Xu Ting, Qi Guo-An, Zhu Jun, Xu Hai-Ming, Chen Guo-Bo
Department of Mathematics, Zhejiang University, Hangzhou, China.
Department of Agricultural and Biotechnology, Zhejiang University, Hangzhou, China.
Front Genet. 2021 Mar 5;12:612045. doi: 10.3389/fgene.2021.612045. eCollection 2021.
The estimation of heritability has been an important question in statistical genetics. Due to the clear mathematical properties, the modified Haseman-Elston regression has been found a bridge that connects and develops various parallel heritability estimation methods. With the increasing sample size, estimating heritability for biobank-scale data poses a challenge for statistical computation, in particular that the calculation of the genetic relationship matrix is a huge challenge in statistical computation. Using the Haseman-Elston framework, in this study we explicitly analyzed the mathematical structure of the key term ( ), the trace of high-order term of the genetic relationship matrix, a component involved in the estimation procedure. In this study, we proposed two estimators, which can estimate ( ) with greatly reduced sampling variance compared to the existing method under the same computational complexity. We applied this method to 81 traits in UK Biobank data and compared the chromosome-wise partition heritability with the whole-genome heritability, also as an approach for testing polygenicity.
遗传力估计一直是统计遗传学中的一个重要问题。由于具有清晰的数学性质，改进的哈斯曼 - 埃尔斯顿回归已成为连接和发展各种并行遗传力估计方法的桥梁。随着样本量的增加，对生物样本库规模的数据进行遗传力估计对统计计算提出了挑战，特别是遗传关系矩阵的计算在统计计算中是一个巨大的挑战。在本研究中，我们使用哈斯曼 - 埃尔斯顿框架，明确分析了关键项（ ）的数学结构，即遗传关系矩阵高阶项的迹，它是估计过程中涉及的一个组成部分。在本研究中，我们提出了两种估计器，在相同计算复杂度下，与现有方法相比，它们可以以大大降低的抽样方差来估计（ ）。我们将此方法应用于英国生物样本库数据中的81个性状，并将染色体分区遗传力与全基因组遗传力进行比较，这也是一种检验多基因性的方法。
