Ehoche O Grace, Arojju Sai Krishna, Jahufer M Z Zulfi, Jauregui Ruy, Larking Anna C, Cousins Greig, Tate Jennifer A, Lockhart Peter J, Griffiths Andrew G
Grasslands Research Centre, AgResearch Ltd, Private Bag 11008, Palmerston North, 4442, New Zealand.
Massey University, Private Bag 11222, Palmerston North, 4442, New Zealand.
Theor Appl Genet. 2025 Jan 23;138(1):34. doi: 10.1007/s00122-025-04819-w.
Genomic selection using white clover multi-year-multi-site data showed predicted genetic gains through integrating among-half-sibling-family phenotypic selection and within-family genomic selection were up to 89% greater than half-sibling-family phenotypic selection alone. Genomic selection, an effective breeding tool used widely in plants and animals for improving low-heritability traits, has only recently been applied to forages. We explored the feasibility of implementing genomic selection in white clover (Trifolium repens L.), a key forage legume which has shown limited genetic improvement in dry matter yield (DMY) and persistence traits. We used data from a training population comprising 200 half-sibling (HS) families evaluated in a cattle-grazed field trial across three years and two locations. Combining phenotype and genotyping-by-sequencing (GBS) data, we assessed different two-stage genomic prediction models, including KGD-GBLUP developed for low-depth GBS data, on DMY, growth score, leaf size and stolon traits. Predictive abilities were similar among the models, ranging from -0.17 to 0.44 across traits, and remained stable for most traits when reducing model input to 100-120 HS families and 5500 markers, suggesting genomic selection is viable with fewer resources. Incorporating a correlated trait with a primary trait in multi-trait prediction models increased predictive ability by 28-124%. Deterministic modelling showed integrating among-HS-family phenotypic selection and within-family genomic selection at different selection pressures estimated up to 89% DMY genetic gain compared to phenotypic selection alone, despite a modest predictive ability of 0.3. This study demonstrates the potential benefits of combining genomic and phenotypic selection to boost genetic gains in white clover. Using cost-effective GBS paired with a prediction model optimized for low read-depth data, the approach can achieve prediction accuracies comparable to traditional models, providing a viable path for implementing genomic selection in white clover.
利用白三叶多年多点数据进行的基因组选择表明，通过整合半同胞家系间表型选择和家系内基因组选择，预测的遗传增益比仅进行半同胞家系表型选择高出89%。基因组选择是一种在植物和动物中广泛用于改善低遗传力性状的有效育种工具，最近才应用于牧草。我们探讨了在白三叶（Trifolium repens L.）中实施基因组选择的可行性，白三叶是一种关键的豆科牧草，在干物质产量（DMY）和持久性性状方面的遗传改良有限。我们使用了来自一个训练群体的数据，该群体由200个半同胞（HS）家系组成，在三年和两个地点的牛放牧田间试验中进行了评估。结合表型数据和测序基因分型（GBS）数据，我们评估了不同的两阶段基因组预测模型，包括为低深度GBS数据开发的KGD-GBLUP，用于DMY、生长评分、叶大小和匍匐茎性状。各模型的预测能力相似，各性状的预测能力范围为-0.17至0.44，当将模型输入减少到100-120个HS家系和5500个标记时，大多数性状的预测能力保持稳定，这表明在资源较少的情况下基因组选择是可行的。在多性状预测模型中纳入与主要性状相关的性状，可使预测能力提高28-124%。确定性模型表明，与仅进行表型选择相比，在不同选择压力下整合HS家系间表型选择和家系内基因组选择，估计DMY遗传增益高达89%，尽管预测能力仅为0.3。本研究证明了结合基因组选择和表型选择以提高白三叶遗传增益的潜在益处。使用具有成本效益的GBS并结合针对低读深度数据优化的预测模型，该方法可以实现与传统模型相当的预测准确性，为在白三叶中实施基因组选择提供了一条可行的途径。
