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

Visscher, Peter M., Hemani, Gibran, Vinkhuyzen, Anna A. E., Chen, Guo-Bo, Lee, Sang Hong, Wray, Naomi R., Goddard, Michael E. and Yang, Jian (2014) Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples. PLoS Genetics, 10 4: e1004269.1-e1004269.10. doi:10.1371/journal.pgen.1004269


Author Visscher, Peter M.
Hemani, Gibran
Vinkhuyzen, Anna A. E.
Chen, Guo-Bo
Lee, Sang Hong
Wray, Naomi R.
Goddard, Michael E.
Yang, Jian
Title Statistical Power to Detect Genetic (Co)Variance of Complex Traits Using SNP Data in Unrelated Samples
Journal name PLoS Genetics   Check publisher's open access policy
ISSN 1553-7404
Publication date 2014-01-01
Year available 2014
Sub-type Article (original research)
DOI 10.1371/journal.pgen.1004269
Open Access Status DOI
Volume 10
Issue 4
Start page e1004269.1
End page e1004269.10
Total pages 10
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Language eng
Subject 1311 Genetics
1312 Molecular Biology
1105 Dentistry
1306 Cancer Research
2716 Genetics (clinical)
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 variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.
Keyword Genetics & Heredity
Genetics & Heredity
GENETICS & HEREDITY
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID FT0991360
APP1011506
GM099568
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2015 Collection
UQ Diamantina Institute Publications
 
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