Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection

Abstract

Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits. Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.

Biofortification strategies to increase wheat nutrition and sustaining yield simultaneously

“…The major negative influence of protein content, zinc concentration and yellow pigment content was on grain yield (r = -0.326, -0.219 and -0.201) and 1000 grain weight (r = -0.192, -0.193 and -0.183). The negative association of grain yield with grain protein content which presents a major obstacle in their simultaneous improvement had also been earlier reported by different workers (Austin et al 1980, Pleijel et al 1999, Groos et al 2003, Barneix 2007, Blanco et al 2012, Wurschum et al 2016, Rapp et al 2018. In this study, most of the high protein lines were low yielding, however, some of the lines with high protein content (progeny no.…”

“…Multigenic control along with significant environmental influences for high protein traits (Snape et al 1993;Kunert et al 2007) and micronutrients (Trethowan 2007;Joshi et al 2010) make selections for these more difficult. Generally, a negative correlation between high yield and high protein has been obtained (Simmonds 1995;Blanco et al 2002;Gonzalez-Hernandez et al 2004;Brevis and Dubkovsky 2010;Blanco et al 2012;Wurschum et al 2016;Rapp et al 2018). However, this inverse relationship has been debated theoretically.…”

Multi-trait regressor stacking increased genomic prediction accuracy of sorghum grain composition

“…al. [14] introduced a reaction norm model 28 which introduces the main and interaction effects of markers and environmental covariates by using 29 high-dimensional random variance-covariance structures of markers and environmental covariates. 30 While most of the genomic prediction studies have been on individual traits, breeding programs use 31 selection indices based on several traits to make breeding decisions.…”

“…[23] used 51 near-infrared derived phenotypes in genomic prediction of protein content and end-use quality in 52 wheat. Multi-trait genomic prediction models can simultaneously improve grain yield and protein 53 content despite being negatively correlated [24,29]. In sorghum, grain macronutrients have shown 54 to be inter-correlated among one another [30], which suggests the multi-trait models may increase 55 predictive ability of individual grain quality traits.…”

Nitrogen distribution in the source-sink system of plants and its role in the production process

“…За даними ФАО (http://www.fao.org/faostat/en/#data) стосовно динаміки світового виробництва основних зернових культур -пшениці, рису і кукурудзи, а також кількості вироблених азотних добрив, з урахуванням середнього вмісту азоту в листках [40][41][42], автори праці [6] дійшли висновку, що на зібране зерно у світі припадає чверть азоту, внесеного з добривами. Це відмінний результат, якщо врахувати, що добрива застосовують не тільки до цих трьох зернових культур, що зібране зерно відповідає лише частині азоту в урожаї на стадії зрілості, що частина азоту втрачається при вимиванні, ерозії та денітрифікації, а також залишається в ґрунті.…”