We used a false discovery rate (FDR) (Benjamini & Hochberg, 1995) approach to declare statistical significance. In comparison to controlling a family-wise error rate (e.g., Bonferroni correction), FDR a) provides a better balance between finding true effects versus controlling false discoveries, b) results in comparable standards for declaring significance across studies because it does not directly depend on the number of tests, c) is relatively robust against having correlated tests (Brown & Russell, 1997). FDR is commonly used in high-dimensional applications, including GWAS (Beecham et al., 2009; Dubois et al., 2010; McClay et al., 2011b). We set a FDR threshold of 0.1 for declaring genome-wide significance. This specifies that, on average, 10% of the SNPs declared significant are expected to be false discoveries. Additionally, we discuss suggestive associations at a FDR threshold of 0.2 to reduce the probability of Type II statistical errors, while explicitly noting reduced confidence in these associations. Operationally, FDR was controlled using q-values, which are FDRs calculated using the p-value of the markers as thresholds for declaring significance (Storey & Tibshirani, 2003).
