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Common Genomic Tools and Their Implementations in Genetic Improvement of Cereals

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Genomics of Cereal Crops

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Abstract

Genomics is an interdisciplinary approach that characterizes and quantifies important genes of an organism through mapping, editing, functional, and structural studies. Genomics-based strategies have revolutionized the genetic enhancement of crops by dissecting the complex architecture of various important traits in plants. Incorporate genomic tools in cereal breeding can assist in enhancing their yield and productivity. Owing to the advancement of genomic resources and novel molecular markers, genome-wide association studies (GWAS) via tagging of important genes or markers linked to useful agronomic and physiological traits and QTL mapping have become an important approach utilized in marker assisted breeding for improvement or introgression of these traits. In addition, advancements in sequencing technologies provide complete sequences of various important cereals that further provide insights into their complex genomes. Knowledge of genes can assist in manipulating their response toward stress and transferring important genes to other crop species via conventional breeding methods, marker assisted breeding or transgenic approaches for crop improvement. The present chapter provides the detailed methodology of GWAS and QTL mapping along with advanced sequencing technologies, which enhance the genomic resources in cereals, map important agronomic traits, and provide linked markers for marker assisted breeding which are essential for genetic improvement of crops.

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References

  1. Ray DK, Mueller ND, West PC et al (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One 8(6):e66428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Food and Agriculture Organization of the United Nations (FAO) (2009) High-level expert forum-how to feed the world in 2050. Food and Agriculture Organization of the United Nations (FAO), Rome

    Google Scholar 

  3. Stoskopf NC (1985) Cereal grain crops. Reston Publishing Company

    Google Scholar 

  4. Zenda T, Liu S, Dong A et al (2021) Advances in cereal crop genomics for resilience under climate change. Life 11(6):502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Khush GS (2001) Green revolution: the way forward. Nat Rev Genet 2(10):815–822

    Article  CAS  PubMed  Google Scholar 

  6. Parry MA, Hawkesford MJ (2012) An integrated approach to crop genetic improvement. J Integr Plant Biol 54(4):250–259

    Article  PubMed  Google Scholar 

  7. Pandurangan S, Workman C, Nilsen K et al (2022) Introduction to marker-assisted selection in wheat breeding. In: Bilichak A, Laurie JD (eds) Accelerated breeding of cereal crops, Springer protocols handbooks. Humana, New York, NY

    Google Scholar 

  8. Huang Y, Liu H, Xing Y (2018) Next-generation sequencing promoted the release of reference genomes and discovered genome evolution in cereal crops. Curr Issues Mol Biol 27(1):37–50

    Article  PubMed  Google Scholar 

  9. Perez-de-Castro AM, Vilanova S, Cañizares J et al (2012) Application of genomic tools in plant breeding. Curr Genomics 13(3):179–195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Varshney RK, Dubey A (2009) Novel genomic tools and modern genetic and breeding approaches for crop improvement. J Plant Biochem Biotechnol 18(2):127–138

    Article  CAS  Google Scholar 

  11. Hasan N, Choudhary S, Naaz N et al (2021) Recent advancements in molecular marker-assisted selection and applications in plant breeding programmes. J Genet Eng Biotechnol 19(1):1–26

    Article  Google Scholar 

  12. Kumar S, Kumar M, Mir RR et al (2021) Advances in molecular markers and their use in genetic improvement of wheat. In: Wani SH, Mohan A, Singh GP (eds) Physiological, molecular, and genetic perspectives of wheat improvement. Springer, Cham

    Google Scholar 

  13. Thomson MJ, Ismail AM, McCouch SR et al (2009) Marker assisted breeding. In: Pareek A, Sopory S, Bohnert H (eds) Abiotic stress adaptation in plants. Springer, Dordrecht, pp 451–469

    Chapter  Google Scholar 

  14. Nadeem MA, Nawaz MA, Shahid MQ et al (2018) DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnol Biotechnol Equip 32(2):261–285

    Article  CAS  Google Scholar 

  15. Arabzai MG, Gul H (2021) Application techniques of molecular marker and achievement of marker assisted selection (MAS) in three major crops rice, wheat and maize. Int J Res Appl Sci Biotechnol 8(1):82–93

    Article  Google Scholar 

  16. Prasanna BM, Pixley K, Warburton ML et al (2010) Molecular marker-assisted breeding options for maize improvement in Asia. Mol Breed 26(2):339–356

