Agriculture Victoria, Centre for AgriBioscience, AgriBio, Bundoora, VIC, Australia.
School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Narrabri, NSW, Australia.
Theor Appl Genet. 2021 Oct;134(10):3339-3350. doi: 10.1007/s00122-021-03900-4. Epub 2021 Jul 12.
Genomic selection enabled accurate prediction for the concentration of 13 nutritional element traits in wheat. Wheat biofortification is one of the most sustainable strategies to alleviate mineral deficiency in human diets. Here, we investigated the potential of genomic selection using BayesR and Bayesian ridge regression (BRR) models to predict grain yield (YLD) and the concentration of 13 nutritional elements in grains (B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P and Zn) using a population of 1470 spring wheat lines. The lines were grown in replicated field trials with two times of sowing (TOS) at 3 locations (Narrabri-NSW, all lines; Merredin-WA and Horsham-VIC, 200 core lines). Narrow-sense heritability across environments (locations/TOS) ranged from 0.09 to 0.45. Co, K, Na and Ca showed low to negative genetic correlations with other traits including YLD, while the remaining traits were negatively correlated with YLD. When all environments were included in the reference population, medium to high prediction accuracy was observed for the different traits across environments. BayesR had higher average prediction accuracy for mineral concentrations (r = 0.55) compared to BRR (r = 0.48) across all traits and environments but both methods had comparable accuracies for YLD. We also investigated the utility of one or two locations (reference locations) to predict the remaining location(s), as well as the ability of one TOS to predict the other. Under these scenarios, BayesR and BRR showed comparable performance but with lower prediction accuracy compared to the scenario of predicting reference environments for new lines. Our study demonstrates the potential of genomic selection for enriching wheat grain with nutritional elements in biofortification breeding.
基因组选择可实现对小麦 13 种营养元素浓度的精准预测。小麦生物强化是缓解人类饮食中矿物质缺乏最可持续的策略之一。在这里，我们使用 BayesR 和贝叶斯岭回归（BRR）模型，研究了基因组选择在预测籽粒产量（YLD）和 13 种营养元素（B、Ca、Co、Cu、Fe、K、Mg、Mn、Mo、Na、Ni、P 和 Zn）浓度方面的潜力，该研究基于 1470 个春小麦株系群体，这些株系在三个地点（新南威尔士州纳拉伯里-所有株系；西澳大利亚州默里丁和维多利亚州霍舍姆-200 个核心株系）的两次播种（TOS）重复田间试验中种植。环境（地点/TOS）间的狭义遗传力范围从 0.09 到 0.45。Co、K、Na 和 Ca 与其他性状（包括 YLD）表现出低到负的遗传相关性，而其余性状与 YLD 呈负相关。当将所有环境纳入参考群体时，不同性状在不同环境下的预测准确性中等至较高。当所有环境都包含在参考群体中时，BayesR 对不同性状的平均预测准确性（r=0.55）高于 BRR（r=0.48），但两种方法对 YLD 的预测准确性相当。我们还研究了使用一个或两个地点（参考地点）来预测其余地点，以及一个 TOS 预测另一个 TOS 的能力。在这些情况下，BayesR 和 BRR 表现出相当的性能，但与预测新株系参考环境的情况相比，预测准确性较低。本研究表明，基因组选择在生物强化育种中富集小麦籽粒营养元素方面具有潜力。
