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

Twin concordance to estimate heritability

Heritable and non-heritable factors Castillo-Fernandez, Genome Medicine2014 6:60 Heritable factors Shared environmental factors Nonshared environmental factors

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

Genome-wide linkage analysis of an autosomal recessive hypotrichosis identifies a novel P2RY5 mutation Petukhova Genomics

Genome-wide linkage analysis of an autosomal recessive hypotrichosis identifies a novel P2RY5 mutation Petukhova Genomics

Genome-wide linkage analysis of an autosomal recessive hypotrichosis identifies a novel P2RY5 mutation Petukhova Genomics

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  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

MTMR9 SLC35G5 TDH C8orf12  FAM167A BLK  LINC00208 P values from Stage 1 meta GWAS Genetics of rheumatoid arthritis contributes to biology and drug discovery. Okada et al RA association in Europeans in BLK regulatory region

Systemic Lupus Erythematosus Rheumatoid Arthritis Dermatomyositis Sjögren’s Syndrome Systemic Sclerosis Kawasaki Disease Anti-phospholipid Syndrome European / Caucasian Chinese-Han Japanese African American Hispanic Korean Asian Simpfendorfer et al. Arthritis & Rheumatology Controls n=2,134 RA cases n=2,526 Association of the BLK risk haplotype with autoimmune disease across ancestral groups

Simpfendorfer et al. Arthritis & Rheumatology Candidate causal alleles in the BLK autoimmune disease-risk haplotype 1bp insertion1bp deletion Histone mark peaks from B lymphocytes

Functional Analysis Does your gene make sense?  pathway  function  cell type  expression  animal models PTPN22: first non-MHC gene associated with RA (TCR signaling)

Sharing of risk genes between autoimmune diseases indicates involvement in a shared autoimmune disease development mechanism NHGRI GWAS catalog Autoimmunity risk genes/loci from GWAS

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