Abstract：
Joint genomic prediction (GP) is an attractive method to improve the accuracy of GP by combining information from multiple populations. However, many factors can negatively influence the accuracy of joint GP, such as differences in linkage disequilibrium phasing between single nucleotide polymorphisms (SNPs) and causal variants, minor allele frequencies and causal variants\' effect sizes across different populations. The objective of this study was to investigate whether the imputed high-density genotype data can improve the accuracy of joint GP using genomic best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP), multi-trait GBLUP (MT-GBLUP) and GBLUP based on genomic relationship matrix considering heterogenous minor allele frequencies across different populations (wGBLUP). Three traits, including days taken to reach slaughter weight, backfat thickness and loin muscle area, were measured on 67 276 Large White pigs from two different populations, for which 3334 were genotyped by SNP array. The results showed that a combined population could substantially improve the accuracy of GP compared with a single-population GP, especially for the population with a smaller size. The imputed SNP data had no effect for single population GP but helped to yield higher accuracy than the medium-density array data for joint GP. Of the four methods, ssGLBUP performed the best, but the advantage of ssGBLUP decreased as more individuals were genotyped. In some cases, MT-GBLUP and wGBLUP performed better than GBLUP. In conclusion, our results confirmed that joint GP could be beneficial from imputed high-density genotype data, and the wGBLUP and MT-GBLUP methods are promising for joint GP in pig breeding.
摘要：
联合基因组预测（GP）是一种有吸引力的方法，可以通过组合来自多个种群的信息来提高GP的准确性。然而,许多因素会对联合GP的准确性产生负面影响，例如单核苷酸多态性（SNP）和因果变异之间的连锁不平衡定相差异，次要等位基因频率和因果变异在不同人群中的影响大小。这项研究的目的是调查是否输入的高密度基因型数据可以使用基因组最佳线性无偏预测（GBLUP）提高联合GP的准确性，单步GBLUP（ssGBLUP），多性状GBLUP（MT-GBLUP）和基于基因组关系矩阵的GBLUP，考虑了不同群体中异质次要等位基因频率（wGBLUP）。三个特征，包括达到屠宰体重所需的天数，背脂厚度和腰肌面积，对来自两个不同种群的67276头大型白猪进行了测量，通过SNP阵列对3334进行了基因分型。结果表明，与单种群GP相比，组合种群可以大大提高GP的准确性，特别是对于人口规模较小的人群。估算的SNP数据对单个种群GP没有影响，但有助于产生比联合GP的中密度阵列数据更高的准确性。在这四种方法中，ssGLBUP表现最好,但是ssGBLUP的优势随着更多个体的基因分型而降低。在某些情况下,MT-GBLUP和wGBLUP的表现优于GBLUP。总之,我们的结果证实，联合GP可以从估算的高密度基因型数据中获益，wGBLUP和MT-GBLUP方法有望用于猪育种中的联合GP。
