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A Method for Detecting Pleiotropy

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This method tests for independent genetic effects on two traits, estimating the parameter of pleiotropy and comparing null and alternative hypotheses using MANOVA, FCP, RCM, and SUM tests.

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A Method for Detecting Pleiotropy

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  1. A Method for Detecting Pleiotropy Ingrid Borecki, Qunyuan Zhang, Michael Province Division of Statistical Genomics Washington University School of Medicine

  2. Biological question: Does a genetic variant have independent effects on both of two traits? Pleiotropy Statistical question: Can the correlation or a portion of the correlation between two traits be explained by a genetic variant?

  3. Compound null: no pleiotropy Alternative: pleiotropy Hypotheses & Models Y1 Y2 X Y1 Y2 Y1 Y2 Y1 Y2 X X X

  4. Statistical Parameter (δ) of Pleiotropy& Hypotheses to Be Tested Compound null: no pleiotropy Alternative: 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 structure When excluding X from the model When including X in the model

  6. Q-Q Plot under the null Testing δ Pleiotropy Estimation Test (PET) Estimated by bootstrapre-sampling 100 times with replacement -LOG10(P)

  7. =Residual of Y1 adjusted by Y2 =Residual of Y2 adjusted by Y1 • 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) Other Methods for Comparison Testing if β1≠0 and β2≠0

  8. Power Comparison PET FCP MANOVA RCM SUM

  9. Power Comparison PET FCP MANOVA RCM SUM

  10. Correlation (WC, HOMA)= 0.542 Application Correlation (TG, CAC)= 0.089

  11. 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. Conclusions

  12. Acknowledgement Ling-Yun Chang (programming & testing) Mary Feitosa (GWAS data and application)

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