Abstract：
Genomic selection (GS) is a method of marker-assisted selection revolutionizing crop improvement, but it can still be optimized. For hybrid breeding between heterozygote parents of different populations or species, specific aspects can be considered to increase GS accuracy: (1) training population genotyping, i.e., only genotyping the hybrid parents or also a sample of hybrid individuals, and (2) marker effects modeling, i.e., using population-specific effects of single nucleotide polymorphism alleles model (PSAM) or across-population SNP genotype model (ASGM). Here, this was investigated empirically for the prediction of the performances of oil palm hybrids for yield traits. The GS model was trained on 352 hybrid crosses and validated on 213 independent hybrid crosses. The training and validation hybrid parents and 399 training hybrid individuals were genotyping by sequencing. Despite the small proportion of hybrid individuals genotyped and low parental heterozygosity, GS prediction accuracy increased on average by 5% (range 1.4-31.3%, depending on trait and model) when training was done using genomic data on hybrids and parents compared with only parental genomic data. With ASGM, GS prediction accuracy increased on average by 3% (- 10.2 to 40%, depending on trait and genotyping strategy) compared with PSAM. We conclude that the best GS strategy for oil palm is to aggregate genomic data of parents and hybrid individuals and to ignore the parental origin of marker alleles (ASGM). To gain a better insight into these results, future studies should examine the respective effect of capturing genetic variability within crosses and taking segregation distortion into account when genotyping hybrid individuals, and investigate the factors controlling the relative performances of ASGM and PSAM in hybrid crops.
摘要：
基因组选择（GS）是一种标记辅助选择的方法，彻底改变了作物改良，但它仍然可以优化。对于不同种群或物种的杂合子亲本之间的杂交育种，可以考虑提高GS准确性的具体方面：(1)培训人群基因分型，即，仅对杂种亲本或杂种个体样本进行基因分型，(2)标记效果建模，即，使用单核苷酸多态性等位基因模型（PSAM）或跨群体SNP基因型模型（ASGM）的群体特异性效应。这里,对油棕杂种产量性状的预测进行了实证研究。GS模型在352个杂交杂交上进行了训练，并在213个独立的杂交上进行了验证。训练和验证杂种亲本和399个训练杂种个体通过测序进行基因分型。尽管杂交个体的基因分型比例小，亲本杂合性低，GS预测精度平均提高了5%(范围1.4-31.3%，取决于性状和模型），当使用杂种和亲本的基因组数据进行训练时，仅与亲本基因组数据进行比较。有了ASGM,GS预测精度平均提高了3%(-10.2至40%，取决于性状和基因分型策略)与PSAM相比。我们得出的结论是，油棕的最佳GS策略是汇总父母和杂种个体的基因组数据，而忽略标记等位基因（ASGM）的父母起源。为了更好地了解这些结果，未来的研究应该检查在杂交中捕获遗传变异性的各自的影响，并在对杂交个体进行基因分型时考虑到分离扭曲，并研究了控制ASGM和PSAM在杂交作物中相对性能的因素。
