Table6_A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study.DOC

The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix (G) in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second G (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne ≅ 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between −0.14 and −0.08 and from −0.62 to −0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne ≅ 250) and less (Ne ≅ 100) genetically diverse populations, respectively, and the bias ranged between −0.36 and −0.32 and from −0.78 to −0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two G matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.

种群的遗传多样性水平与个体单核苷酸多态性（single nucleotide polymorphisms, SNPs）和数量性状位点（quantitative trait loci, QTLs）之间的连锁不平衡（linkage disequilibrium, LD）呈负相关，这会导致高遗传多样性种群的基因组育种值（genomic breeding values, GEBVs）预测能力更低。基于单倍型的预测能够更好地捕捉SNPs与QTLs之间的LD，因此其预测效果往往优于单SNP预测。据此，本研究旨在评估单步基因组最佳线性无偏预测（single-step-genomic best linear unbiased prediction, ssGBLUP）框架下，基于单SNP和单倍型的基因组预测在遗传多样性种群中的准确性与偏差。我们利用已发表的参数模拟了中等遗传力和低遗传力性状的纯种与复合绵羊种群。单倍型基于0.1、0.3和0.6的LD阈值构建。在ssGBLUP分析中，我们使用来自独特单倍型等位基因的伪SNPs构建基因组关系矩阵（genomic relationship matrix, G）。我们设置了多组对比场景：将伪SNPs与非LD聚类SNPs结合、仅使用伪SNPs，或是将单倍型纳入第二个G矩阵（即双关系矩阵场景）。对于中等遗传力性状场景，在遗传多样性最高（有效种群大小Ne ≅ 450）和最低（Ne < 200）的种群中，基于单SNP的GEBV准确性范围为0.41至0.55，基于单倍型的准确性范围为0.17至0.54；基于单SNP或单倍型的偏差范围分别为-0.14至-0.08，以及-0.62至-0.08。对于低遗传力性状场景，在遗传多样性最高（Ne ≅ 250）和最低（Ne ≅ 100）的种群中，基于单SNP的GEBV准确性范围为0.24至0.32，基于单倍型的准确性范围为0.11至0.32；基于单SNP的偏差范围为-0.36至-0.32，基于单倍型的偏差范围为-0.78至-0.33。仅使用基于LD阈值0.3构建的区块对应的伪SNPs时，预测准确性最低且偏差最大（p < 0.05）；而无论遗传多样性水平如何，在两种遗传力水平的所有种群中，仅使用SNPs，或是将独立SNPs与伪SNPs结合构建单个或两个G矩阵时，均可获得最优结果。综上，在遗传多样性种群中，基于单倍型的模型并未提升基因组预测的性能。