Unraveling the genetic architecture of anti-nutritional factors in soybean (Glycine max.) for nutritional enhancement

Abstract

Anti-nutritional factors (ANFs) can reduce nutrient bioavailability for monogastric animals. Therefore, this study aimed to understand the genetic architecture underlying ANF accumulation in soybean. Diversity arrays technology and a spectrophotometric method were employed to generate genotypic and phenotypic data, respectively, and gene mining was performed within 100-kb genomic window. A significant difference was found regarding ANFs content in the genotypes (p < 0.001). Significant SNP markers for phytate were identified on chromosomes 3, 4, 13, and 20 by FarmCPU, and for total trypsin inhibitors (TTI) on 6, 12, and 14 by CMLM models, whereas mrMLM model detected markers on chromosome 3, 12 and 15 for phytate, 4, 9, 13, 17 and 18 for TTI. Genes associated with phytate content include Glyma.03G001600, Glyma.04G194600, Glyma.13G128200, Glyma.20G118700, Glyma.14G213400, and Glyma.16G126400. For TTI, the genes are Glyma.06G074700, Glyma.12G241600, Glyma.14G176700, Glyma.13G052700, and Glyma.18G050400. These genes are primarily linked to plant defense and substrate interactions. Most promising SNP markers for marker-assisted selection aimed at reducing phytate levels include Soy_3_218818 (218,818 bp), Soy_3_241209 (241,209 bp), Soy_4_45462019 (45,462,019 bp), Soy_14_48672982 (48,672,982 bp), and Soy_6_5695090 (5,695,090 bp). For TTI, key markers include Soy_14_43649238 (43,649,238 bp), Soy_12_41339023 (41,339,023 bp), Soy_18_4301721 (4,301,721 bp), and Soy_13_14029215 (14,029,215 bp). These findings offer a valuable foundation for marker-assisted breeding aimed at improving soybean nutritional quality.

Keywords: GWAS; Phytate; SNP markers; Soybean; Trypsin inhibitors.

Fig. 1

(a): Number of SNPs per soybean chromosome. Chromosome 12 and 18 harbor the lowest and highest number of SNPs, respectively. Panel (b) shows the SNP density across soybean genome, where the vertical axis displays the chromosome number, horizontal axis displays chromosome length (1 Mb window), and the various colors represent SNP density or total number of SNPs per window. Chromosomes with high SNP density—such as Chr7, Chr9, Chr16, and Chr18—highlight regions of high genetic variation. These SNP-rich zones (in red) are useful for association mapping, diversity studies, and marker development. Conversely, SNP-poor chromosomes, including Chr2, Chr3, and Chr4, as well as relatively low-density regions on Chr1, Chr10, and Chr11 (green zones), suggest more conserved genomic segments. These regions may reflect low recombination or evolutionary conservation.

Fig. 2

(a)- Average linkage disequilibrium rate. The x-axis shows the distance (kilo base pairs) between SNPs, and the y-axis, the LD value (r²). Panel (b) represents an amplified region from the averaged linkage disequilibrium (a) of ~ 1500 kb. LD decay is shown at around 50-kb at r² = 0.2 and the LD becomes obsolete at around 100-kb.

Fig. 3

Principal component analysis (PCA) showing trends of population distribution (a) and phylogenetic tree (b). The quadrants show a trend of stratification among the genotypes. Numbers 1, 2, 3 and 4 represent four distinct clusters in the population.

Fig. 4

Manhattan and QQ plots for phytate and total trypsin inhibitors. Significant SNPs have hit the threshold and respective QQ-plot depicts the distribution of observed versus expected p-values and the genetic associations (a–d). Among the models tested in GAPIT, FarmCPU and CMLM were the most effective in detecting significant SNP markers for phytate and TTI, respectively. No common markers were identified between GAPIT models. To assess marker detection power and consistency, six mrMLM methods were also applied to the same dataset. From an inter-model perspective, in general, no overlapping SNPs were detected between GAPIT and mrMLM outputs. However, an intra-model comparison revealed that two SNPs were consistently identified by multiple mrMLM methods (SNPs Soy_14_48672982 and Soy_16_26978144 for phytate; and Soy_13_14029215 and Soy_18_4301721 for TTI) suggesting a higher detection consistency and potential sensitivity of mrMLM methods in capturing trait-associated loci compared to the GAPIT models.

Fig. 5

Allelic effects on SNPs for phytate and TTI accumulation. Marker effect evaluated based on the genotypes of each marker exhibiting significant p-values, as identified through GAPIT models are presented in the boxplot “a” for phytate, “b” and “c” for TTI traits.

Fig. 6

Allelic effects on SNPs for phytate and TTI accumulation for each marker exhibiting significant p-values, as identified by mrMLM methods are presented in boxplots “a” and “b” for phytate, whereas “c” and “d” for TTI traits.