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

Chi-Yang Chiu, Jeesun Jung, Yifan Wang, Daniel E. Weeks, Alexander F. Wilson, Joan E. Bailey-Wilson, Christopher I. Amos, James L. Mills, Michael Boehnke, Momiao Xiong, Ruzong Fan

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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.

Original languageEnglish (US)
Pages (from-to)18-34
Number of pages17
JournalGenetic Epidemiology
Volume41
Issue number1
DOIs
StatePublished - Jan 1 2017

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Linear Models
Analysis of Variance
Genes
Multivariate Analysis
Phenotype
Genotype
Students

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Genetics(clinical)

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A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing. / Chiu, Chi-Yang; Jung, Jeesun; Wang, Yifan; Weeks, Daniel E.; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Amos, Christopher I.; Mills, James L.; Boehnke, Michael; Xiong, Momiao; Fan, Ruzong.

In: Genetic Epidemiology, Vol. 41, No. 1, 01.01.2017, p. 18-34.

Research output: Contribution to journalArticle

Chiu, C-Y, Jung, J, Wang, Y, Weeks, DE, Wilson, AF, Bailey-Wilson, JE, Amos, CI, Mills, JL, Boehnke, M, Xiong, M & Fan, R 2017, 'A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing', Genetic Epidemiology, vol. 41, no. 1, pp. 18-34. https://doi.org/10.1002/gepi.22014
Chiu, Chi-Yang ; Jung, Jeesun ; Wang, Yifan ; Weeks, Daniel E. ; Wilson, Alexander F. ; Bailey-Wilson, Joan E. ; Amos, Christopher I. ; Mills, James L. ; Boehnke, Michael ; Xiong, Momiao ; Fan, Ruzong. / A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing. In: Genetic Epidemiology. 2017 ; Vol. 41, No. 1. pp. 18-34.
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