Skip to main content
SpringerLink
Log in

Identification of quantitative trait loci and candidate genes for seed folate content in soybean

  • Original Article
  • Published:
Theoretical and Applied Genetics Aims and scope Submit manuscript

Abstract

Key message

From 61 QTL mapped, a stable QTL cluster of 992 kb was discovered on chromosome 5 for folate content and a putative candidate gene, Glyma.05G237500, was identified.

Abstract

Folate (vitamin B9) is one of the most essential micronutrients whose deficiencies lead to various health defects in humans. Herein, we mapped the quantitative trait loci (QTL) underlying seed folate content in soybean using recombinant inbred lines developed from cultivars, ZH35 and ZH13, across four environments. We identified 61 QTL on 12 chromosomes through composite interval mapping, with phenotypic variance values ranging from 1.68 to 24.68%. A major-effect QTL cluster (qFo-05) was found on chromosome 5, spanning 992 kb and containing 134 genes. Through gene annotation and single-locus haplotyping analysis of qFo-05 in a natural soybean population, we identified seven candidate genes significantly associated with 5MTHF and total folate content in multiple environments. RNA-seq analysis showed a unique expression pattern of a hemerythrin RING zinc finger gene, Glyma.05G237500, between both parental cultivars during seed development, which suggest the gene might regulate folate content in soybean. This is the first study to investigate QTL underlying folate content in soybean and provides new insight for molecular breeding to improve folate content in soybean.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The raw sequence data for RNA-seq reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) at the National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA008483) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. The SNPs used for the single locus haplotyping of candidate genes in qFo-05 and for haplotype analysis of missense variants in Glyma.05G237500 were downloaded from the Soybean Functional Genomics & Breeding database (SoyFGB v2.0) (https://sfgb.rmbreeding.cn/).

References

  • Abdelghany AM, Zhang S, Azam M, Shaibu AS, Feng Y, Li Y, Tian Y, Hong H, Li B, Sun J (2020) Profiling of seed fatty acid composition in 1025 Chinese soybean accessions from diverse ecoregions. Crop J 8:635–644

    Article  Google Scholar 

  • Adhikari P, McNellie J, Panthee DR (2020) Detection of quantitative trait loci (QTL) associated with the fruit morphology of tomato. Genes 11:1117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Agyenim-Boateng KG, Zhang S, Islam MS, Gu Y, Li B, Azam M, Abdelghany AM, Qi J, Ghosh S, Shaibu AS et al (2022) Profiling of naturally occurring folates in a diverse soybean germplasm by HPLC-MS/MS. Food Chem 384:132520

    Article  CAS  PubMed  Google Scholar 

  • Agyenim-Boateng KG, Zhang S, Shohag MJI, Shaibu AS, Li J, Li B, Sun J (2023) Folate biofortification in soybean: challenges and prospects. Agronomy 13:241

    Article  CAS  Google Scholar 

  • Bali S, Robinson BR, Sathuvalli V, Bamberg J, Goyer A (2018) Single Nucleotide Polymorphism (SNP) markers associated with high folate content in wild potato species. PloS One 13:e0193415

    Article  PubMed  PubMed Central  Google Scholar 

  • Bečanović K, Nørremølle A, Neal SJ, Kay C, Collins JA, Arenillas D, Lilja T, Gaudenzi G, Manoharan S, Doty CN, Beck J, Lahiri N, Portales-Casamar E, Warby SC, Connolly C, De Souza RAG, Tabrizi SJ, Hermanson O, Langbehn DR, Hayden MR, Wasserman WW, Leavitt BR, Network RIotEHsD (2015) A SNP in the HTT promoter alters NF-κB binding and is a bidirectional genetic modifier of Huntington disease. Nat Neurosci 18:807–816

    Article  PubMed  Google Scholar 

  • Blancquaert D, De Steur H, Gellynck X, Van Der Straeten D (2014) Present and future of folate biofortification of crop plants. J Exp Bot 65:895–906

    Article  CAS  PubMed  Google Scholar 

  • Cingolani P, Platts A, le Wang L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6:80–92

    Article  CAS  PubMed  Google Scholar 

  • Czeizel AE, Dudás I, Vereczkey A, Bánhidy F (2013) Folate deficiency and folic acid supplementation: the prevention of neural-tube defects and congenital heart defects. Nutrients 5:4760–4775

