Comprehensive phenotypic analysis and quantitative trait locus identification for grain mineral concentration, content, and yield in maize (Zea mays L.)

Abstract

Understanding the correlations of seven minerals for concentration, content and yield in maize grain, and exploring their genetic basis will help breeders to develop high grain quality maize. Biofortification by enhanced mineral accumulation in grain through genetic improvement is an efficient way to solve global nutrient malnutrition, in which one key step is to detect the underlying quantitative trait loci (QTL). Herein, a maize recombinant inbred population (RIL) was field grown to maturity across four environments (two locations × two years). Phenotypic data for grain mineral concentration, content and yield were determined for copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), magnesium (Mg), potassium (K) and phosphorus (P). Significant effects of genotype, location and year were observed for all investigated traits. The strongest location effects were found for Zn accumulation traits probably due to distinct soil Zn availabilities across locations. Heritability (H (2)) of different traits varied with higher H (2) (72-85 %) for mineral concentration and content, and lower (48-63 %) for mineral yield. Significant positive correlations for grain concentration were revealed between several minerals. QTL analysis revealed 28, 25, and 12 QTL for mineral concentration, content and yield, respectively; and identified 8 stable QTL across at least two environments. All these QTL were assigned into 12 distinct QTL clusters. A cluster at chromosome Bin 6.07/6.08 contained 6 QTL for kernel weight, mineral concentration (Mg) and content (Zn, K, Mg, P). Another cluster at Bin 4.05/4.06 contained a stable QTL for Mn concentration, which were previously identified in other maize and rice RIL populations. These results highlighted the phenotypic and genetic performance of grain mineral accumulation, and revealed two promising chromosomal regions for genetic improvement of grain biofortification in maize.

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.…”

“…Association analysis is increasingly used for identification of chromosomal regions affecting mineral accumulations (Alomari et al , 2017; Kumar et al , 2018), and ionomic traits (Shakoor et al , 2016; Ziegler et al , 2018). Yet, quantitative trait loci (QTL) analysis, based on segregating mapping populations, remains an important approach for genetic dissection of elemental accumulation (Gu et al , 2015; Velu et al , 2017; Huang et al , 2017). The recent development of wheat high-throughput SNP genotyping assays (Wang et al , 2014) and the assembly of reference genomes for wild emmer wheat ( Triticum turgidum ssp.…”

Diversity of Maize Kernels from a Breeding Program for Protein Quality III: Ionome Profiling

“…Natural variation of Fe (8.0 to 62 mg kg −1 ) and Zn (12.0 to 58.0 mg kg −1 ) has been extensively reported as a prerequisite for genetic biofortification [36,37]. However, a genetic approach may not always be adequate due to several interacting factors, including lack of readily available germplasm, unfavorable genetic, physiological, and chemical interactions within the ionome [38], and negative relationships with grain yield [17,20]. These perceived shortcomings of genetic biofortification recently led to promoting molecular breeding [38,39] as a means of improving [nutrient] and bioavailability of micronutrients in maize and other grain crops.…”

“…However, and despite their variable effects on nutrient densities and indices (Table 2), starch and protein, but not oil, clustered together in accordance with G(HG) differences (Figure 2). These relationships and cluster-affiliations may render selection of desirable trait combinations much easier; however, due to negative relationships, selection for some nutrient combinations (e.g., Cu and K with N or S; Figure 4) may be more difficult to achieve [17].…”

“…Nutrient density are reflections of a dynamic network of interactions, signifying unique functions and bivariate or multivariate synergistic or antagonistic relationships [8,17]. That nutrients do not behave independently can be gleaned from their joint clustering and associations with one or more nutrient indices (Figures 3 and 4).…”