Network analysis combined with genome-wide association study helps identification of genes related to amino acid contents in soybean
Abstract Background Additional to total protein content, the amino acid (AA) profile is important to the nutritional value of soybean seed. The AA profile in soybean seed is a complex quantitative trait controlled by multiple interconnected genes and pathways controlling the accumulation of each AA....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-024-11163-8 |
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| Summary: | Abstract Background Additional to total protein content, the amino acid (AA) profile is important to the nutritional value of soybean seed. The AA profile in soybean seed is a complex quantitative trait controlled by multiple interconnected genes and pathways controlling the accumulation of each AA. With a total of 621 soybean germplasm, we used three genome-wide association study (GWAS)-based approaches to investigate the genomic regions controlling the AA content and profile in soybean. Among those approaches, the GWAS network analysis we implemented takes advantage of the relationships between specific AAs to identify the genetic control of AA profile. Results For Approach I, GWAS were performed for the content of 24 single AAs under all environments combined. Significant SNPs grouping into 16 linkage disequilibrium (LD) blocks from 18 traits were identified. For Approach II, the individual AAs were grouped by five families according to their metabolic pathways and were examined based on the sum, ratios, and interactions of AAs within the same biochemical family. Significant SNPs grouping into 35 LD blocks were identified, with SNPs associated with traits from the same biochemical family often positioned on the same LD blocks. Approach III, a correlation-based network analysis, was performed to assess the empirical relationships among AAs. Two groups were described by the network topology, Group 1: Ala, Gly, Lys, available Lys (Alys), and Thr and Group 2: Ile and Tyr. Significant SNPs associated with a ratio of connected AAs or a ratio of a single AA to its fully or partially connected metabolic groups were identified within 9 LD blocks for Group 1 and 2 LD blocks for Group 2. Among 40 identified QTL for AA or AA-derived traits, three genomic regions were novel in terms of seed composition traits (oil, protein, and AA content). An additional 24 regions had previously not been specifically associated with the AA content. Conclusions Our results confirmed loci identified from previous studies but also suggested that network approaches for studying AA contents in soybean seed are valuable. Three genomic regions (Chr 5: 41,754,397–41,893,109 bp, Chr 9: 1,537,829–1,806,586 bp, and Chr 20: 31,554,795–33,678,257 bp) were significantly identified by all three approaches. Yet, the majority of associations between a genomic region and an AA trait were approach- and/or environment-specific. Using a combination of approaches provides insights into the genetic control and pleiotropy among AA contents, which can be applied to mechanistic understanding of variation in AA content as well as tailored nutrition in cultivars developed from soybean breeding programs. |
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| ISSN: | 1471-2164 |