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

TLDR

Germplasm diversity and genetic relationships among breeding materials are crucial for crop improvement, and numerous methods—using pedigree, morphological, agronomic, biochemical, and DNA‑based data—have been developed to analyze them. This review focuses on applying statistical tools and techniques to analyze genetic diversity at the intraspecific level in crop plants. Accurate genetic diversity estimates require careful sampling, appropriate data selection, suitable distance measures and clustering methods, and objective relationship determination, with bootstrapping used to address complex data analysis challenges.

Abstract

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.