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Genetic analysis of iron, zinc and grain yield in wheat-Aegilops derivatives using multi-locus GWAS

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Abstract

Background

Wheat is a major staple crop and helps to reduce worldwide micronutrient deficiency. Investigating the genetics that control the concentrations of iron (Fe) and zinc (Zn) in wheat is crucial. Hence, we undertook a comprehensive study aimed at elucidating the genomic regions linked to the contents of Fe and Zn in the grain.

Methods and results

We performed the multi-locus genome-wide association (ML-GWAS) using a panel of 161 wheat-Aegilops substitution and addition lines to dissect the genomic regions controlling grain iron (GFeC), and grain zinc (GZnC) contents. The wheat panel was genotyped using 10,825 high-quality SNPs and phenotyped in three different environments (E1-E3) during 2017–2019. A total of 111 marker-trait associations (MTAs) (at p-value < 0.001) were detected that belong to all three sub-genomes of wheat. The highest number of MTAs were identified for GFeC (58), followed by GZnC (44) and yield (9). Further, six stable MTAs were identified for these three traits and also two pleiotropic MTAs were identified for GFeC and GZnC. A total of 1291 putative candidate genes (CGs) were also identified for all three traits. These CGs encode a diverse set of proteins, including heavy metal-associated (HMA), bZIP family protein, AP2/ERF, and protein previously associated with GFeC, GZnC, and grain yield.

Conclusions

The significant MTAs and CGs pinpointed in this current study are poised to play a pivotal role in enhancing both the nutritional quality and yield of wheat, utilizing marker-assisted selection (MAS) techniques.

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Data Availability

All data needed to support the conclusions are included in this article. Additional data related to this paper can be requested from the corresponding author.

References

  1. White PJ, Broadley MR (2009) Biofortification of crops with seven mineral elements often lacking in human diets–iron, zinc, copper, calcium, magnesium, selenium and iodine. New Phytol 182(1):49–84

    Article  CAS  PubMed  Google Scholar 

  2. Kumar S, Hash CT, Nepolean T, Mahendrakar MD, Satyavathi CT, Singh G, Rathore A, Yadav RS, Gupta R, Srivastava RK (2018) Mapping grain iron and zinc content quantitative trait loci in an iniadi-derived immortal population of pearl millet. Genes 9(5):248

    Article  PubMed  PubMed Central  Google Scholar 

  3. Colasuonno P, Lozito ML, Marcotuli I, Nigro D, Giancaspro A, Mangini G, De Vita P, Mastrangelo AM, Pecchioni N, Houston K (2017) The carotenoid biosynthetic and catabolic genes in wheat and their association with yellow pigments. BMC Genomics 18(1):1–18

    Article  Google Scholar 

  4. Kumar RR, Singh SP, Rai GK, Krishnan V, Berwal M, Goswami S, Vinutha T, Mishra GP, Satyavathi CT, Singh B (2022) Iron and zinc at a cross-road: a trade-off between micronutrients and anti-nutritional factors in pearl millet flour for enhancing the bioavailability. J Food Compos Anal: 104591

  5. Kumar J, Saripalli G, Gahlaut V, Goel N, Meher PK, Mishra KK, Mishra PC, Sehgal D, Vikram P, Sansaloni C (2018) Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS. Euphytica 214(11):1–17

    Article  Google Scholar 

  6. Singh R, Saripalli G, Gautam T, Kumar A, Jan I, Batra R, Kumar J, Kumar R, Balyan HS, Sharma S (2022) Meta-QTLs, ortho-MetaQTLs and candidate genes for grain Fe and Zn contents in wheat (Triticum aestivum L). Physiol Mol Biol Plants 28(3):637–650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gupta P, Balyan H, Sharma S, Kumar R (2021) Biofortification and bioavailability of Zn, Fe and Se in wheat: present status and future prospects. Theor Appl Genet 134(1):1–35

    Article  CAS  PubMed  Google Scholar 

  8. Priyadarshi R, Patel HK, Kumar A (2018) Breeding for nutritional quality enhancement in crops: Biofortification and Molecular Farming. Advanced Molecular Plant breeding: meeting the challenge of Food Security. CRC Press, pp 475–502

