Big genomic data analysis leads to more accurate trait prediction in hybrid breeding for yield enhancement in crop plants

Abstract

In the high-throughput next-generation sequencing (NGS) era, it is feasible to generate huge amount of genotypic data from a large population of a given species. Population size, amount of data generated, and efficient computational analysis are the determining factors for the genomic predictions during crop improvement. Big data would generate more meaningful information from them and predict the trait behaviour more accurately in subsequent breeding cycles. The pace crop improvements and elite variety development through genomic-assisted breeding (GAB) is directly proportional to the advancements in NGS technologies occurred during the last 2 decades. Efficient evaluation of amount of crop genetic stocks is perquisite to exploit their genetic diversity to attain global food security. Making the sense from available genotypic data, genomic prediction has become a promising strategy to accurately explore the potential of large number of accessions deposited in various gene banks across the globe.