000			03596nab|a22004097a|4500
001			66534
003			MX-TxCIM
005			20231101182147.0
008			231031s2023 xxk|||p|op||| 00| 0 eng d
022			_a2517-5025 (Online)
024	8		_ahttps://doi.org/10.1093/insilicoplants/diad002
040			_aMX-TxCIM
041			_aeng
100	1		_aFradgley, N. S. _917394 _8001713762 _gGlobal Wheat Program
245	1	0	_aMulti-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat
260			_aUnited Kingdom : _bOxford University Press, _c2023.
500			_aPeer review
500			_aOpen Access
520			_aCereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement.
546			_aText in English
650		7	_aGenomics _2AGROVOC _91132
650		7	_aPopulation _2AGROVOC _915029
650		7	_aRecurrent selection _2AGROVOC _912374
650		7	_aSimulation _2AGROVOC _98687
650		7	_aTriticum aestivum _2AGROVOC _91296
700	1		_8001712617 _917393 _aGardner, K.A. _gGenetic Resources Program
700	1		_8001712492 _99599 _aBentley, A.R. _gFormerly Global Wheat Program
700	1		_919870 _aHowell, P.
700	1		_8001711711 _95975 _aMackay, I. _gFormerly Excellence in Breeding
700	1		_919868 _aScott, M.F.
700	1		_919872 _aMott, R.
700	1		_917416 _aCockram, J.
773	0		_tin silico Plants _gv. 5, no. 1, art. diad002 _dUnited Kingdom : Oxford University Press, 2023. _x2517-5025
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22730
942			_cJA _n0 _2ddc
999			_c66534 _d66526