Laboratory of Segregation and Recombination Analyses, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
Laboratory of Animal Genetics, Vavilov Institute of General Genetics, the Russian Academy of Sciences, Moscow, Russia.
PLoS Comput Biol. 2022 Jun 2;18(6):e1010172. doi: 10.1371/journal.pcbi.1010172. eCollection 2022 Jun.
Gene-based association analysis is an effective gene-mapping tool. Many gene-based methods have been proposed recently. However, their power depends on the underlying genetic architecture, which is rarely known in complex traits, and so it is likely that a combination of such methods could serve as a universal approach. Several frameworks combining different gene-based methods have been developed. However, they all imply a fixed set of methods, weights and functional annotations. Moreover, most of them use individual phenotypes and genotypes as input data. Here, we introduce sumSTAAR, a framework for gene-based association analysis using summary statistics obtained from genome-wide association studies (GWAS). It is an extended and modified version of STAAR framework proposed by Li and colleagues in 2020. The sumSTAAR framework offers a wider range of gene-based methods to combine. It allows the user to arbitrarily define a set of these methods, weighting functions and probabilities of genetic variants being causal. The methods used in the framework were adapted to analyse genes with large number of SNPs to decrease the running time. The framework includes the polygene pruning procedure to guard against the influence of the strong GWAS signals outside the gene. We also present new improved matrices of correlations between the genotypes of variants within genes. These matrices estimated on a sample of 265,000 individuals are a state-of-the-art replacement of widely used matrices based on the 1000 Genomes Project data.
基于基因的关联分析是一种有效的基因映射工具。最近已经提出了许多基于基因的方法。然而，它们的功效取决于潜在的遗传结构，而在复杂性状中很少知道这种结构，因此，结合这些方法可能是一种通用的方法。已经开发了几种结合不同基于基因的方法的框架。然而，它们都暗示了一组固定的方法、权重和功能注释。此外，它们大多数都使用个体表型和基因型作为输入数据。在这里，我们介绍 sumSTAAR，这是一个使用从全基因组关联研究（GWAS）中获得的汇总统计数据进行基于基因的关联分析的框架。它是 Li 及其同事在 2020 年提出的 STAAR 框架的扩展和修改版本。sumSTAAR 框架提供了更广泛的基于基因的方法来组合。它允许用户任意定义一组这些方法、权重函数以及遗传变异是因果关系的概率。框架中使用的方法经过了改编，可以分析具有大量 SNP 的基因，以减少运行时间。该框架包括多基因修剪过程，以防止基因外强 GWAS 信号的影响。我们还提出了新的改进的基因内变异基因型之间相关性矩阵。这些在 265000 个人的样本上估计的矩阵是广泛使用的基于 1000 基因组计划数据的矩阵的最新替代方法。
