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

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)350-359
Number of pages10
JournalEuropean Journal of Human Genetics
Volume25
Issue number3
DOIs
StatePublished - Feb 1 2017

Fingerprint

Meta-Analysis
Linear Models
Multivariate Analysis
Genotype
Lipids

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

Cite this

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models. / Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E.; Ren, Haobo; Boehnke, Michael; Amos, Christopher I.; Liu, Aiyi; Mills, James L.; Ting Lee, Mei Ling; Xiong, Momiao; Fan, Ruzong.

In: European Journal of Human Genetics, Vol. 25, No. 3, 01.02.2017, p. 350-359.

Research output: Contribution to journalArticle

Chiu, C-Y, Jung, J, Chen, W, Weeks, DE, Ren, H, Boehnke, M, Amos, CI, Liu, A, Mills, JL, Ting Lee, ML, Xiong, M & Fan, R 2017, 'Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models', European Journal of Human Genetics, vol. 25, no. 3, pp. 350-359. https://doi.org/10.1038/ejhg.2016.170
Chiu, Chi-Yang ; Jung, Jeesun ; Chen, Wei ; Weeks, Daniel E. ; Ren, Haobo ; Boehnke, Michael ; Amos, Christopher I. ; Liu, Aiyi ; Mills, James L. ; Ting Lee, Mei Ling ; Xiong, Momiao ; Fan, Ruzong. / Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models. In: European Journal of Human Genetics. 2017 ; Vol. 25, No. 3. pp. 350-359.
@article{1a5932e8a4ab4088807853bd30f0413a,
title = "Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models",
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.",
author = "Chi-Yang Chiu and Jeesun Jung and Wei Chen and Weeks, {Daniel E.} and Haobo Ren and Michael Boehnke and Amos, {Christopher I.} and Aiyi Liu and Mills, {James L.} and {Ting Lee}, {Mei Ling} and Momiao Xiong and Ruzong Fan",
year = "2017",
month = "2",
day = "1",
doi = "10.1038/ejhg.2016.170",
language = "English (US)",
volume = "25",
pages = "350--359",
journal = "European Journal of Human Genetics",
issn = "1018-4813",
publisher = "Nature Publishing Group",
number = "3",

}

TY - JOUR

T1 - Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

AU - Chiu, Chi-Yang

AU - Jung, Jeesun

AU - Chen, Wei

AU - Weeks, Daniel E.

AU - Ren, Haobo

AU - Boehnke, Michael

AU - Amos, Christopher I.

AU - Liu, Aiyi

AU - Mills, James L.

AU - Ting Lee, Mei Ling

AU - Xiong, Momiao

AU - Fan, Ruzong

PY - 2017/2/1

Y1 - 2017/2/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85007179032&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85007179032&partnerID=8YFLogxK

U2 - 10.1038/ejhg.2016.170

DO - 10.1038/ejhg.2016.170

M3 - Article

VL - 25

SP - 350

EP - 359

JO - European Journal of Human Genetics

JF - European Journal of Human Genetics

SN - 1018-4813

IS - 3

ER -