da Costa Weverton Gomes, Bandeira E Souza Massaine, Azevedo Camila Ferreira, Nascimento Moyses, Morgante Carolina Vianna, Borel Jerônimo Constantino, de Oliveira Eder Jorge
Laboratório de Inteligência Computacional e Aprendizado Estatístico - LICAE, Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
Nugene, Embrapa Mandioca e Fruticultura, Cruz das Almas, Bahia, Brazil.
Front Plant Sci. 2024 Dec 16;15:1483340. doi: 10.3389/fpls.2024.1483340. eCollection 2024.
The complexity of selecting for drought tolerance in cassava, influenced by multiple factors, demands innovative approaches to plant selection. This study aimed to identify cassava clones with tolerance to water stress by employing truncated selection and selection based on genomic values for population improvement and genotype evaluation . The Best Linear Unbiased Predictions (BLUPs), Genomic Estimated Breeding Values (GEBVs), and Genomic Estimated Genotypic Values (GETGVs) were obtained based on different prediction models via genomic selection. The selection intensity ranged from 10 to 30%. A wide range of BLUPs for agronomic traits indicate desirable genetic variability for initiating genomic selection cycles to improve cassava's drought tolerance. SNP-based heritability ( ) and broad-sense heritabilities ( ) under water deficit were low magnitude (<0.40) for 8 to 12 agronomic traits evaluated. Genomic predictive abilities were below the levels of phenotypic heritability, varying by trait and prediction model, with the lowest and highest predictive abilities observed for starch content (0.15 - 0.22) and root length (0.34 - 0.36). Some agronomic traits of greater importance, such as fresh root yield (0.29 - 0.31) and shoot yield (0.31 - 0.32), showed good predictive ability, while dry matter content had lower predictive ability (0.16 - 0.22). The G-BLUP and RKHS methods presented higher predictive abilities, suggesting that incorporating kinship effects can be beneficial, especially in challenging environments. The selection differential based on a 15% selection intensity (62 genotypes) was higher for economically significant traits, such as starch content, shoot yield, and fresh root yield, both for population improvement (GEBVs) and for evaluating genotype's performance per (GETGVs). The lower costs of genotyping offer advantages over conventional phenotyping, making genomic selection a promising approach to increasing genetic gains for drought tolerance in cassava and reducing the breeding cycle to at least half the conventional time.
木薯耐旱性选择的复杂性受多种因素影响，需要创新的植物选择方法。本研究旨在通过采用截断选择和基于基因组值的选择来鉴定耐水分胁迫的木薯无性系，以进行群体改良和基因型评估。通过基因组选择，基于不同的预测模型获得了最佳线性无偏预测值（BLUPs）、基因组估计育种值（GEBVs）和基因组估计基因型值（GETGVs）。选择强度范围为10%至30%。农艺性状的广泛BLUPs表明存在理想的遗传变异性，可启动基因组选择周期以提高木薯的耐旱性。对于评估的8至12个农艺性状，基于单核苷酸多态性（SNP）的遗传力（）和广义遗传力（）在水分亏缺条件下较低（<0.40）。基因组预测能力低于表型遗传力水平，因性状和预测模型而异，淀粉含量（0.15 - 0.22）和根长（0.34 - 0.36）的预测能力最低和最高。一些更重要的农艺性状，如鲜根产量（0.29 - 0.31）和地上部产量（0.31 - 0.32），表现出良好的预测能力，而干物质含量的预测能力较低（0.16 - 0.22）。G - BLUP和RKHS方法表现出较高的预测能力，表明纳入亲缘关系效应可能有益，特别是在具有挑战性的环境中。基于15%选择强度（62个基因型）的选择差异对于经济上重要的性状，如淀粉含量、地上部产量和鲜根产量，在群体改良（GEBVs）和评估基因型表现（GETGVs）方面都更高。基因分型成本较低比传统表型分析具有优势，使基因组选择成为增加木薯耐旱性遗传增益并将育种周期缩短至至少传统时间一半的有前途的方法。
