Genome-wide association study of drought tolerance and biomass allocation in wheat

English version

Autoři: Isack Mathew ^aff001; Hussein Shimelis ^aff001; Admire Isaac Tichafa Shayanowako ^aff001; Mark Laing ^aff001; Vincent Chaplot ^aff002
Působiště autorů: African Centre for Crop Improvement, University of KwaZulu-Natal, School of Agricultural, Earth and Environmental Sciences, Pietermaritzburg, South Africa ^aff001; University of KwaZulu-Natal, School of Agricultural, Earth and Environmental Sciences, Pietermaritzburg, South Africa ^aff002; Sorbonne Universities, UPMC, IRD, CNRS, MNHN, Laboratoire d’Océanographie et du Climat: Expérimentations et approches numériques (LOCEAN), IPSL, Paris, France ^aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0225383

Souhrn

Genome wide association studies (GWAS) are important in discerning the genetic architecture of complex traits such as biomass allocation for improving drought tolerance and carbon sequestration potential of wheat. The objectives of this study were to deduce the population structure and marker-trait association for biomass traits in wheat under drought-stressed and non-stressed conditions. A 100-wheat (Triticum aestivum L.) genotype panel was phenotyped for days to heading (DTH), days to maturity (DTM), shoot biomass (SB), root biomass (RB), root to shoot ratio (RS) and grain yield (GY). The panel was sequenced using 15,600 single nucleotide polymorphism (SNPs) markers and subjected to genetic analysis using the compressed mixed linear model (CMLM) at false discovery rate (FDR < 0.05). Population structure analysis revealed six sub-clusters with high membership ancestry coefficient of ≤0.65 to their assigned sub-clusters. A total of 75 significant marker-trait associations (MTAs) were identified with a linkage disequilibrium threshold of 0.38 at 5cM. Thirty-seven of the MTAs were detected under drought-stressed condition and 48% were on the B genome, where most quantitative trait loci (QTLs) for RB, SB and GY were previously identified. There were seven pleiotropic markers for RB and SB that may facilitate simultaneous selection. Thirty-seven putative candidate genes were mined by gene annotation on the IWGSC RefSeq 1.1. The significant MTAs observed in this study will be useful in devising strategies for marker-assisted breeding for simultaneous improvement of drought tolerance and to enhance C sequestration capacity of wheat.

Klíčová slova:

Wheat – Genetic loci – Quantitative trait loci – Phenotypes – Genome-wide association studies – Plant resistance to abiotic stress – Drought adaptation – Linkage disequilibrium

Zdroje

1. Portmann FT, Siebert S, Döll P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling. Global Biogeochem Cy. 2010; 24(1): 1–18.

2. Pfeifer M, Kugler KG, Sandve SR, Zhan B, Rudi H, Hvidsten TR, et al. Genome interplay in the grain transcriptome of hexaploid bread wheat. Science. 2014; 345(6194): 1250091–101. doi: 10.1126/science.1250091 25035498

3. Mapfumo P, Onyango M, Honkponou SK, El Mzouri EH, Githeko A, Rabeharisoa L, et al. Pathways to transformational change in the face of climate impacts: an analytical framework. Climate and Development. 2017; 9(5): 439–51.

4. Poorter H, Nagel O. The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Func Plant Bio. 2000; 27(12): 1191–126.

5. White CA, Sylvester-Bradley R, Berry PM. Root length densities of UK wheat and oilseed rape crops with implications for water capture and yield. J Exp Bot. 2015; 66(8): 2293–303. doi: 10.1093/jxb/erv077 25750427

6. Baldock JA, Skjemstad JO. Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Org Geochem. 2000; 31(7–8): 697–710.

7. Paustian K, Lehmann J, Ogle S, Reay D, Robertson GP, Smith P. Climate-smart soils. Nature. 2016; 532(7597): 49–57. doi: 10.1038/nature17174 27078564

8. Osmont KS, Sibout R, Hardtke CS. Hidden branches: developments in root system architecture. Ann Rev Plant Bio. 2007; 58: 93–113.

