000			03291nab a22003737a 4500
001			G93402
003			MX-TxCIM
005			20240919020946.0
008			210409s2010 xxu|||p|op||| 00| 0 eng d
022			_a1435-0653 (Online)
022			_a0011-183X
024	8		_ahttps://doi.org/10.2135/cropsci2009.01.0053
040			_aMX-TxCIM
041			_aeng
090			_aCIS-5583
100	1		_aFranco, J. _8CFRN01 _gFormerly Genetic Resources Program _9494
245	1	0	_aHierarchical multiple-factor analysis for classifying genotypes based on phenotypic and genetic data
260			_aMadison (USA) : _bCSSA : _bWiley, _c2010.
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			_aA numerical classification problem encountered by breeders and gene-bank curators is how to partition the original heterogeneous population of genotypes into non-overlapping homogeneous subpopulations. The measure of distance that may be defined depends on the type of variables measured (i.e., continuous and/or discrete). The key points are whether and how a distance may be defined using all types of variables to achieve effective classification. The objective of this research was to propose an approach that combines the use of hierarchical multiple-factor analysis (HMFA) and the two-stage Ward Modified Location Model (Ward-MLM) classification strategy that allows (i) combining different types of phenotypic and genetic data simultaneously; (ii) balancing out the effects of the different phenotypic, genetic, continuous, and discrete variables; and (iii) measuring the contribution of each original variable to the new principal axes (PAs). Of the two strategies applied for developing PA scores to be used for clustering genotypes, the strategy that used the first few PA scores to which phenotypic and genetic variables each contributed 50% (i.e., a balanced contribution) formed better groups than those formed by the strategy that used a large number of PA scores explaining 95% of total variability. Phenotypic variables account for much variability in the initial PA; then their contributions decrease. The importance of genetic variables increases in later PAs. Results showed that various phenotypic and genetic variables made important contributions to the new PA. The HMFA uses all phenotypic and genetic variables simultaneously and, in conjunction with the Ward-MLM method, it offers an effective unifying approach for the classification of breeding genotypes into homogeneous groups and for the formation of core subsets for genetic resource conservation.
536			_aGenetic Resources Program
546			_aText in English
591			_aCrop Science Society of America (CSSA)
594			_aCCJL01
650		7	_91134 _aGenotypes _2AGROVOC
650		7	_93634 _aPhenotypes _2AGROVOC
650		7	_aGenetic variation _2AGROVOC _91129
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_919533 _aDesphande, S.
773	0		_tCrop Science _gv. 50, no. 1, p. 105-117 _dMadison (USA) : CSSA : Wiley, 2010. _wG444244 _x1435-0653
856	4		_yAccess only for CIMMYT Staff _uhttps://hdl.handle.net/20.500.12665/303
942			_cJA _2ddc _n0
999			_c27816 _d27816