Sipowicz Pablo, Murad Leite Andrade Mario Henrique, Fernandes Filho Claudio Carlos, Benevenuto Juliana, Muñoz Patricio, Ferrão L Felipe V, Resende Marcio F R, Messina C, Rios Esteban F
Plant Breeding Graduate Program, University of Florida, Gainesville, Florida, USA.
Instituto Nacional de Tecnologia Agropecuaria, Manfredi, Argentina.
Plant Genome. 2025 Mar;18(1):e20526. doi: 10.1002/tpg2.20526. Epub 2024 Dec 5.
Alfalfa (Medicago sativa L.) is a perennial forage legume esteemed for its exceptional quality and dry matter yield (DMY); however, alfalfa has historically exhibited low genetic gain for DMY. Advances in genotyping platforms paved the way for a cost-effective application of genomic prediction in alfalfa family bulks. In this context, the optimization of marker density holds potential to reallocate resources within genomic prediction pipelines. This study aimed to (i) test two genotyping platforms for population structure discrimination and predictive ability (PA) of genomic prediction models (G-BLUP) for DMY, and (ii) explore optimal levels of marker density to predict DMY in family bulks. For this, 160 nondormant alfalfa families were phenotyped for DMY across 11 harvests and genotyped via targeted sequencing using Capture-seq with 17K probes and the DArTag 3K panel. Both platforms discriminated similarly against the population structure and resulted in comparable PA for DMY. For genotyping optimization, different levels of marker density were randomly extracted from each platform. In both cases, a plateau was achieved around 500 markers, yielding similar PA as the full set of markers. For phenotyping optimization, models with 500 markers built with data from five harvests resulted in similar PA compared to the full set of 11 harvests and full set of markers. Altogether, genotyping and phenotyping efforts were optimized in terms of number of markers and harvests. Capture-seq and DArTag yielded similar results and have the flexibility to adjust their panels to meet breeders' needs in terms of marker density.
紫花苜蓿（Medicago sativa L.）是一种多年生豆科牧草，因其品质优良和干物质产量（DMY）高而备受推崇；然而，紫花苜蓿在历史上DMY的遗传增益较低。基因分型平台的进步为在紫花苜蓿家系群体中经济高效地应用基因组预测铺平了道路。在此背景下，标记密度的优化有可能在基因组预测流程中重新分配资源。本研究旨在：（i）测试两种基因分型平台对群体结构的区分能力以及基因组预测模型（G-BLUP）对DMY的预测能力（PA）；（ii）探索预测家系群体中DMY的最佳标记密度水平。为此，对160个非休眠紫花苜蓿家系在11次收获时进行了DMY表型测定，并通过使用17K探针的捕获测序（Capture-seq）和3K DArTag芯片进行靶向测序进行基因分型。两个平台对群体结构的区分相似，并且对DMY的PA相当。为了优化基因分型，从每个平台中随机提取不同水平的标记密度。在这两种情况下，大约500个标记时达到平稳期，其PA与全套标记相似。为了优化表型测定，与使用11次收获的全套数据和全套标记构建的模型相比，使用5次收获的数据构建的具有500个标记的模型具有相似的PA。总之，在标记数量和收获次数方面，基因分型和表型测定工作得到了优化。Capture-seq和DArTag产生了相似的结果，并且具有调整其芯片以满足育种者在标记密度方面需求的灵活性。
