期刊
G3-GENES GENOMES GENETICS
卷 9, 期 7, 页码 2123-2133出版社
OXFORD UNIV PRESS INC
DOI: 10.1534/g3.118.200842
关键词
Genetic gain; Genomic; Selection; Look-ahead; Selection; Simulation; Optimization; Genomic Prediction; GenPred; Shared Data Resources
资金
- Agriculture and Food Research Initiative from the USDA National Institute of Food and Agriculture [2017-67007-26175, 1011702]
- Plant Sciences Institute's Faculty Scholars program at Iowa State University
- NIFA [914521, 2017-67007-26175] Funding Source: Federal RePORTER
New genotyping technologies have made large amounts of genotypic data available for plant breeders to use in their efforts to accelerate the rate of genetic gain. Genomic selection (GS) techniques allow breeders to use genotypic data to identify and select, for example, plants predicted to exhibit drought tolerance, thereby saving expensive and limited field-testing resources relative to phenotyping all plants within a population. A major limitation of existing GS approaches is the trade-off between short-term genetic gain and long-term potential. Some approaches focus on achieving short-term genetic gain at the cost of reduced genetic diversity necessary for long-term gains. In contrast, others compromise short-term progress to preserve long-term potential without consideration of the time and resources required to achieve it. Our contribution is to define a new look-ahead metric for assessing selection decisions, which evaluates the probability of achieving high genetic gains by a specific time with limited resources. Moreover, we propose a heuristic algorithm to identify optimal selection decisions that maximize the look-ahead metric. Simulation results demonstrate that look-ahead selection outperforms other published selection methods.
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