Problems and possibilities of studying malting quality in barley using molecular genetic approaches.

Abstract

About one-third of the world’s barley crop is used for malt production to meet the needs of the brewing industry. In this regard, the study of the genetic basis of malting quality traits and the breeding of malting barley varieties that are adaptive to their growing conditions are relevant throughout the world, particularly in the Russian Federation, where the cultivation and use of foreign malting varieties of barley prevails. The main parameters of malting quality (artificially germinated and dried barley grains) are malt extract, diastatic power, Kolbach index, viscosity, grain protein, wort β-glucan, free amino nitrogen, and soluble protein content. Most of these components are under the control of quantitative trait loci (QTLs) and are affected by environmental conditions, which complicates their study and precise localization. In addition, the phenotypic assessment of malting quality traits requires elaborate, expensive phenotypic analyses. Currently, there are more than 200 QTLs associated with malting parameters, which were identified using biparental mapping populations. Molecular markers are widely used both for mapping QTL loci responsible for malting quality traits and for performing marker-assisted selection (MAS), which, in combination with conventional breeding, makes it possible to create effective strategies aimed at accelerating the process of obtaining new promising genotypes. Nevertheless, the MAS of malting quality traits faces a series of difficulties, such as the low accuracy of localization of QTLs, their ineffectiveness when transferred to another genetic background, and linkage with undesirable traits, which makes it necessary to validate QTLs and the molecular markers linked to them. This review presents the results of studies that used MAS to improve the malting quality of barley, and it also considers studies that searched for associations between genotype and phenotype, carried out using GWAS (genome-wide association study) approaches based on the latest achievements of high-throughput genotyping (diversity array technology (DArT) and single-nucleotide polymorphism markers (SNPs)).