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.
Highlights
Genome-wide association studies (GWAS) have been extremely successfully in identifying genetic variants associated with complex traits and diseases in humans [1]
We have g*N(0,s2GA) and e*N(0,s2e I), where s2G is the genetic variance captured by all single nucleotide polymorphism (SNP), A is the genetic relationship matrix (GRM) estimated from SNPs [3], s2e is the residuals variance and I is an identity matrix
We have derived the approximate sampling variance of the estimate of variance explained by all common SNPs (h2G) for a quantitative trait or case-control study of a disease, and genetic correlation between two quantitative traits, between two diseases, or between a quantitative trait and a disease, using genome-wide SNP data in unrelated individuals
Summary
Genome-wide association studies (GWAS) have been extremely successfully in identifying genetic variants associated with complex traits and diseases in humans [1]. If there are many genes each with a small effect affecting the trait, most of these genetic variants will fail to pass the stringent threshold and remain undetected. This is one of the explanations of the ‘missing heritability’ question, that genetic variants identified from GWAS so far explain a fraction of the heritability for complex traits [2]. The SNP-based approach allows the estimation of the genetic correlation between complex traits measured on different samples [6,7]_ENREF_8.
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