    Article  CAS  Google Scholar 

  17. Loskutov IG (2021) Advances in cereal crops breeding. Plants (Basel) 10:1705

    Article  Google Scholar 

  18. Huang L, Ren X, Wu B et al (2016) Development and deployment of a high-density linkage map identified quantitative trait loci for plant height in peanut (Arachis hypogaea L.). Sci Rep 6(1):1–10

    Article  CAS  Google Scholar 

  19. Korzun V (2003) Molecular markers and their applications in cereals breeding. In: Markers assisted selection: a fast track to increase genetic gain in plant and animal breeding, (International Conference, October, 17–18), pp 18-22

    Google Scholar 

  20. Mather K (1949) Biometrie genetics, 1st edn. Methuen, London

    Google Scholar 

  21. Gilpin BJ, McCallum JA, Frew TJ et al (1997) A linkage map of the pea (Pisum sativum L.) genome containing cloned sequences of known function and expressed sequence tags (ESTs). Theor Appl Genet 95(8):1289–1299

    Article  CAS  Google Scholar 

  22. Takeda S, Matsuoka M (2008) Genetic approaches to crop improvement: responding to environmental and population changes. Nat Rev Genet 9(6):444–457

    Article  CAS  PubMed  Google Scholar 

  23. Roy SJ, Tucker EJ, Tester M (2011) Genetic analysis of abiotic stress tolerance in crops. Curr Opin Plant Biol 14(3):232–239

    Article  CAS  PubMed  Google Scholar 

  24. Gouda G, Sabrinathan S, Gupta MK et al (2021) Quantitative trait locus mapping in rice. In: Gupta MK, Behera L (eds) Applications of bioinformatics in rice research. Springer, Singapore. https://doi.org/10.1007/978-981-16-3997-5_3

    Chapter  Google Scholar 

  25. Blümel M, Dally N, Jung C (2015) Flowering time regulation in crops-what did we learn from Arabidopsis? Curr Opin Biotechnol 32:121–129

    Article  PubMed  CAS  Google Scholar 

  26. Li XH, Wang ZH, Gao SR et al (2008) Analysis of QTL for resistance to head smut (Sporisorium reiliana) in maize. Field Crops Res 106(2):148–155

    Article  Google Scholar 

  27. Isham K, Wang R, Zhao W et al (2021) QTL mapping for grain yield and three yield components in a population derived from two high-yielding spring wheat cultivars. Theor Appl Genet 134:2079–2095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Naz AA, Kunert A, Lind V et al (2008) AB-QTL analysis in winter wheat: II. Genetic analysis of seedling and field resistance against leaf rust in a wheat advanced backcross population. Theor Appl Genet 116(8):1095–1104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gómez G, Álvarez MF, Mosquera T (2011) Association mapping, a method to detect quantitative trait loci: statistical bases. Agron Colomb 29(3):367–376

    Google Scholar 

  30. Soto-Cerda BJ, Vasudevan A, Laroche A et al (2022) Genome-wide association studies (GWAS) in cereals. In: Bilichak A, Laurie JD (eds) Accelerated breeding of cereal crops. Humana, New York, NY, pp 119–132

    Chapter  Google Scholar 

  31. Jannin JL, Walsh B (2002) Association mapping in plant populations. In: Kang MS (ed) Quantitative genetics, genomics and plant breeding. CABI, Wallingford, pp 59–68

    Chapter  Google Scholar 

  32. Gupta PK, Balyan HS, Mir RR et al (2011) QTL analysis, association mapping and marker-assisted selection for some quality traits in bread wheat-An overview of the work done at CCS University, Meerut. J Wheat Res 3(2):1

    CAS  Google Scholar 

  33. Zhu C, Gore M, Buckler ES et al (2008) Status and prospects of association mapping in plants. Plant Genome 1(1)

    Google Scholar 

  34. Rafalski JA (2010) Association genetics in crop improvement. Curr Opin Plant Biol 13(2):174–180

    Article  CAS  PubMed  Google Scholar 

  35. Zhang J, Zhao J, Xu Y et al (2015) Genome-wide association mapping for tomato volatiles positively contributing to tomato flavour. Front Plant Sci 6:1042

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kraakman AT, Niks RE, Van den Berg PM et al (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168(1):435–446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Alseekh S, Kostova D, Bulut M et al (2021) Genome-wide association studies: assessing trait characteristics in model and crop plants. Cell Mol Life Sci 78(15):5743–5754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chen W, Gao Y, Xie W et al (2014) Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 46:714–721

