Ye Haoqiang, Zhang Zipeng, Ren Duanyang, Cai Xiaodian, Zhu Qianghui, Ding Xiangdong, Zhang Hao, Zhang Zhe, Li Jiaqi
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, Guangzhou, China.
Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture and Rural Affairs, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Front Genet. 2022 Jun 9;13:843300. doi: 10.3389/fgene.2022.843300. eCollection 2022.
The size of reference population is an important factor affecting genomic prediction. Thus, combining different populations in genomic prediction is an attractive way to improve prediction ability. However, combining multireference population roughly cannot increase the prediction accuracy as well as expected in pig. This may be due to different linkage disequilibrium (LD) pattern differences between population. In this study, we used the imputed whole-genome sequencing (WGS) data to construct LD-based haplotypes for genomic prediction in combined population to explore the impact of different single-nucleotide polymorphism (SNP) densities, variant representation (SNPs or haplotype alleles), and reference population size on the prediction accuracy for reproduction traits. Our results showed that genomic best linear unbiased prediction (GBLUP) using the WGS data can improve prediction accuracy in multi-population but not within-population. Not only the genomic prediction accuracy of the haplotype method using 80 K chip data in multi-population but also GBLUP for the multi-population (3.4-5.9%) was higher than that within-population (1.2-4.3%). More importantly, we have found that using the haplotype method based on the WGS data in multi-population has better genomic prediction performance, and our results showed that building haploblock in this scenario based on low LD threshold ( = 0.2-0.3) produced an optimal set of variables for reproduction traits in Yorkshire pig population. Our results suggested that whether the use of the haplotype method based on the chip data or GBLUP (individual SNP method) based on the WGS data were beneficial for genomic prediction in multi-population, while simultaneously combining the haplotype method and WGS data was a better strategy for multi-population genomic evaluation.
参考群体的大小是影响基因组预测的一个重要因素。因此，在基因组预测中合并不同群体是提高预测能力的一种有吸引力的方法。然而，在猪中大致合并多参考群体并不能像预期的那样提高预测准确性。这可能是由于群体之间不同的连锁不平衡（LD）模式差异所致。在本研究中，我们使用估算的全基因组测序（WGS）数据构建基于LD的单倍型用于合并群体的基因组预测，以探索不同单核苷酸多态性（SNP）密度、变异表示形式（SNP或单倍型等位基因）和参考群体大小对繁殖性状预测准确性的影响。我们的结果表明，使用WGS数据的基因组最佳线性无偏预测（GBLUP）可以提高多群体中的预测准确性，但不能提高群体内的预测准确性。不仅在多群体中使用80K芯片数据的单倍型方法的基因组预测准确性，而且多群体的GBLUP（3.4 - 5.9%）都高于群体内（1.2 - 4.3%）。更重要的是，我们发现，在多群体中使用基于WGS数据的单倍型方法具有更好的基因组预测性能，并且我们的结果表明，在这种情况下基于低LD阈值（ = 0.2 - 0.3）构建单倍型模块可为约克夏猪群体的繁殖性状产生一组最优变量。我们的结果表明，无论是使用基于芯片数据的单倍型方法还是基于WGS数据的GBLUP（单个SNP方法）都有利于多群体中的基因组预测，而同时结合单倍型方法和WGS数据是多群体基因组评估的更好策略。
