Association signals caused by the vast polygenicity underlying complex traits can be hard to distinguish from confounders related to sample relatedness and population stratification. To effectively disentangle this issue, we used the software LD Score v1.0 to analyze the summary statistics of our association analyses and estimate the contribution of confounding biases to our results by LDSR61. An LD reference was generated from 1KGPp3 after restricting this dataset to strictly unrelated individuals and retaining only markers with MAF >0.01. To improve accuracy, the summary statistics used as input were refined by discarding all indels and restricting SNPs to those with INFO >0.9 and MAF >0.01, a total of 5.16 million SNPs. The resulting LD score intercept for the CLOZUK GWAS was 1.085 ± 0.010, which compared to a mean χ2 of 1.417 indicates a polygenic contribution of at least 80%. For the CLOZUK + PGC meta-analysis, the LD score intercept was 1.075 ± 0.014 (mean χ2 = 1.960), which supports more than 90% of the signal being driven by polygenic architecture. Both of these figures are in line with those
