GWAS Fine-Mapping for Soybean First Pod Insertion Height Identifies Candidate Genomic Regions Involved in Plant Development

Background/Objectives: Soybean first pod insertion height (FPIH) is a key trait established during plant development, but its genetic architecture in Eurasian germplasm remains largely unknown. Methods: We performed a GWAS and fine-mapping for FPIH using 180 Eurasian varieties (SoySNP50K array, imputed to ~4M SNPs) phenotyped in four Russian environments (2021–2022), using two models: a residual-based and a covariate-adjusted. SuSiE fine-mapping was applied to refine candidate loci. Results: The covariate-adjusted model demonstrated better control of genomic inflation (λ = 1.08 vs. 1.16) and higher SNP heritability (0.381 vs. 0.014); the lower heritability in the residual-based model was expected, as this model removes environmental main effects and the genetic variance associated with them. Thus, the models are complementary: one captures stable genetic effects, the other highlights environment-dependent signals. SuSiE refined three loci. On chromosome 13, two stable independent signals (Gm13_30553403, Gm13_30909346; PIP ≥ 0.99) were identified near MYB83 and BEN1, consistent across both models. On chromosome 4, the signal shifted to Gm04_36933515 (PIP = 0.9999) near COBL4/IRX6, although model dependency was observed, and this locus is not currently recommended for marker development. On chromosome 5, the original GWAS SNP was not causal; two tightly linked SNPs (Gm05_38427309 and Gm05_38710046, r2 = 0.714, PIP ≥ 0.99) formed a haplotype block located near ARR1/ARR2 and a B-box/CCT domain gene. Crucially, this signal was absent in the residual-based model (max PIP = 0.33), suggesting that the chromosome 5 locus may modulate developmental plasticity rather than exerting a direct main effect on FPIH. However, as we did not perform formal G × E testing, we present this as a hypothesis requiring further validation. The environment-dependent behavior of this locus indicates that it should be used with caution in breeding programs and validated under specific target environments. Conclusions: Chromosome 13 SNPs provide stable genetic associations across environments, whereas the chromosome 5 block requires environment-specific validation. This work provides the first fine-mapped GWAS for FPIH in Russian soybean germplasm and highlights how model choice can uncover or obscure environment-dependent genetic loci affecting plant development.

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Journal
Genes
Published
2026-08-24
DOI
https://doi.org/10.3390/genes17090994
Primary Topic
Soybean genetics and cultivation
Type
article
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article

GWAS Fine-Mapping for Soybean First Pod Insertion Height Identifies Candidate Genomic Regions Involved in Plant Development

A. V. Kirichenko, G. R. Svishcheva, Irina V. Zorkoltseva, Sergey V. Kiryukhin et al.
Genes
Soybean genetics and cultivation
article

GWAS Fine-Mapping for Soybean First Pod Insertion Height Identifies Candidate Genomic Regions Involved in Plant Development

A. V. Kirichenko, G. R. Svishcheva, Irina V. Zorkoltseva, Sergey V. Kiryukhin, Elena A. Salina, Dmitriy A. Potapov, Revmira I. Polyudina, Sergey O. Gurinovich, Veronika I. Panarina
article en

Abstract

Background/Objectives: Soybean first pod insertion height (FPIH) is a key trait established during plant development, but its genetic architecture in Eurasian germplasm remains largely unknown. Methods: We performed a GWAS and fine-mapping for FPIH using 180 Eurasian varieties (SoySNP50K array, imputed to ~4M SNPs) phenotyped in four Russian environments (2021–2022), using two models: a residual-based and a covariate-adjusted. SuSiE fine-mapping was applied to refine candidate loci. Results: The covariate-adjusted model demonstrated better control of genomic inflation (λ = 1.08 vs. 1.16) and higher SNP heritability (0.381 vs. 0.014); the lower heritability in the residual-based model was expected, as this model removes environmental main effects and the genetic variance associated with them. Thus, the models are complementary: one captures stable genetic effects, the other highlights environment-dependent signals. SuSiE refined three loci. On chromosome 13, two stable independent signals (Gm13_30553403, Gm13_30909346; PIP ≥ 0.99) were identified near MYB83 and BEN1, consistent across both models. On chromosome 4, the signal shifted to Gm04_36933515 (PIP = 0.9999) near COBL4/IRX6, although model dependency was observed, and this locus is not currently recommended for marker development. On chromosome 5, the original GWAS SNP was not causal; two tightly linked SNPs (Gm05_38427309 and Gm05_38710046, r2 = 0.714, PIP ≥ 0.99) formed a haplotype block located near ARR1/ARR2 and a B-box/CCT domain gene. Crucially, this signal was absent in the residual-based model (max PIP = 0.33), suggesting that the chromosome 5 locus may modulate developmental plasticity rather than exerting a direct main effect on FPIH. However, as we did not perform formal G × E testing, we present this as a hypothesis requiring further validation. The environment-dependent behavior of this locus indicates that it should be used with caution in breeding programs and validated under specific target environments. Conclusions: Chromosome 13 SNPs provide stable genetic associations across environments, whereas the chromosome 5 block requires environment-specific validation. This work provides the first fine-mapped GWAS for FPIH in Russian soybean germplasm and highlights how model choice can uncover or obscure environment-dependent genetic loci affecting plant development.

GenesVol. 17(9)
Krasnoyarsk Scientific Center (RU), Siberian Research Institute of Agriculture and Peat (RU), Institute of Cytology and Genetics (RU), All-Russia Research Institute of Legumes and Groat Crops (RU), Vavilov Institute of General Genetics (RU)
Life in Land
Openalex Percentile: Top 11%
Soybean genetics and cultivation
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