  9. Joshi A, Crossa J, Arun B, Chand R, Trethowan R, Vargas M, Ortiz-Monasterio I (2010) Genotype × environment interaction for zinc and iron concentration of wheat grain in eastern Gangetic plains of India. Field Crops Res 116(3):268–277

    Article  Google Scholar 

  10. Velu G, Singh R, Huerta-Espino J, Peña R, Arun B, Mahendru-Singh A, Mujahid MY, Sohu V, Mavi G, Crossa J (2012) Performance of biofortified spring wheat genotypes in target environments for grain zinc and iron concentrations. Field Crops Res 137:261–267

    Article  Google Scholar 

  11. Srinivasa J, Arun B, Mishra VK, Singh GP, Velu G, Babu R, Vasistha NK, Joshi AK (2014) Zinc and iron concentration QTL mapped in a Triticum spelta × T. aestivum cross. Theor Appl Genet 127(7):1643–1651

    Article  CAS  PubMed  Google Scholar 

  12. Tiwari C, Wallwork H, Arun B, Mishra VK, Velu G, Stangoulis J, Kumar U, Joshi AK (2016) Molecular mapping of quantitative trait loci for zinc, iron and protein content in the grains of hexaploid wheat. Euphytica 207(3):563–570

    Article  CAS  Google Scholar 

  13. Gupta S, Das S, Dikshit HK, Mishra GP, Aski MS, Bansal R, Tripathi K, Bhowmik A, Kumar S (2021) Genotype by Environment Interaction Effect on Grain Iron and Zinc Concentration of Indian and Mediterranean Lentil genotypes. Agronomy 11(9):1761

    Article  CAS  Google Scholar 

  14. Rathan ND, Krishna H, Ellur RK, Sehgal D, Govindan V, Ahlawat AK, Krishnappa G, Jaiswal JP, Singh JB, Sv S (2022) Genome-wide association study identifies loci and candidate genes for grain micronutrients and quality traits in wheat (Triticum aestivum L). Sci Rep 12(1):1–15

    Article  Google Scholar 

  15. Kumar A, Jain S, Elias EM, Ibrahim M, Sharma LK (2018) An overview of QTL identification and marker-assisted selection for grain protein content in wheat. Eco-friendly agro-biological techniques for enhancing crop productivity:245–274

  16. Cakmak I (2010) Biofortification of cereals with zinc and iron through fertilization strategy. In: 19th world congress of soil science: 1–6

  17. Yadava DK, Hossain F, Mohapatra T (2018) Nutritional security through crop biofortification in India: Status & future prospects. Indian J Med Res 148(5):621

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Erenstein O, Chamberlin J, Sonder K (2021) Estimating the global number and distribution of maize and wheat farms. Glob Food Sect. 30:100558

    Article  Google Scholar 

  19. Bouis HE, Hotz C, McClafferty B, Meenakshi JV, Pfeiffer WH (2011) Biofortification: a new tool to reduce micronutrient malnutrition. Food Nutr Bull 32(1suppl1):S31–S40

    Article  PubMed  Google Scholar 

  20. Gupta PK, Balyan HS, Sharma S, Kumar R (2021) Biofortification and bioavailability of Zn, Fe and Se in wheat: present status and future prospects. Theor Appl Genet 134:1–35

    Article  CAS  PubMed  Google Scholar 

  21. Rasheed A, Hao Y, Xia X, Khan A, Xu Y, Varshney RK, He Z (2017) Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Mol Plant 10(8):1047–1064

    Article  CAS  PubMed  Google Scholar 

  22. Sun C, Dong Z, Zhao L, Ren Y, Zhang N, Chen F (2020) The wheat 660K SNP array demonstrates great potential for marker-assisted selection in polyploid wheat. Plant Biotechnol J 18(6):1354–1360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yano K, Yamamoto E, Aya K, Takeuchi H, Lo PC, Hu L, Yamasaki M, Yoshida S, Kitano H, Hirano K, Matsuoka M (2016) Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet 48(8):927–934

