000			02917nab a22004457a 4500
001			G90478
003			MX-TxCIM
005			20240919020946.0
008			220516s2008 ne |||p|op||| 00| 0 eng d
022			_a1573-5060 (Online)
022			_a0014-2336
024	8		_ahttps://doi.org/10.1007/s10681-007-9594-0
040			_aMX-TxCIM
041			_aeng
090			_aCIS-5315
100	1		_aMalosetti, M. _912182
245	1	2	_aA multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize ( Zea mays L.)
260			_aDordrecht (Netherlands) : _bSpringer, _c2008.
340			_aComputer File|Printed
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0014-2336
500			_aPeer review
500			_aOpen Access
520			_aDespite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance–covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities.
536			_aGeneration Challenge Program|Genetic Resources Program
546			_aText in English
591			_aSpringer
594			_aINT1991|CCJL01
650		7	_aGenetic Correlation _99128 _2AGROVOC
650		7	_aMathematical models _93706 _2AGROVOC
650		0	_aField Experimentation _98629 _2AGROVOC
650		7	_aQuantitative Trait Loci _91853 _2AGROVOC
650		7	_aGene Interaction _99058 _2AGROVOC
700	1		_9835 _aRibaut, J.M. _gIntegrated Breeding Platform _8INT1991
700	1		_aVargas, M. _93542
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aEeuwijk, F.A. van _99549
773	0		_tEuphytica _n635209 _gv. 161, no. 1-2, p. 241-257 _dDordrecht (Netherlands) : Springer, 2008. _wG444298 _x0014-2336
856	4		_yOpen Access through DSpace _uhttp://hdl.handle.net/10883/3067
942			_cJA _2ddc _n0
999			_c27033 _d27033