analyses in small family cohorts and more recently on genome-wide association analyses (GWAS) in large case- control cohorts, fueled by rapid advances in next generation sequencing (NGS). Numerous AD-associated genomic variations are present at a common frequency in the general population, making these variants of public health significance. However, known AD-associated variants explain only a fraction of the expected heritability. In this dissertation, we demonstrate that systems biology applications that integrate evolutionary genomics, rare variants and structural variation can dissect the genetic architecture of AD and elucidate its heritability. We identified several complex human diseases, including AD and other brain disorders, as potential targets of natural selection forces in diverse world populations. Further evidence of natural selection forces affecting AD was revealed when we identified an association between eye color, a trait under strong selection, and AD. These findings provide strong support for conducting GWAS on brain disorder phenotypes. However, with the ever-increasing abundance of rare genomic variants and large cohorts of multi-ethnic samples, population stratification becomes a serious confounding factor for GWAS. To address this problem, we designed a novel approach to identify ancestry informative single nucleotide polymorphisms (SNPs) for population stratification adjustment in association analyses. Furthermore, to leverage untyped variants from genotyping arrays – particularly rare variants – for GWAS and meta-analysis through rapid imputation, we designed a tool that converts genotype definitions across various array platforms. To further elucidate the genetic heritability of brain disorders, we designed approaches aimed at identifying Copy Number Variations (CNVs) and viral insertions into the human genome. We conducted the first CNV-based whole genome meta-analysis for AD. We also designed an integrated approach to estimate the sensitivity of NGS- based methods of viral insertion detection. For the first time in the literature, we identified herpesvirus in NGS data from an Alzheimer’s disease brain sample. The work in this dissertation represents a three-faceted advance in our understanding of brain disease etiology: 1) evolutionary genomic insights, 2) novel resources and tools to leverage rare variants, and 3) the discovery of disease-associated structural genomic aberrations. Our findings have broad implications on the genetics of complex human disease and hold promise for delivering clinically useful knowledge and resources. CITATIONS Material from this dissertation has been published in the following form: Sulovari, A., Zhu, Z., Li, D.., (2017). Genome-wide Meta-analysis of Copy Number Variations with Alcohol Dependence. The Pharmacogenomics Journal, (00):1-8 Sulovari, A., Chen, Y., Hudziak, J.J., Li, D.., (2017). Atlas of Human Diseases Influenced by Genetic Variants with Extreme Allele Frequency Differences. Human Genetics, 136(1):39-54. Sulovari, A., Kranzler, H. R., Farrer, L. A., Gelernter, J., Li, D.., (2015). Eye color: A potential Indicator of Alcohol Dependence Risk in European Americans. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 168B(5):347-353. Sulovari, A., Kranzler, H. R., Farrer, L. A., Gelernter, J., Li, D.., (2015). Further Analyses Support the Association Between Light Eye Color and Alcohol Dependence. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 168(8):757- 760. Sulovari, A., and Li, D.., (2014). GACT: a Genome Build and Allele Definition Conversion Tool for SNP Imputation and Meta-analysis in Genetic Association Studies. BMC Genomics, 15:610 ii DEDICATION This work is dedicated to my parents and my sister, who have always supported and loved me unconditionally, despite us being several thousand miles apart; to my life partner, Zoë, for making everything brighter and making me a better person. From the bottom of my heart: Thank you! iii ACKOWLEDGEMENTS I have many people I would like to acknowledge. First and foremost, I would like to thank my advisor Dr. Dawei Li, who took me into his lab as his first graduate student. Thank you for accepting me into your lab and for pushing me to improve at both a personal and professional level. I would also like to thank my lab mates, past and present: Xun Chen, Guangchen Liu, Michael Mariani, Jason Kost, David Miserak, and Acadia Moeyersoms. Our friendship will have a lasting positive impact on me for many more years to come. I am very grateful to my thesis committee members for ensuring that I became the best scientist that I could during my time at UVM, and for keeping my training and education at the forefront; Drs. James Hudziak, Russell Tracy and Jeffrey Bond: Thank you! I want to acknowledge the CMB community, particularly Drs. Nicholas Heintz and Matthew Poynter and the administrators over the years: Erin Montgomery, Kirstin van Luling, Jessica Deaette, Carrie Perkins, and Haley Bradstreet. I am grateful to the MMG department, especially Dr. Susan Wallace and to the administrators for their continued help and support: Barbara Drapelick, Helen Brunelle, France Roy and Anne MacLeod. I want to thank the UVM office of international education, especially Emma Swift and Evan Mills. I want to thank my first year mentors, especially Dr. Stephen Everse. Last but not least, the people who were there when I needed them the most: Drs. Alan Rubin and Claire Verschraegen. To all of you, and everyone else in the UVM community that I have had the pleasure to know and/or learn from: Thank you for making the past five years the best experience I could have wished for! iv TABLE OF CONTENTS CITATIONS ...................................................................................................................... ii DEDICATION.................................................................................................................. iii ACKOWLEDGEMENTS ............................................................................................... iv LIST OF TABLES .......................................................................................................... xii LIST OF FIGURES ........................................................................................................ xv CHAPTER 1: INTRODUCTION .................................................................................... 1 Prevalence .................................................................................................................................... 1 Neurobiology ............................................................................................................................... 2 Genetic heritability....................................................................................................................... 5 Genome-wide linkage studies ...................................................................................................... 6 Candidate gene studies ................................................................................................................. 6 Genome-wide association studies (GWAS) ................................................................................. 8 Gene by environment interaction ................................................................................................. 9 Gene by drug interaction .............................................................................................................. 9 Missing heritability ...................................................................................................................
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