A general approach to testing for pleiotropy with rare and common variants - PubMed (original) (raw)

Comparative Study

. 2017 Feb;41(2):163-170.

doi: 10.1002/gepi.22011. Epub 2016 Nov 30.

Affiliations

Comparative Study

A general approach to testing for pleiotropy with rare and common variants

Sharon M Lutz et al. Genet Epidemiol. 2017 Feb.

Abstract

Through genome-wide association studies, numerous genes have been shown to be associated with multiple phenotypes. To determine the overlap of genetic susceptibility of correlated phenotypes, one can apply multivariate regression or dimension reduction techniques, such as principal components analysis, and test for the association with the principal components of the phenotypes rather than the individual phenotypes. However, as these approaches test whether there is a genetic effect for at least one of the phenotypes, a significant test result does not necessarily imply pleiotropy. Recently, a method called Pleiotropy Estimation and Test Bootstrap (PET-B) has been proposed to specifically test for pleiotropy (i.e., that two normally distributed phenotypes are both associated with the single nucleotide polymorphism of interest). Although the method examines the genetic overlap between the two quantitative phenotypes, the extension to binary phenotypes, three or more phenotypes, and rare variants is not straightforward. We provide two approaches to formally test this pleiotropic relationship in multiple scenarios. These approaches depend on permuting the phenotypes of interest and comparing the set of observed P-values to the set of permuted P-values in relation to the origin (e.g., a vector of zeros) either using the Hausdorff metric or a cutoff-based approach. These approaches are appropriate for categorical and quantitative phenotypes, more than two phenotypes, common variants and rare variants. We evaluate these approaches under various simulation scenarios and apply them to the COPDGene study, a case-control study of chronic obstructive pulmonary disease in current and former smokers.

Keywords: GWAS; pleiotropy; qualitative phenotypes; quantitative phenotypes; rare variant analysis.

© 2016 WILEY PERIODICALS, INC.

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Conflict of interest statement

Conflict of Interest Disclosure

The authors have no conflict of interest to declare.

Figures

Figure 1

Figure 1

For 2 normally distributed phenotypes, _β_2 = 0.2 and _β_1 varies from 0 to 0.5. For _β_1 = 0 (e.g. the null hypotheses of no pleiotropic effect), the PET-B, ad-hoc method (e.g. checking if each phenotype has a p-value less than 0.05 for the association with the SNP), and the 2 proposed approaches all maintain the type 1 error rate. The CCA and CMA approaches do not maintain the type 1 error rate since they are testing that at least one phenotype is associated with the SNP, which is true since _β_2 = 0.2. For _β_1 > 0, all 4 methods (PET-B, the ad-hoc method and the 2 proposed methods) have similar power.

Figure 2

Figure 2

For 5 normally distributed phenotypes, we evaluated the type-1 error rate of the proposed approaches when one phenotype is not associated with the SNP, but the other 4 phenotypes are strongly associated with the SNP. We fixed _β_1 = 0 and _β_2 = _β_3 = _β_4 = _β_5 vary from 0 to 1 by 0.1. Note that the ad hoc approach and the cut-off based permutation approaches both maintain the type 1 error rate, but the Hausdorff based approach has a slightly inflated type 1 error rate of 0.06 to 0.07.

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