GATE: an efficient procedure in study of pleiotropic genetic associations

Wei Zhang,Larry L Tang,James L Mills,Liu Yang,Qizhai Li,Yuanchang Sun,Aiyi Liu

doi:10.1186/s12864-017-3928-7

Abstract

BackgroundThe association studies on human complex traits are admittedly propitious to identify deleterious genetic markers. Compared to single-trait analyses, multiple-trait analyses can arguably make better use of the information on both traits and markers, and thus improve statistical power of association tests prominently. Principal component analysis (PCA) is a well-known useful tool in multivariate analysis and can be applied to this task. Generally, PCA is first performed on all traits and then a certain number of top principal components (PCs) that explain most of the trait variations are selected to construct the test statistics. However, under some situations, only utilizing these top PCs would lead to a loss of important evidences from discarded PCs and thus makes the capability compromised.MethodsTo overcome this drawback while keeping the advantages of using the top PCs, we propose a group accumulated test evidence (GATE) procedure. By dividing the PCs which is sorted in the descending order according to the corresponding eigenvalues into a few groups, GATE integrates the information of traits at the group level.ResultsSimulation studies demonstrate the superiority of the proposed approach over several existing methods in terms of statistical power. Sometimes, the increase of power can reach 25%. These methods are further illustrated using the Heterogeneous Stock Mice data which is collected from a quantitative genome-wide association study.ConclusionsOverall, GATE provides a powerful test for pleiotropic genetic associations.

Highlights

The association studies on human complex traits are admittedly propitious to identify deleterious genetic markers
*Correspondence: liqz@amss.ac.cn †Equal contributors 1Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China Full list of author information is available at the end of the article rs964184, rs1367117, rs1042034, and rs174546 are concurrently associated with four complex traits including total cholesterol, high and low density lipoprotein, and triglycerides [1, 5], and the Single nucleotide polymorphism (SNP) rs2476601 has been reported to be associated with five traits including rheumatoid arthritis [6], Crohn’s disease [7], systemic lupus erythematosus [8], type I diabetes [9], and Graves’ disease [10]
We propose a procedure called group accumulated test evidence (GATE) to test for the asscoaition between multiple traits and a single marker

Summary

Introduction

The association studies on human complex traits are admittedly propitious to identify deleterious genetic markers. The joint analysis of the associations between multiple traits and a single marker is becoming popular nowadays, and many methods have been put forward in the literature [5, 11,12,13,14,15,16,17,18,19] Speaking, these methods can be classified into two categories: univariate analyses and multivariate analyses. The canonical correlation analysis (CCA) and principal component analysis (PCA) are two common dimension-reduction approaches. Both of them have been widely applied in pleiotropic genetic association studies [17, 24, 25]

Methods

Results

Discussion

Conclusion