In GWAS studies, SNP heritability measures the proportion of phenotypic variance explained by all measured SNPs. Accurate estimation of SNP heritability can help us better understand the degree to which measured genetic variants influence phenotypes. Over the last decade, a variety of statistical methods and software tools have been developed for SNP heritability estimation with different data types including genotype array data, imputed genotype data, whole-genome sequencing data, RNA sequencing data, and bisulfite sequencing data. However, a thorough technical review of these methods, especially from a statistical and computational viewpoint, is currently missing. To fill this knowledge gap, we present a comprehensive review on a broad category of recently developed and commonly used SNP heritability estimation methods. We focus on their modeling assumptions; their interconnected relationships; their applicability to quantitative, binary and count phenotypes; their use of individual level data versus summary statistics, as well as their utility for SNP heritability partitioning. We hope that this review will serve as a useful reference for both methodologists who develop heritability estimation methods and practitioners who perform heritability analysis.

在GWAS研究中，SNP遗传力可衡量由所有测得的SNP解释的表型变异的比例。SNP遗传力的准确估计可以帮助我们更好地了解所测遗传变异影响表型的程度。在过去的十年中，已经开发出了用于统计SNP遗传力的各种统计方法和软件工具，这些数据具有不同的数据类型，包括基因型阵列数据，估算的基因型数据，全基因组测序数据，RNA测序数据和亚硫酸氢盐测序数据。但是，目前缺少对这些方法的全面技术审查，尤其是从统计和计算的角度来看。为了填补这一知识空白，我们对最近开发和常用的SNP遗传力估计方法的广泛类别进行了全面的回顾。我们关注他们的建模假设；他们相互联系的关系；它们对定量，二元和计数表型的适用性；它们对单个级别数据与摘要统计数据的使用，以及对SNP遗传性分区的实用性。我们希望这篇评论对开发遗传力估算方法的方法论者和进行遗传力分析的实践者都可以提供有用的参考。