    Article  CAS  PubMed  Google Scholar 

  24. Pang Y, Liu C, Wang D, Amand PS, Bernardo A, Li W, He F, Li L, Wang L, Yuan X, Dong L (2020) High-resolution genome-wide association study identifies genomic regions and candidate genes for important agronomic traits in wheat. Mol Plant 13(9):311–1327

    Article  Google Scholar 

  25. Gupta PK (2021) Quantitative genetics: pan-genomes, SVs, and k-mers for GWAS. Trends Genet 37(10):868–871

    Article  CAS  PubMed  Google Scholar 

  26. Jones E, Sullivan H, Bhattramakki D, Smith J (2007) A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (Zea mays L). Theor Appl Genet 115(3):361–371

    Article  CAS  PubMed  Google Scholar 

  27. Manickavelu A, Hattori T, Yamaoka S, Yoshimura K, Kondou Y, Onogi A, Matsui M, Iwata H, Ban T (2017) Genetic nature of elemental contents in wheat grains and its genomic prediction: toward the effective use of wheat landraces from Afghanistan. PLoS ONE 12(1):e0169416

    Article  PubMed  PubMed Central  Google Scholar 

  28. Malik P, Kumar J, Singh S, Sharma S, Meher PK, Sharma MK, Roy JK, Sharma PK, Balyan HS, Gupta PK (2021) Single-trait, multi-locus and multi-trait GWAS using four different models for yield traits in bread wheat. Mol Breed 41(7):1–21

    Article  Google Scholar 

  29. Saini DK, Chopra Y, Singh J, Sandhu KS, Kumar A, Bazzer S, Srivastava P (2022) Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. Mol Breed 42(1):1–52

    Article  PubMed  Google Scholar 

  30. Liu Y, Chen Y, Yang Y, Zhang Q, Fu B, Cai J, Guo W, Shi L, Wu J, Chen Y (2021) A thorough screening based on QTLs controlling zinc and copper accumulation in the grain of different wheat genotypes. Environ Sci Pollut Res 28(12):15043–15054

    Article  CAS  Google Scholar 

  31. Gupta PK, Kulwal PL, Jaiswal V (2014) Association mapping in crop plants: opportunities and challenges. Adv Genet 85:109–147

    Article  CAS  PubMed  Google Scholar 

  32. Tibbs Cortes L, Zhang Z, Yu J (2021) Status and prospects of genome-wide association studies in plants. Plant Genome 14(1):e20077

    Article  CAS  PubMed  Google Scholar 

  33. Malik MA, Lundervold AS, Michoel T (2022) rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes. arXiv preprint arXiv:220400067

  34. Malik P, Kumar J, Sharma S, Meher PK, Balyan HS, Gupta PK, Sharma S (2022) GWAS for main effects and epistatic interactions for grain morphology traits in wheat. Physiol Mol Biol Plants 28(3):651–668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Huang M, Liu X, Zhou Y, Summers RM, Zhang Z (2019) BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience 8(2):giy154

    Article  PubMed  Google Scholar 

  36. Rawat N, Tiwari VK, Singh N, Randhawa GS, Singh K, Chhuneja P, Dhaliwal HS (2009) Evaluation and utilization of Aegilops and wild Triticum species for enhancing iron and zinc content in wheat. Genet Res Crop Evol 56(1):53–64

    Article  Google Scholar 

  37. Murray M, Thompson W (1980) Rapid isolation of high molecular weight plant DNA. Nuc Acids Res 8(19):4321–4326

    Article  CAS  Google Scholar 

  38. Sansaloni C, Petroli C, Jaccoud D, Carling J, Detering F, Grattapaglia D, Kilian A Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus. In: BMC proceedings: 2011. BioMed Central: 1–2

  39. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23(19):2633–2635

    Article  CAS  PubMed  Google Scholar 

  40. Wang Y, Jodoin PM, Porikli F, Konrad J, Benezeth Y, Ishwar P, CDnet (2014) An expanded change detection benchmark dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops: 387–394

  41. Jorhem L, Engman J, Venäläinen (2000) Determination of lead, cadmium, zinc, copper, and iron in foods by atomic absorption spectrometry after microwave digestion: NMKL1 collaborative study. J AOAC Int 83(5):1189–1203

