Chunk #84 — 7.0 Recommendations to Advance Endophenotype Genetics — 7.3 Adequate power to detect individual effects is crucial but almost never attained in existing endophenotype genetic association studies — 7.3.1. Power and sampling schemes in GWAS
We suggest that, once an endophenotype has been chosen for GWAS, power to detect genetic variants can be increased in two primary ways, motivated by classic work in psychology (McClelland, 2000) and illustrated very simply by the well-known formula for calculating the standard error of the slope sβ^ for arbitrary predictor X (e.g., a genetic variant) in multiple linear regression: sβ^=MSEnVX(1−RX2), where MSE is the mean square error, n is the number of observations (individuals), VX is the variance of X, and (1−RX2) is the proportion of variation in X not shared with other variables in the model. Minimizing sβ^ results in a smaller standard error and tighter confidence intervals; in short, more power. Clearly, doubling sample size, for example, will reduce sβ^ and increase power, which illustrates the role of sample size in determining power, but doubling the other terms in the denominator (or halving MSE) will have exactly the same effect as doubling n. Thus, doubling the variance in X will have exactly the same influence on power as doubling the sample size. When X is the genetic
