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Eur J Hum Genet. 2017 Feb;25(3):350-359. doi: 10.1038/ejhg.2016.170. Epub 2016 Dec 21.

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate 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, MD USA.
2
Laboratory of Epidemiology and Biometry, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
3
Division of Pulmonary Medicine, Allergy and Immunology, The University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
4
Department of Human Genetics and Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
5
Data Paradise Inc, Belle Mead, NJ, USA.
6
Department of Biostatistics, The University of Michigan, Ann Arbor, MI, USA.
7
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
8
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
9
Department of Epidemiology and Biostatistics, University of Maryland College Park, College Park, MD, USA.
10
Human Genetics Center, University of Texas-Houston, Houston, TX, USA.

Abstract

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

PMID:
28000696
PMCID:
PMC5315507
DOI:
10.1038/ejhg.2016.170
[Indexed for MEDLINE]
Free PMC Article

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