    Article  PubMed  PubMed Central  Google Scholar 

  • Dong W, Cheng Z, Xu J, Zheng T, Wang X, Zhang H, Jie W, Wan J (2014) Identification of QTLs underlying folate content in milled rice. J Integr Agric 13:1827–1834

    Article  CAS  Google Scholar 

  • Feng Y, Zhang S, Li J, Pei R, Tian L, Qi J, Azam M, Agyenim-Boateng KG, Shaibu AS, Liu Y et al (2022) Dual-function C2H2-type zinc-finger transcription factor GmZPF7 contributes to isoflavone accumulation in soybean. New Phytol 237:1794–1809

    Article  PubMed  Google Scholar 

  • Gorelova V, Ambach L, Rébeillé F, Stove C, Van Der Straeten D (2017) Folates in plants: research advances and progress in crop biofortification. Front Chem 5:21

    Article  PubMed  PubMed Central  Google Scholar 

  • Guo W, Lian T, Wang B, Guan J, Yuan D, Wang H, Safiul Azam FM, Wan X, Wang W, Liang Q (2019) Genetic mapping of folate QTLs using a segregated population in maize. J Integr Plant Biol 61:675–690

    Article  CAS  PubMed  Google Scholar 

  • Herbig AK, Stover PJ (2002) Regulation of folate metabolism by iron. In: Massaro EJ, Rogers JM (eds) Folate and human development. Humana Press, Totowa, pp 241–262

    Chapter  Google Scholar 

  • Hu B, Jin J, Guo A, Zhang H, Luo J, Gao G (2014) GSDS 2.0: an upgraded gene feature visualization server. Bioinformatics 31:1296–1297

    Article  PubMed  PubMed Central  Google Scholar 

  • Jha AB, Gali KK, Zhang H, Purves RW, Tar’an B, Vandenberg A, Warkentin TD (2020) Folate profile diversity and associated SNPs using genome wide association study in pea. Euphytica 216:18

    Article  CAS  Google Scholar 

  • Kobayashi T, Nagasaka S, Senoura T, Itai RN, Nakanishi H, Nishizawa NK (2013) Iron-binding haemerythrin RING ubiquitin ligases regulate plant iron responses and accumulation. Nat Commun 4:2792

    Article  PubMed  Google Scholar 

  • Kobayashi T, Nishizawa NK (2014) Iron sensors and signals in response to iron deficiency. Plant Sci 224:36–43

    Article  CAS  PubMed  Google Scholar 

  • Korasick DA, Kandoth PK, Tanner JJ, Mitchum MG, Beamer LJ (2020) Impaired folate binding of serine hydroxymethyltransferase 8 from soybean underlies resistance to the soybean cyst nematode. J Biol Chem 295:3708–3718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kumawat G, Xu D (2021) A major and stable quantitative trait locus qSS2 for seed size and shape traits in a soybean RIL population. Front Genet 12:646102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lakhina V, McReynolds M, Grimes DT, Rabinowitz JD, Burdine RD, Murphy CT (2019) ZIP-5/bZIP transcription factor regulation of folate metabolism is critical for aging axon regeneration. bioRxiv:727719

  • Li H, Ye G, Wang J (2007) A modified algorithm for the improvement of composite interval mapping. Genetics 175:361–374

    Article  PubMed  PubMed Central  Google Scholar 

  • Li S, Wang J, Zhang L (2015) Inclusive composite interval mapping of QTL by environment interactions in biparental populations. PLoS One 10:e0132414

    Article  PubMed  PubMed Central  Google Scholar 

  • Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930

    Article  CAS  PubMed  Google Scholar 

  • Lin H, Falchetto R, Mosca PJ, Shabanowitz J, Hunt DF, Hamlin JL (1996) Mimosine targets serine hydroxymethyltransferase. J Biol Chem 271:2548–2556

    Article  CAS  PubMed  Google Scholar 

  • Liu T, Gu Y, Wang J, Sun J, Qiu L (2022) Mapping soybean protein QTLs based on high-density genetic map. Acta Agron Sin. https://kns.cnki.net/kcms/detail/11.1809.S.20221019.1603.006.html

  • Mackay TF (2009) Q&A: genetic analysis of quantitative traits. J Biol 8:23

    Article  PubMed  PubMed Central  Google Scholar 

  • Martin CJ, Torkamaneh D, Arif M, Pauls KP (2021) Genome-wide association study of seed folate content in common bean. Front Plant Sci 12:1829

