Quantitative trait loci analysis of zinc efficiency and grain zinc concentration in wheat using whole genome average interval mapping

Abstract

Zinc (Zn) deficiency is a widespread problem which reduces yield and grain nutritive value in many cereal growing regions of the world. While there is considerable genetic variation in tolerance to Zn deficiency (also known as Zn efficiency), phenotypic selection is difficult and would benefit from the development of molecular markers. A doubled haploid population derived from a cross between the Zn inefficient genotype RAC875-2 and the moderately efficient genotype Cascades was screened in three experiments to identify QTL linked to growth under low Zn and with the concentrations of Zn and iron (Fe) in leaf tissue and in the grain. Two experiments were conducted under controlled conditions while the third examined the response to Zn in the field. QTL were identified using an improved method of analysis, whole genome average interval mapping. Shoot biomass and shoot Zn and Fe concentrations showed significant negative correlations, while there were significant genetic correlations between grain Zn and Fe concentrations. Shoot biomass, tissue and grain Zn concentrations were controlled by a number of genes, many with a minor effect. Depending on the traits and the site, the QTL accounted for 12-81% of the genetic variation. Most of the QTL linked to seedling growth under Zn deficiency and to Zn and Fe concentrations were associated with height genes with greater seedling biomass associated with lower Zn and Fe concentrations. Four QTL for grain Zn concentration and a single QTL for grain Fe concentration were also identified. A cluster of adjacent QTL related to the severity of symptoms of Zn deficiency, shoot Zn concentration and kernel weight was found on chromosome 4A and a cluster of QTL associated with shoot and grain Fe concentrations and kernel Plant Soil (weight was found on chromosome 3D. These two regions appear promising areas for further work to develop markers for enhanced growth under low Zn and for Zn and Fe uptake. Although there was no significant difference between the parents, the grain Zn concentration ranged from 29 to 43 mg kg −1 within the population and four QTL associated with grain Zn concentration were identified. These were located on chromosomes 3D, 4B, 6B and 7A and they described 92% of the genetic variation. Each QTL had a relatively small effect on grain Zn concentration but combining the four high Zn alleles increased the grain Zn by 23%. While this illustrates the potential for pyramiding genes to improve grain Zn, breeding for increased grain Zn concentration requires identification of individual QTL with large effects, which in turn requires construction and testing of new mapping populations in the future.

Genome-wide association mapping of zinc and iron concentration in barley landraces from Ethiopia and Eritrea

“…These values are comparable to a correlation coefficient of 0.64 reported between iron and zinc concentration for barley grown in a field trial in Syria (El-Haramein and Grando, 2010). A significant correlation between grain zinc and iron concentrations was also reported in wheat (Genc et al, 2009). This suggests that the traits might have some common genetic mechanisms underlying their uptake, accumulation, or concentration.…”

How metal hyperaccumulating plants can advance Zn biofortification

“…Quantitative trait loci (QTLs) controlling Zn accumulation have been mapped in several species, e.g. in wheat (Genc et al 2009), the common bean (Blair et al 2011), and barley (Lonergan et al 2009;Mamo et al 2014). However, there is practically no definitive information on genes underlying the variation.…”

Molecular mapping of the grain iron and zinc concentration, protein content and thousand kernel weight in wheat (Triticum aestivum L.)

“…However, in most studies on identification of QTLs for Fe [28–35], Zn [28–30, 32–37], PC [28, 32–33, 38–39], and TKW [40–45] the size of mapping population was limited. The present study was aimed to identify the genomic region(s) associated with grain Fe, Zn, PC, and TKW in a set of the 286 RILs.…”