title: Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.
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.
authors.affiliation: Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892 fanr@mail.nih.gov.
authors.forename: Ruzong
authors.initials: R
authors.lastname: Fan
authors.affiliation: Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Yifan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109.
authors.forename: Michael
authors.initials: M
authors.lastname: Boehnke
authors.affiliation: Division of Pulmonary Medicine, Allergy, and Immunology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15224.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: Department of Genetics, Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599.
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
authors.affiliation: Regeneron Pharmaceuticals, Basking Ridge, New Jersey 07920.
authors.forename: Haobo
authors.initials: H
authors.lastname: Ren
authors.affiliation: Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94185.
authors.forename: Iryna
authors.initials: I
authors.lastname: Lobach
authors.affiliation: Human Genetics Center, University of Texas, Houston, Texas 77225.
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
mesh_terms: D000704:Analysis of Variance; D015331:Cohort Studies; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D050356:Lipid Metabolism / Q000235:genetics; D008297:Male; D015201:Meta-Analysis as Topic; D019655:Quantitative Trait, Heritable
keywords: association mapping; common variants; complex traits; functional data analysis; meta-analysis; quantitative trait loci; rare variants