9. Collard BC, Mackill DJ. Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B Biol Sci. 2008; 363(1491): 557–72. doi: 10.1098/rstb.2007.2170 17715053

10. Korte A, Farlow A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods. 2013; 9(1): 29–39.

11. Breseghello F, Sorrells ME. Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics. 2006; (2): 1165–77. doi: 10.1534/genetics.105.044586 16079235

12. Crossa J, Burgueno J, Dreisigacker S, Vargas M, Herrera-Foessel SA, Lillemo M, et al. Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure. Genetics. 2007; 177(3): 1889–1913. doi: 10.1534/genetics.107.078659 17947425

13. Charmet G, Masood-Quraishi U, Ravel C, Romeuf I, Balfourier F, Perretant MR, et al. Genetics of dietary fibre in bread wheat. Euph. 2009; 170(1–2): 155–168.

14. Qiu X, Gong R, Tan Y, Yu S. Mapping and characterization of the major quantitative trait locus qSS7 associated with increased length and decreased width of rice seeds. Theor App Gen. 2012; 125(8): 1717–26.

15. Liu Y, Lin Y, Gao S, Li Z, Ma J, Deng M, et al. A genome‐wide association study of 23 agronomic traits in Chinese wheat landraces. The Plant Journal. 2017; 91(5): 861–73. doi: 10.1111/tpj.13614 28628238

16. Mwadzingeni L, Shimelis H, Rees DJ, Tsilo TJ. Genome-wide association analysis of agronomic traits in wheat under drought-stressed and non-stressed conditions. PloS one. 2017; 12(2): e0171692. doi: 10.1371/journal.pone.0171692 28234945

17. Sukumaran S, Reynolds MP, Sansaloni C. Genome-wide association analyses identify QTL hotspots for yield and component traits in durum wheat grown under yield potential, drought, and heat stress environments. Front Plant Sci. 2018; 9: 81–96. doi: 10.3389/fpls.2018.00081 29467776

18. Maccaferri M, Ricci A, Salvi S, Milner SG, Noli E, Martelli PL, et al. A high‐density, SNP‐based consensus map of tetraploid wheat as a bridge to integrate durum and bread wheat genomics and breeding. Plant Biotechnol J. 2015; 13(5): 648–63. doi: 10.1111/pbi.12288 25424506

19. Ye H, Song L, Chen H, Valliyodan B, Cheng P, Ali L, et al. A major natural genetic variation associated with root system architecture and plasticity improves waterlogging tolerance and yield in soybean. Plant Cell Environ. 2018; 41(9): 2169–82. doi: 10.1111/pce.13190 29520811

20. Uga Y, Sugimoto K, Ogawa S, Rane J, Ishitani M, Hara N, et al. Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat Genet. 2013; 45(9): 1097. doi: 10.1038/ng.2725 23913002

21. Varshney RK, Thudi M, Nayak SN, Gaur PM, Kashiwagi J, Krishnamurthy L, et al. Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.). Theor App Gen. 2014; 127(2): 445–462.

22. Fleury D, Jefferies S, Kuchel H, Langridge P. Genetic and genomic tools to improve drought tolerance in wheat. J Exp Bot. 2010; 61(12): 3211–22. doi: 10.1093/jxb/erq152 20525798

23. Li L, Peng Z, Mao X, Wang J, Chang X, Reynolds M, et al. Genome-wide association study reveals genomic regions controlling root and shoot traits at late growth stages in wheat. Ann Bot. 2019; 1–14.

24. Thorup-Kristensen K, Cortasa MS, Loges R. Winter wheat roots grow twice as deep as spring wheat roots, is this important for N uptake and N leaching losses? Plant and Soil. 2009; 322(1–2): 101–14.

25. Department of Agriculture, Forestry and Fisheries (DAFF). Wheat Production Guideline. 2010. Pretoria, South Africa

26. Mathew I, Shimelis H, Mutema M, Clulow A, Zengeni R, Mbava N, et al. Selection of wheat genotypes for biomass allocation to improve drought tolerance and carbon sequestration into soils. J Agron Crop Sci. 2019; 205(4): 385–400.

