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Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data

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Title Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data
 
Creator López Cruz, Marco
Dreisigacker, Susanne
Crespo-Herrera, Leonardo A.
Bentley, Alison R.
Singh, Ravi P.
Poland, Jesse A.
Shrestha, Sandesh
Huerta Espino, Julio
Velu, Govindan
Juliana, Philomin
Mondal, Suchismita
Pérez Rodriguez, Paulino
Crossa, José
 
Subject marker-assisted selection
training
wheat
breeding
 
Description 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.
 
Date 2022-12
2022-12-23T12:17:36Z
2022-12-23T12:17:36Z
 
Type Journal Article
 
Identifier Lopez‐Cruz, M., Dreisigacker, S., Crespo‐Herrera, L., Bentley, A. R., Singh, R., Poland, J., Shrestha, S., Huerta‐Espino, J., Govindan, V., Juliana, P., Mondal, S., Pérez‐Rodríguez, P., & Crossa, J. (2022). Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data. Plant Genome. https://hdl.handle.net/10883/22199
1940-3372
https://hdl.handle.net/10568/126293
https://doi.org/10.1002/tpg2.20254
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format application/pdf
 
Publisher Wiley
 
Source Plant Genome