A Genome-Wide Association Study
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“A Very Stinking Herbe” Is Cilantro Love/Hate a Genetic Trait? So Fresh Or So Clean? Methods Results and Discussion Referenc
A Genetic Variant Near Olfactory Receptor Genes Associates With Cilantro Preference S. Wu, J.Y. Tung, A.B. Chowdry, A.K. Kiefer, J.L. Mountain, N. Eriksson 23andMe, Inc., Mountain View, CA “A very stinking herbe”! Many people love the taste of fresh cilantro (Coriandrum sativum, also called coriander), while Oh, coriander.! others despise it with every fiber of their being. The debate has raged for centuries. Pliny extolled If "pure evil# had a taste?! its “refreshing” properties, a medieval herbalist claimed that the leaves had a “venomous quality,” It would taste like you.! by SilverSpoon at and, more recently, beloved culinary icon Julia Child opined that cilantro had “kind of a dead taste” IHateCilantro.com! and that she would “pick it out if [she] saw it and throw it on the floor.”! So fresh or so clean?! Is cilantro love/hate a genetic trait?! Cilantro"s dual nature may lie in its key aroma components, which consist of It is not known why cilantro is so differentially perceived. Cilantro various aldehydes. The unsaturated aldehydes (mostly decanal and preference varies with ancestry and may be influenced by dodecanal) are described as fruity, green, and pungent; the (E)-2-alkenals exposure. Genetic factors are also thought to play a role but to as soapy, fatty, pungent, and “like cilantro.” Of the many descriptors that date no studies have found genetic variants influencing cilantro “haters” use, soapy is the most common.! taste preference. ! Methods! Results and discussion! We conducted the first-ever genome-wide association study of cilantro preference. Participants were drawn from the more than 180,000 genotyped customers of 23andMe, Inc., a consumer genetics company.! Phenotype data collection! Participants reported their age and answered Figure 1. -
Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Plenary and Platform Abstracts
American Society of Human Genetics 68th Annual Meeting PLENARY AND PLATFORM ABSTRACTS Abstract #'s Tuesday, October 16, 5:30-6:50 pm: 4. Featured Plenary Abstract Session I Hall C #1-#4 Wednesday, October 17, 9:00-10:00 am, Concurrent Platform Session A: 6. Variant Insights from Large Population Datasets Ballroom 20A #5-#8 7. GWAS in Combined Cancer Phenotypes Ballroom 20BC #9-#12 8. Genome-wide Epigenomics and Non-coding Variants Ballroom 20D #13-#16 9. Clonal Mosaicism in Cancer, Alzheimer's Disease, and Healthy Room 6A #17-#20 Tissue 10. Genetics of Behavioral Traits and Diseases Room 6B #21-#24 11. New Frontiers in Computational Genomics Room 6C #25-#28 12. Bone and Muscle: Identifying Causal Genes Room 6D #29-#32 13. Precision Medicine Initiatives: Outcomes and Lessons Learned Room 6E #33-#36 14. Environmental Exposures in Human Traits Room 6F #37-#40 Wednesday, October 17, 4:15-5:45 pm, Concurrent Platform Session B: 24. Variant Interpretation Practices and Resources Ballroom 20A #41-#46 25. Integrated Variant Analysis in Cancer Genomics Ballroom 20BC #47-#52 26. Gene Discovery and Functional Models of Neurological Disorders Ballroom 20D #53-#58 27. Whole Exome and Whole Genome Associations Room 6A #59-#64 28. Sequencing-based Diagnostics for Newborns and Infants Room 6B #65-#70 29. Omics Studies in Alzheimer's Disease Room 6C #71-#76 30. Cardiac, Valvular, and Vascular Disorders Room 6D #77-#82 31. Natural Selection and Human Phenotypes Room 6E #83-#88 32. Genetics of Cardiometabolic Traits Room 6F #89-#94 Wednesday, October 17, 6:00-7:00 pm, Concurrent Platform Session C: 33. -
High Expression of an Unknown Long Noncoding RNA RP11-290L1.3 from GDM Macrosomia and Its Effect on Preadipocyte Differentiation
ID: 20-0584 10 2 Y Lin, Y Zhand, L Xu et al. RP11 enhanced preadipocyte 10:2 191–204 differentiation RESEARCH High expression of an unknown long noncoding RNA RP11-290L1.3 from GDM macrosomia and its effect on preadipocyte differentiation Yu Lin1,*, Yingying Zhang1,*, Lei Xu2,*, Wei Long1, Chunjian Shan1, Hongjuan Ding1, Lianghui You1, Chun Zhao1 and Zhonghua Shi1 1State Key Laboratory of Reproductive Medicine, Women’s Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, People’s Republic of China 2Maternal and Child Health Care Hospital of Dongchangfu District, Liaocheng, People’s Republic of China Correspondence should be addressed to C Zhao or Z Shi: [email protected] or [email protected] *(Y Lin, Y Zhang and L Xu contributed equally to this work) Abstract Aims: Gestational diabetes mellitus (GDM)-induced macrosomia is predominantly Key Words characterized by fat accumulation, which is closely related to adipocyte