    Article  Google Scholar 

  • McWilliams DA, Berglund DR, Endres GJ (1999) Soybean: growth and management quick guide. NDSU Extension Service. Available at http://www.ag.ndsu.edu/pubs/plantsci/rowcrops/a1174/a1174w.htm

  • Ooijen JW, Ooijen JW, Verlaat J, Ooijen JW, Tol J, Dalén J, Buren JBV, Meer JWM, Krieken JH, Ooijen JW, Kessel JS, Van O, Voorrips RE, Heuvel LP (2006) JoinMap® 4, Software for the calculation of genetic linkage maps in experimental populations. Kyazma B.V., Wageningen

  • Oppenheim EW, Adelman C, Liu X, Stover PJ (2001) Heavy chain ferritin enhances serine hydroxymethyltransferase expression and de novo thymidine biosynthesis. J Biol Chem 276:19855–19861

    Article  CAS  PubMed  Google Scholar 

  • Oppenheim EW, Nasrallah IM, Mastri MG, Stover PJ (2000) Mimosine is a cell-specific antagonist of folate metabolism. J Biol Chem 275:19268–19274

    Article  CAS  PubMed  Google Scholar 

  • Owusu EY, Karikari B, Kusi F, Haruna M, Amoah RA, Attamah P, Adazebra G, Sie EK, Issahaku M (2021) Genetic variability, heritability and correlation analysis among maturity and yield traits in Cowpea (Vigna unguiculata (L) Walp) in Northern Ghana. Heliyon 7:e07890

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pascual L, Desplat N, Huang BE, Desgroux A, Bruguier L, Bouchet JP, Le QH, Chauchard B, Verschave P, Causse M (2015) Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era. Plant Biotechnol J 13:565–577

    Article  CAS  PubMed  Google Scholar 

  • Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL (2016) Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc 11:1650–1667

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Qi Z, Zhang X, Qi H, Xin D, Che D (2017) Identification and validation of major QTLs and epistatic interactions for seed oil content in soybeans under multiple environments based on a high-density map. Euphytica 213:162

    Article  Google Scholar 

  • Rodríguez-Celma J, Chou H, Kobayashi T, Long TA, Balk J (2019) Hemerythrin E3 ubiquitin ligases as negative regulators of iron homeostasis in plants. Front Plant Sci 10:98

    Article  PubMed  PubMed Central  Google Scholar 

  • Said JI, Lin Z, Zhang X, Song M, Zhang J (2013) A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton. BMC Genomics 14:776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Selote D, Matthiadis A, Gillikin JW, Sato MH, Long TA (2018) The E3 ligase BRUTUS facilitates degradation of VOZ1/2 transcription factors. Plant Cell Environ 41:2463–2474

    Article  CAS  PubMed  Google Scholar 

  • Song L, Yu D, Zheng H, Wu G, Sun Y, Li P, Wang J, Wang C, Lv B, Tang X (2021) Weighted gene co-expression network analysis unveils gene networks regulating folate biosynthesis in maize endosperm. 3 Biotech 11:441

    Article  PubMed  PubMed Central  Google Scholar 

  • Strandler HS, Patring J, Jägerstad M, Jastrebova J (2015) Challenges in the determination of unsubstituted food folates: impact of stabilities and conversions on analytical results. J Agric Food Chem 63:2367–2377

    Article  CAS  PubMed  Google Scholar 

  • Strobbe S, Van Der Straeten D (2017) Folate biofortification in food crops. Curr Opin Biotechnol 44:202–211

    Article  CAS  PubMed  Google Scholar 

  • Suh JR, Oppenheim EW, Girgis S, Stover PJ (2000) Purification and properties of a folate-catabolizing enzyme. J Biol Chem 275:35646–35655

    Article  CAS  PubMed  Google Scholar 

  • Wang X, You B, Yin F, Chen C, He H, Liu F et al (2023) A presumed missense variant in the U2AF2 gene causes exon skipping in neurodevelopmental diseases. J Hum Genet. https://doi.org/10.1038/s10038-023-01128-2

    Article  PubMed  Google Scholar 

  • Xiao Y, Yu Y, Xie L, Li K, Guo X, Li G, Liu J, Li G, Hu J (2022) A genome-wide association study of folates in sweet corn kernels. Front Plant Sci 13:1004455

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu F (2021) The analysis of folate-dependent transcription factor zinc finger protein 410. Dissertation, Liberty University.

