[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2521":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":59},2521,"From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00122-026-05373-9","Sugarcane (Saccharum spp.) underpins global sugar and bioenergy supply and is increasingly valued as a renewable biomass feedstock. Sustained improvement in commercial traits and resilience is constrained by long breeding cycles, clonal propagation, multi-stage testing, and a highly polyploid, heterozygous, and frequently aneuploid genome with substantial non-additive genetic variation. Genomic selection has demonstrated value for predicting elite-clone performance, yet its operational use remains limited at earlier decision points, including family selection, parent evaluation, and cross design. This review examines the biological, statistical, and genomic factors that shape these decisions, with emphasis on the Australian breeding context based on progeny assessment trials (PATs), clonal assessment trials (CATs), and final assessment trials (FATs). We evaluate challenges arising from family plot means, the use of different full-sib samples as nominal family replicates, spatial heterogeneity, competition, genotype-by-environment interaction, and the partitioning of additive and non-additive effects. We also assess the integration of pedigree and genomic relationship, genotype representation, allele-dosage estimation, aneuploidy, genomic prediction models, and training-population design. We then consider genomic prediction of cross performance and constrained mate allocation as approaches for improving expected family performance, accounting for cross-specific non-additive effects and managing relatedness. We propose a decision-centred framework that links family and clonal data across breeding stages, tracks the propagation of information and uncertainty, and supports parent recycling and cross allocation. We conclude with a practical research agenda for stage-integrated mixed-model and single-step analyses that connect early family evaluation with genomic prediction and cross-level decision support in sugarcane breeding.","甘蔗（\u003Ci>Saccharum\u003C\u002Fi> spp.）支撑着全球糖料与生物能源供应，并作为可再生生物质原料日益受到重视。商业性状与适应性的持续改良受到长育种周期、无性繁殖、多阶段测试以及高度多倍体、杂合且常为非整倍体基因组的制约，后者具有大量非加性遗传变异。基因组选择在预测优良无性系表现方面已显示出价值，但其在更早期决策节点（包括家系选择、亲本评价和杂交设计）中的实际应用仍然有限。本文综述了影响这些决策的生物学、统计学和基因组学因素，重点聚焦于基于后代评估试验（PATs）、无性系评估试验（CATs）和最终评估试验（FATs）的澳大利亚育种背景。我们评估了由家系小区均值、使用不同全同胞样本作为名义家系重复、空间异质性、竞争、基因型×环境互作以及加性与非加性效应划分所带来的挑战。我们还评估了系谱与基因组亲缘关系的整合、基因型代表性、等位基因剂量估计、非整倍性、基因组预测模型和训练群体设计。随后，我们考虑了杂交表现的基因组预测和约束配对分配作为改善预期家系表现的方法，同时兼顾杂交特异性非加性效应并管理亲缘关系。我们提出了一个以决策为中心的框架，该框架连接育种各阶段的家系与无性系数据，追踪信息和不确定性的传播，并支持亲本循环利用与杂交分配。最后，我们提出了一个面向阶段整合混合模型和单步分析的实际研究议程，将早期家系评价与基因组预测及甘蔗育种中的杂交水平决策支持相连接。",null,"Theoretical and Applied Genetics","2026-09-12T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"核心期刊综述，系统梳理甘蔗育种中基因组预测与亲本选配的统计与基因组策略，方法新颖、专业深度高，对作物智能育种有参考价值，但属细分领域学术进展，公共影响范围有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业人工智能","种业振兴","分子育种","基因组选择","甘蔗育种",0,"10.1007\u002Fs00122-026-05373-9",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":50,"card":51,"direction":57,"ingested_from":58},"W7212382599",[36,39,41,44,47],{"name":37,"orcid":38},"Andrew Rigby","https:\u002F\u002Forcid.org\u002F0009-0006-3626-3875",{"name":40,"orcid":9},"Felicity Atkin",{"name":42,"orcid":43},"Ben J. Hayes","https:\u002F\u002Forcid.org\u002F0000-0002-5606-3970",{"name":45,"orcid":46},"Lee T. Hickey","https:\u002F\u002Forcid.org\u002F0000-0001-6909-7101",{"name":48,"orcid":49},"Seema Yadav","https:\u002F\u002Forcid.org\u002F0000-0001-7191-7770","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs00122-026-05373-9.pdf",{"tldr":52,"method":53,"finding":54,"direction":55,"opportunity":56},"综述甘蔗育种早期决策的统计与基因组策略，提出连接家系与无性系数据的决策框架。","基于澳大利亚PAT\u002FCAT\u002FFAT试验，整合系谱与基因组关系、等位基因剂量及预测","基因组预测与约束交配分配可优化家系表现，需处理非加性效应和亲缘关系。","农业人工智能与决策模型","开发阶段整合的混合模型与单步分析，将早期家系评估与基因组预测及杂交决策支持相连。","农业遥感与作物表型","openalex","2026-09-15T23:30:16.012181Z"]