27. Payne RW, Murray D A, Harding S A. An introduction to the GenStat command language. 2017. Hemel Hempstead, UK: VSN International.

28. Edwards CE, Ewers BE, Weinig C. Genotypic variation in biomass allocation in response to field drought has a greater effect on yield than gas exchange or phenology. BMC Plant Biol. 2016; 16(1): 185. doi: 10.1186/s12870-016-0876-3 27558796

29. Allard RW, Allard RW. Principles of plant breeding. John Wiley & Sons. 1999.

30. Gillespie JH, Turelli M. Genotype-environment interactions and the maintenance of polygenic variation. Genetics. 1989; 121(1): 129–38. 17246488

31. Herzig P, Maurer A, Draba V, Sharma R, Draicchio F, Bull H, et al. Contrasting genetic regulation of plant development in wild barley grown in two European environments revealed by nested association mapping. J Exp Bot. 2018; 69(7): 1517–31. doi: 10.1093/jxb/ery002 29361127

32. Huang J, Ge X, Sun M. Modified CTAB protocol using a silica matrix for isolation of plant genomic DNA. Biotechniques. 2000; 28(3): 432–4. doi: 10.2144/00283bm08 10723554

33. Cruz VMV, Kilian A, Dierig DA. Development of DArT marker platforms and genetic diversity assessment of the US collection of the new oilseed crop lesquerella and related species. PLoS One. 2013; 8(5): e64062. doi: 10.1371/journal.pone.0064062 23724020

34. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155(2): 945–59. 10835412

35. Gupta S, Kumari K, Muthamilarasan M, Parida SK, Prasad M. Population structure and association mapping of yield contributing agronomic traits in foxtail millet. Plant Cell Rep. 2014; 33(6): 881–93. doi: 10.1007/s00299-014-1564-0 24413764

36. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol Eco Res. 2015; 15(5): 1179–1191.

37. Valluru R, Reynolds MP, Davies WJ, Sukumaran S. Phenotypic and genome‐wide association analysis of spike ethylene in diverse wheat genotypes under heat stress. New Phyt. 2017; 214(1): 271–83.

38. Ma F, Xu Y, Ma Z, Li L, An D. Genome-wide association and validation of key loci for yield-related traits in wheat founder parent Xiaoyan 6. Mol Breed. 2018; 38(7): 91–106.

39. Lehnert H, Serfling A, Friedt W, Ordon F. Genome-Wide Association Studies Reveal Genomic Regions Associated With the Response of Wheat (Triticum aestivum L.) to Mycorrhizae Under Drought Stress Conditions. Front Plant Sci. 2018; 9: 1728–1752 doi: 10.3389/fpls.2018.01728 30568663

40. Molero G, Joynson R, Pinera‐Chavez FJ, Gardiner LJ, Rivera‐Amado C, Hall A, et al. Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant Biotech J. 2019; 17(7): 1276–1288.

41. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, et al. GAPIT: genome association and prediction integrated tool. Bioinformatics. 2012; 28(18): 2397–9. doi: 10.1093/bioinformatics/bts444 22796960

42. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing J R Stat Soc Series B Stat Methodol. 1995; 57(1): 289–300.

43. Shin JH, Blay S, McNeney B, Graham J. LDheatmap: an R function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. J Stat Softw. 2006; 16(3): 1–10.

44. R Core Team. R: A Language and Environment for Statistical Computing. 2017. Vienna: R Core Team.

45. The International Wheat Genome Sequencing Consortium (IWGSC), IWGSC RefSeq principal investigators, Appels R, Eversole K, Feuillet C, Keller B, Rogers J, et al. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science. 2018; 361: eaar7191. doi: 10.1126/science.aar7191 P.

46. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005; 21(18): 3674–3646. doi: 10.1093/bioinformatics/bti610 16081474

47. Hassani-Pak K, Castellote M, Esch M, Hindle M, Lysenko A, Taubert J, et al. Developing integrated crop knowledge networks to advance candidate gene discovery. App Trans Gen. 2016; 11: 18–26. doi: 10.1016/j.atg.2016.10.003

48. Hassani-Pak, K. KnetMiner-An integrated data platform for gene mining and biological knowledge discovery. PhD Thesis. Bielefeld University, Germany. 2017. Available from: https://pub.uni-bielefeld.de/record/2915227

49. Dalal A, Attia Z, Moshelion M. To Produce or to Survive: How Plastic Is Your Crop Stress Physiology? Front Plant Sci. 2017; 8: 2067. doi: 10.3389/fpls.2017.02067 29259613

50. Mathew I, Shimelis H, Mwadzingeni L, Zengeni R, Mutema M, Chaplot V. Variance components and heritability of traits related to root: shoot biomass allocation and drought tolerance in wheat. Euph. 2018; 214(12): 225–237.

51. Tian J, Deng Z, Zhang K, Yu H, Jiang X, Li C. Genetic Analysis Methods of Quantitative Traits in Wheat. In: Genetic Analyses of Wheat and Molecular Marker-Assisted Breeding, Volume 1 2015. Springer, Dordrecht. pp. 13–40

52. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Eco. 2005; 14(8): 2611–20.

53. Mogga M, Sibiya J, Shimelis H, Lamo J, Yao N. Diversity analysis and genome-wide association studies of grain shape and eating quality traits in rice (Oryza sativa L.) using DArT markers. PloS one. 2018; 13(6): e0198012. doi: 10.1371/journal.pone.0198012 29856872

54. Kristensen PS, Jahoor A, Andersen JR, Cericola F, Orabi J, Janss LL, et al. Genome-wide association studies and comparison of models and cross-validation strategies for genomic prediction of quality traits in advanced winter wheat breeding lines. Front Plant Sci. 2018; 9:69. doi: 10.3389/fpls.2018.00069 29456546

55. Jost LO. GST and its relatives do not measure differentiation. Mol Ecol. 2008; 17(18): 4015–26. doi: 10.1111/j.1365-294x.2008.03887.x 19238703

56. Hao C, Wang L, Ge H, Dong Y, Zhang X. Genetic diversity and linkage disequilibrium in Chinese bread wheat (Triticum aestivum L.) revealed by SSR markers. PLOS one. 2011; 6(2): e17279. doi: 10.1371/journal.pone.0017279 21365016

57. Vos‐Fels KP, Qian L, Parra‐Londono S, Uptmoor R, Frisch M, Keeble‐Gagnère G, et al. Linkage drag constrains the roots of modern wheat. Plant Cell Environ. 2017; 40(5): 717–25. doi: 10.1111/pce.12888 28036107

58. Ayalew H, Liu H, Börner A, Kobiljski B, Liu C, Yan G. Genome-wide association mapping of major root length QTLs under PEG induced water stress in wheat. Front Plant Sci. 2018; 9: 1759–1763. doi: 10.3389/fpls.2018.01759 30555498

59. Onyemaobi I, Ayalew H, Liu H, Siddique KH, Yan G. Identification and validation of a major chromosome region for high grain number per spike under meiotic stage water stress in wheat (Triticum aestivum L.). PloS one. 2018; 13(3): e0194075. doi: 10.1371/journal.pone.0194075 29518125

60. Beyer S, Daba S, Tyagi P, Bockelman H, Brown-Guedira G, Mohammadi M. Loci and candidate genes controlling root traits in wheat seedlings—a wheat root GWAS. Funct Integr Genomics. 2018; (1): 91–107. doi: 10.1007/s10142-018-0630-z 30151724

61. Cvrcková F. Are plant formins integral membrane proteins? Genome Biol. 2000; research001-1

62. Huang C., Zhang R., Gui J., Zhong Y., & Li L. The receptor-like kinase AtVRLK1 regulates secondary cell wall thickening. Plant Physiol. 2018; 177(2): 671–683. doi: 10.1104/pp.17.01279 29678858

