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Genet Epidemiol. 2017 Jan;41(1):18-34. doi: 10.1002/gepi.22014. Epub 2016 Dec 5.

A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.

Author information

1
Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA.
2
Laboratory of Epidemiology and Biometry, National Institute on Alcohol, Abuse and Alcoholism, NIH, Bethesda, MD, USA.
3
Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
4
Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
5
Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA.
6
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
7
Epidemiology Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NIH), Bethesda, MD, USA.
8
Department of Biostatistics, School of Public Health, The University of Michigan, Ann Arbor, MI, USA.
9
Human Genetics Center, University of Texas-Houston, Houston, TX, USA.

Abstract

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models that perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.

KEYWORDS:

association mapping; common variants; complex traits; functional data analysis; multivariate analysis of variance (MANOVA); multivariate functional linear models (MFLM); quantitative trait loci; rare variants

PMID:
27917525
PMCID:
PMC5154843
DOI:
10.1002/gepi.22014
[Indexed for MEDLINE]
Free PMC Article

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