Speed Doug, Cai Na, Johnson Michael R, Nejentsev Sergey, Balding David J
UCL Genetics Institute, University College London, London, UK.
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
Nat Genet. 2017 Jul;49(7):986-992. doi: 10.1038/ng.3865. Epub 2017 May 22.
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%.
单核苷酸多态性(SNP)遗传力,即由SNP解释的表型变异比例,已针对数百种性状进行了报道。其估计需要对全基因组遗传力的分布做出强有力的先验假设,但目前的假设尚未得到充分检验。通过分析大量人类性状的估算数据,我们凭经验得出了一个模型,该模型能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,我们改进后的模型得出的常见SNP遗传力估计值平均比广泛使用的软件GCTA得出的估计值高43%(标准差3%),比最近提出的扩展版GCTA-LDMS得出的估计值高25%(标准差2%)。此前,据报道DNA酶I超敏位点可解释79%的SNP遗传力;使用我们改进后的遗传力模型,其估计贡献仅为24%。