1 / 12

A Method for Detecting Pleiotropy

A Method for Detecting Pleiotropy. Ingrid Borecki, Qunyuan Zhang, Michael Province Division of Statistical Genomics Washington University School of Medicine. Biological question : Does a genetic variant have independent effects on both of two traits? . Pleiotropy. Statistical question :

tevin
Download Presentation

A Method for Detecting Pleiotropy

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. 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)

More Related