1 / 38

Molecular & Genetic Epi 217 Association Studies: Indirect

Molecular & Genetic Epi 217 Association Studies: Indirect. John Witte. Homework, Question 4: Haplotypes. ID MTHFR_C677T MTHFR_A1298C Haplotypes? 959 CC AA C-A / C-A 1044 CC AC C-A / C-C 147 CT AA C-A / T-A 123 CT AC C-A / T-C or C-C / T-A.

metea
Download Presentation

Molecular & Genetic Epi 217 Association Studies: Indirect

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Molecular & Genetic Epi 217Association Studies: Indirect John Witte

  2. Homework, Question 4: Haplotypes ID MTHFR_C677T MTHFR_A1298C Haplotypes? 959 CC AA C-A / C-A 1044 CC AC C-A / C-C 147 CT AA C-A / T-A 123 CT AC C-A / T-C or C-C / T-A • Genotypes 677TT and 1298CC never observed together: Suggests most • Probable haplotype, and potential selection or chance. • Rare variants: not necessarily lethal, especially those that are associated • with late onset diseases.

  3. 3 SNPs in the TAS2R38 Gene P A V P A I P V V P V I A A V A A I A V V A V I

  4. TASR: 3 SNPs form Haplotypes P A V Taster Non-taster A V I

  5. TAS2R38 Haplotype Function

  6. TASR Genotyping Results

  7. G/C 3 G/A 2 T/C 4 G/C 5 A/T 1 A/C 6 G G A A G T G A C C C C C C C C T T A A G G C C high r2 high r2 high r2 • SNPs are correlated (aka Linkage Disequilibrium) Too many MTHFR SNPsSolution: Tag SNP Selection Pairwise Tagging: SNP 1 SNP 3 SNP 6 3 tags in total Test for association: SNP 1 SNP 3 SNP 6 Carlson et al. (2004) AJHG 74:106

  8. Coverage: Measurement Error in TagSNPs

  9. Common Measures of Coverage • Threshold Measures • e.g., 73% of SNPs in the complete set are in LD with at least one SNP in the genotyping set at r2> 0.8 • Average Measures • e.g., Average maximum r2 = 0.84

  10. Coverage and Sample Size • Sample size required for Direct Association, n • Sample size for Indirect Association n* = n/ r2 • For r2 = 0.8, increase is 25% • For r2 = 0.5, increase is 100%

  11. Tag SNPs Database Resources http://www.hapmap.org http://gvs.gs.washington.edu/GVS/index.jsp

  12. HapMap • Re-sequencing to discover millions of additional SNPs; deposited to dbSNP. • SNPs from dbSNP were genotyped • Looked for 1 SNP every 5kb • SNP Validation • Polymorphic • Frequency • Haplotype and Linkage Disequilibrium Estimation • LD tagging SNPs

  13. HapMap Phase III Populations • ASW African ancestry in Southwest USA • CEU Utah residents with Northern and Western European ancestry from the CEPH collection • CHB Han Chinese in Beijing, China • CHD Chinese in Metropolitan Denver, Colorado • GIH Gujarati Indians in Houston, Texas • JPT Japanese in Tokyo, Japan • LWK Luhya in Webuye, Kenya • MEX Mexican ancestry in Los Angeles, California • MKK Maasai in Kinyawa, Kenya • TSI Toscani in Italia • YRI Yoruba in Ibadan, Nigeria

  14. Tag SNPs: HapMap

  15. Tag SNPs: HapMap

  16. Tag SNPs: HapMap & Haploview http://www.broad.mit.edu/mpg/haploview/

  17. Tag SNPs: HapMap & Haploview

  18. Tag SNPs: HapMap & Haploview

  19. Tag SNPs: HapMap & Haploview

  20. Tag SNPs: HapMap & Haploview

  21. Tag SNPs: HapMap Summary • Identified 33 common MTHR SNPs (MAF > 5%) among Caucasians • Forced in 3 potentially functional/previously associated SNPs • Identified tag based on pairwise tagging • 15 tags SNPs could capture all 33 MTHR SNPs (mean r2 = 97%) • Note: number of SNPs required varies from gene to gene and from population to population

  22. 1K Genomes Project

  23. Genome-wide Assocation Studies (GWAS)

  24. One- and Two-Stage GWA Designs Two-Stage Design One-Stage Design SNPs SNPs 1,2,3,……………………………,M 1,2,3,……………………………,M 1,2,3,………………………,N 1,2,3,………………………,N samples Stage 1 Samples Samples Stage 2 markers

  25. One-Stage Design SNPs Samples Two-Stage Design Joint analysis Replication-based analysis SNPs SNPs Samples Stage 1 Stage 1 Samples Stage 2 Stage 2

  26. Multistage Designs • Joint analysis has more power than replication • p-value in Stage 1 must be liberal • Lower cost—do not gain power • http://www.sph.umich.edu/csg/abecasis/CaTS/index.html

  27. Complex diseases Physical activity Genetic susceptibility Obesity Hyperlipidemia Diet Diabetes Complex diseases: Many causes = many causal pathways! Vulnerable plaques Hypertension MI Atherosclerosis

  28. Pathways • Many websites / companies provide ‘dynamic’ graphic models of molecular and biochemical pathways. • Example: BioCarta: http://www.biocarta.com/ • May be interested in potential joint and/or interaction effects of multiple genes in one pathway.

  29. Interactions • “The interdependent operation of two or more causes to produce or prevent an effect” • “Differences in the effects of one or more factors according to the level of the remaining factor(s)” • Last, 2001

  30. Why look for interactions? • Improve detection of genetic (& environmental) risks. • Understand etiology/biology • New hypotheses? • Diagnostics • Prevention and interventions

  31. 19 2.8 Micronutrient X 0.6 0.2 0.1 Environmental exposure Y 25 2.7 5.2 Other gene Z Drinker? 16 2.1 0.1 0.1 Within particular subgroups, effect of gene may be quite high or low 21 Dilution of effects Gene A OR=1.5

  32. Statistical vs. Biological Interactions • Not identical. • One hypothesizes biological interaction • But ‘tests’ for statistical interaction • Does statistical evidence support our biological hypothesis?

  33. Additive “effect” RER = (OR(E,G)-1)/((OR(E,g)-1)+(OR(e,G)-1)) = (2.4-1)/((2.0-1)+(1.4-1)) = 1.0 2.8/2.0 7.8/2.0 = 1.0 = 2.8  =  = 1.4/1.0 1.4/1.0 Multiplicative “effect” (ORs, RRs) Multiplicative interaction (ORs, RRs) Departure from =1 is a multiplicative interaction Multiplicative vs. Additive Interactions RER = relative excess risk

  34. Additive interaction: G1 and E5: independent risk factors Multiplicative interaction: G2 and E2: work through same pathway Two possible causal pathways: additive and multiplicative interaction for colorectal cancer If factors are not known to act independently, use multiplicative. Brennan, P. Carcinogenesis 2002 23:381-387

  35. Analysis of Multiple Genes • Joint / Additive • Multiplicative • Increasing complexity

  36. More Complex Modeling • Multifactor-dimensionality reduction • (Moore & Williams, Ann Med 2002) • Logic regression • (Kooperberg & Ruczinski, Genetic Epi 2005) • Multi-loci analysis • (Marchini, Donnelly, Cardon, Nat Genet 2005) • Bayesian epistasis association mapping • (Zhang & Liu, Nat Genet 2007)

More Related