Journal article

Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples

Peter M Visscher, Gibran Hemani, Anna AE Vinkhuyzen, Guo-Bo Chen, Sang Hong Lee, Naomi R Wray, Michael E Goddard, Jian Yang

PLOS GENETICS | PUBLIC LIBRARY SCIENCE | Published : 2014

Abstract

We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling va..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council


Awarded by Australian National Health and Medical Research Council


Awarded by National Institutes of Health


Funding Acknowledgements

This research was supported by the Australian Research Council (FT0991360, DP1093502 and DP130102666), the Australian National Health and Medical Research Council (APP1011506, APP1047956, APP1048853 and APP1052684), and the National Institutes of Health (GM099568, GM075091 and MH100141). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.