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Hierarchical multiple-factor analysis for classifying genotypes based on phenotypic and genetic data

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Relation http://oar.icrisat.org/90/
http://dx.doi.org/10.2135/cropsci2009.01.0053
 
Title Hierarchical multiple-factor analysis for classifying genotypes based on phenotypic and genetic data
 
Creator Franco, J
Crossa, J
Desphande, S
 
Subject Genetics and Genomics
 
Description A numerical classifi cation 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 defi ned depends on the type of variables
measured (i.e., continuous and/or discrete).
The key points are whether and how a distance
may be defi ned using all types of variables to
achieve effective classifi cation. 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 Modifi
ed Location Model (Ward-MLM) classifi cation
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 fi rst 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 classifi cation of breeding genotypes into
homogeneous groups and for the formation of
core subsets for genetic resource conservation.
 
Publisher Crop Science Society of America
 
Date 2010
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights —
 
Identifier http://oar.icrisat.org/90/1/CropSci50_1_105-117_2010.pdf
Franco, J and Crossa, J and Desphande, S (2010) Hierarchical multiple-factor analysis for classifying genotypes based on phenotypic and genetic data. Crop Science, 50 (1). pp. 105-117.