SNP heritability, the proportion of phenotypic variation explained by genotyped SNPs, is an important parameter in understanding the genetic architecture underlying various diseases and traits. Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale datasets and by the restrictions in access to individual-level data. These limitations have motivated the development of methods that only require summary statistics. While the availability of publicly accessible summary statistics makes them widely applicable, these methods lack the accuracy of methods that utilize individual genotypes. Here we present a SUMmary statistics-based Randomized Haseman-Elston regression (SUM-RHE), a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes, while exclusively relying on summary statistics. SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population, which can be efficiently estimated and readily shared for public use. Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared to other summary statistic methods and on par with methods that rely on individual-level data.

中文翻译：

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具有个体基因型水平精度的可扩展的基于汇总统计的遗传力估计方法


SNP 遗传力，即由基因型 SNP 解释的表型变异的比例，是了解各种疾病和性状背后的遗传结构的重要参数。旨在根据个体基因型和表型数据估计 SNP 遗传力的方法受到其扩展到生物银行规模数据集的能力以及获取个体水平数据的限制的限制。这些限制促使人们开发出只需要汇总统计的方法。虽然可公开获取的汇总统计数据使其广泛适用，但这些方法缺乏利用个体基因型的方法的准确性。在这里，我们提出了一种基于汇总统计的随机 Haseman-Elston 回归 (SUM-RHE)，该方法可以估计复杂表型的 SNP 遗传力，其准确性与需要个体基因型的方法相当，同时完全依赖于汇总统计。 SUM-RHE 采用全基因组关联研究 (GWAS) 汇总统计数据和参考人群获得的统计数据，可以有效地估计并轻松共享以供公众使用。我们的结果表明，SUM-RHE 获得的 SNP 遗传力估计值比其他汇总统计方法要准确得多，并且与依赖个体水平数据的方法相当。