The utility of RFLP (restriction fragment length polymorphism), RAPD (random-amplified polymorphic DNA), AFLP (amplified fragment length polymorphism) and SSR (simple sequence repeat, microsatellite) markers in soybean germplasm analysis was determined by evaluating information content (expected heterozygosity), number of loci simultaneously analyzed per experiment (multiplex ratio) and effectiveness in assessing relationships between accessions. SSR markers have the highest expected heterozygosity (0.60), while AFLP markers have the highest effective multiplex ratio (19). A single parameter, defined as the marker index, which is the product of expected heterozygosity and multiplex ratio, may be used to evaluate overall utility of a marker system. A comparison of genetic similarity matrices revealed that, if the comparison involved both cultivated (Glycine max) and wild soybean (Glycine soja) accessions, estimates based on RFLPs, AFLPs and SSRs are highly correlated, indicating congruence between these assays. However, correlations of RAPD marker data with those obtained using other marker systems were lower. This is because RAPDs produce higher estimates of interspecific similarities. If the comparisons involvedG. max only, then overall correlations between marker systems are significantly lower. WithinG. max, RAPD and AFLP similarity estimates are more closely correlated than those involving other marker systems.

The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis

Phosphate solubilizing bacteria from subtropical soil and their tricalcium phosphate solubilizing abilities

Knowledge about germplasm diversity and genetic relationships among breeding materials could be an invaluable aid in crop improvement strategies. A number of methods are currently available for analysis of genetic diversity in germplasm accessions, breeding lines, and populations. These methods have relied on pedigree data, morphological data, agronomic performance data, biochemical data, and more recently molecular (DNA-based) data. For reasonably accurate and unbiased estimates of genetic diversity, adequate attention has to be devoted to (i) sampling strategies; (ii) utilization of various data sets on the basis of the understanding of their strengths and constraints; (iii) choice of genetic distance measure(s), clustering procedures, and other multivariate methods in analyses of data; and (iv) objective determination of genetic relationships. Judicious combination and utilization of statistical tools and techniques, such as bootstrapping, is vital for addressing complex issues related to data analysis and interpretation of results from different types of data sets, particularly through clustering procedures. This review focuses on application of statistical tools and techniques in analysis of genetic diversity at the intraspecific level in crop plants.

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Analysis of Genetic Diversity in Crop Plants—Salient Statistical Tools and Considerations

The set of most-parsimonious trees for a data matrix may include several distinct classes (islands) of trees. An island is defined as a collection of trees, all less than a specified length, each tree connected to every other tree in the island through a series of trees, and each one differing from the next by a single rearrangement of branches. One advantage of this method of defining classes of trees is that islands are easily found by tree-searching programs. Multiple islands can be discovered by conducting many searches using a tree-searching program, each search beginning with a different tree. Among 37 data matrices examined, all 8 data matrices with two or more islands have retention indices less than 0.67. Trees are generally more similar within islands than among islands, as judged by analysis of partition metric distances between trees. As a consequence, trees in different islands may have different implications for character evolution, and for this reason should be sought. (Parsimony; phylogeny; tree searching; opti- mization; multiple optima; islands of trees; character evolution.)

The discovery and importance of multiple islands of most-parsimonious trees

The relatively new two-block partial least-squares method for analyzing the covariance between two sets of variables is described and contrasted with the well-known method of canonical correlation analysis. Their statistical properties, types of answers, and visualization techniques are discussed. Examples are given to show its usefulness in comparing two sets of variables—especially when one or both of the sets of variables are shape variables from a geometric morphometric study. (Canonical correlation; covariance; morphometrics; Mus musculus domesticus; partial least squares; Plethodon; visualization.)

https://www.jstor.org/stable/info/2585291

FJ Rohlf

Papers

NTSYS-pc: numerical taxonomy and multivariate analysis system