    Article  CAS  PubMed  Google Scholar 

  42. Bates D, Maechler M, Bolker B, Walker S, Christensen RH, Singmann H, Dai B (2014) lme4 Linear mixed-effects models using Eigen and S4. R package version 1.1–7. In. 2015

  43. Allard RW (1999) Principles of plant breeding. John Wiley & Sons

  44. Wang J, Zhang Z (2021) GAPIT Version 3: boosting power and accuracy for genomic association and prediction. Genom Proteom Bioinform 19(4):629–640

    Article  Google Scholar 

  45. VanRaden PM (2007) Efficient estimation of breeding values from dense genomic data. J Dairy Sci 90(1):374–375

    Google Scholar 

  46. Welch RM, Graham RD (2004) Breeding for micronutrients in staple food crops from a human nutrition perspective. J Exp Bot 55(396):353–364

    Article  CAS  PubMed  Google Scholar 

  47. Alemu A, Suliman S, Hagras A, Thabet S, Al-Abdallat A, Abdelmula AA, Tadesse W (2021) Multi-model genome-wide association and genomic prediction analysis of 16 agronomic, physiological and quality related traits in ICARDA spring wheat. Euphytica 217:1–22

    Article  Google Scholar 

  48. Kumar D, Sharma S, Sharma R, Pundir S, Singh VK, Chaturvedi D, Singh B, Kumar S, Sharma S (2021) Genome-wide association study in hexaploid wheat identifies novel genomic regions associated with resistance to root lesion nematode (Pratylenchus thornei). Sci Rep 11(1):3572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sandhu KS, Mihalyov PD, Lewien MJ, Pumphrey MO, Carter AH (2021) Genomic selection and genome-wide association studies for grain protein content stability in a nested association mapping population of wheat. Agronomy 11(12):2528

    Article  CAS  Google Scholar 

  50. Lee MH, Park J, Kim KH, Kim KM, Kang CS, Lee GE, Choi JY, Shon J, Ko JM, Choi C (2023) Genome-wide Association study of Arabinoxylan Content from a 562 Hexaploid Wheat Collection. Plants 12(1):184

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Singh VK, Chaturvedi D, Pundir S, Kumar D, Sharma R, Kumar S, Sharma S, Sharma S (2023) GWAS scans of cereal cyst nematode (Heterodera avenae) resistance in indian wheat germplasm. Mol Genet Genom 298(3):579–601

    Article  CAS  Google Scholar 

  52. Saini DK, Devi P, Kaushik P (2020) Advances in genomic interventions for wheat biofortification: a review. Agronomy 10(1):62

    Article  CAS  Google Scholar 

  53. Gupta PK, Balyan HS, Sharma S, Kumar R (2020) Genetics of yield, abiotic stress tolerance and biofortification in wheat (Triticum aestivum L). Theor Appl Genet 133(5):1569–1602

    Article  PubMed  Google Scholar 

  54. Tiwari VK, Rawat N, Chhuneja P, Neelam K, Aggarwal R, Randhawa GS, Dhaliwal HS, Keller B, Singh K (2009) Mapping of quantitative trait loci for grain iron and zinc concentration in diploid A genome wheat. J Hered 100(6):771–776

    Article  CAS  PubMed  Google Scholar 

  55. Crespo-Herrera LA, Govindan V, Stangoulis J, Hao Y, Singh RP (2017) QTL mapping of grain Zn and Fe concentrations in two hexaploid wheat RIL populations with ample transgressive segregation. Front Plant Sci 8:1800

    Article  PubMed  PubMed Central  Google Scholar 

  56. Krishnappa G, Singh AM, Chaudhary S, Ahlawat AK, Singh SK, Shukla RB, Jaiswal JP, Singh GP, Solanki IS (2017) Molecular mapping of the grain iron and zinc concentration, protein content and thousand kernel weight in wheat (Triticum aestivum L). PLoS ONE 12(4):e0174972

