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<p>Selecting for high grain yield is an intrinsic target of most crop breeding programs. However, the genetic complexity of the trait and its strong interactions with environmental factors pose significant challenges, hindering quick and efficient genetic improvement. This scenario is especially evident in oats (<i>Avena sativa</i> L.), as genetic gain for grain yield has historically been low when compared to major crops. The objective of this study was to leverage data collected in an oat breeding program, identifying molecular markers and genomic regions associated with grain yield and related traits and exploring if this genetic information could enhance genomic prediction models for oat grain yield. Association mapping was conducted to investigate genetic associations for grain yield, plant height, heading date, lodging, snapback, and crown rust severity in six oat breeding populations evaluated in multiple environments in South Dakota over six different years. Thirty-eight genomic regions associated with the traits were considered most promising because of their significance in multiple populations and/or environments, suggesting their potential as valuable genetic sources. When specific markers from the significant genomic regions were considered as fixed effects in linear and additive genomic prediction models, prediction accuracies for grain yield did not improve. However, using only the markers identified through association mapping, instead of the whole marker set, for model development improved prediction accuracy from approximately 4–6%. These results provide valuable information for increasing genetic gains for grain yield in oats and other crops.</p>

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Unraveling genomic regions and optimizing genomic predictions for grain yield and yield-related traits in oats (Avena sativa L.)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月16日

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  • Guilherme Oliveira,
  • Sumandeep Bazzer,
  • Jason Fiedler,
  • Raja Sekhar Nandety,
  • Jean-Luc Jannik,
  • Melanie Caffe

摘要

Selecting for high grain yield is an intrinsic target of most crop breeding programs. However, the genetic complexity of the trait and its strong interactions with environmental factors pose significant challenges, hindering quick and efficient genetic improvement. This scenario is especially evident in oats (Avena sativa L.), as genetic gain for grain yield has historically been low when compared to major crops. The objective of this study was to leverage data collected in an oat breeding program, identifying molecular markers and genomic regions associated with grain yield and related traits and exploring if this genetic information could enhance genomic prediction models for oat grain yield. Association mapping was conducted to investigate genetic associations for grain yield, plant height, heading date, lodging, snapback, and crown rust severity in six oat breeding populations evaluated in multiple environments in South Dakota over six different years. Thirty-eight genomic regions associated with the traits were considered most promising because of their significance in multiple populations and/or environments, suggesting their potential as valuable genetic sources. When specific markers from the significant genomic regions were considered as fixed effects in linear and additive genomic prediction models, prediction accuracies for grain yield did not improve. However, using only the markers identified through association mapping, instead of the whole marker set, for model development improved prediction accuracy from approximately 4–6%. These results provide valuable information for increasing genetic gains for grain yield in oats and other crops.