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Marker-assisted identification of maize genotypes with improved protein quality

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Russian Journal of Genetics: Applied Research

Abstract

Currently, more than 70% of maize is used for food and fodder; therefore, an improvement in grain quality can increase its nutritive and energy value. A deficiency of two essential amino acids (lysine and tryptophan) significantly reduces the nutritional quality of maize proteins. However, in comparison to conventional maize varieties, opaque2 (o2) mutants have greater contents of lysine and tryptophan in their endosperm proteins and their bioavailability is better. The aim of the study was to identify the maize accessions with high-quality protein. A collection of maize accessions of various ecogeographical origins was studied by molecular methods. This approach was expected to improve the maize’s breeding efficiency. We collected 54 maize genotypes differing in grain quality performance. Amplification with three specific markers for the opaque-2 gene (phi057, phi112, and umc1066) revealed homozygous recessive o2 genotypes associated with an improved nutritional quality of the protein. UREA-PAG electrophoresis of zein proteins was used for Quality Protein Maize (QPM) identification. In addition to the mutant o2 allele, QPM contains genetic modifiers that convert the starchy endosperm of the o2 mutant to the hard vitreous phenotype. The selected QPM accessions are of interest for maize breeding programs aimed at improving grain quality. The use of the markers for o2 and modifier genes accelerates the development of QPM varieties and significantly reduces the labor and financial costs of their production.

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Correspondence to O. A. Orlovskaya.

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Original Russian Text © O.A. Orlovskaya, S.V. Kubrak, S.I. Vakula, L.V. Khotyleva, A.V. Kilchevsky, 2015, published in Vavilovskii Zhurnal Genetiki i Selektsii, 2015, Vol. 19, No. 3, pp. 333–338.

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Orlovskaya, O.A., Kubrak, S.V., Vakula, S.I. et al. Marker-assisted identification of maize genotypes with improved protein quality. Russ J Genet Appl Res 6, 367–372 (2016). https://doi.org/10.1134/S2079059716040158

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  • DOI: https://doi.org/10.1134/S2079059716040158

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