Genomic Selection and Its Application in Pearl Millet Improvement

Pearl millet [Pennisetum glaucum (L.) R. Br] is a staple grain for about 90 million people in India, sub-Saharan Africa, and South Asia. Genomic selection is a new tool that helps to identify better lines among experimental cultivars in plant breeding programs. Genomic selection examines the phenotypes and high-density marker scores of lines in a population to predict breeding values. The integration of all marker information in the prediction model contributes to the effectiveness of genomic selection by eliminating biased marker effect estimations and collecting more of the variance associated with small-effect quantitative trait loci (QTL). The whole genome sequence of pearl millet has recently been sequenced, allowing genomic selection models to be used to improve the selection process in the pearl millet breeding program. Genomic selection, which employs genomic-estimated breeding values of individuals obtained from genome-wide markers to identify candidates for the next breeding cycle, is a powerful tool for enhancing quantitative traits. Models used for genomic selection frequently encounter problems when the number of markers exceeds the number of phenotypic data. To address this issue and enhance prediction accuracy, genomic selection models and algorithms such as Bayesian, Gaussian, and machine learning have been used. This chapter focuses extensively on the transition from conventional selection techniques used in plant breeding to the genomic selection, the underlying statistical models and methods used for this purpose, the current state of genomic selection research in pearl millet, and the prospects for its successful application in the development of climate resilient pearl millet varieties suitable for different end users.