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Potential and Optimization of Genomic Selection for Fusarium Head Blight Resistance in Six‐Row Barley
Author(s) -
Lorenz A. J.,
Smith K.P.,
Jannink J.L.
Publication year - 2012
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2011.09.0503
Subject(s) - biology , quantitative trait locus , hordeum vulgare , linkage disequilibrium , selection (genetic algorithm) , population , fusarium , genetic marker , genetics , marker assisted selection , doubled haploidy , allele , agronomy , gene , poaceae , artificial intelligence , haplotype , computer science , demography , sociology
Fusarium head blight (FHB),is a devastating disease of barley ( Hordeum vulgare L.), causing reductions in yield and quality. Marker‐based selection for resistance to FHB and lowered deoxynivalenol (DON) grain concentration would save considerable costs and time associated with phenotyping. A marker‐based selection approach called genomic selection (GS) uses genomewide marker information to predict genetic value. We used a cross‐validation approach that separated training sets from validation sets by both entry and environment. We used this framework to test the potential of GS for genetic improvement of FHB and DON as well as test the effect of different factors on prediction accuracy. Prediction accuracy for FHB was found to be as high as 0.72 and that for DON was found to be as high as 0.68. Little difference was found between marker effect estimation methods in terms of prediction of entry genetic value. The extensive linkage disequilibrium (LD) present in this population allowed the marker set to be reduced to 384 markers and training population (TP) size to be reduced 200 with little effect on prediction accuracy. We found little to no advantage to combining subpopulations that correspond to neighboring breeding programs to increase TP size. Apparently, little genetic information is shared between subpopulations, either because of different marker–quantitative trait loci (QTL) linkage phases, different segregating QTL, or nonadditive gene action.