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The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from ancestors to improve prediction accuracy. The genomic best linear unbiased predictions (GBLUPs) are performed by borrowing information through kinship relationships between individuals. Multigeneration data usually becomes heterogeneous with complex family relationship patterns that are increasingly entangled with each generation. Under these conditions, historical data may not be optimal for model training as the accuracy could be compromised. The sparse selection index (SSI) is a method for training set (TRN) optimization, in which training individuals provide predictions to some but not all predicted subjects. We added an additional trimming process to the original SSI (trimmed SSI) to remove less important training individuals for prediction. Using a large multigeneration (8 yr) wheat (Triticum aestivum L.) grain yield dataset (n = 68,836), we found increases in accuracy as more years are included in the TRN, with improvements of ∼0.05 in the GBLUP accuracy when using 5 yr of historical data relative to when using only 1 yr. The SSI method showed a small gain over the GBLUP accuracy but with an important reduction on the TRN size. These reduced TRNs were formed with a similar number of subjects from each training generation. Our results suggest that the SSI provides a more stable ranking of genotypes than the GBLUP as the TRN becomes larger.
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Journal
Plant Genome
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Place of Publication
USA
Publisher
John Wiley & Sons Inc.
Related Datasets
CGIAR Initiatives
Initiative
Accelerated Breeding
Breeding Resources
Breeding Resources
Impact Area
Nutrition, health & food security
Poverty reduction, livelihoods & jobs
Poverty reduction, livelihoods & jobs
Action Area
Genetic Innovation
Donor or Funder
Bill & Melinda Gates Foundation (BMGF)
CGIAR Trust Fund
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund
National Institute of Food and Agriculture
United States Agency for International Development (USAID)
CGIAR Trust Fund
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund
National Institute of Food and Agriculture
United States Agency for International Development (USAID)