Fig. 3: Naïve approaches to addressing AM induced bias and the impact of missing data.

a Simulations employing the same parameters described in Fig. 1 demonstrate that neither partitioned nor principal component adjusted approaches mitigate the impact of AM on HE (\({\hat{h}}_{{{{{{\rm{HE}}}}}}}^{2}\)) or REML (\({\hat{h}}_{{{{{{\rm{REML}}}}}}}^{2}\)) heritability estimates. Additionally, simulations confirm that LDSC is subject to equivalent biases. Single component: standard single genomic variance component models. Single comp. + 10 PCs: included the first ten within-sample PCs as covariates. Partitioned: included four annotation-based variance components generated by median splits of within-sample minor allele frequencies and LD scores. b Simulations demonstrate that conclusions regarding estimator bias do not change when some of the influence of causal variants is not captured by measured SNPs. Simulations employed the same parameters as above except that 0, 50, or 75% of randomly selected SNPs (both causal and non-causal) were dropped. As expected, estimates were attenuated when SNPs were dropped but overall patterns remained consistent.