1. Genetic Analysis in Human Disease

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

3. 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

4. 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

5. 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

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

7. The spectrum of genetic effects in complex diseases

8. 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

9. 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

10. Linkage vs. Association Analysis 5M

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

12. Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

13. 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

14. Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

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

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

17. Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

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

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

20. 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?

21. 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

22. 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

25. Influence of Admixture Not all Subjects are the same

26. 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?

27. 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

28. 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

29. 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

30. 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

31. 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)

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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