Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits
Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits
复制标题
DOI：
10.1038/s41588-018-0108-x
复制
发表时间：
2018-05-01
期刊：
影响因子：
30.8
通讯作者：
Keller, Matthew C.
中科院分区：
文献类型：
--
作者：
Evans, Luke M.;Tahmasbi, Rasool;Keller, Matthew C.
已经开发了多种方法来估计狭义遗传力h（2），使用无关个体的单核苷酸多态性（SNP）。然而，尚未对这些方法进行全面评价，导致文献中的混乱和差异。我们提出了迄今为止这些方法的最彻底和最现实的比较。我们使用数千个真实的全基因组序列来模拟不同遗传结构和混杂变量下的表型，并且我们使用阵列、估算或全基因组序列SNP来获得“SNP-遗传性”估计。我们表明，SNP遗传力可以高度敏感的频率，效应大小和水平的潜在的因果变异的连锁不平衡的假设，但根据次要等位基因频率和连锁不平衡的方法，bin SNP在广泛的遗传结构和可能的混杂因素，这些假设不太敏感。这些调查结果为最佳做法和正确解释公布的估计数提供了指导。
Multiple methods have been developed to estimate narrow-sense heritability, h(2), using single nucleotide polymorphisms (SNPs) in unrelated individuals. However, a comprehensive evaluation of these methods has not yet been performed, leading to confusion and discrepancy in the literature. We present the most thorough and realistic comparison of these methods to date. We used thousands of real whole-genome sequences to simulate phenotypes under varying genetic architectures and confounding variables, and we used array, imputed, or whole genome sequence SNPs to obtain 'SNP- heritability' estimates. We show that SNP-heritability can be highly sensitive to assumptions about the frequencies, effect sizes, and levels of linkage disequilibrium of underlying causal variants, but that methods that bin SNPs according to minor allele frequency and linkage disequilibrium are less sensitive to these assumptions across a wide range of genetic architectures and possible confounding factors. These findings provide guidance for best practices and proper interpretation of published estimates.