differentiation. f long noncoding RNA An unknown long noncoding RNA RP11-290L1.3, referred to as RP11, was identified to f GDM be dramatically upregulated in the umbilical cord blood of women with GDM-induced f preadipocyte macrosomia in our previous study. We conducted this study to identify the function of differentiation RP11 in GDM-induced macrosomia. f macrosomia Methods: The effects of RP11 gain- and loss-of-function on HPA-v (human preadipocytes- f fat accumulation visceral) adipogenesis were determined with lentivirus mediated cell transduction. The mRNA and protein expression levels of adipogenesis makers were evaluated by qPCR/Western blot. Then, we performed the microarray and pathway analysis to explore the possible mechanisms by which RP11 regulates adipogenesis. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
WO 2017/214553 Al 14 December 2017 (14.12.2017) W !P O PCT
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2017/214553 Al 14 December 2017 (14.12.2017) W !P O PCT (51) International Patent Classification: AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, C12N 15/11 (2006.01) C12N 15/113 (2010.01) CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, (21) International Application Number: HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, PCT/US20 17/036829 KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (22) International Filing Date: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, 09 June 2017 (09.06.2017) OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY,TH, TJ, TM, TN, (25) Filing Language: English TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (26) Publication Language: English (84) Designated States (unless otherwise indicated, for every (30) Priority Data: kind of regional protection available): ARIPO (BW, GH, 62/347,737 09 June 2016 (09.06.2016) US GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, 62/408,639 14 October 2016 (14.10.2016) US UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/433,770 13 December 2016 (13.12.2016) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, (71) Applicant: THE GENERAL HOSPITAL CORPO¬ MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, RATION [US/US]; 55 Fruit Street, Boston, Massachusetts TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, 021 14 (US). -
DUX4-Induced Constitutive DNA Damage and Oxidative Stress Contribute to Aberrant Differentiation of Myoblasts from FSHD Patients
Free Radical Biology and Medicine 99 (2016) 244–258 Contents lists available at ScienceDirect Free Radical Biology and Medicine journal homepage: www.elsevier.com/locate/freeradbiomed Original article DUX4-induced constitutive DNA damage and oxidative stress contribute to aberrant differentiation of myoblasts from FSHD patients Petr Dmitriev a,b,1, Yara Bou Saada a,1, Carla Dib a, Eugénie Ansseau d, Ana Barat a, Aline Hamade e, Philippe Dessen c, Thomas Robert c, Vladimir Lazar c, Ruy A.N. Louzada g, Corinne Dupuy g, Vlada Zakharova f, Gilles Carnac b, Marc Lipinski a, Yegor S. Vassetzky a,f,n a UMR 8126, Univ. Paris-Sud, CNRS, Institut de Cancérologie Gustave-Roussy, F-94805 Villejuif, France b PhyMedExp, University of Montpellier, INSERM U1046, CNRS UMR 9214, F-34295 Montpellier cedex 5, France c Functional Genomics Unit, Institut de Cancérologie Gustave-Roussy, F-94805 Villejuif, France d Laboratory of Molecular Biology, University of Mons, 20 place du Parc, B700 Mons, Belgium e ER030-EDST, Department of Life and Earth Sciences, Faculty of Sciences II, Lebanese University, Lebanon f Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, 119991 Moscow, Russia g UMR 8200, Univ., Paris-Sud, CNRS, Institut de Cancérologie Gustave-Roussy, F-94805 Villejuif, France article info abstract Article history: Facioscapulohumeral dystrophy (FSHD) is one of the three most common muscular dystrophies in the Received 31 March 2016 Western world, however, its etiology remains only partially understood. Here, we provide evidence of Received in revised form constitutive DNA damage in in vitro cultured myoblasts isolated from FSHD patients and demonstrate 4 August 2016 oxidative DNA damage implication in the differentiation of these cells into phenotypically-aberrant Accepted 8 August 2016 myotubes. -
Integrative Clinical Sequencing in the Management of Refractory Or
