Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models. Issue 4 (9th June 2015)

• Record Type: Journal Article
• Authors: Fan, Ruzong; Wang, Yifan; Boehnke, Michael; Chen, Wei; Li, Yun; Ren, Haobo; Lobach, Iryna; Xiong, Momiao
• Is Part Of: Genetics. Volume 200:Issue 4(2015)
• Page Start: 1089
• Page End: 1104
• Publication Date: 2015-06-09
• Subjects: meta-analysis -- rare variants -- common variants -- association mapping -- quantitative trait loci -- complex traits -- functional data analysis; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.115.178343
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F -distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F -distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P -values of the proposed LRT and F -distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies.