Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

被引：0
作者：
Chi-yang Chiu
Jeesun Jung
Wei Chen
Daniel E Weeks
Haobo Ren
Michael Boehnke
Christopher I Amos
Aiyi Liu
James L Mills
Mei-ling Ting Lee
Momiao Xiong
Ruzong Fan
机构：
[1] Biostatistics and Bioinformatics Branch,Division of Intramural Population Health Research
[2] Eunice Kennedy Shriver National Institute of Child Health and Human Development,Division of Pulmonary Medicine
[3] National Institutes of Health,Department of Human Genetics and Biostatistics
[4] Laboratory of Epidemiology and Biometry,Department of Biostatistics
[5] National Institute on Alcohol Abuse and Alcoholism,Department of Biomedical Data Science
[6] National Institutes of Health,Division of Intramural Population Health Research
[7] Allergy and Immunology,Department of Epidemiology and Biostatistics
[8] The University of Pittsburgh Medical Center,undefined
[9] University of Pittsburgh,undefined
[10] Data Paradise Inc,undefined
[11] The University of Michigan,undefined
[12] Geisel School of Medicine at Dartmouth,undefined
[13] Epidemiology Branch,undefined
[14] Eunice Kennedy Shriver National Institute of Child Health and Human Development,undefined
[15] National Institutes of Health,undefined
[16] University of Maryland College Park,undefined
[17] Human Genetics Center,undefined
[18] University of Texas–Houston,undefined
[19] 11Current address: Department of Biostatistics,undefined
[20] Bioinformatics,undefined
[21] and Biomathematics,undefined
[22] 4000 Reservoir Road NW,undefined
[23] Building D-180,undefined
[24] Georgetown University Medical Center,undefined
[25] Washington,undefined
[26] DC 20057,undefined
[27] USA.,undefined
来源：
European Journal of Human Genetics | 2017年 / 25卷
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摘要：
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.
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页码：350 / 359
页数：9
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