000			03756nab a22005177a 4500
999			_c30435 _d30435
001			G98565
003			MX-TxCIM
005			20240919020947.0
008			121211b |||p||p||||||| |z||| |
022			_a1432-2242 (Revista en electrónico)
022	0		_a0040-5752
024	8		_ahttps://doi.org/10.1007/s00122-013-2243-1
040			_aMX-TxCIM
090			_aCIS-7454
100	1		_91934 _aJarquín, D.
245	1	2	_aA reaction norm model for genomic selection using high-dimensional genomic and environmental data
260			_c2014
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0040-5752
500			_aPeer review
500			_aOpen Access
520			_aKey message: New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. Abstract: In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17?34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.
536			_aGenetic Resources Program
546			_aEnglish
591			_aCIMMYT Informa No. 1874
594			_aINT3239|CCJL01
595			_aCSC
650		7	_aWheat _gAGROVOC _2 _91310
650		7	_94709 _aYield increases _2AGROVOC
650		7	_91133 _aGenotype environment interaction _2AGROVOC
650		7	_91937 _aNucleotide sequence _2AGROVOC
650		7	_91848 _aGenetic markers _2AGROVOC
700	1		_aCalus, M., _ecoaut.
700	1		_aCampos, G. de los, _ecoaut.
700	1		_aCheyron, P.D., _ecoaut.
700	1		_aDaucourt, J., _ecoaut.
700	1		_aGuerreiro, L., _ecoaut.
700	1		_aLacaze, X., _ecoaut.
700	1		_aLorgeou, J., _ecoaut.
700	1		_aPerez, P., _ecoaut.
700	1		_aPiraux, F., _ecoaut.
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tTheoretical and Applied Genetics _gv. 127, no. 3, p. 595-607
856	4		_uhttps://hdl.handle.net/10883/19773 _yOpen Access through DSpace
942			_cJA _2ddc _n0