Genetic Analysis in Human Disease

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

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 -7 Validation/ replication Dense mapping/Sequencing Functional Analysis

Validation Independent replication set Genotyping platform Analysis 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 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 Environmental Epigenetic MicroRNA Microbiome 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

GWAS- What have we found? 3,800 SNPs identified for 427 diseases and traits

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 and the reality Few causal variants have been identified Clinical heterogeneity and complexity of disease Genetic results don’t account for all of disease risk

Pathway Analysis – Crohn’s disease

Personalized Medicine "5P" Health Care Personalized medicine is: Predictive: Uses state-of-the-art molecular and diagnostic tools to precisely predict individual health risks and outcomes Personalized: Is informed by each person’s unique clinical, social, genetic, genomic, and environmental profile Preventive: Emphasizes wellness and prevention to stop disease before it progresses Preemptive: Incorporates action-oriented, individualized health planning Participatory: Empowers each patient to participate in their own care, with coordinated support from their health care team http://www.dukepersonalizedmedicine.org/what_is_personalized_medicine

Things to remember You can never have too many samples You can never collect too much information on a subject The more you know about the disease and your subjects, the more homogeneous your study will be and the less interference from “population” noise you will have.

Questions True/ False Association studies are comprised of many multiplex families With 100 randomly chosen polymorphisms and 10,000 diverse human subjects you have a high probability of finding the causative polymorphism for your disease of interest It’s better to ascertain all of your case subjects in one small town and all of your control subjects in a distant small town so there is no overlap in genetic composition. The ability to combine data from different large studies to perform a meta-analysis can result in identifying new loci which were not significant in the original studies If it weren’t for admixture we would not be able to study complex genetics.