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

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.

Common genetic variation and schizophrenia polygenic risk influence neurocognitive performance in young adulthood

“…Even though precise h 2 SNP estimates could not be obtained, presumably because of the small sample size and the increased standard errors [Visscher PM et al, 2014], we found that a significant proportion of the phenotypic variance for both verbal and spatial WM accuracy (VNB, SNB) can be attributed to common genetic variation ( P  = 0.0003 and P  = 0.0004 respectively), with at least 38% of variance explained by all genotyped SNPs. Lower h 2 SNP was observed for sustained attention/vigilance ( P  = 0.03).…”

Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for Thirteen Cancer Types

“…Although our study had low power to detect modest but possibly important correlations between specific pairs of cancers (28), the distribution of test statistics over all pairs of cancers deviated little from the null (Figure 1), suggesting that at most a small fraction have modest or high (eg, ρ > 0.3) correlations (Supplementary Tables 18-19, available online). Four pairs of sites that show notable correlation in our study are bladder and lung, testes and kidney, DLBCL and CLL, and DLBCL and osteosarcoma.…”

Does natural selection favour taller stature among the tallest people on earth?

“…While genotype information is available for a subsample of the LifeLines study, the selection of data for the current study means that the sample available for genetic analyses is rather small (approx. 2000 per sex), and would be severely underpowered for any statistical approach using non-family members, particularly for the bivariate model [19].…”