    Article  PubMed  PubMed Central  Google Scholar 

  57. Velu G, Tutus Y, Gomez-Becerra HF, Hao Y, Demir L, Kara R, Crespo-Herrera LA, Orhan S, Yazici A, Singh RP (2017) QTL mapping for grain zinc and iron concentrations and zinc efficiency in a tetraploid and hexaploid wheat mapping populations. Plant Soil 411(1):81–99

    Article  CAS  Google Scholar 

  58. Wang S, Wang Z, Gao Y, Liu L, Yu R, Jin J, Luo L, Hui X, Li F, Li M (2017) EDTA alone enhanced soil zinc availability and winter wheat grain zn concentration on calcareous soil. Environ Exp Bot 141:19–27

    Article  CAS  Google Scholar 

  59. Rathan ND, Sehgal D, Thiyagarajan K, Singh R, Singh AM, Govindan V (2021) Identification of genetic loci and candidate genes related to grain zinc and iron concentration using a zinc-enriched wheat ‘Zinc-Shakti’. Front Genet 12:652653

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kumar J, Mishra A, Kumar A, Kaur G, Sharma H, Kaur S, Sharma S, Devi K, Garg M, Pandey AK (2022) Whole genome re-sequencing of indian wheat genotypes for identification of genomic variants for grain iron and zinc content. Mol Biol Rep: 1–11

  61. Pu ZE, Ma Y, He QY, Chen GY, Wang JR, Liu YX, Jiang QT, Wei L, Dai SF, Wei YM (2014) Quantitative trait loci associated with micronutrient concentrations in two recombinant inbred wheat lines. J Integrat Agric 13(11):2322–2329

    Article  CAS  Google Scholar 

  62. Liu S, Zhong H, Meng X, Sun T, Li Y, Pinson SR, Chang SK, Peng Z (2020) Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods. BMC Plant Biol 20(1):1–18

    Article  Google Scholar 

  63. Çakmak İ, Torun A, Millet E, Feldman M, Fahima T, Korol A, Nevo E, Braun H, Özkan H (2004) Triticum dicoccoides: an important genetic resource for increasing zinc and iron concentration in modern cultivated wheat. Soil Sci Plant Nutr 50(7):1047–1054

    Article  Google Scholar 

  64. Peleg Z, Cakmak I, Ozturk L, Yazici A, Jun Y, Budak H, Korol AB, Fahima T, Saranga Y (2009) Quantitative trait loci conferring grain mineral nutrient concentrations in durum wheat × wild emmer wheat RIL population. Theor Appl Genet 119(2):353–369

    Article  CAS  PubMed  Google Scholar 

  65. Chatzav M, Peleg Z, Ozturk L, Yazici A, Fahima T, Cakmak I, Saranga Y (2010) Genetic diversity for grain nutrients in wild emmer wheat: potential for wheat improvement. Ann Bot 105(7):1211–1220

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Cu ST, Guild G, Nicolson A, Velu G, Singh R, Stangoulis J (2020) Genetic dissection of zinc, iron, copper, manganese and phosphorus in wheat (Triticum aestivum L.) grain and rachis at two developmental stages. Plant Sci 291:11033859

    Article  Google Scholar 

  67. Crespo-Herrera LA, Velu G, Singh RP (2016) Quantitative trait loci mapping reveals pleiotropic effect for grain iron and zinc concentrations in wheat. Ann Appl Biol 169:27–35

    Article  CAS  Google Scholar 

  68. Pu ZE, Ma YU, He QY, Chen GY, Wang JR, Liu YX, Jiang QT, Li W, Dai SF, Wei YM, Zheng YL (2014) Quantitative trait loci associ- ated with micronutrient concentrations in two recombinant inbred wheat lines. J Integrat Agri 13:2322–2329

    Article  CAS  Google Scholar 

  69. Liu J, Wu B, Singh RP, Velu G (2019) QTL mapping for micronutrients concentration and yield component traits in a hexaploid wheat mapping population. J Cereal Sci 88:57–64

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Hu J, Wang X, Zhang G, Jiang P, Chen W, Hao Y, Ma X, Xu S, Jia J, Kong L, Wang H (2020) QTL mapping for yield-related traits in wheat based on four RIL populations. Theor Appl Genet 133:917–933

