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Genetic Association in Multivariate Phenotypic Data: Power in Five Models

Published online by Cambridge University Press:  21 February 2012

Camelia C. Minica ,
Dorret I. Boomsma ,
Sophie van der Sluis  and
Conor V. Dolan
Camelia C. Minica
Affiliation:
Department of Psychology, FMG, University of Amsterdam, The Netherlands.
Dorret I. Boomsma
Affiliation:
Biological Psychology, VU University, The Netherlands.
Sophie van der Sluis
Affiliation:
Functional Genomics Section, Department of Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University and VU University Medical Center, The Netherlands.
Conor V. Dolan*
Affiliation:
Department of Psychology, FMG, University of Amsterdam, The Netherlands. c.v.dolan@uva.nl
*
*Address for correspondence: Conor V. Dolan, Department of Psychology, FMG, University of Amsterdam, Roetersstraat 15, 1018 WB, Amsterdam, The Netherlands.

Abstract

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This article concerns the power of various data analytic strategies to detect the effect of a single genetic variant (GV) in multivariate data. We simulated exactly fitting monozygotic and dizygotic phenotypic data according to single and two common factor models, and simplex models. We calculated the power to detect the GV in twin 1 data in an ANOVA of phenotypic sum scores, in a MANOVA, and in exploratory factor analysis (EFA), in which the common factors are regressed on the genetic variant. We also report power in the full twin model, and power of the single phenotype ANOVA. The results indicate that (1) if the GV affects all phenotypes, the sum score ANOVA and the EFA are most powerful, while the MANOVA is less powerful. Increasing phenotypic correlations further decreases the power of the MANOVA; and (2) if the GV affects only a subset of the phenotypes, the EFA or the MANOVA are most powerful, while sum score ANOVA is less powerful. In this case, an increase in phenotypic correlations may enhance the power of MANOVA and EFA. If the effect of the GV is modeled directly on the phenotypes in the EFA, the power of the EFA is approximately equal to the power of the MANOVA.

Keywords

powerassociationmultivariateunivariateexact data simulationgenetic covariance structures
Type
Articles
Information
Twin Research and Human Genetics , Volume 13 , Issue 6 , 01 December 2010 , pp. 525 - 543
Copyright
Copyright © Cambridge University Press 2010