Genetical and Mathematical Properties of Similarity and Dissimilarity Coefficients Applied in Plant Breeding and Seed Bank Management

Abstract

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.

Genetic diversity of Albanian pea (Pisum sativum L.) landraces assessed by morphological traits and molecular markers

“…Genetic similarity/distances assessed by RBIP marker data. Genetic similarity assessed by RBIP markers data, using the Jaccard index of similarity (Nei 1973(Nei , 1978Reif et al 2005) show the presence of similarity and differences between Albanian local pea landraces. Comparisons of data and cluster analysis range pea landraces into different clusters.…”

“…In the case of RBIP analysis, a fourth state, namely complete absence of any PCR product corresponding to primer site mutation (Jing et al 2005) was added. Genetic similarity coefficients were calculated using the Jaccard index of similarity (Nei 1973(Nei , 1978Reif et al 2005) using SPSS 12 software (SPSS 2003).…”

Variabilidade genética entre acessos de açaizeiro utilizando marcadores microssatélites

“…O nível de polimorfismo e as estimativas dos coeficientes de dissimilaridades genéticas entre o i-ésimo e o j-ésimo acesso foram obtidos no software TFPGA v. 1,3, a partir das frequências alélicas. A distância utilizada foi a de Roger ( ), conforme Reif et al (2005 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 e 27 Chaves, PA População natural 28, 29, 30, 31, 32, 33, 34, 35, 36, 37 e 38 Muaná, PA Propriedade rural 39, 40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67 (Weir, 1996).…”

Population structure and genetic diversity of maize inbreds derived from tropical hybrids

“…Relevant applications include the identification of diverse parental combinations to create segregating progeny with maximum genetic variability, introgression of desirable genes from diverse germplasm into the available genetic base, identification of adaptation patterns by linking genetic variability to environmental data, understanding the genetic relationships between major groups of inbred lines used in crosses, assigning inbred lines to specific heterotic groups, and plant varietal protection (Mohammadi and Prasanna, 2003;Reif et al, 2005;Jombart et al, 2009;Laurentin, 2009). The observed genetic diversity is affected by the data (morphological, biochemical, and molecular marker data), the sample size of markers and individuals genotyped, the criterion for defining the optimal number of groups, and the amount of data lost (Mohammadi and Prasanna, 2003).…”