Ye S, Song H, Ding X, Zhang Z, Li J
Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, National Engineering Research Centre for Breeding Swine Industry, College of Animal Science, South China Agricultural University, No. 483, Wushan Road, Tianhe District, 510642Guangzhou, China.
Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, No. 2, Yuanmingyuan West Road, Haidian District, 100193Beijing, China.
Animal. 2020;14(8):1555-1564. doi: 10.1017/S1751731120000506. Epub 2020 Mar 25.
Combining different swine populations in genomic prediction can be an important tool, leading to an increased accuracy of genomic prediction using single nucleotide polymorphism (SNP) chip data compared with within-population genomic. However, the expected higher accuracy of multi-population genomic prediction has not been realized. This may be due to an inconsistent linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTL) across populations, and the weak genetic relationships across populations. In this study, we determined the impact of different genomic relationship matrices, SNP density and pre-selected variants on prediction accuracy using a combined Yorkshire pig population. Our objective was to provide useful strategies for improving the accuracy of genomic prediction within a combined population. Results showed that the accuracy of genomic best linear unbiased prediction (GBLUP) using imputed whole-genome sequencing (WGS) data in the combined population was always higher than that within populations. Furthermore, the use of imputed WGS data always resulted in a higher accuracy of GBLUP than the use of 80K chip data for the combined population. Additionally, the accuracy of GBLUP with a non-linear genomic relationship matrix was markedly increased (0.87% to 15.17% for 80K chip data, and 0.43% to 4.01% for imputed WGS data) compared with that obtained with a linear genomic relationship matrix, except for the prediction of XD population in the combined population using imputed WGS data. More importantly, the application of pre-selected variants based on fixation index (Fst) scores improved the accuracy of multi-population genomic prediction, especially for 80K chip data. For BLUP|GA (BLUP approach given the genetic architecture), the use of a linear method with an appropriate weight to build a weight-relatedness matrix led to a higher prediction accuracy compared with the use of only pre-selected SNPs for genomic evaluations, especially for the total number of piglets born. However, for the non-linear method, BLUP|GA showed only a small increase or even a decrease in prediction accuracy compared with the use of only pre-selected SNPs. Overall, the best genomic evaluation strategy for reproduction-related traits for a combined population was found to be GBLUP performed with a non-linear genomic relationship matrix using variants pre-selected from the 80K chip data based on Fst scores.
在基因组预测中合并不同猪群可能是一种重要工具，与群体内基因组预测相比，这会提高使用单核苷酸多态性（SNP）芯片数据进行基因组预测的准确性。然而，多群体基因组预测预期的更高准确性尚未实现。这可能是由于群体间SNP与数量性状基因座（QTL）之间的连锁不平衡（LD）不一致，以及群体间的遗传关系较弱。在本研究中，我们使用合并的约克夏猪群体确定了不同基因组关系矩阵、SNP密度和预选变异对预测准确性的影响。我们的目标是提供提高合并群体内基因组预测准确性的有用策略。结果表明，在合并群体中使用推算的全基因组测序（WGS）数据进行基因组最佳线性无偏预测（GBLUP）的准确性始终高于群体内的准确性。此外，对于合并群体，使用推算的WGS数据进行GBLUP的准确性始终高于使用80K芯片数据。此外，与线性基因组关系矩阵相比，使用非线性基因组关系矩阵的GBLUP准确性显著提高（80K芯片数据提高0.87%至15.17%，推算的WGS数据提高0.43%至4.01%），但在合并群体中使用推算的WGS数据预测XD群体时除外。更重要的是，基于固定指数（Fst）分数应用预选变异提高了多群体基因组预测的准确性，尤其是对于80K芯片数据。对于BLUP|GA（给定遗传结构的BLUP方法），与仅使用预选SNP进行基因组评估相比，使用具有适当权重的线性方法构建权重相关性矩阵可提高预测准确性，尤其是对于总产仔数。然而，对于非线性方法，与仅使用预选SNP相比，BLUP|GA的预测准确性仅略有提高甚至降低。总体而言，发现合并群体繁殖相关性状的最佳基因组评估策略是使用基于Fst分数从80K芯片数据中预选的变异，通过非线性基因组关系矩阵进行GBLUP。
