Chunk #52 — 6.0 How Do Electrophysiological Endophenotypes Compare with Other Quantitative Traits? — 6.1 Are endophenotype effect sizes larger than those of other phenotypes?
report variance accounted for but did report standardized effects, we computed an approximation using the formula: r2 = 2β2(1 – MAF)MAF, where β is a standardized effect size estimate of the variant when the residual variance is ~1, and MAF is the minor allele frequency for the variant. Unstandardized effects were converted to r2 by converting the p-value to its implied t-distribution value, and then converting that with the formula r2 = t2/(t2 + df), where df was set equal to the sample size reported for each such genetic variant. While an exhaustive study of quantitative trait genetic architecture is infeasible here, we attempted to select broadly from the domain of quantitative phenotypes, ranging from heritable medical biomarkers (metabolites, cholesterol levels, bone mineral density), to endophenotypes (antisaccade eye movements, resting heart rate, intracranial volume, and hippocampal volume), to physical phenotypes (height, BMI), to psychological phenotypes (personality, education). Most of these studies have large enough samples (median sample size N~78,000; see Figure 1) to have detected the largest genetic effects, and are thus likely to provide only slightly overestimated effect sizes of these variants. The lone exception is the single antisaccade hit from our work, which we expect to be significantly
