Phenomic and genomic prediction of yield on multiple locations in winter wheat.
Phenomic and genomic prediction of yield on multiple locations in winter wheat.
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DOI：
10.3389/fgene.2023.1164935
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发表时间：
2023
影响因子：
3.7
通讯作者：
Ober, Eric S.
中科院分区：
文献类型：
--
作者：
Jackson, Robert;Buntjer, Jaap B.;Bentley, Alison R.;Lage, Jacob;Byrne, Ed;Burt, Chris;Jack, Peter;Berry, Simon;Flatman, Edward;Poupard, Bruno;Smith, Stephen;Hayes, Charlotte;Barber, Tobias;Love, Bethany;Gaynor, R. Chris;Gorjanc, Gregor;Howell, Phil;Mackay, Ian J.;Hickey, John M.;Ober, Eric S.
基因组选择最近已成为谷物育种策略的一部分。然而，复杂性状（如产量）的线性基因组预测模型的局限性在于，这些模型无法适应基因型对环境的影响，这通常在多个地点的试验中观察到。在这项研究中，我们研究了如何通过收集大量的表型标记，使用高通量现场表型分析，以及它是否可以提高GS预测的准确性，这种环境变化可以被捕获。为此，选择了44株冬小麦（Triticum aestivum L.）在2年内在两个地点培育包括2，994个品系的优良种群，以接近实际育种计划中的试验规模。在不同的生长阶段，收集了多光谱和高光谱相机的遥感数据，以及传统的地面可视作物评估分数，每个地块收集了大约100个不同的数据变量。测试了各种数据类型的谷物产量的预测能力，有或没有全基因组标记数据集。单独使用表型性状的模型具有比基因组数据（大约R2 = 0.1）更大的预测值（R2 = 0.39-0.47）。通过组合性状和标记数据的预测能力的平均改善比最佳的仅表型模型高6%-12%，并且当来自一个完整位置的数据用于预测整个第二位置的产量时表现最佳。结果表明，遗传增益育种计划可以增加利用大量的表型变量，利用遥感在田间试验中，虽然在育种周期的表型选择可以最有利可图的应用阶段仍然有待回答。
Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39–0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%–12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.
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影响因子：
13.8
作者：
Allen AM;Winfield MO;Burridge AJ;Downie RC;Benbow HR;Barker GL;Wilkinson PA;Coghill J;Waterfall C;Davassi A;Scopes G;Pirani A;Webster T;Brew F;Bloor C;Griffiths S;Bentley AR;Alda M;Jack P;Phillips AL;Edwards KJ
通讯作者：
Edwards KJ
影响因子：
5.4
作者：
Jarquin, Diego;Crossa, Jose;Lacaze, Xavier;Du Cheyron, Philippe;Daucourt, Joelle;Lorgeou, Josiane;Piraux, Francis;Guerreiro, Laurent;Perez, Paulino;Calus, Mario;Burgueno, Juan;de los Campos, Gustavo
通讯作者：
de los Campos, Gustavo
影响因子：
5.4
作者：
Heslot, Nicolas;Akdemir, Deniz;Jannink, Jean-Luc
通讯作者：
Jannink, Jean-Luc
影响因子：
2.3
作者：
Aguate, Fernando M.;Trachsel, Samuel;de los Campos, Gustavo
通讯作者：
de los Campos, Gustavo
影响因子：
4.2
作者：
Battenfield, Sarah D.;Guzman, Carlos;Poland, Jesse A.
通讯作者：
Poland, Jesse A.