Innovations in Assessment Approaches of Plant Genetic Diversity

Global issues of poverty, economic development, and sustainability are brought on by the world’s continuously growing population, particularly in developing economies globally. Breeders always strive to genetically improve crop performance to meet the world’s food demands. The existence of various alleles in the gene pool, and consequently various genotypes within populations, is reflected in the genetic diversity among individuals. The first step in improving crops is to comprehend genetic diversity and how it relates to agricultural performance. High throughput molecular marker technologies viz., Next Generation Sequencing (NGS) and deep sequencing or ultra-high throughput sequencing technologies, open new avenues for understanding empirical questions and made it possible to characterize more germplasm in more efficient and cost-effective way. Today, it is possible to assess or quantify the genetic diversity of an organism in increasingly complex ways using a variety of analytical statistical tools and genetic diversity indices. Recently, the examination of plant diversity multivariate features has become prominent due to the high interpretative capability of machine learning techniques. This chapter covers the concepts and sources of genetic diversity, as well as the modern pioneering analytical methods and methodologies, including molecular markers and machine learning algorithms, enabling their thorough and accurate assessment in the postgenomic era. Discussions are illuminating for future research and may encourage researchers to implement genetic diversity considerations into new levels of plant breeding.