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 Vol 10: Statistical Power to Detect Genetic CoVariance of Complex Traits Using SNP Data in Unrelated Samples.


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This article is from PLoS Genetics, volume 10.AbstractWe 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 covariation 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.



Autor: Visscher, Peter M.; Hemani, Gibran; Vinkhuyzen, Anna A. E.; Chen, Guo-Bo; Lee, Sang Hong; Wray, Naomi R.; Goddard, Michael E.; Yang, Jian

Fuente: https://archive.org/







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