Premium
Genetical and Mathematical Properties of Similarity and Dissimilarity Coefficients Applied in Plant Breeding and Seed Bank Management
Author(s) -
Reif J. C.,
Melchinger A. E.,
Frisch M.
Publication year - 2005
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2005.0001
Subject(s) - germplasm , similarity (geometry) , biology , genetic similarity , euclidean distance , basis (linear algebra) , genetic distance , statistics , microbiology and biotechnology , mathematics , computer science , genetic variation , genetics , artificial intelligence , botany , genetic diversity , population , gene , image (mathematics) , demography , geometry , sociology
A proper choice of a dissimilarity measure is important in surveys investigating genetic relationships among germplasm with molecular marker data. The objective of our study was to examine 10 dissimilarity coefficients widely used in germplasm surveys, with special focus on applications in plant breeding and seed banks. In particular, we (i) investigated the genetical and mathematical properties of these coefficients, (ii) examined consequences of these properties for different areas of application in plant breeding and seed banks, and (iii) determined relationships between these 10 coefficients. The genetical and mathematical concepts of the coefficients were described in detail. A Procrustes analysis of a published data set consisting of seven CIMMYT maize populations demonstrated close affinity between Euclidean, Rogers’, modified Rogers’, and Cavalli‐Sforza and Edwards’ distance on one hand, and Nei's standard and Reynolds dissimilarity on the other hand. Our investigations show that genetical and mathematical properties of dissimilarity measures are of crucial importance when choosing a genetic dissimilarity coefficient for analyzing molecular marker data. The presented results assist experimenters to extract the maximum amount of information from genetic data and, thus, facilitate the interpretation of findings from molecular marker studies on a theoretically sound basis.