  Genome-wide association studies (GWAS) are commonly employed to study the
genetic basis of complex traits and diseases, and a key question is how much
heritability could be explained by all variants in GWAS. One widely used
approach that relies on summary statistics only is LD score regression (LDSC),
however the approach requires certain assumptions on the SNP effects (all SNPs
contribute to heritability and each SNP contributes equal variance). More
flexible modeling methods may be useful. We previously developed an approach
recovering the true z-statistics from a set of observed z-statistics with an
empirical Bayes approach, using only summary statistics. However, methods for
standard error (SE) estimation are not available yet, limiting the
interpretation of results and applicability of the approach. In this study we
developed several resampling-based approaches to estimate the SE of SNP-based
heritability, including two jackknife and three parametric bootstrap methods.
Simulations showed that delete-d-jackknife and parametric bootstrap approaches
provide good estimates of the SE. Particularly, the parametric bootstrap
approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. In
addition, we applied our method to estimate SNP-based heritability of 12
immune-related traits (levels of cytokines and growth factors) to shed light on
their genetic architecture. We also implemented the methods to compute the sum
of heritability explained and the corresponding SE in an R package SumVg,
available at https://github.com/lab-hcso/Estimating-SE-of-total-heritability/ .
In conclusion, SumVg may provide a useful alternative tool for SNP heritability
and SE estimates, which does not rely on distributional assumptions of SNP
effects.
