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用于冬季杂交玉米多环境选择的增强贝叶斯模型：使用“ProbBreed”评估籽粒产量

Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using 'ProbBreed'.

作者信息

Basnet Bikas, Kunwar Chitra Bahadur, Upreti Umisha

机构信息

Faculty of Agriculture, Agriculture and Forestry University, Bharatpur, 13712, Nepal.

National Maize Research Program, Rampur, Chitwan, Nepal.

出版信息

Plant Methods. 2025 Jan 29;21(1):8. doi: 10.1186/s13007-025-01327-2.

DOI:10.1186/s13007-025-01327-2
PMID:39881335
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11776175/
Abstract

BACKGROUND

Crossover interactions stemming from phenotypic plasticity complicate selection decisions when evaluating hybrid maize with superior grain yield and consistent performance. Consequently, a two-year, region-wide investigation of 45 hybrids maize across Nepal was performed with the aim of disclosing both site and wide adapted hybrids. Utilizing an innovative "ProbBreed" package, based on Bayesian probability analysis of randomized complete block designs with three replicated trials at each station, this study substantively streamlines hybrids maize selection.

RESULTS

This finding revealed substantial genetic, environmental, and interactive influences on grain yield (p < 0.05). Among the hybrids, DKC9149 (8.8 tons/ha) emerged as the elite with probability coefficient of (0.39), followed by NK6607(0.35 & 8.6 tons/ha). Joint probability analysis identified RMH1899 super (0.23 & 8.3 tons/ha), followed by RMH 666 (0.15 & 8.4 tons/ha) and Uttam 121 (0.11 & 8.6 tons/ha), all of which accounted for overall environmental conditions. Additionally, over the years, DKC 9149, NK 6607(0.18 & 8.6 tons/ha), GK 3254(0.18 & 8.5 tons/ha), Shann 111(0.12 & 8.4 tons/ha), Sweety 1(0.13 & 8.4 tons/ha), and ADV 756(0.10 & 8.2 tons/ha) consistently demonstrated superior performance and stability. Delving with site specific recommendations include Nepalgunj: RMH 9999(8.5 tons/ha), NK 6607(8.6 tons/ha); Parwanipur: DKC 9149, MM 2033(8.5 tons/ha); Rampur: ADV 756, DKC 9149, MM 2929(8.6 tons/ha); and Tarahara: GK 3254(8.5 tons/ha), NK 6607(8.6 tons/ha), Uttam 121.

CONCLUSION

Thus, Selected hybrids are predicted to outperform within the recommended domain. Over and above, integrating genomic information into Bayesian models expected to enhance prediction accuracy and expedite breeding progress.

摘要

背景

在评估具有优异籽粒产量和稳定表现的杂交玉米时，表型可塑性产生的交叉互作会使选择决策变得复杂。因此，在尼泊尔开展了一项为期两年、覆盖全地区的对45个杂交玉米品种的调查，旨在找出适合特定地点以及广泛适应的杂交品种。本研究利用基于贝叶斯概率分析的创新型“ProbBreed”软件包，对随机完全区组设计进行分析，每个试验站设置三次重复试验，从而大幅简化了杂交玉米的选择过程。

结果

该研究结果表明，遗传、环境及交互作用对籽粒产量有显著影响（p < 0.05）。在这些杂交品种中，DKC9149（8.8吨/公顷）表现最为突出，概率系数为（0.39），其次是NK6607（0.35及8.6吨/公顷）。联合概率分析确定RMH1899特级品种（0.23及8.3吨/公顷），其次是RMH 666（0.15及8.4吨/公顷）和Uttam 121（0.11及8.6吨/公顷），所有这些品种都考虑了整体环境条件。此外，多年来，DKC 9149、NK 6607（0.18及8.6吨/公顷）、GK 3254（0.18及8.5吨/公顷）、Shann 111（0.12及8.4吨/公顷）、Sweety 1（0.13及8.4吨/公顷）和ADV 756（0.10及8.2吨/公顷）一直表现出卓越的性能和稳定性。针对特定地点的推荐品种包括：尼泊尔根杰：RMH 9999（8.5吨/公顷）、NK 6607（8.6吨/公顷）；帕尔瓦尼布尔：DKC 9149、MM 2033（8.5吨/公顷）；拉姆布尔：ADV 756、DKC 9149、MM 2929（8.6吨/公顷）；以及塔拉哈拉：GK 3254（8.5吨/公顷）、NK 6607（8.6吨/公顷）、Uttam 121。

结论

因此，预计所选杂交品种在推荐区域内表现会更优。此外，将基因组信息整合到贝叶斯模型中有望提高预测准确性并加快育种进程。

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