  • Zhang M, Liu S, Wang Z, Yuan Y, Zhang Z, Liang Q, Yang X, Duan Z, Liu Y, Kong F, Liu B, Ren B, Tian Z (2021) Progress in soybean functional genomics over the past decade. Plant Biotechnol J 20:256–282

    Article  PubMed  PubMed Central  Google Scholar 

  • Zheng T, Li Y, Li Y, Zhang S, Ge T, Wang C, Zhang F, Faruquee M, Zhang L, Wu X, Tian Y, Jiang S, Xu J, Qiu L (2022) A general model for “germplasm-omics” data sharing and mining: a case study of SoyFGB v2.0. Sci Bull 67:1716–1719

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all and sundry who contributed their time and effort to this study.

Funding

This work was supported by the Ministry of Science and Technology (2021YFD1201605), National Natural Science Foundation of China (32161143033, 32272178 and 32001574), and CAAS (Chinese Academy of Agricultural Sciences) Agricultural Science and Technology Innovation Project (2060302–2).

Author information

Author notes

  1. Kwadwo Gyapong Agyenim-Boateng, Shengrui Zhang and Rongzhe Gu contributed equally to this work.

Authors and Affiliations

  1. The National Engineering Laboratory for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Kwadwo Gyapong Agyenim-Boateng, Shengrui Zhang, Shibi Zhang, Jie Qi, Muhammad Azam, Caiyou Ma, Yecheng Li, Yue Feng, Yitian Liu, Jing Li & Junming Sun

  2. The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/ Key Laboratory of Germplasm and Biotechnology (MARA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Rongzhe Gu & Lijuan Qiu

  3. MARA Key Laboratory of Soybean Biology (Beijing), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Bin Li

Authors
  1. Kwadwo Gyapong Agyenim-Boateng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengrui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rongzhe Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shibi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muhammad Azam
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Caiyou Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yecheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yue Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yitian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Bin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Lijuan Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Junming Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KGAB, SZ and RG contributed to the formal analysis, investigation; methodology, software, writing—original draft, review and editing and data curation. SZ, JQ, MA, CM, YL, MA, YF, HF, YL, and JL were involved in the investigation and methodology. LB, LQ and JS assisted in the conceptualisation, funding acquisition, project administration, supervision, resources, writing—review and editing.

Corresponding authors

Correspondence to Bin Li, Lijuan Qiu or Junming Sun.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose. On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Communicated by Istvan Rajcan.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

122_2023_4396_MOESM1_ESM.docx

Fig. S1. Genetic linkage map of RIL population derived between ZH35 and ZH13. Fig. S2. Conserved protein domains of Glyma.05G237500 with homologs of other legumes and Arabidopsis. Table S1. Information on the linkage group of the RIL population derived from ZH35 and ZH13. Table S3. Protein sequences of candidate gene, Glyma.05G237500 and its homologs in other plants. Table S4. Folate content of parents, ZH35 and ZH13 across different environments. Table S5. QTL for soybean folate monoglutamates in RIL population across four environments. Table S6. List of gene models identified in qFo-05. Table S7. List of predicted transcription factors in qFo-05. Table S8. Seven genes with significant loci for 5MTHF and total folate across four environments. Table S9. Gene annotation of candidate genes. Table S10. Expression in transcripts per million (TPM) of candidate genes for parents, ZH35 and ZH13, during seed development. Table S11. Folate contents (µg/100g DW) of ZH13 at different stages of seed development. Sampling times, S1-S7, represent the different sampling time points in sequential order, from the beginning of seed filling to final maturity. Table S13. Modifier SNP and InDel variants in the promoter region of Glyma.05G237500. Table S14. Genotype and phenotype data of RIL population used in this study.

Table S2. Total folate and 5MTHF contents of soybean natural population evaluated in three environments.

Table S12. Polymorphic SNP and InDel variants of Glyma.05G237500.

Table S14. Genotype and phenotype data of RIL population used in this study.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agyenim-Boateng, K.G., Zhang, S., Gu, R. et al. Identification of quantitative trait loci and candidate genes for seed folate content in soybean. Theor Appl Genet 136, 149 (2023). https://doi.org/10.1007/s00122-023-04396-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00122-023-04396-w

Search

Navigation