GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits

BackgroundIn designing genome-wide association (GWA) studies it is important to calculate statistical power. General statistical power calculation procedures for quantitative measures often require information concerning summary statistics of distributions such as mean and variance. However, with genetic studies, the effect size of quantitative traits is traditionally expressed as heritability, a quantity defined as the amount of phenotypic variation in the population that can be ascribed to the genetic variants among individuals. Heritability is hard to transform into summary statistics. Therefore, general power calculation procedures cannot be used directly in GWA studies. The development of appropriate statistical methods and a user-friendly software package to address this problem would be welcomed.ResultsThis paper presents GWAPower, a statistical software package of power calculation designed for GWA studies with quantitative traits, where genetic effect is defined as heritability. Based on several popular one-degree-of-freedom genetic models, this method avoids the need to specify the non-centrality parameter of the F-distribution under the alternative hypothesis. Therefore, it can use heritability information directly without approximation. In GWAPower, the power calculation can be easily adjusted for adding covariates and linkage disequilibrium information. An example is provided to illustrate GWAPower, followed by discussions.ConclusionsGWAPower is a user-friendly free software package for calculating statistical power based on heritability in GWA studies with quantitative traits. The software is freely available at: http://dl.dropbox.com/u/10502931/GWAPower.zip

[1]  Philip S Rosenberg,et al.  PGA: power calculator for case-control genetic association analyses , 2008, BMC Genetics.

[2]  J. Pritchard,et al.  Linkage disequilibrium in humans: models and data. , 2001, American journal of human genetics.

[3]  Chad Haynes,et al.  PAWE-3D: visualizing power for association with error in case-control genetic studies of complex traits , 2005, Bioinform..

[4]  Mitchell R. Lucas,et al.  ParentChecker: a computer program for automated inference of missing parental genotype calls and linkage phase correction , 2012, BMC Genetics.

[5]  Jun Shao,et al.  Mathematical Statistics: Exercises and Solutions , 2005 .

[6]  G. Abecasis,et al.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies , 2006, Nature Genetics.

[7]  N Risch,et al.  The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling. , 1998, Genome research.

[8]  Jacques Fellay,et al.  A Whole-Genome Association Study of Major Determinants for Host Control of HIV-1 , 2007, Science.

[9]  Pak Chung Sham,et al.  Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits , 2003, Bioinform..

[10]  Chad Haynes,et al.  Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies , 2005, BMC Genetics.

[11]  David M. Evans,et al.  Genome-wide association analysis identifies 20 loci that influence adult height , 2008, Nature Genetics.

[12]  P. Sham,et al.  Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data. , 2000, American journal of human genetics.

[13]  Elizabeth T. Cirulli,et al.  Common Genetic Variation and the Control of HIV-1 in Humans , 2009, PLoS genetics.