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022			_a2160-1836
024	8		_ahttps://doi.org/10.1093/g3journal/jkaf031
040			_aMX-TxCIM
041			_aeng
100	1		_aTessele, A. _938420
245	1	0	_aImproving genomic selection in hexaploid wheat with sub-genome additive and epistatic models
260			_aBethesda, MD (United States of America) : _bOxford University Press, _c2025.
500			_aPeer review
500			_aOpen Access
520			_aThe goal of wheat breeding is the development of superior cultivars tailored to specific environments, and the identification of promising crosses is crucial for the success of breeding programs. Although genomic estimated breeding values were developed to estimate additive effects of genotypes before testing as parents, application has focused on predicting performance of candidate lines, ignoring non-additive genetic effects. However, non-additive genetic effects are hypothesized to be especially important in allopolyploid species due to the interaction between homeologous genes. The objectives of this study were to model additive and additive-by-additive epistatic effects to better delineate the genetic architecture of grain yield in wheat and to improve the accuracy of genomewide predictions. The dataset utilized consisted of 3740 F5:6 experimental lines tested in the K-State wheat breeding program across the years 2016 and 2018. Covariance matrices were calculated based on whole and sub-genome marker data and the natural and orthogonal interaction approach (NOIA) was used to estimate variance components for additive and additive-by-additive epistatic effects. Incorporating epistatic effects in additive models resulted in non-orthogonal partitioning of genetic effects but increased total genetic variance and reduced deviance information criteria. Estimation of sub-genome effects indicated that genotypes with the greatest whole genome effects often combine sub-genomes with intermediate to high effects, suggesting potential for crossing parental lines which have complementary sub-genome effects. Modeling epistasis in either whole-genome or sub-genome models led to a marginal (3%) improvement in genomic prediction accuracy, which could result in significant genetic gains across multiple cycles of breeding.
546			_aText in English
597			_fBreeding for Tomorrow _dKansas Wheat Commission _dKansas Wheat Alliance, Inc. _uhttps://hdl.handle.net/10568/179269
650		7	_aAdditives _2AGROVOC _914443
650		7	_aGenomes _2AGROVOC _91131
650		7	_aHexaploidy _2AGROVOC _92020
650		7	_aWheat _2AGROVOC _91310
650		7	_aBreeding _2AGROVOC _91029
700	1		_aGonzález-Diéguez, D.O. _8I1707522 _gGlobal Wheat Program _gBreeding Modernization and Innovation Platform _926628
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aJohnson, B.E. _938421
700	1		_aMorris, G.P. _94848
700	1		_aFritz, A.K. _93758
773	0		_tG3: Genes, Genomes, Genetics _gv. 15, no. 4, art. jkaf031 _dBethesda, MD (United States of America) : Oxford University Press, 2025. _x2160-1836 _w56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/35532
942			_cJA _n0 _2ddc
999			_c68663 _d68655