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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
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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 single-nucleotide 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
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. PLoSComputBiol 8(12)
Getting StartedQuestion 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 Sib pairs Trios 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
GWASSo you have a hit: p< 5 x10-7 • Validation/ replication • Dense mapping/Sequencing • Functional Analysis
Validation • Independent replication set • Same inclusion/exclusion subject criteria • Sample size • Genotyping platform • 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 BLK MTMR9 TDH LINC00208 FAM167A SLC35G5 C8orf12 P values from Stage 1 meta GWAS Genetics of rheumatoid arthritis contributes to biology and drug discovery. Okada et al. 2013.
Association of the BLK risk haplotype with autoimmune disease across ancestral groups Controls n=2,134 RA cases n=2,526 European / Caucasian Systemic Lupus Erythematosus Chinese-Han Rheumatoid Arthritis Japanese Dermatomyositis African American Sjögren’s Syndrome Systemic Sclerosis Hispanic Anti-phospholipid Syndrome Simpfendorfer et al. Arthritis & Rheumatology 2015. Asian Kawasaki Disease Korean
Candidate causal alleles in the BLK autoimmune disease-risk haplotype Histone mark peaks from B lymphocytes 1bp insertion 1bp deletion Simpfendorfer et al. Arthritis & Rheumatology 2015.
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. • Viral, bacterial, environmental • Genetic effect is small and your sample size wasn’t big enough to detect it. • CDCV vs CDRV • Phenotype /or demographics too heterogeneous • Too many outliers • Wrong controls. • 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