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Extending Finlay–Wilkinson regression with environmental covariates 环境协变量下Finlay-Wilkinson回归的扩展
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-20 DOI: 10.1111/pbr.13130
Hans-Peter Piepho, Justin Blancon
Finlay–Wilkinson regression is a popular method for analysing genotype–environment interaction in series of plant breeding and variety trials. It involves a regression on the environmental mean, indexing the productivity of an environment, which is driven by a wide array of environmental factors. Increasingly, it is becoming feasible to characterize environments explicitly using observable environmental covariates. Hence, there is mounting interest to replace the environmental index with an explicit regression on such observable environmental covariates. This paper reviews the development of such methods. The focus is on parsimonious models that allow replacing the environmental index by regression on synthetic environmental covariates formed as linear combinations of a larger number of observable environmental covariates. Two new methods are proposed for obtaining such synthetic covariates, which may be integrated into genotype-specific regression models, that is, criss-cross regression and a factor-analytic approach. The main advantage of such explicit modelling is that predictions can be made also for new environments where trials have not been conducted. A published dataset is employed to illustrate the proposed methods.
Finlay-Wilkinson回归是一种在植物育种和品种试验中常用的基因型-环境相互作用分析方法。它涉及对环境平均值的回归,对环境的生产力进行索引,这是由一系列广泛的环境因素驱动的。越来越多地,使用可观察的环境协变量来明确地描述环境变得可行。因此,人们越来越有兴趣用对这些可观察到的环境协变量的显式回归来取代环境指数。本文综述了这类方法的发展。重点是简化模型,该模型允许通过对合成环境协变量的回归来取代环境指数,这些环境协变量是由大量可观察到的环境协变量组成的线性组合。本文提出了两种新的方法来获得这些合成协变量,它们可以整合到基因型特异性回归模型中,即交叉回归和因子分析法。这种明确的模型的主要优点是,在没有进行试验的新环境中也可以做出预测。使用已发布的数据集来说明所提出的方法。
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引用次数: 0
Characterization of resistance to angular leaf spot of common bean (Phaseolus vulgaris L.) breeding line SPS50HB 菜豆(Phaseolus vulgaris L.)选育系SPS50HB对角斑病抗性的研究
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-12 DOI: 10.1111/pbr.13128
Tigist Firew, Y. Rezene, Hewan Demissie
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引用次数: 0
Genomic selection and enablers for agronomic traits in maize (Zea mays): A review 玉米(Zea mays)农艺性状的基因组选择和使能因子研究进展
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-12 DOI: 10.1111/pbr.13127
Rodreck Gunundu, H. Shimelis, J. Mashilo
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引用次数: 0
Hybrid breeding for fall armyworm resistance: Combining ability and hybrid prediction 秋粘虫抗性杂交育种:配合力与杂交预测
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-11 DOI: 10.1111/pbr.13129
Isaac Kamweru, Y. Beyene, Bruce Anani, V. Adetimirin, B. Prasanna, M. Gowda
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引用次数: 0
Responses of differentially expressed proteins and endogenous hormones in winter rapeseed (Brassica rapa L.) roots under water deficit stress 水分亏缺胁迫下冬油菜根系差异表达蛋白和内源激素的响应
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-10 DOI: 10.1111/pbr.13126
C. Mi, Yanning Zhao, Qian Wang, Chao Sun, Yusong Zhang, Chao Zhou, Youmin Liu, Zigang Liu, Liangbin Lin
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引用次数: 0
Status of yam (Dioscorea spp.) in the Democratic Republic of Congo 薯蓣在刚果民主共和国的地位
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-10 DOI: 10.1111/pbr.13123
I. I. Adejumobi, P. Agre, Adewumi S. Adeyinka, I.M. Cipriano, J. Adheka, D. Onautshu
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引用次数: 0
Identification of potential sources of mungbean yellow mosaic India virus resistance in black gram (Vigna mungo) and expression of antioxidants and R‐genes modulating resistance response in cultivated and its two wild relatives 黑革(Vigna mungo)绿豆黄花叶印度病毒抗性潜在来源的鉴定以及栽培及其两个野生近缘种抗氧化剂和调控抗性反应的R基因的表达
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-10 DOI: 10.1111/pbr.13122
A. Tripathi, C. Singh, Mukul Kumar, Shalini Purwar, Anuj Mishra, Dharmendra Kumar, Ashutosh Kumar Singh, S Kumar, Sanjay K. Singh, N. Singh
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引用次数: 1
Combined transcriptome and proteome analyses between the cytoplasmic male sterile line and its maintainer line in Welsh onion (Allium fistulosum) 大葱细胞质雄性不育系及其保持系的转录组和蛋白质组联合分析
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-07 DOI: 10.1111/pbr.13125
Junxiang Jia, Guodong Yang, Na Li, Xintao Jiang, Lianwei Cui
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引用次数: 0
Designing optimal training sets for genomic prediction using adversarial validation with probit regression 利用概率回归对抗性验证设计基因组预测的最优训练集
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-07-04 DOI: 10.1111/pbr.13124
O. Montesinos-López, A. Montesinos‐López
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引用次数: 0
Accelerating gene discovery in climate‐resilient and nutrient‐rich major and minor millets through genome‐wide association studies: Progress and prospects 通过全基因组关联研究加速气候适应性强、营养丰富的主要和次要小米的基因发现:进展和前景
IF 2 4区 农林科学 Q2 AGRONOMY Pub Date : 2023-06-15 DOI: 10.1111/pbr.13120
Renganathan Vellaichamy Gandhimeyyan, Vanniarajan Chockalingam, R. Muthurajan, Renuka Raman
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引用次数: 0
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Plant Breeding
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