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 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

Use of two-block partial least-squares to study covariation in shape.

Many problems in comparative biology and biological anthropology require meaningful definitions of “relative size” and “shape” Here we review the distinguishing features of ratios and residuals and their relationships to other methods of “size-adjustment” for continuous data Eleven statistical techniques are evaluated in reference to one broadly interspecific data set (craniometries of adult Old World monkeys) and one narrowly intraspecific data set (anthropometries of adult Native American males) Three different types of residuals are compared to three versions of shape ratios, and these are contrasted to “cscores,” Penrose shape, and multivariate adjustments based on the first principal component of the logged variance-covariance matrix; all methods are also compared to raw and logged raw data In order to help us identify appropriate; methods for size-adjustment, geometrically similar or “isometric” versions of the male vervet and the Inuit male were created by scalar multiplication of all variables The geometric mean of all variables is used as overall “size” throughout this investigation, but our conclusions would be the same for most other size variables



Residual adjustments failed to correctly identify individuals of the same shape in both sampkles Like residuals, cscores are also sample-specific and incorrectly attribute different shape values to individuals known to be identical in shape Multivariate “residuals” (eg, discarding the first principal component and Burnaby's method) are plagued by similar problems If one of the goals of an analysis is to identify individuals (OTUs) of the same shape after accounting for overalll size differences, then none of these methods can be recommended We also reject the assertion that size-adjusted variables should be unciorrelated with size of “size-free”; rather, whether or not shape covaries with size is an important empirical determination in any analysis Without explicit similarity criteria, “lines of subtraction” can be very misleading



Only variables in the Mosimann family of shape rations allowed us to identify sized individuals of the same shape (“Iso-OUTs”) Residuals from isometric lines in logarithmic space, projections of logged data to a plane orthogonal to an isometric vector, and Penrose shape distance based on logged data are also part of this shape family Shape defined in this manner can be significantly correlated with size in allometric data sets (eg, guenon craniometrics); ratio shape differences may be largely independent of size in narrowly intraspecific or intrasexual data sets (eg, Native American anthropometrics) Log-transformations of shape variables are not always necessary or desirable We hope our findings enciourage other workers to question the assumptions and utility of residuals as size-adjusted data and to explore shape and relative size within Mosimann's explicitly geometric framework © 1995 Wiley-Liss, Inc

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ajpa.1330380608

F.J. Rohlf

Papers

NTSYS-pc Numerical Taxonomy and Multivariate Analysis System, version 2.1: Owner manual