We compared the field standard procedure of using vTotal versus our proposed approach of using ∑EffNk for 12 major, binary traits for which sufficient cohort-level information was available: We used the same population prevalences from the original GWAS publications from which the summary statistics were derived. We quantified bias here as the proportional difference across ∑EffNk and vTotal (i.e., hl2EstimateforvTotalhl2estimatefor∑EffNk−1). Consistent with simulation findings, real data results revealed that in all cases using vTotal produced a deflated estimate of liability-scale heritability relative to ∑EffNk. This bias ranged from as little as 1.3% for autism spectrum disorder to as much as 28.1% for alcohol use disorder and 31.8% for bipolar disorder (17) (Table 2). In all but one instance, the heritability estimates reported in the corresponding manuscripts most closely matched those produced from using vTotal (Table S2 in Supplement 2). The exception was the most recent release (Freeze 3) of the Psychiatric Genomics Consortium bipolar summary statistics (17), which reports a liability-scale heritability consistent with using ∑EffNk.
