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Genetics. 2015 Aug;200(4):1089-104. doi: 10.1534/genetics.115.178343. Epub 2015 Jun 9.

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

Author information

1
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.
2
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.
3
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109.
4
Division of Pulmonary Medicine, Allergy, and Immunology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15224.
5
Department of Genetics, Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599.
6
Regeneron Pharmaceuticals, Basking Ridge, New Jersey 07920.
7
Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94185.
8
Human Genetics Center, University of Texas, Houston, Texas 77225.

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.

KEYWORDS:

association mapping; common variants; complex traits; functional data analysis; meta-analysis; quantitative trait loci; rare variants

PMID:
26058849
PMCID:
PMC4574252
DOI:
10.1534/genetics.115.178343
[Indexed for MEDLINE]
Free PMC Article

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