We used a second method to measure chip heritability of AUDIT that is implemented by Linkage Disequilibrium Score Regression Coefficient (LDSC; Bulik-Sullivan et al. 2015b). To standardize the input file (GWAS summary statistics), we followed quality controls as implemented by the LDSC python software package. We used pre-calculated LD scores (“eur_w_ld_chr/” files (Finucane et al. 2015); MHC region excluded) for each SNP using individuals of European ancestry from the 1000 Genomes project, suitable for LD score analysis in European populations. We restricted the analysis to well-imputed SNPs: the SNPs were filtered to HapMap3 SNPs (International HapMap 3 Consortium et al. 2010), and were required to have a minor allele frequency (MAF) above 1%. InDels, structural variants, strand-ambiguous SNPs, and SNPs with extremely large effect sizes (χ2 > 80) were removed. In addition, this approach allowed us to distinguish between genomic inflation attributed to polygenic signal, from confounding biases such as population stratification or polygenicity (LD Score regression intercept > 1; Bulik-Sullivan et al. 2015b; Bulik-Sullivan et al. 2015a). As expected under polygenicity, we observed inflation of the median test statistic
