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

被引：243

作者：

Visscher, Peter M. ^{[1
,2
]}

Hemani, Gibran ^{[1
,2
]}

Vinkhuyzen, Anna A. E. ^{[1
]}

Chen, Guo-Bo ^{[1
]}

Lee, Sang Hong ^{[1
]}

Wray, Naomi R. ^{[1
]}

Goddard, Michael E. ^{[3
,4
]}

Yang, Jian ^{[1
,2
]}

机构：

[1] Univ Queensland, Queensland Brain Inst, Brisbane, Qld, Australia

[2] Univ Queensland, Diamantina Inst, Translat Res Inst, Brisbane, Qld, Australia

[3] Univ Melbourne, Dept Food & Agr Syst, Parkville, Vic 3052, Australia

[4] Dept Primary Ind, Biosci Res Div, Bundoora, Vic, Australia

来源：

PLOS GENETICS | 2014年 / 10卷 / 04期

基金：

澳大利亚研究理事会; 英国医学研究理事会; 美国国家卫生研究院;

关键词：

COMPONENT LINKAGE ANALYSIS; GENOME-WIDE ASSOCIATION; COMMON SNPS; CORRELATION COEFFICIENT; MAXIMUM-LIKELIHOOD; LARGE PROPORTION; HUMAN HEIGHT; VARIANCE; HERITABILITY; DISEASES;

D O I：

10.1371/journal.pgen.1004269

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

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.

引用

页数：10

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