Assessment of genetic variability for grain nutrients from diverse regions: potential for wheat improvement

Abstract

Background A total of 150 bread wheat genotypes representing 121 Indian and 29 Turkish origin were screened for nutrient concentrations and grain protein content. Elemental and grain protein composition were studied by Inductively Coupled Plasma-Atomic Emission Spectrophotometer and LECO analyser, respectively. The study was performed to determine the variability in nutrient concentrations present in the collected wheat genetic material from two countries.ResultsSeveral fold variations among genotypes existed for almost all the elements. Three major components of principal component analysis (PCA) revealed 60.8% variation among the genotypes. Nutrient variables segregated into two groups, one group containing all the macroelements except sulphur; and another cluster containing proteins and all the microelements except Zn and Mn. Pearson correlation analysis and heat-map were in accordance with each other determining strong positive association between P–K, Mn–Zn, Mg–S and Cu–protein content. Also, PCA and hierarchical grouping divided all the Indian and Turkish genotypes in two main clusters.ConclusionsNutritional profile differentiated the genotypes from two countries into separate groups. However, some of the varieties were closely associated and indicated the success of global wheat exchange programs. While most of the correlations were in agreement with the previous studies, non-association of zinc with grain protein content directed towards its control by some other genetic factors. Some of the experimental wheat varieties with promising nutrient content have been suggested for future wheat advancement programs. Results obtained will be supportive for breeders involved in wheat biofortification programs, food industries and people relying on whole grain wheat products.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-016-3586-2) contains supplementary material, which is available to authorized users.

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within the wheat grain ionome

“…The phenotypic correlations, observed in our study, corroborate well with what we know about the biochemical composition of cereal grains, and are in agreement with other studies of phenotypic correlations within grain ionome of major cereals, such as sorghum (Shakoor et al , ), maize (Baxter et al , ; Gu et al , ), rice (Stangoulis et al , ) and wheat (Morgounov et al , ; Gomez‐Becerra et al , ; Pandey et al , ). Most of these studies reported positive correlations of the essential metal microelements Cu‐Fe‐Zn with grain protein content and S, and the essential metal macroelements Ca‐K‐Mg with P in cereal grains.…”

“…Positive associations within cereal grain ionomes were reported recently (Pandey et al , ; Shakoor et al , ), calling for identification of the underlying factors. One of the explanations of this phenomenon at the genetic level is the pleiotropic effect of specific genes (Tester, ); another one is the ‘dilution effect’ (Davis, ; Shewry et al , ), when the increase in accumulation of carbohydrates dilutes the concentration of minerals in the seeds.…”

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within wheat grain ionome

“…The phenotypic associations, observed in our study, corroborate well with what we know about the biochemical composition of cereal grains and are in agreement with other studies of phenotypic associations within grain ionome of major cereals, such as sorghum (Shakoor et al , 2016), maize (Baxter et al , 2013; Gu et al , 2015), rice (Stangoulis et al , 2007), and wheat (Morgounov et al , 2007; Gomez-Becerra et al , 2010b; Pandey et al , 2016). Most of these studies reported positive associations of the essential metal microelements- Cu-Fe-Zn with GPC and S, and the essential metal macroelements Ca-K-Mg with P in cereal grains.…”

“…Positive associations within cereal grain ionomes were reported recently (Shakoor et al , 2016; Pandey et al , 2016), calling for identification of the underlying factors. One of the explanations of this phenomenon at the genetic level is pleiotropic effect of specific genes (Tester, 1990); another one is “dilution effect” (Davis, 2009; Shewry et al , 2016) when the increase in accumulation of carbohydrates dilutes the concentration of minerals in the seeds.…”

Variability and Site Dependence of Grain Mineral Contents in Tetraploid Wheats

“…Moreover, the correlation matrix based on Pearson's coefficient was calculated for all the measured elements by using MetaboAnalyst 4.0, which is a web-based tool for the visualization of metabolomics [35,36]. This approach is the most widely used in this type of data [31,37] in order to assess the existence of a possible linear relationship between minerals for both the two cultivation sites [38]. Multivariate statistical analyses and graphics were obtained using SIMCA 14 software, (Sartorius Stedim Biotech, Umeå, Sweden) [39].…”