Supplementary Online Content Mody RJ, Wu Y-M, Lonigro RJ, et al. Integrative Clinical Sequencing in the Management of Children and Young Adults With Refractory or Relapsed CancerJAMA. doi:10.1001/jama.2015.10080. eAppendix. Supplementary appendix This supplementary material has been provided by the authors to give readers additional information about their work. © 2015 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/29/2021 SUPPLEMENTARY APPENDIX Use of Integrative Clinical Sequencing in the Management of Pediatric Cancer Patients *#Rajen J. Mody, M.B.B.S, M.S., *Yi-Mi Wu, Ph.D., Robert J. Lonigro, M.S., Xuhong Cao, M.S., Sameek Roychowdhury, M.D., Ph.D., Pankaj Vats, M.S., Kevin M. Frank, M.S., John R. Prensner, M.D., Ph.D., Irfan Asangani, Ph.D., Nallasivam Palanisamy Ph.D. , Raja M. Rabah, M.D., Jonathan R. Dillman, M.D., Laxmi Priya Kunju, M.D., Jessica Everett, M.S., Victoria M. Raymond, M.S., Yu Ning, M.S., Fengyun Su, Ph.D., Rui Wang, M.S., Elena M. Stoffel, M.D., Jeffrey W. Innis, M.D., Ph.D., J. Scott Roberts, Ph.D., Patricia L. Robertson, M.D., Gregory Yanik, M.D., Aghiad Chamdin, M.D., James A. Connelly, M.D., Sung Choi, M.D., Andrew C. Harris, M.D., Carrie Kitko, M.D., Rama Jasty Rao, M.D., John E. Levine, M.D., Valerie P. Castle, M.D., Raymond J. Hutchinson, M.D., Moshe Talpaz, M.D., ^Dan R. Robinson, Ph.D., and ^#Arul M. Chinnaiyan, M.D., Ph.D. CORRESPONDING AUTHOR (S): # Arul M. -
Application of Microrna Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease
Article Application of microRNA Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease Jennifer L. Major, Rushita A. Bagchi * and Julie Pires da Silva * Department of Medicine, Division of Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; [email protected] * Correspondence: [email protected] (R.A.B.); [email protected] (J.P.d.S.) Supplementary Tables Methods Protoc. 2021, 4, 5. https://doi.org/10.3390/mps4010005 www.mdpi.com/journal/mps Methods Protoc. 2021, 4, 5. https://doi.org/10.3390/mps4010005 2 of 25 Table 1. List of all hsa-miRs identified by Human microRNA Disease Database (HMDD; v3.2) analysis. hsa-miRs were identified using the term “genetics” and “circulating” as input in HMDD. Targets CAD hsa-miR-1 Targets IR injury hsa-miR-423 Targets Obesity hsa-miR-499 hsa-miR-146a Circulating Obesity Genetics CAD hsa-miR-423 hsa-miR-146a Circulating CAD hsa-miR-149 hsa-miR-499 Circulating IR Injury hsa-miR-146a Circulating Obesity hsa-miR-122 Genetics Stroke Circulating CAD hsa-miR-122 Circulating Stroke hsa-miR-122 Genetics Obesity Circulating Stroke hsa-miR-26b hsa-miR-17 hsa-miR-223 Targets CAD hsa-miR-340 hsa-miR-34a hsa-miR-92a hsa-miR-126 Circulating Obesity Targets IR injury hsa-miR-21 hsa-miR-423 hsa-miR-126 hsa-miR-143 Targets Obesity hsa-miR-21 hsa-miR-223 hsa-miR-34a hsa-miR-17 Targets CAD hsa-miR-223 hsa-miR-92a hsa-miR-126 Targets IR injury hsa-miR-155 hsa-miR-21 Circulating CAD hsa-miR-126 hsa-miR-145 hsa-miR-21 Targets Obesity hsa-mir-223 hsa-mir-499 hsa-mir-574 Targets IR injury hsa-mir-21 Circulating IR injury Targets Obesity hsa-mir-21 Targets CAD hsa-mir-22 hsa-mir-133a Targets IR injury hsa-mir-155 hsa-mir-21 Circulating Stroke hsa-mir-145 hsa-mir-146b Targets Obesity hsa-mir-21 hsa-mir-29b Methods Protoc. -
Identification of Genetic Factors Underpinning Phenotypic Heterogeneity in Huntington’S Disease and Other Neurodegenerative Disorders
Identification of genetic factors underpinning phenotypic heterogeneity in Huntington’s disease and other neurodegenerative disorders. By Dr Davina J Hensman Moss A thesis submitted to University College London for the degree of Doctor of Philosophy Department of Neurodegenerative Disease Institute of Neurology University College London (UCL) 2020 1 I, Davina Hensman Moss confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Collaborative work is also indicated in this thesis. Signature: Date: 2 Abstract Neurodegenerative diseases including Huntington’s disease (HD), the spinocerebellar ataxias and C9orf72 associated Amyotrophic Lateral Sclerosis / Frontotemporal dementia (ALS/FTD) do not present and progress in the same way in all patients. Instead there is phenotypic variability in age at onset, progression and symptoms. Understanding this variability is not only clinically valuable, but identification of the genetic factors underpinning this variability has the potential to highlight genes and pathways which may be amenable to therapeutic manipulation, hence help find drugs for these devastating and currently incurable diseases. Identification of genetic modifiers of neurodegenerative diseases is the overarching aim of this thesis. To identify genetic variants which modify disease progression it is first necessary to have a detailed characterization of the disease and its trajectory over time. In this thesis clinical data from the TRACK-HD studies, for which I collected data as a clinical fellow, was used to study disease progression over time in HD, and give subjects a progression score for subsequent analysis. In this thesis I show blood transcriptomic signatures of HD status and stage which parallel HD brain and overlap with Alzheimer’s disease brain.