Joint Analysis of Multiple Phenotypes in Association Studies based on Cross-Validation Prediction Error

Qiuying Sha,Shuanglin Zhang,Xinlan Yang

doi:10.1038/s41598-018-37538-y

Qiuying Sha, Shuanglin Zhang + Show 1 more

Open Access

https://doi.org/10.1038/s41598-018-37538-y

Copy DOI

Journal: Scientific Reports	Publication Date: Jan 31, 2019
Citations: 8	License type: open-access

Affiliation: Michigan Technological University

Abstract

In genome-wide association studies (GWAS), joint analysis of multiple phenotypes could have increased statistical power over analyzing each phenotype individually to identify genetic variants that are associated with complex diseases. With this motivation, several statistical methods that jointly analyze multiple phenotypes have been developed, such as O’Brien’s method, Trait-based Association Test that uses Extended Simes procedure (TATES), multivariate analysis of variance (MANOVA), and joint model of multiple phenotypes (MultiPhen). However, the performance of these methods under a wide range of scenarios is not consistent: one test may be powerful in some situations, but not in the others. Thus, one challenge in joint analysis of multiple phenotypes is to construct a test that could maintain good performance across different scenarios. In this article, we develop a novel statistical method to test associations between a genetic variant and Multiple Phenotypes based on cross-validation Prediction Error (MultP-PE). Extensive simulations are conducted to evaluate the type I error rates and to compare the power performance of MultP-PE with various existing methods. The simulation studies show that MultP-PE controls type I error rates very well and has consistently higher power than the tests we compared in all simulation scenarios. We conclude with the recommendation for the use of MultP-PE for its good performance in association studies with multiple phenotypes.

Highlights

Several methods to detect association using multiple phenotypes simultaneously have been introduced in recent years
We develop a novel statistical method to test the association between a genetic variant and Multiple Phenotypes based on cross-validation Prediction Error (MultP-PE)
A similar conclusion has been reached in some published papers[2,6,7]; (4) O’Brien method (OB) is comparable to MultiPhen, Optimal weight method (OW), and multivariate analysis of variance (MANOVA) in models 1 and 2, but has almost no power when the genetic effects have different directions; (5) that uses Extended Simes procedure (TATES) is more powerful than MultiPhen, OW, and MANOVA in model 2, but is less powerful than MultiPhen, OW, and MANOVA in models 3 and 4

Summary

Introduction

Several methods to detect association using multiple phenotypes simultaneously have been introduced in recent years. O’Brien method (OB) is proposed to combines test statistics obtained from association test for each individual phenotype[5]. Some other variable reduction methods have been proposed to test for the association between a genetic variant and the linear combination of multiple phenotypes rather than the original phenotypes[12,13,14]. There are many proposed methods for joint analysis of multiple phenotypes, the performance of these methods under a wide range of scenarios is not consistent[6]: one test may be powerful in some situations, but not in the others. We develop a novel statistical method to test the association between a genetic variant and Multiple Phenotypes based on cross-validation Prediction Error (MultP-PE). Our simulation studies show that MultP-PE controls the type I error rates very well and has consistently higher power than other methods we compared in all simulation scenarios

Methods

Results

Conclusion