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Genetic analysis for detection of genes associated to drought tolerance in rice accessions belonging to north east India

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Abstract

Introduction

The North East (NE) India is rich in biodiversity and also considered as the secondary centre for origin of rice. The NE rice accessions was characterized previously using genetic markers and morphological traits. Simultaneously, genome-wide association studies (GWAS) reveal significant marker-trait associations for the drought tolerance traits.

Methods and results

The genetic diversity and population structure of 296 NE rice accessions were studied using 96,712 single nucleotide polymorphism (SNP) markers distributed across 12 chromosomes. The accessions were clustered into two major sub-groups (SG). A total of 91 accessions were assembled as SG1 and 114 accessions as SG2, while the remaining 91 were admixture genotypes. A total of 200 genotypes belonging to different groups were phenotyped for yield component traits under drought and control conditions. The GWAS was performed to identify significant marker-trait associations (MTAs). Consequently, 47 MTAs were detected under drought, exhibiting 0.02–9.95% of phenotypic variance (P.V.). Whereas 58 MTAs were discovered under control conditions, showing a 0.01–9.74% contribution to the phenotype. Through in-silico mining of QTLs, 2999 genes were identified. Among these; only 22 genes were directly associated with stress response.

Conclusion

These QTLs/genes may be deployed for marker-assisted pyramiding to improve drought tolerance in popular drought susceptible rice varieties.

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References

  1. Bernier J, Atlin GN, Serraj R, Kumar A, Spaner D (2008) Breeding upland rice for drought resistance. J Sci Food Agric 88:927–939

    CAS  Google Scholar 

  2. Verma RK, Dey PC, Chetia SK, Modi MK (2017) Development of advanced breeding lines of rice for drought tolerance based on physiological and yield traits. Oryza 54:169–173

    Google Scholar 

  3. Metzker ML (2010) Sequencing technologies-the next generation. Nat Rev Genet 11:31–46

    CAS  PubMed  Google Scholar 

  4. Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol 26:1135–1145

    CAS  PubMed  Google Scholar 

  5. Mishra A, Singh PK, Bhandawat A, Sharma V, Sharma V, Singh P, Roy J et al (2022) Analysis of SSR and SNP markers: bioinformatics. Academic Press, Cambridge, pp 131–144

    Google Scholar 

  6. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR et al (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456:53–59

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Rothberg JM, Hinz W, Rearick TM, Schultz J, Mileski W, Davey M, Leamon JH, Johnson K, Milgrew MJ, Edwards M et al (2011) An integrated semiconductor device enabling non-optical genome sequencing. Nature 475:348–352

    CAS  PubMed  Google Scholar 

  8. Rhoads A, Au KF (2015) PacBio sequencing and its applications. Genom Proteom Bioinform 13:278–289

    Google Scholar 

  9. Bayega A, Fahiminiya S, Oikonomopoulos S, Ragoussis J (2018) Current and future methods for mRNA analysis: a drive toward single molecule sequencing gene expression analysis. Humana Press, New York, pp 209–241

    Google Scholar 

  10. Lu F, Lipka AE, Glaubitz J, Elshire R, Cherney JH, Casler MD, Buckler ES, Costich DE (2013) Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet 9:e1003215

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH (2011) Stacks: building and genotyping loci de novo from short-read sequences. Genes Genom Genet 1:171–182

    CAS  Google Scholar 

  12. Melo AT, Bartaula R, Hale I (2016) GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data. BMC Bioinf 17:1–15

    Google Scholar 

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

    CAS  Google Scholar 

  14. Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES (2014) TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9:e90346

    PubMed  PubMed Central  Google Scholar 

  15. Torkamaneh D, Laroche J, Bastien M, Abed A, Belzile F (2017) Fast-GBS: a new pipeline for the efficient and highly accurate calling of SNPs from genotyping-by-sequencing data. BMC Bioinfo 18:1–7

    Google Scholar 

  16. Brachi B, Morris GP, Borevitz JO (2011) Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol 12:1–8

    Google Scholar 

  17. Ogura T, Busch W (2015) From phenotypes to causal sequences: using genome wide association studies to dissect the sequence basis for variation of plant development. Curr Opin Plant Biol 23:98–108

