Abstract
Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.
Highlights
Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development
In the wake of the enormous genomic advances, 15 years back we proposed the concept of GAB for accelerating crop improvement [1]
Innovative tools and technologies have been instrumental in improving our understanding of genome structure and function, providing the genetic underpinnings of important trait architectures
Summary
Genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. The increased genome sequence information in crops has improved gene mapping strategies used to discover and map genome-wide allelic variation. Genetic improvement of complex traits demanded efficient breeding methods to facilitate identification and subsequent exploitation of hitherto unexplained trait variation attributable to a vast number of small-effect QTLs. breeding methods like genomic selection (GS), that exploit genome-wide marker information, became more relevant to continuous population improvement and improving the rate of genetic gain [6]. Using BUSCO sets to assess the completeness of a genome sequence was deemed robust in comparison with conventional parameters, including k-mer
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