Introduction to Genetic Epidemiology

1. Introduction to Genetic Epidemiology 5th Annual Interdisciplinary Genetic Research Course: Medical, Public Health, Biostatistical & Bioethical Approaches October 6, 2008 T.H. Beaty, Johns Hopkins School of Public Health

2. Genetic Epidemiology

3. Landmarks in Genetics

4. Landmarks in Genetics (cont�d)

5. Summarizing Genetic History

6. Landmarks in Epidemiology

7. Landmarks in Epidemiology

8. 50 years of genetic epidemiologyMorton (2006) J HUM GENET 51:269-277 Before DNA polymorphisms (1956-1979) Linkage analysis (LOD scores) began with very few markers Population genetics measured linkage disequilibrium (LD) by D� & r2 Heritability (h2) began with twin studies & evolved into path analysis with genetic & non-genetic causal factors Pre-genome period (1980-2001) Linkage analysis expanded to multipoint analysis Even for Mendelian diseases, peaks are hard to narrow With �complex� phenotypes, it is worse Association studies proposed as alternative (Risch & Merikangas 1996 SCIENCE 273:1516-1517) Family based association tests are useful alternative

9. 50 years of genetic epidemiology (cont�d) Post-genome period (>2002) Sequence of the human genome allows greater understanding of structure of genes, their physical position & (maybe) their function HapMap offers virtually unlimited markers, but SNPs are not equally polymorphic in all populations LD & haplotype blocks vary among populations Associated markers are only predictive, but can identify causal genes Study design will be critical Is 500K always better than 100K?

10. Comparing genetics & epidemiology

11. Comparing genetics & epidemiology � (cont�d)

12. Central questions in Genetic Epidemiology Does the trait cluster in families? Can familial clustering be explained by genes or shared environment? What is the best model of inheritance? Can we locate genes for complex diseases/traits? How does the gene control risk of disease?

13. Extending basic questions in genetic epidemiology(Burton et al. 2005 Lancet 366:941-951)

14. Useful References from Lancet 2005

15. Study designs for central questions

16. Study design for central questions (cont�d)

17. Nature is not linear�

18. Different levels of study

19. Study designs can (& do) overlap

20. 1. Population based designs Population comparisons (=ecological design) Migrant studies Do people who move from a low risk environment to a high risk environment change their risk? Consider issues of self-selection, assimilation, etc. Admixture studies Does disease risk parallel genetic admixture (% of genes of distinct ancestry)? Admixture is only estimated Human populations are not constant Vital records can be an important resource, especially birth defects & disease registries Does risk of disease change among offspring of �incross vs. outcross� matings?

21. 2. Case-Control Designs Case-unrelated control can identify genetic risk factors Genetic index (e.g. inbreeding) Genetic marker Genetic marker can be a risk factor due to Direct effect of marker in �causal pathway� Indirect effect due to linkage disequilibrium (LD) association between a high risk allele at an unobserved causal gene & observed marker allele

22. 2. Case-control designs (cont�d) Conventional case-control design: Representative sample from case & control populations Tests for difference in allele or genotypic frequencies Problems with confounding (�population stratification�) Case-related control design Representative sample of cases & their unaffected sibs (or cousins) Minimize chances of confounding Overmatched for genetic background: less statistical power Can test for linkage directly

23. Variations on case-control design Incomplete case-controls designs can test for Gene-Environment interaction (GxE) Case-only designs Incomplete variations (G & E on cases, only E on controls, etc.) Family based controls Create controls from parental mating type Under Ho, marker alleles are transmitted to case as often as not Rejecting Ho implies linkage & linkage disequilibrium (=association) Simplex families can now contribute to tests for linkage

24. 3. Family Designs

25. Families come in different shapes & sizes Sample fixed sets of relatives Adoption studies address fundamental questions about genes vs. environment Adoptee, adoptive parents, biological parents, unrelated sibs in adoptive family Twins: estimate heritability by comparing MZ & DZ twins Affected sib pairs (+parents) to test for linkage Are these representative of all families?

