澳大利亚昆士兰大学Jian Yang团队开发出一种资源高效的大型数据混合模型关联分析工具。11月25日，《自然—遗传学》在线发表了该研究成果。

研究人员开发了一种基于混合线性模型（MLM）的工具（命名为fastGWA），该工具可通过主成分控制人口分层，并通过稀疏的遗传关系矩阵来控制生物库规模数据的全基因组关联（GWA）分析的相关性。

通过广泛的模型，研究人员证明fastGWA是可靠、强大且资源高效的工具。随后，研究人员在UK Biobank（UKB）中对来自456422个个体的阵列基因分型，和估算样本中的2173个性状，以及来自46191个个体的全基因组测序样本中的2048个性状应用了fastGWA。

据悉，全基因组关联研究（GWAS）已被广泛用于检测遗传变异与表型之间关联的实验设计中。人口分层和相关性是两个主要的混杂因素，有可能导致GWAS测试统计数据膨胀，从而导致虚假关联。基于MLM的方法可用于解释样本结构。但是，在诸如UKB之类的生物库样本中进行的GWA分析通常超过了大多数现有的基于MLM工具的能力，尤其是在特征数量众多的情况下。

附：英文原文

Title: A resource-efficient tool for mixed model association analysis of large-scale data

Author: Longda Jiang, Zhili Zheng, Ting Qi, Kathryn E. Kemper, Naomi R. Wray, Peter M. Visscher, Jian Yang

Issue&Volume: 2019-11-25

Abstract: 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.

DOI: 10.1038/s41588-019-0530-8

Source: https://www.nature.com/articles/s41588-019-0530-8