Integrating Advanced Molecular, Genomic, and Speed Breeding Methods for Genetic Improvement of Stress Tolerance in Rice

Abstract

AbstractConventional plant breeding methods have contributed significantly to improving economic traits toward the development and release of elite cultivars of several crop species, including rice. Generation advancement through conventional methods is time-consuming and requires at least ten cycles to stabilize the variability generated before it is released for commercial cultivation. Recent advances in genomics and molecular tools have accelerated the breeding methods that can handle large datasets more precisely and efficiently, enabling marker-assisted selection, gene pyramiding for multiple stress tolerance, and gene stacking for introgression of multiple traits into an elite background. A comprehensive breeding approach is required which includes novel techniques that allow for rapid inbred line development and evaluation. Recently, a rapid generation advancement technique called “speed breeding” has been proposed to accelerate the generation advancement by shortening the generation cycle in several crops, including rice. Genomic selection (GS) is another novel breeding approach utilizing genome-wide high-throughput, cost-effective molecular markers for genetic evaluation. GS can increase genetic gain for quantitative traits, such as grain yield and stress resistance traits, by reducing the length of the selection cycle. Genomic selection combined with “speed breeding” (SB) could speed up the breeding cycle, even more allowing for rapid generation advancement. Prior to field trials in a target environment, an integrated breeding technique could comprise quick derivation of line, phenotyping of yield-associated variables, and indirect phenotypic and multivariate GS for relevant traits. The present chapter focuses on the genetic improvement of rice through speed breeding methods, genomic selection approaches, and advanced genomic tools available in rice.KeywordsSpeed breedingRapid generation advancementGenomic selectionRice