The genome-wide association study (GWAS) has been widely used as an experimental design to detect associations between genetic variants and a phenotype. Two major confounding factors, population stratification and relatedness, could potentially lead to inflated GWAS test statistics and hence to spurious associations. Mixed linear model (MLM)-based approaches can be used to account for sample structure. However, genome-wide association (GWA) analyses in biobank samples such as the UK Biobank (UKB) often exceed the capability of most existing MLM-based tools especially if the number of traits is large. Here, we develop an MLM-based tool (fastGWA) that controls for population stratification by principal components and for relatedness by a sparse genetic relationship matrix for GWA analyses of biobank-scale data. We demonstrate by extensive simulations that fastGWA is reliable, robust and highly resource-efficient. We then apply fastGWA to 2,173 traits on array-genotyped and imputed samples from 456,422 individuals and to 2,048 traits on whole-exome-sequenced samples from 46,191 individuals in the UKB.

中文翻译：

---

一种用于大规模数据混合模型关联分析的资源高效工具。

全基因组关联研究 (GWAS) 已被广泛用作检测遗传变异与表型之间关联的实验设计。人口分层和相关性这两个主要混杂因素可能会导致 GWAS 测试统计数据膨胀，从而导致虚假关联。基于混合线性模型 (MLM) 的方法可用于解释样本结构。然而，英国生物样本库 (UKB) 等生物样本库样本中的全基因组关联 (GWA) 分析通常超出了大多数现有的基于 MLM 的工具的能力，尤其是在性状数量很大的情况下。在这里，我们开发了一种基于 MLM 的工具 (fastGWA)，该工具通过主要成分控制种群分层，并通过稀疏遗传关系矩阵控制相关性，用于生物样本库规模数据的 GWA 分析。我们通过广泛的模拟证明 fastGWA 可靠、健壮且资源效率高。然后，我们将 fastGWA 应用于来自 456,422 个个体的芯片基因分型和估算样本的 2,173 个特征，以及来自 UKB 中 46,191 个个体的全外显子组测序样本的 2,048 个特征。