SKAT is a kernel-based method(39–41) that allows for variants within a gene to have different directions of effect. We used the linear kernel function reported in Wu et al.(39), which sums the weighted genetic similarity across markers within a region (here, a gene). The square root of the weight followed a Beta(MAF; a1, a2) distribution with parameters a1=1 and a2=25, which weights rarer variants (e.g., MAF< 1%) more heavily than more common variants (1% < MAF < 5%). The method tests the null hypothesis that the distribution of the SNP effects within a gene has zero mean and covariance τK, where K is the kernel value and τ is the variance component attributable to the SNPs. The value of τ thus depends only on the magnitude of the SNP effects, and not their direction of effect.
