000			03436nab a22005177a 4500
001			G90190
003			MX-TxCIM
005			20240919020946.0
008			210702s2007 xxu|||p|op||| 00| 0 eng d
022			_a1943-2631 (Online)
022			_a0016-6731
024	8		_ahttps://doi.org/10.1534/genetics.107.078659
040			_aMX-TxCIM
041			_aeng
090			_aCIS-5124
100	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
245	1	0	_aAssociation analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure
260			_aUSA : _bGenetics Society of America, _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=1943-2631
520			_aLinkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker–trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype x environment interaction, thereby making possible the identification of markers contributing to both additive and additive x additive interaction effects of traits.
536			_aGenetic Resources Program|Global Wheat Program
546			_aText in English
594			_aINT2692|CCJL01|INT3239|INT1511|INT2833|INT0610
650		7	_2AGROVOC _91265 _aSoft wheat
650		7	_2AGROVOC _91136 _aGermplasm
650		7	_2AGROVOC _91848 _aGenetic markers
650		7	_2AGROVOC _96025 _aLinear models
650		7	_2AGROVOC _91133 _aGenotype environment interaction
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
700	1		_9851 _aDreisigacker, S. _gGlobal Wheat Program _8INT2692
700	1		_93542 _aVargas, M.
700	1		_92073 _aHerrera-Foessel, S.
700	1		_91659 _aLillemo, M.
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_9341 _aTrethowan, R.M.
700	1		_94138 _aWarburton, M.L.
700	1		_aFranco, J. _8CFRN01 _gFormerly Genetic Resources Program _9494
700	1		_aReynolds, M.P. _gGlobal Wheat Program _8INT1511 _9831
700	1		_95274 _a Crouch, J.H.
700	1		_95322 _aOrtiz, R.
773	0		_tGenetics _n634959 _gv. 177, no. 3, p. 1889-1913 _dUSA : Genetics Society of America, 2007. _wG444332 _x0016-6731
856	4		_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/652
942			_cJA _2ddc _n0
999			_c26902 _d26902