Genetic Analysis in Human Disease

Learning Objectives Describe the differences between a linkage analysis and an association analysis Identify potentially confounding factors in a genetic study Define missing heritability

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

Power of Genetic Analysis Success stories  Age-related Macular Degeneration  Crohn’s Disease  Allopecia Areata  Type1 Diabetes Not so successful  Ovarian Cancer  Obesity

The spectrum of genetic effects in complex diseases

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

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)  Case - control

Linkage vs. Association Analysis 5M

Linkage Studies- all in the family Family based method to map location of disease causing loci Families  Multiplex  Trios  Sib pairs

Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

Association Studies – numbers game Genome-Wide Association Studies (GWAS)  Tests the whole genome for a statistical association between a marker and a trait in unrelated cases and controls Affecteds Controls

Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

So you have a hit: p< 5 x10 Validation/ replication Dense mapping/Sequencing Functional Analysis -7

Validation Independent replication set  Same inclusion/exclusion subject criteria  Sample size Genotyping platform  Same polymorphism Analysis Different ethnic group (added bonus)

Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

Dense Mapping/Sequencing Identifies the boundaries of your signal  close in on the target gene/ causal variant  find other (common or rare) variants

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

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 Not asking the right question.  wrong statistics, wrong model

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

Influence of Admixture Not all Subjects are the same

Missing heritability Except for a few diseases (AMD, T1D) genetics explains less than 50% of risk.  Large number of genes with small effects Other influences?

Other Contributors Any change in gene expression can influence disease state- not always related directly to DNA sequence Environmental Epigenetic MicroRNA Microbiome Copy Number Variation Gene-Gene Interactions Alternative splice sites/transcription start sites

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

GWAS – what have we found? 3800 SNPs identified for 427 diseases and traits Only 7% in coding regions >50% in DNAse sensitive sites, presumed regulatory regions

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

Genome-Wide Association Studies The potential clinical applications  Risk prediction Type 1 Diabetes (MHC and 50 loci)  Disease subtyping/classification MODY: HNF1A- C- reactive protein biomarker  Drug development Ribavirin- induced anemia: ITPA variants protective  Drug toxicity/ adverse effects MCR4 SNPs and extreme SGA-induced weight gain (Manolio 2013)

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