Division of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xin jian South Road, 030001, Shanxi, China.
Social Medicine, School of Public Health, Shanxi Medical University, No.56 Xin jian South Road, 030001, Shanxi, China.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac067.
Over the past decade, statistical methods have been developed to estimate single nucleotide polymorphism (SNP) heritability, which measures the proportion of phenotypic variance explained by all measured SNPs in the data. Estimates of SNP heritability measure the degree to which the available genetic variants influence phenotypes and improve our understanding of the genetic architecture of complex phenotypes. In this article, we review the recently developed and commonly used SNP heritability estimation methods for continuous and binary phenotypes from the perspective of model assumptions and parameter optimization. We primarily focus on their capacity to handle multiple phenotypes and longitudinal measurements, their ability for SNP heritability partition and their use of individual-level data versus summary statistics. State-of-the-art statistical methods that are scalable to the UK Biobank dataset are also elucidated in detail.
在过去的十年中,已经开发出了统计方法来估计单核苷酸多态性(SNP)遗传力,该方法用于衡量数据中所有测量的 SNP 解释表型方差的比例。SNP 遗传力的估计值衡量了可用遗传变异对表型的影响程度,并增进了我们对复杂表型遗传结构的理解。在本文中,我们从模型假设和参数优化的角度,综述了最近开发的用于连续和二项表型的常用 SNP 遗传力估计方法。我们主要关注它们处理多个表型和纵向测量的能力、SNP 遗传力划分的能力以及使用个体水平数据与汇总统计信息的能力。还详细阐述了可扩展到英国生物库数据集的最先进的统计方法。