Exploring the optimal strategy of imputation from SNP array to whole-genome sequencing data in farm animals

文献类型： 外文期刊

第一作者： Jiang, Yifan

作者： Jiang, Yifan;Ding, Xiangdong;Song, Hailiang;Gao, Hongding;Zhang, Qin

作者机构：

关键词： imputation; accuracy; whole genome sequencing; livestock; poultry

期刊名称：FRONTIERS IN GENETICS （ 影响因子：4.772； 五年影响因子：4.933 ）

ISSN：

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Genotype imputation from BeadChip to whole-genome sequencing (WGS) data is a cost-effective method of obtaining genotypes of WGS variants. Beagle, one of the most popular imputation software programs, has been widely used for genotype inference in humans and non-human species. A few studies have systematically and comprehensively compared the performance of beagle versions and parameter settings of farm animals. Here, we investigated the imputation performance of three representative versions of Beagle (Beagle 4.1, Beagle 5.0, and Beagle 5.4), and the effective population size (Ne) parameter setting for three species (cattle, pig, and chicken). Six scenarios were investigated to explore the impact of certain key factors on imputation performance. The results showed that the default Ne (1,000,000) is not suitable for livestock and poultry in small reference or low-density arrays of target panels, with 2.47%-10.45% drops in accuracy. Beagle 5 significantly reduced the computation time (4.66-fold-13.24-fold) without an accuracy loss. In addition, using a large combined-reference panel or high-density chip provides greater imputation accuracy, especially for low minor allele frequency (MAF) variants. Finally, a highly significant correlation in the measures of imputation accuracy can be obtained with an MAF equal to or greater than 0.05.

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