A method for combining molecular markers and phenotypic attributes for classifying plant genotypes

被引:106
作者
Franco, J
Crossa, J
Ribaut, JM
Betran, J
Warburton, ML
Khairallah, M
机构
[1] Univ Republica, Fac Agron, Montevideo 12900, Uruguay
[2] CIMMYT, Int Maize & Wheat Improvement Ctr, Mexico City 06600, DF, Mexico
[3] Texas A&M Univ, Dept Crop Sci, College Stn, TX 77843 USA
关键词
molecular markers; fragments; cluster analysis; simple matching coefficients; analysis of molecular variance; mixture models;
D O I
10.1007/s001220100641
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Classifying genotypes into clusters based on DNA fingerprinting, and/or agronomic attributes. for studying genetic and phenotypic diversity is a common practice. Researchers are interested in knowing the minimum number of fragments (and markers) needed for finding the underlying structural patterns of diversity in a population of interest. and using this information in conjunction with the phenotypic attributes to obtain more precise clusters of genotypes. The objectives of this study are to present: (1) a retrospective method of analysis for selecting a minimum number of fragments (and markers) from a study needed to produce the same classification of genotypes as that obtained using all the fragments (and markers), and (2) a classification strategy for genotypes that allows the combination of the minimum set of fragments with available phenotypic attributes. Results obtained on seven experimental data sets made up of different plant species, number of individuals per species' and number of markers, showed that the retrospective analysis did indeed find few relevant fragments (and markers) that best discriminated the genotypes. In two data sets, the classification strategy of combining the information on the relevant minimum fragments with the available morpho-agronomic attributes produced compact and well-differentiated groups of genotypes.
引用
收藏
页码:944 / 952
页数:9
相关论文
共 20 条
[1]  
[Anonymous], 1997, NTSYSpc, Numerical taxonomy and multivariate analysis system, Version 202
[2]  
[Anonymous], 1987, CLUSTAN USER MANUAL
[3]   THE MIXTURE METHOD OF CLUSTERING APPLIED TO 3-WAY DATA [J].
BASFORD, KE ;
MCLACHLAN, GJ .
JOURNAL OF CLASSIFICATION, 1985, 2 (01) :109-125
[4]  
EXCOFFIER L, 1992, GENETICS, V131, P479
[5]   A two-stage, three-way method for classifying genetic resources in multiple environments [J].
Franco, J ;
Crossa, J ;
Villaseñor, J ;
Castillo, A ;
Taba, S ;
Eberhart, SA .
CROP SCIENCE, 1999, 39 (01) :259-267
[6]   A sequential clustering strategy for classifying gene bank accessions [J].
Franco, J ;
Crossa, J ;
Diaz, J ;
Taba, S ;
Villasenor, J ;
Eberhart, SA .
CROP SCIENCE, 1997, 37 (05) :1656-1662
[7]   Classifying genetic resources by categorical and continuous variables [J].
Franco, J ;
Crossa, J ;
Villasenor, J ;
Taba, S ;
Eberhart, SA .
CROP SCIENCE, 1998, 38 (06) :1688-1696
[8]   GENERAL COEFFICIENT OF SIMILARITY AND SOME OF ITS PROPERTIES [J].
GOWER, JC .
BIOMETRICS, 1971, 27 (04) :857-&
[9]  
Hartl D.L., 1997, PRINCIPLES POPULATIO
[10]   Mixture separation for mixed-mode data [J].
Lawrence, CJ ;
Krzanowski, WJ .
STATISTICS AND COMPUTING, 1996, 6 (01) :85-92