2025-08-07 イリノイ大学アーバナ・シャンペーン校
＜関連情報＞
- https://aces.illinois.edu/news/genomic-techniques-can-streamline-breeding-grain-quality
- https://acsess.onlinelibrary.wiley.com/doi/10.1002/tpg2.70068
- https://acsess.onlinelibrary.wiley.com/doi/10.1002/tpg2.20457
マーカー共変量と多形質ゲノム選択モデルを実装し、米（Oryza sativa L.）の製粉性、外観、調理性、食用品質を向上させる Implementing marker covariates and multi-trait genomic selection models to improve grain milling, appearance, cooking, and edible quality in rice (Oryza sativa L.)
Anup Dhakal, Maribel Cruz, Katherine Loaiza, Juan Cuasquer, Juan Rosas, Eduardo Graterol, Juan David Arbelaez
The Plant Genome Published: 13 July 2025
DOI:https://doi.org/10.1002/tpg2.70068
Abstract
Rice (Oryza sativa L.) is a staple food for over half of the world’s population. With population growth, socioeconomic changes, and shifting consumer lifestyles, the demand for high-quality rice has surged. Understanding consumer preferences for rice quality traits is crucial for breeders to effectively address evolving market needs. Rice breeding programs assess various quality aspects, including grain shape, appearance, milling efficiency, and cooking and eating qualities. Molecular-based approaches like marker-assisted selection and genomic selection (GS) offer promising opportunities to enhance breeding efficiency. In this study, our goal was to build upon our previous findings and improve the predictive ability of GS for primary grain milling and cooking and eating quality traits by incorporating trait marker covariates and highly heritable, high-throughput secondary traits in multi-trait genomic selection strategies (MT-GS). By including amylose content and gelatinization temperature functional markers as covariates in GS models, we improved the predictive ability for primary cooking and eating traits from 21% to 44%. Additionally, integrating secondary traits into MT-GS increased the predictive ability for milling quality traits from 13.5% to 18% and for cooking and eating traits from 4.6% to 50%. Overall, our study demonstrates the feasibility of incorporating whole-genome markers, trait markers, and secondary trait information to enhance the predictive ability of GS for grain milling, cooking, and eating qualities in rice.
Plain Language Summary
Rice is a staple food for over half of the world’s population. As cultural, social, and economic changes shape consumer preferences, the demand for high-quality rice has risen. Breeding programs that can quickly adapt to these shifts are better positioned to develop cultivars tailored to markets with specific grain quality profiles. New molecular and statistical tools offer a way to enhance this adaptability. In this study, we explored how integrating new molecular and phenotypic data can improve the prediction accuracy of genomic selection models for grain milling, cooking, and edible quality traits in rice. Our results showed a significant increase in prediction accuracy when these new data sources were incorporated. Breeding programs can apply our approach to more efficiently improve a range of grain quality traits in rice.
多形質ゲノム選択を実装し、オーツ麦（Avena sativa L.）の穀物製粉品質を向上させる Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.)
Anup Dhakal, Jesse Poland, Laxman Adhikari, Ethan Faryna, Jason Fiedler, Jessica E. Rutkoski, Juan David Arbelaez
The Plant Genome Published: 19 May 2024
DOI:https://doi.org/10.1002/tpg2.20457
Abstract
Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.
Plain Language Summary
Oats must fulfill strict industry grain milling quality standards for food use. Phenotyping for milling quality traits is delayed until the later stages of breeding. Most advanced selection candidates may fail to meet those milling requirements despite having good agronomics. Grain morphometric traits, for example, length and width, can be phenotyped earlier in the breeding pipeline at a fraction of the cost. Including morphometric traits in genomic prediction models can improve their performance and allow breeders to make selections faster and cheaper.
