Open AccessGenomic Selection for Predicting Fusarium Head Blight Resistance in a Wheat Breeding Program
Author(s) -
Arruda Marcio P.,
Brown Patrick J.,
Lipka Alexander E.,
Krill Allison M.,
Thurber Carrie,
Kolb Frederic L.
Publication year - 2015
Publication title -
the plant genome
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 41
ISSN - 1940-3372
DOI - 10.3835/plantgenome2015.01.0003
Subject(s) - biology , best linear unbiased prediction , imputation (statistics) , statistics , single nucleotide polymorphism , quantitative trait locus , lasso (programming language) , genetics , selection (genetic algorithm) , genotype , mathematics , missing data , artificial intelligence , computer science , gene , world wide web
Genomic selection (GS) is a breeding method that uses marker–trait models to predict unobserved phenotypes. This study developed GS models for predicting traits associated with resistance to Fusarium head blight (FHB) in wheat ( Triticum aestivum L.). We used genotyping‐by‐sequencing (GBS) to identify 5054 single‐nucleotide polymorphisms (SNPs), which were then treated as predictor variables in GS analysis. We compared how the prediction accuracy of the genomic‐estimated breeding values (GEBVs) was affected by (i) five genotypic imputation methods (random forest imputation [RFI], expectation maximization imputation [EMI], k ‐nearest neighbor imputation [kNNI], singular value decomposition imputation [SVDI], and the mean imputation [MNI]); (ii) three statistical models (ridge‐regression best linear unbiased predictor [RR‐BLUP], least absolute shrinkage and operator selector [LASSO], and elastic net); (iii) marker density ( p = 500, 1500, 3000, and 4500 SNPs); (iv) training population (TP) size ( n TP = 96, 144, 192, and 218); (v) marker‐based and pedigree‐based relationship matrices; and (vi) control for relatedness in TPs and validation populations (VPs). No discernable differences in prediction accuracy were observed among imputation methods. The RR‐BLUP outperformed other models in nearly all scenarios. Accuracies decreased substantially when marker number decreased to 3000 or 1500 SNPs, depending on the trait; when sample size of the training set was less than 192; when using pedigree‐based instead of marker‐based matrix; or when no control for relatedness was implemented. Overall, moderate to high prediction accuracies were observed in this study, suggesting that GS is a very promising breeding strategy for FHB resistance in wheat.