Liu Jia, Gock Andrew, Ramm Kerrie, Stops Sandra, Phongkham Tanya, Norman Adam, Eastwood Russell, Stone Eric, Dillon Shannon
Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT, Australia.
Biology Data Science Institute (BDSI), College of Science, Australian National University, Canberra, ACT, Australia.
Front Plant Sci. 2025 May 6;16:1506434. doi: 10.3389/fpls.2025.1506434. eCollection 2025.
The adoption of novel molecular strategies such as genomic selection (GS) in crop breeding have been key to maintaining rates of genetic gain through increased efficiency and shortening the cycle of evaluation relative to conventional selection. In the search for improved methodologies that incorporate novel sources of variation for the assessment of genetic merit, GS remains a focus of crop breeding research globally. Here we explored the role transcriptome data could play in enhancing GS in wheat.
Across 286 wheat lines, we integrated phenotype and multi-omic data from controlled environment and field experiments including ca. 40K single nucleotide polymorphisms (SNP), abundance data for ca. 50K transcripts as well as meta-data (e.g. categorical environments) to predict individual genetic merit for two agronomic traits, flowering time and height. We evaluated the performance of different model scenarios based on linear (GBLUP) and Gaussian/nonlinear (RKHS) regression in the Bayesian analytical framework. These models explored the relative contributions of different combinations of additive genomic (G), transcriptomic (T) and environment (E), with and without considering non-additive epistasis, dominance and genotype by environment ( × ) random effects.
In controlled environments, where traits were measured under contrasting daylength regimes (long and short days), transcriptome abundance outperformed other random effects when considered independently, while the model combining SNP, environment and × marginally outperformed the transcriptome. The best performing model for prediction of both flowering and height combined all data types, where the GBLUP framework showed slightly better performance overall compared with RKHS across all tests. Under field conditions, we found that models combining all variables were superior using the RKHS framework. However, the relative contribution of the transcriptome was reduced.
Our results show there is a predictive advantage to direct inclusion of the transcriptome for genomic evaluation in wheat breeding for traits where × is a factor. However, the complexity and cost of generating transcriptome data are likely to limit its feasibility for commercial breeding at this stage. We demonstrate that combining less costly environmental covariates with conventional genomic data provides a practical alternative with similar gains to the transcriptome when environments are well characterised.
在作物育种中采用诸如基因组选择（GS）等新的分子策略，对于通过提高效率和缩短相对于传统选择的评估周期来维持遗传增益率至关重要。在寻求纳入新变异来源以评估遗传价值的改进方法时，基因组选择仍然是全球作物育种研究的重点。在此，我们探讨了转录组数据在增强小麦基因组选择方面可能发挥的作用。
在286个小麦品系中，我们整合了来自控制环境和田间试验的表型和多组学数据，包括约40K个单核苷酸多态性（SNP）、约50K个转录本的丰度数据以及元数据（如分类环境），以预测两个农艺性状（开花时间和株高）的个体遗传价值。我们在贝叶斯分析框架中评估了基于线性（GBLUP）和高斯/非线性（RKHS）回归的不同模型方案的性能。这些模型探讨了加性基因组（G）、转录组（T）和环境（E）不同组合的相对贡献，同时考虑了和不考虑非加性上位性、显性和基因型与环境互作（×）随机效应的情况。
在控制环境中，即在不同日长条件（长日照和短日照）下测量性状时，单独考虑转录组丰度优于其他随机效应，而结合SNP、环境和互作的模型略优于转录组。预测开花和株高的最佳模型组合了所有数据类型，其中在所有测试中GBLUP框架总体表现略优于RKHS。在田间条件下，我们发现使用RKHS框架组合所有变量的模型更优。然而，转录组的相对贡献降低了。
我们的结果表明，对于基因型与环境互作是一个因素的性状，在小麦育种的基因组评估中直接纳入转录组具有预测优势。然而，生成转录组数据的复杂性和成本可能会限制其在现阶段商业育种中的可行性。我们证明，当环境特征明确时，将成本较低的环境协变量与传统基因组数据相结合提供了一种实用的替代方法，与转录组具有相似的效果。
