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Genetic Analysis in Human Disease Kim R. Simpfendorfer, PhD Robert S.Boas Center for Genomics & Human Genetics The Feinstein Institute for Medical Research Learning Objectives Describe the differences between a linkage analysis and an association analysis Identify potentially confounding factors in a genetic study Describe why a disease associated singlenucleotide polymorphism is not necessarily the causal disease variant Question: 1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects? A) Phenotype, gender and age B) Phenotype, gender and income C) Gender, age and income D) Age, income and education Question: 2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong? A) Recruited too many subjects B) Population was too homogeneous C) Not enough subjects D) Genotyped using only one platform Question: 3) You’ve made it to the big time. From your GWAS analysis you have significant hits in known genes. What’s the next step? A) End of story, move on to the next study B) Develop new drugs C) Replication/validation D) Patent the SNPs Aims of Genetic Analysis in Human Disease McCarthy Nature Genetics Reviews The contributions of genetic and environmental factors to human diseases Rare Genetics simple Unifactorial High recurrence rate Common Genetics complex Multifactorial Low recurrence rate Twin concordance to estimate heritability Heritable and non-heritable factors Heritable factors Shared environmental factors Nonshared environmental factors Castillo-Fernandez, Genome Medicine2014 6:60 The spectrum of genetic effects in complex diseases Bush WS and Moore JH - Bush WS, Moore JH (2012) Chapter 11: Genome-Wide Association Studies. PLoS Comput Biol 8(12) Getting Started Question to be answered Which gene(s) are responsible for genetic susceptibility for Disease A? What is the measurable difference Clinical phenotype biomarkers, drug response, outcome Who is affected Demographics male/female, ethnic/racial background, age Genome Wide Study Design Linkage (single gene diseases: cystic fibrosis, Huntington’s disease, Duchene's Muscular Dystrophy) Families Association (complex diseases: RA, SLE, breast cancer, autism, allopecia, AMD, Alzheimer’s) Families Case - control Linkage vs. Association Analysis Ott Nat Rev Gen 2011 Linkage Studies- all in the family Family based method to map location of disease causing loci Trios Sib pairs Multiplex families Abo BMC Bioinformatics 2010 Genome-wide linkage analysis of an autosomal recessive hypotrichosis identifies a novel P2RY5 mutation Petukhova Genomics 92 2008 Genome-wide linkage analysis of an autosomal recessive hypotrichosis identifies a novel P2RY5 mutation Petukhova Genomics 92 2008 Genome-wide linkage analysis of an autosomal recessive hypotrichosis identifies a novel P2RY5 mutation Petukhova Genomics 92 2008 Genome-wide linkage analysis of an autosomal recessive hypotrichosis identifies a novel P2RY5 mutation Petukhova Genomics 92 2008 GWAS Lasse Folkersen Genome wide association study & meta-analysis Case-control SLE Meta-analysis RA GWAS So you have a hit: p< 5 x10-7 Validation/ replication Dense mapping/Sequencing Functional Analysis Validation Independent replication set Genotyping platform Same inclusion/exclusion subject criteria Sample size Same polymorphism Analysis Different ethnic group (added bonus) Dense Mapping/Sequencing Identifies the boundaries of your signal close in on the target gene/ causal variant find other (common or rare) variants Imputation and haplotype analysis Identifies the boundaries of your signal close in on the target gene/ causal variant find other (common or rare) variants RA association in Europeans in BLK regulatory region MTMR9 TDH SLC35G5 P values from Stage 1 meta GWAS Genetics of rheumatoid arthritis contributes to biology and drug discovery. Okada et al. 2013. FAM167A C8orf12 BLK LINC00208 Association of the BLK risk haplotype with autoimmune disease across ancestral groups Controls RA cases n=2,134 n=2,526 Simpfendorfer et al. Arthritis & Rheumatology 2015. Systemic Lupus Erythematosus Rheumatoid Arthritis Dermatomyositis Sjögren’s Syndrome Systemic Sclerosis Anti-phospholipid Syndrome Kawasaki Disease European / Caucasian Chinese-Han Japanese African American Hispanic Asian Korean Candidate causal alleles in the BLK autoimmune disease-risk haplotype Histone mark peaks from B lymphocytes Simpfendorfer et al. Arthritis & Rheumatology 2015. 1bp insertion 1bp deletion Functional Analysis Does your gene make sense? pathway function cell type expression animal models PTPN22: first non-MHC gene associated with RA (TCR signaling) Autoimmunity risk genes/loci from GWAS NHGRI GWAS catalog Sharing of risk genes between autoimmune diseases indicates involvement in a shared autoimmune disease development mechanism Perfect vs Imperfect Worlds Perfect world Linkage and/or GWAS – identify causative gene polymorphism for your disease Publish Imperfect world nothing significant identify genes that have no apparent influence in your disease of interest Now what? What Happened? Disease has no genetic component. Genetic effect is small and your sample size wasn’t big enough to detect it. Too many outliers Wrong controls. CDCV vs CDRV Phenotype /or demographics too heterogeneous Viral, bacterial, environmental Population stratification; admixture Genotyping platform does not detect CNVs Not asking the right question. wrong statistics, wrong model Influence of Admixture Not all Subjects are the same Meta-Analysis – Bigger is better Meta-analysis - combines genetic data from multiple studies; allows identification of new loci Rheumatoid Arthritis Lupus Crohn’s disease Alzheimer’s Schizophrenia Autism Candidate gene association success story: PCSK9 Cohen NEJM 2006 Genome-Wide Association Studies The promise Better understanding of biological processes leading to disease pathogenesis Development of new treatments Identify non-genetic influences of disease Better predictive models of risk Genome-Wide Association Studies The reality Few causal variants have been identified Clinical heterogeneity and complexity of disease Genetic results don’t account for all of disease risk Question: 1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects? A) Phenotype, gender and age B) Phenotype, gender and income C) Gender, age and income D) Age, income and education Answer: 1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects? A) Phenotype, gender and age Question: 2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong? A) Recruited too many subjects B) Population was too homogeneous C) Not enough subjects D) Genotyped using only one platform Answer: 2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong? C) Not enough subjects Question: 3) You’ve made it to the big time. From your GWAS analysis you have significant hits in known genes. What’s the next step? A) End of story, move on to the next study B) Develop new drugs C) Replication/validation D) Patent the SNPs Answer: 3) You’ve made it to the big time. From your GWAS analysis you have significant hits in known genes. What’s the next step? C) Replication/validation