Testing an optimally weighted combination of common and/or rare variants with multiple traits.

Zhenchuan Wang,Shurong Fang,Kui Zhang,Shuanglin Zhang,Qiuying Sha,Qizhai Li

doi:10.1371/journal.pone.0201186

Zhenchuan Wang, Shurong Fang + Show 4 more

Open Access

https://doi.org/10.1371/journal.pone.0201186

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Abstract

Recently, joint analysis of multiple traits has become popular because it can increase statistical power to identify genetic variants associated with complex diseases. In addition, there is increasing evidence indicating that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single genetic variant. However, these methods by analyzing one variant at a time may not be ideal for rare variant association studies because of the allelic heterogeneity as well as the extreme rarity of rare variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.

Highlights

The motivations to develop this method are based on the following: (1) for complex diseases, multiple correlated traits are usually measured in genetic association studies; (2) there is increasing evidence demonstrating that pleiotropy is a widespread phenomenon in complex diseases [5]; and (3) there is a shortage of gene-based approaches for multiple traits
We used extensive simulation studies to compare the performance of TOWmuT with Multivariate Analysis of Variance (MANOVA), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), gene association with multiple traits (GAMuT) and Single-TOW
The results from real data analysis showed that the proposed method has great potential in gene-based association study for complex diseases with multiple phenotypes such as Chronic obstructive pulmonary disease (COPD)

Summary

Introduction

Several statistical methods have been developed to test the association between multiple traits and a single common variant. Statistical methods for rare variant association studies with a single trait have been developed.

Results

Conclusion