    CAS  PubMed  Google Scholar 

  18. Verma RK, Chetia SK, Baishya S, Sharma V, Sharma H, Modi MK (2022) GWAS to spot candidate genes associated with grain quality traits in diverse rice accessions of North East India. Mol Biol Rep 49:5365–5377

    CAS  PubMed  Google Scholar 

  19. Verma RK, Chetia SK, Dey PC, Rahman A, Saikia S, Sharma V, Sharma H, Sen P, Modi MK (2021) Genome-wide association studies for agronomical traits in winter rice accessions of Assam. Genomics 113:1037–1047

    CAS  PubMed  Google Scholar 

  20. Verma RK, Chetia SK, Dey PC, Sharma V, Baruah AR, Modi MK (2017) Development of advanced breeding lines for high grain yield under drought stress in elite rice genetic background. Res Crop 18:705–710

    Google Scholar 

  21. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchel SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Li H, Durbin R (2009) Fast and accurate short read alignment with burrows-wheeler transform. Bioinfo 25:1754–1760

    CAS  Google Scholar 

  23. Qi P, Gimode D, Saha D, Schröder S, Chakraborty D, Wang X, Dida MM, Malmberg RL, Devos KM (2018) UGbS-Flex, a novel bioinformatics pipeline for imputation-free SNP discovery in polyploids without a reference genome: finger millet as a case study. BMC Plant Biol 18:1–9

    CAS  Google Scholar 

  24. Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Volante A, Tondelli A, Aragona M, Valente MT, Biselli C, Desiderio F, Bagnaresi P, Matic S, Gullino ML, Infantino A, Spadaro D (2017) Identification of bakanae disease resistance loci in japonica rice through genome wide association study. Rice 10:1–6

    Google Scholar 

  26. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinfo 28:2397–2399

    CAS  Google Scholar 

  27. Longmei N, Gill GK, Zaidi PH, Kumar R, Nair SK, Hindu V, Vinayan MT, Vikal Y (2021) Genome wide association mapping for heat tolerance in sub-tropical maize. BMC Genom 22:1–4

    Google Scholar 

  28. Myles S, Chia JM, Hurwitz B, Simon C, Zhong GY, Buckler E, Ware D (2010) Rapid genomic characterization of the genus vitis. PLoS ONE 5:e8219

    PubMed  PubMed Central  Google Scholar 

  29. Huang X, Zhao Y, Li C, Wang A, Zhao Q, Li W, Guo Y, Deng L, Zhu C, Fan D et al (2012) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44:32–39

    Google Scholar 

  30. Xu X, Liu X, Ge S, Jensen JD, Hu F, Li X, Dong Y, Gutenkunst RN, Fang L, Huang L, Li J (2012) Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nat Biotechnol 30:105–111

    CAS  Google Scholar 

  31. Verma RK, Chetia SK, Rahman A, Dey PC, Sen P, Modi MK (2017) Study on genetic diversity and population structure of upland rice accessions using SSR markers associated with grain yield under drought. Crop Res 52:180–187

    Google Scholar 

  32. Mahalle MD, Dey PC, Chetia SK, Baruah AR, Ahmed T, Sarma RN, Kaldate RC, Kumar A, Singh SK, Modi MK (2021) Association mapping for yield traits under drought stress in Autumn rice germplasm collection of Assam. J Plant Biochem Biotechnol 30:26–36

    CAS  Google Scholar 

  33. Khush GS (1997) Origin, dispersal, cultivation and variation of rice. Plant Mol Biol 35:25–34

    CAS  PubMed  Google Scholar 

  34. Sharma V, Verma RK, Dey PC, Chetia SK, Baruah AR, Modi MK (2017) QTLs associated with yield attributing traits under drought stress in upland rice cultivar of Assam. Oryza 54:253–257

    Google Scholar 

  35. Kumar A, Verulkar SB, Dixit S, Chauhan B, Bernier J, Venuprasad R, Zhao D, Shrivastava MN (2009) Yield and yield-attributing traits of rice (Oryza sativa L.) under lowland drought and suitability of early vigor as a selection criterion. Field Crop Res 114:99–107

    Google Scholar 

  36. Lafitte HR, Price AH, Courtois B (2004) Yield response to water deficit in an upland rice mapping population: associations among traits and genetic markers. Theor App Genet 109:1237–1246

