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

Abstract

Summary Dissection of the genetic basis of wheat ionome is crucial for understanding the physiological and biochemical processes underlying mineral accumulation in seeds, as well as for efficient crop breeding. Most of the elements essential for plants are metals stored in seeds as chelate complexes with phytic acid or sulfur‐containing compounds. We assume that the involvement of phosphorus and sulfur in metal chelation is the reason for strong phenotypic correlations within ionome. Adjustment of element concentrations for the effect of variation in phosphorus and sulfur seed content resulted in drastic change of phenotypic correlations between the elements. The genetic architecture of wheat grain ionome was characterized by quantitative trait loci (QTL) analysis using a cross between durum and wild emmer wheat. QTL analysis of the adjusted traits and two‐trait analysis of the initial traits paired with either P or S considerably improved QTL detection power and accuracy, resulting in the identification of 105 QTLs and 617 QTL effects for 11 elements. Candidate gene search revealed some potential functional associations between QTLs and corresponding genes within their intervals. Thus, we have shown that accounting for variation in P and S is crucial for understanding of the physiological and genetic regulation of mineral composition of wheat grain ionome and can be implemented for other plants.

Grain Protein Content QTLs Identified in a Durum × Wild Emmer Wheat Mapping Population Tested in Five Environments

“…Members of NRT1/PTR family of proton-coupled transporters, which are involved in nitrogen metabolism in different plant species (Fang et al 2013; Naz et al 2017; Castro-Rodriguez et al 2017; Corratge-Faillie and Lacombe 2017) and, hence, influencing protein accumulation, were identified within five QTL intervals in the S×Y population and were selected as promising CGs. Sulfur metabolism was shown to be an important factor that can alter nitrogen accumulation (Howarth et al 2008; Dai et al 2015; Bonnot et al 2017), following this idea glutamate and glutathione related CGs were identified within intervals of seven QTLs in the S×Y population and also was shown for QTLs in the L×G population (Fatiukha et al 2019b).…”

“…QTL analysis of the L×G RIL population revealed 8 loci with WEW alleles that conferred increased GPC, using low-density SSR-based map (Peleg et al 2009). Further analysis, using high resolution SNP-based map, expanded the number of loci with contribution of WEW alleles from 8 to 14 (Fatiukha et al 2019b). The identification of common or meta-QTLs between independent populations can help to improve QTL interval confidence, as demonstrated by Quraishi et al (2017), and can be used for confirmation of detected effects in different genetic backgrounds.…”

“…SNP-based arrays developed for bread wheat (e.g. the 90K and 15K arrays) were proved to be highly efficient also for construction of genetic maps based on WEW as one of the parents, including studies related to wheat chromosome evolution (Jorgensen et al 2017), wheat domestication (Nave et al 2016; Golan et al 2018), QTL analysis of drought adaptation (Fatiukha et al 2019a) and nutrient quality of wheat grains (Fatiukha et al 2019b). In addition, high-density genetic map of durum Svevo × WEW Zavitan (S×Z) RIL population (Avni et al 2014a) served as a base for anchoring of WEW genome assembly scaffolds (Avni et al 2017).…”