Genetic Analysis in Human Disease

1. Genetic Analysis in Human Disease

2. Power of Genetic Analysis • Success stories • Age-related Macular Degeneration • Crohn’s Disease • AllopeciaAreata • Type1 Diabetes • Not so successful • Ovarian Cancer • Obesity

3. 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

4. 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

5. Linkage vs. Association Analysis 5M

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

7. Staged Genetic Analysis - RALinkage/Association/Candidate Gene

8. 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 Controls Affecteds

9. Staged Genetic Analysis - RALinkage/Association/Candidate Gene

10. -7 So you have a hit: p< 5 x10 • Validation/ replication • Dense mapping/Sequencing • Functional Analysis

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

12. Staged Genetic Analysis - RALinkage/Association/Candidate Gene

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

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

15. 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?

16. 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

17. 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

19. Influence of Admixture • Not all Subjects are the same

20. 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?

21. 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

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

23. 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

24. Pathway Analysis – Crohn’s disease

25. 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

26. 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.

27. 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.