Genome wide association study to detect genetic regions related to isoflavone content in a mutant soybean population derived from radiation breeding

Abstract

Isoflavones are major secondary metabolites that are exclusively produced by legumes, including soybean. Soy isoflavones play important roles in human health as well as in the plant defense system. The isoflavone content is influenced by minor-effect quantitative trait loci, which interact with polygenetic and environmental factors. It has been difficult to clarify the regulation of isoflavone biosynthesis because of its complex heritability and the influence of external factors. Here, using a genotype-by-sequencing-based genome-wide association mapping study, 189 mutant soybean genotypes (the mutant diversity pool, MDP) were genotyped on the basis of 25,646 high-quality single nucleotide polymorphisms (SNPs) with minor allele frequency of >0.01 except for missing data. All the accessions were phenotyped by determining the contents of 12 isoflavones in the soybean seeds in two consecutive years (2020 and 2021). Then, quantitative trait nucleotides (QTNs) related to isoflavone contents were identified and validated using multi-locus GWAS models. A total of 112 and 46 QTNs related to isoflavone contents were detected by multiple MLM-based models in 2020 and 2021, respectively. Of these, 12 and 5 QTNs were related to more than two types of isoflavones in 2020 and 2021, respectively. Forty-four QTNs were detected within the 441-Kb physical interval surrounding Gm05:38940662. Of them, four QTNs (Gm05:38936166, Gm05:38936167, Gm05:38940662, and Gm05:38940717) were located at Glyma.05g206900 and Glyma.05g207000, which encode glutathione S-transferase THETA 1 (GmGSTT1), as determined from previous quantitative trait loci annotations and the literature. We detected substantial differences in the transcript levels of GmGSTT1 and two other core genes (IFS1 and IFS2) in the isoflavone biosynthetic pathway between the original cultivar and its mutant. The results of this study provide new information about the factors affecting isoflavone contents in soybean seeds and will be useful for breeding soybean lines with high and stable concentrations of isoflavones.

Novel SNP markers for flowering and seed quality traits in faba bean (Vicia faba L.): characterization and GWAS of a diversity panel

“…It should be noted that the choice of MAF threshold affects the power of GWAS and is commonly set to 0.05 ( Alqudah et al., 2020 ). However, to capture rare alleles in diverse panels which can be of interest as targets for further genetic and biological studies, the MAF threshold in the GWAS can be lowered to not exclude any potential markers of interest ( Stanton-Geddes et al., 2013 ; Arkwazee et al., 2022 ; Kim et al., 2022 ). In the current study, the MAF was therefore set to >0.01, however, it is noteworthy, that the markers identified as significantly associated with traits all had a MAF exceeding 0.05.…”

Differentially Expressed Genes Related to Isoflavone Biosynthesis in a Soybean Mutant Revealed by a Comparative Transcriptomic Analysis

“…Isoflavones were extracted from soybean seeds at different developmental stages, including the reproductive stages R5, R6, R6.5, R7, and R8, with three replicates per stage as described by Kim et al [ 35 ]. The seeds were dried and ground to a fine powder using the Geno/Grinder tissue homogenizer (SPEX SamplePrep LLC., Metuchen, NJ, USA), after which 20 mg powdered material was transferred to 2 mL microcentrifuge tubes containing 1 mL 80% ( v / v ) methanol.…”

Folate Biofortification in Soybean: Challenges and Prospects

“…High-throughput genotyping methods, such as genotyping by sequencing (GBS), can be used to genotype a large number of accessions [102], while phenotyping methods can be used to measure the folate content of these accessions. Linkage mapping, genomewide association studies (GWAS) [103], and bulked segregant analysis-sequencing (BSA-Seq) [104] can then be used to identify quantitative trait loci (QTL) [105] or quantitative trait nucleotides (QTN) [106] that are associated with folate content. RNA-Seq can be combined with identified QTL or QTN and analysed using weighted gene co-expression network analysis (WGCNA) to group genes with similar expression patterns and identify candidate genes [107].…”