利用遗传相似性表示进行遗传力估计
Heritability estimation using genetic similarity representation
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中文总结 AI 辅助
提出基于遗传相似性表示的SMILE方法，通过加权Gram矩阵建模，解决GWAS中遗传力估计对异质性效应和连锁不平衡不稳健的问题，并在模拟和UK Biobank数据中验证了其稳健性。
中文摘要 AI 辅助
我们引入了一种相似性表示框架，用于在全基因组关联研究（GWAS）中进行稳健的遗传力估计。该问题与具有大量预测变量的线性模型中的信噪比估计问题相似。传统的固定效应和随机效应遗传力估计方法通常对回归系数或设计（基因型）矩阵施加限制性假设。这些假设通常因遗传变异（回归系数）的异质性效应（这些效应取决于基因型分布）以及由于连锁不平衡导致的基因型间相关性而被违反。这导致在实践中遗传力估计的不稳健。为克服这些局限性，我们提出了一种相似性表示方法（SMILE），该方法通过Gram矩阵对结果相似性与遗传相似性之间的关系进行建模。SMILE使用基因型的加权Gram矩阵来表示遗传相似性，其中使用数据相关的权重矩阵来将异质性变异效应从基因型分布中分离出来。SMILE将经典随机效应模型作为特例包含在内，并通过不需要精确估计精度矩阵或回归系数来改进固定效应模型。我们开发了一种可扩展的实现，用于高效分析大型生物样本库GWAS数据。广泛的模拟实验和对英国生物样本库数据的分析表明，所提出的方法在一系列遗传架构下相对于现有方法具有稳健性，并表明SMILE为遗传力估计提供了一种通用方法。
英文摘要
We introduce a similarity representation framework for robust heritability estimation in Genome-Wide Association Studies (GWAS). This problem parallels the signal-to-noise ratio estimation problem in linear models with a large number of predictors. Traditional fixed- and random-effects methods for heritability estimation often impose restrictive assumptions on regression coefficients or the design (genotype) matrix. These assumptions are usually violated by the heterogeneous effects of genetic variants (regression coefficients) that depend on the genotype distribution and the correlation among genotypes due to linkage disequilibrium. This leads to the non-robust estimation of heritability in practice. To overcome these limitations, we propose a SiMILarity rEpresentation method (SMILE) which models the relationship between the outcome similarity and genetic similarity through Gram matrices. SMILE represents genetic similarity using a weighted Gram matrix of genotypes, where a data-dependent weight matrix is used to disentangle the heterogeneous variant effects from the genotype distribution. SMILE includes the classical random-effects model as a special case and improves the fixed-effects model by not requiring accurate estimation of the precision matrix or the regression coefficients. We develop a scalable implementation for efficient analysis of large biobank GWAS data. Extensive simulations and the analysis of the UK Biobank data demonstrate the robustness of the proposed method over the existing methods across a range of genetic architectures, and show that SMILE provides a versatile approach for heritability estimation.
