engleski

Comparison of genomic selection models to predict wheat quality traits in a biparental population

Selection for wheat (Triticum aestivum L.) grain quality traits is often costly and time consuming since it requires extensive phenotyping in the process of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The main aim of this study was to investigate several different models, RR-BLUP and Bayesian models, for predicting wheat grain quality traits using a biparental population. Two varieties (Bezostaja and Klara) differing in all HMW glutenin subunits were selected, and a set of 139 recombinant inbred lines were derived from a cross between these two parents. All RILs were evaluated at two locations (Osijek and Slavonski Brod) during three years for the quality traits grain protein content (GPC), wet gluten content (WGC) and mixograph midline peak height (MPT). Population was genotyped using the DArTseq technology and a total of 1087 SNPs were used for genomic selection. Response of the prediction accuracy of quality traits to changes in heritability and model applied were determined, in order to propose an optimal scenario for genomic selection within biparental wheat populations. Impact of genotype imputation on the performance of models for genomic prediction was also assessed. Results showed that trait heritability and genotype imputation significantly affect predictive ability of selected models when using biparental wheat population.

wheat quality ; RILs ; genomic selection ; SNPs ; imputation ; heritability

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