    CAS  Google Scholar 

  37. Ranawake AL, Amarasinghe UGS (2014) Relationship of yield and yield related traits of some traditional rice cultivars in Sri Lanka as described by correlation analysis. J Sci Res Rep 3:2395–2403

    Google Scholar 

  38. Bhadru D, Reddy DL, Ramesha MS (2011) Correlation and path coefficient analysis of yield and yield contributing traits in rice hybrids and their parental lines. Elect J Plant Breed 2:112–116

    Google Scholar 

  39. Verma RK, Dey PC, Chetia SK, Modi MK (2017) Correlation coefficient of yield traits in advanced breeding lines under drought stress. Bull Env Pharmacol Life Sci 2:372–375

    Google Scholar 

  40. Liu N, Xue Y, Guo Z, Li W, Tang J (2016) Genome-wide association study identifies candidate genes for starch content regulation in maize kernels. Front Plant Sci 7:1046

    PubMed  PubMed Central  Google Scholar 

  41. Verma RK, Chetia SK, Dey PC, Baruah AR, Modi MK (2017) Mapping of QTLs for grain yield and its component traits under drought stress in elite rice variety of Assam. Int J Curr Microbiol App Sci 6:1443–1455

    CAS  Google Scholar 

  42. Verma RK, Chetia SK, Tamuly A, Sharma V, Dey PC, Sen P, Modi MK (2021) Characterization of winter rice (Oryza sativa L.) germplasm of North East India using morphological traits. Indian J Trad Knowl 20(3):838–845

    Google Scholar 

  43. Zhang K, Kuraparthy V, Fang H, Zhu L, Sood S, Jones DC (2019) High-density linkage map construction and QTL analyses for fiber quality, yield and morphological traits using Cotton SNP63K array in upland cotton (Gossypium hirsutum L.). BMC Genom 20:1–26

    Google Scholar 

  44. Sharma V, Jambaladinni K, Singh N, Mishra N, Kumar A, Kumar R (2022) Understanding environmental associated abiotic stress response in plants under changing climate. In Molecular response and genetic engineering for stress in plants: abiotic stress, Vol 1, IOP Publishing. https://doi.org/10.1088/978-0-7503-4921-5ch1

  45. Almagro L, Gómez Ros LV, Belchi-Navarro S, Bru R, Ros Barceló A, Pedreño MA (2009) Class III peroxidases in plant defence reactions. J Exp Bot 60:377–390

    CAS  PubMed  Google Scholar 

  46. Gautam A, Sharma V, Chauhan C, Panwar RK, Tewari SK, Verma SK (2022) Unraveling the molecular and genetic basis of plant responses to heat stress. In Molecular response and genetic engineering for stress in plants: abiotic stress, Vol 1. IOP Publishing. https://doi.org/10.1088/978-0-7503-4921-5ch7

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Acknowledgements

The authors gratefully acknowledge DBT-NECAB for providing the financial assistance and RARS, Titabar for providing the logistic support to the field work. The authors also acknowledge the contribution of Dr. Sushil K. Singh in collection of leaf samples for genotyping.

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Authors and Affiliations

  1. DBT-North East Centre for Agricultural Biotechnology, Jorhat, 785013, Assam, India

    Rahul K. Verma, Sushil K. Singh & Bidyut K. Sarmah

  2. Regional Agricultural Research Station, Titabar, 785630, Assam, India

    Sanjay K. Chetia

  3. Department of Agricultural Biotechnology, Assam Agricultural University, Jorhat, 785013, Assam, India

    Vinay Sharma & Mahendra K. Modi

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  1. Rahul K. Verma
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  2. Sanjay K. Chetia
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  3. Vinay Sharma
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  6. Mahendra K. Modi
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Contributions

MKM for supervision of overall research programme, MKM; BKS; SKC; RKV for designing the field and lab experiments, SKC for provided the genetic resources, RKV and VS performed the research work and data analysis, MKM; RKV; VS for writing and editing the manuscript.

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Correspondence to Mahendra K. Modi.

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Verma, R.K., Chetia, S.K., Sharma, V. et al. Genetic analysis for detection of genes associated to drought tolerance in rice accessions belonging to north east India. Mol Biol Rep 50, 1993–2006 (2023). https://doi.org/10.1007/s11033-022-08145-y

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