Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models. Issue 2 (29th December 2015)

• Record Type: Journal Article
• Authors: Fan, Ruzong; Wang, Yifan; Chiu, Chi-yang; Chen, Wei; Ren, Haobo; Li, Yun; Boehnke, Michael; Amos, Christopher I; Moore, Jason H; Xiong, Momiao
• Is Part Of: Genetics. Volume 202:Issue 2(2016)
• Page Start: 457
• Page End: 470
• Publication Date: 2015-12-29
• Subjects: meta-analysis -- rare variants -- common variants -- association mapping -- complex traits -- functional data analysis; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.115.180869
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: We developed generalized functional linear models (GFLMs) to perform a meta-analysis of multiple case-control studies to evaluate the relationship of genetic data to dichotomous traits adjusting for covariates. Unlike the previously developed meta-analysis for sequence kernel association tests (MetaSKATs), which are based on mixed-effect models to make the contributions of major gene loci random, GFLMs are fixed models; i.e., genetic effects of multiple genetic variants are fixed. Based on GFLMs, we developed chi-squared-distributed Rao's efficient score test and likelihood-ratio test (LRT) statistics to test for an association between a complex dichotomous trait and multiple genetic variants. We then performed extensive simulations to evaluate the empirical type I error rates and power performance of the proposed tests. The Rao's efficient score test statistics of GFLMs are very conservative and have higher power than MetaSKATs when some causal variants are rare and some are common. When the causal variants are all rare [ i.e., minor allele frequencies (MAF) < 0.03], the Rao's efficient score test statistics have similar or slightly lower power than MetaSKATs. The LRT statistics generate accurate type I error rates for homogeneous genetic-effect models and may inflate type I error rates for heterogeneous genetic-effect models owing to the large numbers of degrees of freedom and have similar or slightly higher power than the Rao's efficient score test statistics. GFLMs were applied to analyze genetic data of 22 gene regions of type 2 diabetes data from a meta-analysis of eight European studies and detected significant association for 18 genes ( P < 3.10 × 10 −6 ), tentative association for 2 genes ( HHEX and HMGA2 ; P ≈ 10 −5 ), and no association for 2 genes, while MetaSKATs detected none. In addition, the traditional additive-effect model detects association at gene HHEX . GFLMs and related tests can analyze rare or common variants or a combination of the two and can be useful in whole-genome and whole-exome association studies.