26. Families come in different shapes & sizes (cont�d) Sample nuclear families (parents & offspring) Measure familial aggregation/correlation Fit models of inheritance Collect data on family history in extended families Expected risk of disease can be computed as (person-years at risk) * (age specific risk) Requires good information on baseline incidence rates Expected number of cases (E) based on population risk per person-year Observed number of cases (O) typically by report Compute Family History Score as Poisson statistic:

27. Family History Scores Summarize familial risk in families ascertained through probands (cases/controls) Kerber (1995 GENET EPI 12:291-301) Breast cancer cases & controls drawn from the Utah Population Data Base Can be used to identify highest risk families Schwartz et al (1988 AM J EPI 128:524-535) Cancer risk in families of cases drawn from a cancer registry Can be useful for public health

28. Public Health uses for family history

30. CDC resources:

31. CDC resourceshttp://hugenavigator.net/

32. If you sample families in a representative manner,� Quantitative traits or a common qualitative phenotype can be used to Estimate heritability (h2) or Find best fitting model of inheritance (segregation analysis) If genetic markers are available, these families can be used to Test for linkage to unobserved genes controlling qualitative phenotype Drs. Liang & Xu will discuss this tomorrow Search for quantitative traits loci (QTL) that control quantitative phenotypes

33. Family studies & representative sampling (cont�d) Joint models for segregation analysis & linkage are feasible Linkage analysis is still limited to families informative for meiosis Multiplex families with >1 affected Simplex families have only 1 affected member Linkage will always reflect a subset of all families Heterogeneity between simplex & multiplex families should be considered

34. Families ascertained through proband Proband (typically affected) brings the rest of family into the study Segregation analysis can identify the �best� model of inheritance if ascertainment is considered Models have many parameters to estimate Even so they may not completely correct Families vary considerably in �information content� Correcting for ascertainment bias is necessary

35. Linkage vs. Association Requires multiplex families Bigger is better Guaranteed to work for Mendelian diseases Genome wide studies are feasible Still useful for complex diseases Locus heterogeneity (linked & unlinked families) is a problem Meta-analysis may strengthen evidence but narrowing peaks is still hard Unrelated cases & controls can be used Can incorporate tests for G, E, GxE, GxG, etc. Meta-analysis can measure consistency across studies Or lack thereof Allelic heterogeneity is a problem Different high risk alleles Genome wide studies are now feasible (but expensive) Interpreting them is a challenge

36. Genes as risk factors Epidemiology study designs treat genetic markers as a risk factor Test Ho: Genotype (G) is independent of risk, P(case) Odds ratio (OR) measures association between marker & risk of disease OR(case|G+)=(AD)/(CB) Dr. Liang will discuss this

37. What can you do with a genetic risk factor? Are genes just inherited risk factors? How can you use genetic risk factors in public health? Causal mutations can be used to screen Women at high risk of breast cancer for BRCA1 & 2 mutants Couples at risk of having CF child Linked markers can be used for genetic counseling or mapping Genetic markers that are true risk factors can be used in screening But you must be confident in the estimated risks e4 for Alzheimier�s Diseae? Is there an intervention? These may depend on population or environment

38. �Big Picture�:Public Health Genetics is different from Genetic Epidemiology Public health genetics is broader than genetic epidemiology Application vs. Research Screening, intervention, & treatment are part of public health genetics Policy is key part of public health genetics

39. Public Health Genetics (cont�d) Deals with both Mendelian & complex diseases Mendelian diseases in the aggregate are a major public health burden Screening the population can identify �high risk� individuals or groups Screening for complex diseases will be more demanding & will require greater efforts to validate estimates of risk

40. Trends in science: Genomic Medicine & Human Genome Epidemiology Khoury, Little & Burke (2004) Human Genome Epidemiology Oxford Univ Press Recent advances in genetics hold considerable promise for medicine & public health Many reports of genes for common diseases, few are consistent There is some �hype� involved What to do with new information as it emerges? How to validate them? How to act on them?

41. Trends in science (cont�d) Genomic medicine could predict risk of common diseases based on genotypes Genetic vs genomic ?One gene vs. many genes Pharmacogenomics could tailor pharmaceutical treatment based on genotypes Both require solid epidemiologic data to generate & confirm predictive value of genotype on risk This requires many studies, not one This may vary among populations This may depend on environment

42. Continuum from gene discovery to disease prevention (Khoury et al, 2004)

43. Summary of introduction Genetic epidemiology is a wide ranging scientific discipline Focus on identifying genes involved in complex diseases Variety of study designs are used Variety of statistical methods are available Complex diseases are complex Nature has many surprises awaiting us