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

PM Visscher; G Hemani; AAE Vinkhuyzen; GB Chen; SH Lee; NR Wray; ME Goddard; J Yang

Journal article

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

PM Visscher, G Hemani, AAE Vinkhuyzen, GB Chen, SH Lee, NR Wray, ME Goddard, J Yang

Plos Genetics | Published : 2014

DOI: 10.1371/journal.pgen.1004269

Download PDF

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..

View full abstract

University of Melbourne Researchers

Mike Goddard Author

Related Projects (2)

Why is most of the genetic variance for complex traits undetected by large powerful screens of common variants?

These variants have been associated with phenotypic outcomes in a range of species. Paradoxically, even very large studies have only account..

ESTIMATION OF NON-ADDITIVE GENETIC VARIANCE FOR COMPLEX TRAITS USING GENOME-WIDE SINGLE NUCLEOTIDE POLYMORPHYISMS AND SEQUENCE DATA

Finding genes for traits of importance in agriculture, ecology and human health depends on understanding the genetic basis of these traits. ..

Grants

Awarded by National Health and Medical Research Council

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.