Extended Data Fig. 4: Comparison with GRE.

Hou et al.²¹ proposed GRE, a method for estimating SNP heritability without specifying a heritability model. GRE requires individual level data and that there are more individuals than the number of SNPs on the largest chromosome. Here we compare estimates from GRE to those from SumHer for the 14 UKBb GWAS. To run GRE, we follow the instructions at www.github.com/bogdanlab/h2-GRE; to satisfy the sample size requirement, we use only the 623k directly-genotyped SNPs (Hou et al. did likewise). For SumHer, we consider ten heritability models; to enable a fair comparison with GRE, we always restrict the reference panel to genotyped SNPs. The first three plots compare estimates of SNP heritability from GRE and SumHer. It is noticeable that when using only genotyped SNPs, changing the heritability model has a much smaller impact on estimates of SNP heritability than when using imputed SNPs (Supplementary Table 3); this reflects that with fewer SNPs, the impact of the prior assumptions is reduced. Nonetheless, if we consider GRE estimates to be the ‘gold standard’, then this analysis indicates that the LDAK-Thin, GCTA-LDMS-R, GCTA-LDMS-I, BLD-LDAK, BLD-LDAK + Alpha and Baseline LD Models produce more accurate estimates of SNP heritability than the GCTA, LDAK, LDAK + 24Fun and Baseline Models. In the fourth plot, the solid and dashed lines mark the point estimate and 95% confidence intervals for the gradient when regressing onto the Akaike Information Criterion (AIC) the absolute difference between estimates from SumHer and GRE (when performing this regression, we include an indicator for trait, to reflect that AIC will tend to be lower for more heritable traits). If we again consider GRE estimates to be the gold standard, then the fact that the gradient is significantly positive (P < 10⁻⁶) indicates that lower AIC implies more accurate estimates of SNP heritability.