Statistical Methods for Testing Genetic Pleiotropy

Overview

Journal Genetics

Publisher Oxford University Press

Specialty Genetics

Date 2016 Aug 17

PMID 27527515

Citations 32

Authors

Daniel J Schaid

Xingwei Tong

Beth Larrabee

Richard B Kennedy

Gregory A Poland

Jason P Sinnwell

Affiliations

Soon will be listed here.

Abstract

Genetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that [Formula: see text] traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.

Citing Articles

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Pleiotropic genetic association analysis with multiple phenotypes using multivariate response best-subset selection.

Guo H, Li T, Wang Z BMC Genomics. 2023; 24(1):759.

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A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.

St-Pierre J, Oualkacha K Int J Biostat. 2022; 19(2):369-387.

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Genetic pleiotropy underpinning adiposity and inflammation in self-identified Hispanic/Latino populations.

Anwar M, Baldassari A, Polikowsky H, Sitlani C, Highland H, Chami N BMC Med Genomics. 2022; 15(1):192.

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Multivariate genome-wide association study of depression, cognition, and memory phenotypes and validation analysis identify 12 cross-ethnic variants.

Sun J, Wang W, Zhang R, Duan H, Tian X, Xu C Transl Psychiatry. 2022; 12(1):304.

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