The prediction of clonal genetic value for yield is challenging in oil palm (Elaeis guineensis Jacq.). Currently, clonal selection involves two stages of phenotypic selection (PS): ortet preselection on traits with sufficient heritability among a small number of individuals in the best crosses in progeny tests, and final selection on performance in clonal trials. The present study evaluated the efficiency of genomic selection (GS) for clonal selection. The training set comprised almost 300 Deli × La Mé crosses phenotyped for eight palm oil yield components and the validation set 42 Deli × La Mé ortets. Genotyping-by-sequencing (GBS) revealed 15,054 single nucleotide polymorphisms (SNP). The effects of the SNP dataset (density and percentage of missing data) and two GS modeling approaches, ignoring (ASGM) and considering (PSAM) the parental origin of alleles, were assessed. The results showed prediction accuracies ranging from 0.08 to 0.70 for ortet candidates without data records, depending on trait, SNP dataset and modeling. ASGM was better (on average slightly more accurate, less sensitive to SNP dataset and simpler), although PSAM appeared interesting for a few traits. With ASGM, the number of SNPs had to reach 7,000, while the percentage of missing data per SNP was of secondary importance, and GS prediction accuracies were higher than those of PS for most of the traits. Finally, this makes possible two practical applications of GS, that will increase genetic progress by improving ortet preselection before clonal trials: (1) preselection at the mature stage on all yield components jointly using ortet genotypes and phenotypes, and (2) genomic preselection on more yield components than PS, among a large population of the best possible crosses at nursery stage.

中文翻译：

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基因组预测改善油棕（Elaeis guineensis Jacq.）杂交种的克隆选择

在油棕（Elaeis guineensis Jacq.）中预测产量的克隆遗传价值具有挑战性。目前，克隆选择涉及表型选择 (PS) 的两个阶段：对后代测试中最佳杂交中少数个体具有足够遗传力的性状进行 ortet 预选，以及在克隆试验中对性能进行最终选择。本研究评估了基因组选择 (GS) 的克隆选择效率。训练集包括近 300 个 Deli × La Mé 杂交，针对八个棕榈油产量成分进行表型分析，验证集包含 42 个 Deli × La Mé ortets。测序基因分型 (GBS) 揭示了 15,054 个单核苷酸多态性 (SNP)。SNP 数据集（缺失数据的密度和百分比）和两种 GS 建模方法的影响，评估忽略 (ASGM) 和考虑 (PSAM) 等位基因的亲本来源。结果表明，根据性状、SNP 数据集和建模，对于没有数据记录的 ortet 候选者的预测准确度在 0.08 到 0.70 之间。ASGM 更好（平均而言更准确，对 SNP 数据集不太敏感且更简单），尽管 PSAM 对一些特征似乎很有趣。使用 ASGM，SNP 的数量必须达到 7,000，而每个 SNP 的缺失数据百分比是次要的，对于大多数性状，GS 预测精度高于 PS。最后，这使得 GS 的两个实际应用成为可能，这将通过在克隆试验前改进 ortet 预选来增加遗传进展：（1）在成熟阶段对所有产量成分联合使用 ortet 基因型和表型进行预选，