Germplasm

Demography

Biotechnology

Biology

Diversity (politics)

Computer science

Anthropology

Computational biology

Evolutionary biology

Genetic diversity

Crop

Bootstrapping (finance)

Mathematics

Agronomy

Econometrics

Population

Sociology

Artificial intelligence

Cluster analysis

Salient

Machine learning

Crop Science

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.

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