000			03299nab a22003857a 4500
001			G96960
003			MX-TxCIM
005			20240919020947.0
008			210809s2012 ts |||p|op||| 00| 0 eng d
022			_a1875-5488 (Online)
022			_a1389-2029
024	8		_ahttps://doi.org/10.2174/138920212800543066
040			_aMX-TxCIM
041			_aeng
090			_aCIS-6768
100	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
245	1	0	_aFrom genotype x environment interaction to gene x environment interaction
260			_aUnited Arab Emirates : _bBentham Science Publishers, _c2012.
500			_aPeer review
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=1389-2029
520			_aHistorically in plant breeding a large number of statistical models has been developed and used for studying genotype x environment interaction. These models have helped plant breeders to assess the stability of economically important traits and to predict the performance of newly developed genotypes evaluated under varying environmental conditions. In the last decade, the use of relatively low numbers of markers has facilitated the mapping of chromosome regions associated with phenotypic variability (e.g., QTL mapping) and, to a lesser extent, revealed the differetial response of these chromosome regions across environments (i.e., QTL x environment interaction). QTL technology has been useful for marker-assisted selection of simple traits; however, it has not been efficient for predicting complex traits affected by a large number of loci. Recently the appearance of cheap, abundant markers has made it possible to saturate the genome with high density markers and use marker information to predict genomic breeding values, thus increasing the precision of genetic value prediction over that achieved with the traditional use of pedigree information. Genomic data also allow assessing chromosome regions through marker effects and studying the pattern of covariablity of marker effects across differential environmental conditions. In this review, we outline the most important models for assessing genotype x environment interaction, QTL x environment interaction, and marker effect (gene) x environment interaction. Since analyzing genetic and genomic data is one of the most challenging statistical problems researchers currently face, different models from different areas of statistical research must be attempted in order to make significant progress in understanding genetic effects and their interaction with environment.
536			_aGenetic Resources Program
546			_aText in English
591			_aCIMMYT Informa No. 1807
594			_aCCJL01
595			_aCSC
650		7	_aGenotype environment interaction _2AGROVOC _91133
650		7	_aQuantitative Trait Loci _2AGROVOC _91853
650		7	_aEnvironmental factors _2AGROVOC _94558
650		7	_aGenetic markers _2AGROVOC _91848
650		7	_aMarker-assisted selection _2AGROVOC _910737
773	0		_tCurrent Genomics _gv. 13, no. 3, p. 225-244 _dUnited Arab Emirates : Bentham Science Publishers, 2012. _x1389-2029
856	4		_uhttps://hdl.handle.net/20.500.12665/283 _yAccess only for CIMMYT Staff
942			_cJA _2ddc _n0
999			_c29380 _d29380