期刊
THEORETICAL AND APPLIED GENETICS
卷 134, 期 1, 页码 279-294出版社
SPRINGER
DOI: 10.1007/s00122-020-03696-9
关键词
-
资金
- Bill and Melinda Gates Foundation
- United States Agency for International Development (USAID) through the Stress Tolerant Maize for Africa (STMA) [OPP1134248]
- CGIAR Research Program MAIZE
- Genomic & Open-source Breeding Informatics Initiative [OPP1093167]
- Government of Australia
- Government of Belgium
- Government of Canada
- Government of China
- Government of France
- Government of India
- Government of Japan
- Government of Korea
- Government of Mexico
- Government of Netherlands
- Government of New Zealand
- Government of Norway
- Government of Sweden
- Government of Switzerland
- Government of United Kingdom
- Government of United States
- World Bank
- CIMMYT Global Maize Program, under CGIAR Research Program MAIZE
- Robbins Lab at Cornell University
- Bill and Melinda Gates Foundation [OPP1093167] Funding Source: Bill and Melinda Gates Foundation
The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT's maize breeding programs. By optimizing training set design strategies, the use of related data for genomic prediction can be efficient, leading to improved prediction accuracy. Using multiple bi-parental populations as training sets and selecting them using algorithms to maximize relatedness can significantly improve prediction accuracy in genomic selection.
Key message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a test-half-predict-half approach. Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT's maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or test-half-predict-half can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据