000			03140nab a22004337a 4500
001			G89744
003			MX-TxCIM
005			20240919020945.0
008			210409s2007 xxu|||p|op||| 00| 0 eng d
022			_a1435-0653 (Online)
024	8		_ahttps://doi.org/10.2135/cropsci2006.09.0564
040			_aMX-TxCIM
090			_aCIS-4957
100	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
245	1	0	_aModeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes
260			_aMadison (USA) : _bCSSA : _bWiley, _c2007.
340			_aComputer File|Printed
500			_aPeer review
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0011-183X
520			_aIn self-pollinated species, the variance–covariance matrix of breeding values of the genetic strains evaluated in multienvironment trials (MET) can be partitioned into additive effects, additive 3 additive effects, and their interaction with environments. The additive relationship matrix A can be used to derive the additive 3 additive genetic variance–covariance relationships among strains,A˜ . This study shows how to separate total genetic effects into additive and additive 3 additive and how to model the additive 3 environment interaction and additive 3 additive 3 environment interaction by incorporating variance–covariance structures constructed as the Kronecker product of a factor-analytic model across sites and the additive (A) and additive 3 additive relationships (A˜ ), between strains. Two CIMMYT international trials were used for illustration. Results show that partitioning the total genotypic effects into additive and additive 3 additive and their interactions with environments is useful for identifying wheat (Triticum aestivum L.) lines with high additive effects (to be used in crossing programs) as well as high overall production. Some lines and environments had high positive additive 3 environment interaction patterns, whereas other lines and environments showed a different additive 3 additive 3 environment interaction pattern
536			_aGenetic Resources Program
546			_aText in English
591			_aCrop Science Society of America (CSSA)
591			_aMcLaren, C. : No CIMMYT Affiliation
594			_aINT3239|CCJL01
650		7	_aGenetic variation _2AGROVOC _91129
650		7	_98947 _aBreeding Value _2AGROVOC
650		7	_aGermplasm _2AGROVOC _91136
650		7	_93706 _aMathematical models _2AGROVOC
650		7	_91296 _aTriticum aestivum _2AGROVOC
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_99555 _aCornelius, P.L.
700	1		_9341 _aTrethowan, R.M.
700	1		_92578 _aMcLaren, C. _gIntegrated Breeding Platform _8CMCG01
700	1		_919534 _aKrishnamachari, A.
773	0		_tCrop Science _n634600 _gv. 47, no. 1, p. 311-320 _dMadison (USA) : CSSA : Wiley, 2007. _wG444244 _x1435-0653
856	4		_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/763
942			_cJA _2ddc _n0
999			_c26598 _d26598