SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but current assumptions have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency (MAF), linkage disequilibrium (LD) and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (s.d. 3%) higher than those obtained from the widely used software GCTA and 25% (s.d. 2%) higher than those from the recently proposed extension GCTA-LDMS. Previously, DNase I hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model, their estimated contribution is only 24%.

中文翻译：

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复杂人类特征中SNP遗传力的重新评估

已经报道了数百种性状的SNP遗传力，即由SNP解释的表型变异的比例。它的估计需要关于基因组中遗传力分布的强有力的先验假设，但目前的假设尚未经过充分检验。通过分析大量人类特征的估算数据，我们凭经验得出了一个模型，该模型可以更准确地描述遗传力如何随次要等位基因频率（MAF），连锁不平衡（LD）和基因型确定性而变化。在19个性状中，我们改进的模型可导致常见SNP遗传力的估计值比从广泛使用的软件GCTA获得的平均SNP遗传力平均高43％（标准差3％），比最近提出的扩展GCTA的平均SNP遗传力高25％（sd 2％） -LDMS。之前，据报道，DNase I超敏位点解释了79％的SNP遗传性；使用我们改进的遗传力模型，他们的贡献估计仅为24％。