    Article  CAS  PubMed  Google Scholar 

  71. Morel M, Crouzet J, Gravot A, Auroy P, Leonhardt N, Vavasseur A, Richaud P (2009) AtHMA3, a P1B-ATPase allowing Cd/Zn/Co/Pb vacuolar storage in Arabidopsis. Plant Physiol 149(2):894–904

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Arora S, Cheema J, Poland J, Uauy C, Chhuneja P (2019) Genome-wide association mapping of grain micronutrients concentration in Aegilops tauschii. Front Plant Sci 10:54

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors acknowledge Dr. Khem Singh Gill Akal College of Agriculture, Eternal University for providing the required infrastructure and research facilities. We also acknowledge DeLCON (DBT-electronic library consortium), Gurugram, India, for the online journal access.

Funding

The authors acknowledge the Department of Biotechnology Govt. of India for funds under BT/NABI-Flagship/2018 on wheat biofortification.

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Author notes

  1. Sukhwinder Singh

    Present address: USDA-ARS, Southeast Area, Subtropical Horticulture Research Station, 13601 Old Cutler Road, Miami, FL, 33158, USA

Authors and Affiliations

  1. Department of Genetics-Plant Breeding and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, Sirmaur, 173101, India

    Harneet Kaur, Prachi Sharma, Neeraj Kumar Vasistha, Vikrant Tyagi, H S Dhaliwal & Imran Sheikh

  2. National Agri-Food Biotechnology Institute, Sector-81, Mohali, Punjab, 140306, India

    Jitendra Kumar

  3. Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, U.P., 250004, India

    Vikas Kumar Singh

  4. Department of Genetics and Plant Breeding, Rajiv Gandhi University, Itanagar, India

    Neeraj Kumar Vasistha

  5. Department of Biotechnology, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India

    Vijay Gahlaut

  6. University Center for Research and Development, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India

    Vijay Gahlaut

  7. Department of Environmental Studies, University of Delhi, Delhi, 110007, India

    Shailender Kumar Verma

  8. International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico

    Sukhwinder Singh

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Contributions

Conceptualized by HSD and IS. JK, VG and VKS performed formal analysis. HK and PS performed the experiments. HSD supervised. IS, JK, VKS and SKV wrote the original draft. SS, VT, NKV, VG and PV reviewed and edited the manuscript. IS did project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Vijay Gahlaut or Imran Sheikh.

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Additional File 1:

Figure S1. (a) Manhattan plot displaying Blink model of GWAS-analysis for three phenotypic data of environment first: Fe, Zn, and Yield (b) QQ-plots of the observed and the expected p values of the GWAS model for Fe, Zn and Yield to visualize the false positives of the implemented models. Figure S2. (a) Manhattan plot displaying Blink model of GWAS-analysis for three phenotypic data of environment first: Fe, Zn, and Yield (b) QQ-plots of the observed and the expected p values of the GWAS model for Fe, Zn and Yield to visualize the false positives of the implemented models. Figure S3. (a) Manhattan plot displaying Blink model of GWAS-analysis for three phenotypic data of environment first: Fe, Zn, and Yield (b) QQ-plots of the observed and the expected p values of the GWAS model for Fe, Zn and Yield to visualize the false positives of the implemented models.

Additional File 2:

Table S1.Phenotypic data of all three traits. Table S2. Significant MTAs for GFeC detected at p-value < 0.001. Table S3. Significant MTAs for GZnC detected at p-value < 0.001. Table S4. Significant MTAs for yield detected at p-value < 0.001. Table S5. List of putative candidate genes for GFeC. Table S6. List of putative candidate genes for GZnC. Table S7. List of putative candidate genes for yield. Table S8 Important candidate genes based on their function. Table S9. Summary of orthologs identified in the different crops.

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Kaur, H., Sharma, P., Kumar, J. et al. Genetic analysis of iron, zinc and grain yield in wheat-Aegilops derivatives using multi-locus GWAS. Mol Biol Rep 50, 9191–9202 (2023). https://doi.org/10.1007/s11033-023-08800-y

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