Identification of loci controlling mineral element concentration in soybean seeds

Abstract

Background Mineral nutrients play a crucial role in the biochemical and physiological functions of biological systems. The enhancement of seed mineral content via genetic improvement is considered as the most promising and cost-effective approach compared alternative means for meeting the dietary needs. The overall objective of this study was to perform a GWAS of mineral content (Ca, K, P and S) in seeds of a core set of 137 soybean lines that are representative of the diversity of early maturing soybeans cultivated in Canada (maturity groups 000-II). Results This panel of 137 soybean lines was grown in five environments (in total) and the seed mineral content was measured using a portable x-ray fluorescence (XRF) spectrometer. The association analyses were carried out using three statistical models and a set of 2.2 million SNPs obtained from a combined dataset of genotyping-by-sequencing and whole-genome sequencing. Eight QTLs significantly associated with the Ca, K, P and S content were identified by at least two of the three statistical models used (in two environments) contributing each from 17 to 31% of the phenotypic variation. A strong reproducibility of the effect of seven out these eight QTLs was observed in three other environments. In total, three candidate genes were identified involved in transport and assimilation of these mineral elements. Conclusions There have been very few GWAS studies to identify QTLs associated with the mineral element content of soybean seeds. In addition to being new, the QTLs identified in this study and candidate genes will be useful for the genetic improvement of soybean nutritional quality through marker-assisted selection. Moreover, this study also provides details on the range of phenotypic variation encountered within the Canadian soybean germplasm.

Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed

“…Using soybean varieties with low calcium content can partially solve the stone seed issue. Soybean seed calcium content varies between cultivars (0.19%–0.52%) and has a heritability of 41%–63% (Chen et al., 2001; Malle, Morrison, et al., 2020; B. Zhang et al., 2008). Zhang et al.…”

Mapping of partial resistance to Phytophthora sojae in soybean PIs using whole‐genome sequencing reveals a major QTL

“…Association analysis was performed using GAPIT (Tang et al, 2016) with a pruned catalog (r 2 > 0.9) of 691K SNPs and the CDW values. As we used a very large catalog of markers, and each statistical model can lead to the identification of false positives, only the intersection of results for three different statistical models, enriched compressed mixed linear model (ECMLM) (Li et al, 2014), fixed and random model circulating probability unification (FarmCPU) (Kaler et al, 2020), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) (Huang et al, 2019), incorporating a correction for both population structure (Q matrix generated with fastSTRUCTURE) and kinship (K matrix estimated with TASSEL 5), were reported as previously proposed by Malle et al (2020). The ECMLM is an improved form of the most used GWAS model, the mixed linear model, by clustering individuals into groups by using clustering algorithms such as the unweighted pair group method with arithmetic mean.…”

“…This can facilitate the identification of the causal variant underlying phenotypic diversity (Koboldt et al., 2013). Concomitantly, however, such large single‐nucleotide polymorphism (SNP) datasets have increased the probability of identifying false‐positive marker–trait associations, making it necessary to use multiple sophisticated statistical models to identify robust QTL associated with the phenotype (Huang et al., 2019; Kaler et al., 2020; Malle et al., 2020).…”

“…This can facilitate the identification of the causal variant underlying phenotypic diversity (Koboldt et al, 2013). Concomitantly, however, such large single-nucleotide polymorphism (SNP) datasets have increased the probability of identifying false-positive markertrait associations, making it necessary to use multiple sophisticated statistical models to identify robust QTL associated with the phenotype (Huang et al, 2019;Kaler et al, 2020;Malle et al, 2020). The Plant Genome Though extensive genotyping is important for the success of a GWAS, the evaluation of a broad array of relevant germplasm with an efficient phenotyping method is at least as important for success in identifying desirable alleles contributing to resistance against P. sojae.…”

Functional Characterization of a (E)-β-Ocimene Synthase Gene Contributing to the Defense against Spodoptera litura

“…Soybean ( Glycine max ), a herbaceous annual plant in the family Fabaceae, is among the most important crops in the world, playing irreplaceable roles in providing humans and livestock with high-quality proteins and balanced fatty acids [ 1 , 2 , 3 , 4 , 5 ]. Phytonutrients such as saponins, vitamins, and flavonoids in soybean also contribute special health nutrition and pharmacological effects to humans [ 6 , 7 , 8 , 9 ]. The issue of how to guarantee or improve soybean yield and nutrition has attracted special attention from both botanists and breeders around the world.…”