We estimate standard errors using a block jackknife over SNPs with 200 equally-sized blocks of adjacent SNPs [16]. This gives us an empirical covariance matrix of coefficient estimates. In the baseline analysis, to evaluate whether a category is enriched for heritability, we want to test whether h2(C)h2>∣C∣M. This is the same as testing whether the per-SNP heritability is greater in the category than out of the category; i.e., whether h2(C)∣C∣-h2-h2(C)M-∣C∣>0. Because our estimates of the regression coefficients are approximately normally distributed, and therefore h2(C)h2 is not normally distributed but h2(C)∣C∣-h2-h2(C)M-∣C∣ is, we use the latter expression to test for significance. Because this expression is linear in the coefficients, we can estimate its standard error using the covariance matrix for the coefficient estimates, and then we compute a z-score to test for significance. This procedure is well-calibrated; see Figure 1. We also report jackknife standard errors of the proportion of heritability even though this is not what we use to assess significance.
