Presentation on theme: "A Method for Detecting Pleiotropy"— Presentation transcript:
1 A Method for Detecting Pleiotropy Ingrid Borecki, Qunyuan Zhang, Michael ProvinceDivision of Statistical GenomicsWashington University School of Medicine
2 Pleiotropy Biological question: Does a genetic variant have independent effects on both of two traits?Statistical question:Can the correlation or a portion of the correlation between two traits be explained by a genetic variant?
3 Hypotheses & Models Compound null: no pleiotropy Alternative: XY1Y2Y1Y2Y1Y2XXX
4 Statistical Parameter (δ) of Pleiotropy & Hypotheses to Be Tested Compound null:no pleiotropyAlternative:pleiotropy
5 Estimating δ Two traits are simultaneously fit into a mixed model T is the trait indicating variable; R is block diagonal covariance matrix (after re-ordering by individuals), with blocks corresponding to the individuals and each block having the compound-symmetry structureWhen excluding X from the modelWhen including X in the model
6 Pleiotropy Estimation Test (PET) Testing δQ-Q Plot under the nullPleiotropy Estimation Test (PET)Estimated by bootstrap re-sampling 100 timeswith replacement-LOG10(P)
7 Other Methods for Comparison MANOVA (Wilks' test, wrong null)FCP: Fisher’s combined p-value test (meta-analysis ignoring correlations, wrong null)RCM: Reverse compound model (two tests)SUM: Simple univariate model (two tests)Testing if β1≠0 and β2≠0=Residual of Y1 adjusted by Y2=Residual of Y2 adjusted by Y1
10 Application SNP WC HOMA PET Cov(%) 1 3.33E-06 8.35E-06 2.87E-09 1.74 2 Correlation (WC, HOMA)= 0.542SNPWCHOMAPETCov(%)13.33E-068.35E-062.87E-091.7421.77E-048.96E-068.33E-071.2932.25E-038.06E-061.93E-061.3942.29E-044.04E-065.68E-061.1552.28E-044.15E-064.91E-0561.92E-049.84E-067.18E-051.2871.42E-023.02E-057.68E-051.04Correlation (TG, CAC)= 0.089SNPTGCACPETCov(%)11.83E-185.95E-012.57E-013.6327.47E-013.28E-096.76E-010.4232.61E-021.85E-051.30E-045.36
11 Conclusions The PET Method Tests proper compound null for pleiotropy; Gives estimation of covariance due to pleiotropy;Has greater power other alternatives;Under mixed model framework, can easily be expanded to other data (covariates, family data etc.) ;Practical to GWAS data (with 300 blades, R version takes less than 1 day for the analysis of 2M SNPs and ~3000 subjects) ;Must be fit to primary phenotype and (typed or imputed) genotype data.
12 Acknowledgement Ling-Yun Chang (programming & testing) Mary Feitosa (GWAS data and application)