63. Seok MS, You YN, Park HJ, Lee SS, Aigen F, Luan S, et al. AtFKBP16‐1, a chloroplast lumenal immunophilin, mediates response to photosynthetic stress by regulating PsaL stability. Physiol Plant. 2014; 150(4): 620–31. doi: 10.1111/ppl.12116 24124981

64. Rizhsky L, Liang H, Shuman J, Shulaev V, Davletova S, Mittler R. When defense pathways collide. The response of Arabidopsis to a combination of drought and heat stress. Plant Physiol. 2004; 134(4): 1683–1696. doi: 10.1104/pp.103.033431 15047901

65. Fang C, Ma Y, Wu S, Liu Z, Wang Z, Yang R, et al. Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol. 2017; 18(161): 1–14.

66. Guo Z, Yang W, Chang Y, Ma X, Tu H, Xiong F, et al. Genome-wide association studies of image traits reveal genetic architecture of drought resistance in rice. Mol Plant. 2018; 11(6): 789–805. doi: 10.1016/j.molp.2018.03.018 29614319

67. Richards RA, Rebetzke GJ, Watt M, Condon AT, Spielmeyer W, Dolferus R. Breeding for improved water productivity in temperate cereals: phenotyping, quantitative trait loci, markers and the selection environment. Funct Plant Biol. 2010; 37(2): 85–97.

68. Kim SR, Yang JI, Moon S, Ryu CH, An K, Kim KM, et al. Rice OGR1 encodes a pentatricopeptide repeat–DYW protein and is essential for RNA editing in mitochondria. Plant J. 2009; 59(5): 738–749. doi: 10.1111/j.1365-313X.2009.03909.x 19453459

69. Černý M, Alena K, Wolfgang H, Lena F, Ondřej N, Gabriela R, et al. Proteome and metabolome profiling of cytokinin action in Arabidopsis identifying both distinct and similar responses to cytokinin down-and up-regulation. J Exp Bot. 2013; 64(14): 4193–4206. doi: 10.1093/jxb/ert227 24064926

70. Dai C, Lee YL, Lee IC, Nam HG, Kwak JM. Calmodulin 1 regulates senescence and ABA response in Arabidopsis. Front Plant Sci. 2018; 9:803. doi: 10.3389/fpls.2018.00803 30013580

71. Luo Y, Tang Y, Zhang X, Li W, Chang Y, Pang D, et al. Interactions between cytokinin and nitrogen contribute to grain mass in wheat cultivars by regulating the flag leaf senescence process. Crop J. 2018; 6(5): 538–551.

72. Edae EA, Byrne PF, Haley SD, Lopes MS, Reynolds MP. Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor App Gen. 2014; 127(4): 791–807.

73. Reimer S, Pozniak CJ, Clarke FR, Clarke JM, Somers DJ, Knox RE, et al. Association mapping of yellow pigment in an elite collection of durum wheat cultivars and breeding lines. Genome. 2008; 51(12): 1016–1025. doi: 10.1139/G08-083 19088814

74. Maccaferri M, Sanguineti MC, Noli E, Tuberosa R. Population structure and long-range linkage disequilibrium in a durum wheat elite collection. Mol Breed. 2005; 15(3): 271–290.

75. Neumann K, Kobiljski B, Denčić S, Varshney RK, Börner A. Genome-wide association mapping: a case study in bread wheat (Triticum aestivum L.). Mol Breed. 2011; 27(1): 37–58.

76. Nakamura S, Makiko C, Stehno Z, Holubec V, Morishige H, Pourkheirandish M, et al. Diversification of the promoter sequences of wheat Mother of FT and TFL1 on chromosome 3A. Mol Breed. 2015; 35(8): 164–173.

Genome-wide association study of drought tolerance and biomass allocation in wheat

Souhrn

Klíčová slova:

Zdroje

PLOS One

Získaná hemofilie - Povědomí o nemoci a její diagnostika

Eozinofilní granulomatóza s polyangiitidou