    Article  CAS  PubMed  Google Scholar 

  39. Breseghello F, Sorrells ME (2006) Association analysis as a strategy for improvement of quantitative traits in plants. Crop Sci 46(3):1323–1330

    Article  Google Scholar 

  40. Yang Q, Li Z, Li W et al (2013) CACTA-like transposable element in ZmCCT attenuated photoperiod sensitivity and accelerated the postdomestication spread of maize. Proc Natl Acad Sci 110(42):16969–16974

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bouchet S, Servin B, Bertin P et al (2013) Adaptation of Maize to Temperate Climates: Mid-Density Genome-Wide Association Genetics and Diversity Patterns Reveal Key Genomic Regions, with a Major Contribution of the Vgt2 (ZCN8) Locus. PLoS One 8:e71377

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Huang X, Zhao Y, Wei X 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

    Article  CAS  Google Scholar 

  43. Muñoz-Amatriaín M, Cuesta-Marcos A, Endelman JB et al (2014) The USDA barley core collection: genetic diversity, population structure, and potential for genome-wide association studies. PLoS One 9:e94688

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Famoso AN, Zhao K, Clark RT et al (2011) Genetic Architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genet 7:e1002221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ayalew H, Ma X, Yan G (2015) Screening wheat (Triticum spp.) genotypes for root length under contrasting water regimes: potential sources of variability for drought resistance breeding. J Agron Crop Sci 201:189–194

    Article  CAS  Google Scholar 

  46. Pace J, Gardner C, Romay C et al (2015) Genome-wide association analysis of seedling root development in maize (Zea mays L.). BMC Genomics 16:47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pace J, Lee N, Naik HS et al (2014) Analysis of maize (Zea mays L.) seedling roots with the high-throughput image analysis tool ARIA (automatic root image analysis). PLoS One 9:e108255

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Sanchez DL, Liu S, Ibrahim R et al (2018) Genome-wide association studies of doubled haploid exotic introgression lines for root system architecture traits in maize (Zea mays L.). Plant Sci 268:30–38

    Article  CAS  PubMed  Google Scholar 

  49. Biscarini F, Cozzi P, Casella L et al (2016) Genome-wide association study for traits related to plant and grain morphology, and root architecture in temperate rice accessions. PLoS One 11:e0155425

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Li X, Guo Z, Lv Y et al (2017) Genetic control of the root system in rice under normal and drought stress conditions by genome-wide association study. PLoS Genet 13:e1006889

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Yang W, Guo Z, Huang C et al (2015) Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer. J Exp Bot 66:5605–5615

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Yang W, Guo Z, Huang C et al (2014) Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5:5087

    Article  CAS  PubMed  Google Scholar 

  53. Guo Z, Yang W, Chang Y et al (2018) Genome-wide association studies of image traits reveal genetic architecture of drought resistance in rice. Mol Plant 11:789–805

    Article  CAS  PubMed  Google Scholar 

  54. Wu D, Sato K, Ma JF (2015) Genome-wide association mapping of cadmium accumulation in different organs of barley. New Phytol 208:817–829

    Article  CAS  PubMed  Google Scholar 

  55. Zhao J, Yang W, Zhang SY et al (2018) Genome-wide association study and candidate gene analysis of rice cadmium accumulation in grain in a diverse rice collection. Rice 11:61

    Article  PubMed  PubMed Central  Google Scholar 

  56. Zhao X, Luo L, Cao Y et al (2018) Genome-wide association analysis and QTL mapping reveal the genetic control of cadmium accumulation in maize leaf. BMC Genomics 19:91

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Zhao Z, Zhang H, Fu Z et al (2018) Genetic-based dissection of arsenic accumulation in maize using a genome-wide association analysis method. Plant Biotechnol J 16:1085–1093

    Article  CAS  PubMed  Google Scholar 

  58. Frouin J, Labeyrie A, Boisnard A et al (2019) Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains. PLoS One 14:e0217516

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hindu V, Palacios-Rojas N, Babu R et al (2018) Identification and validation of genomic regions influencing kernel zinc and iron in maize. Theor Appl Genet 131:1443–1457

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Alomari DZ, Eggert K, von Wirén N et al (2017) Genome-wide association study of calcium accumulation in grains of European wheat cultivars. Front Plant Sci 8:1–11

    Article  Google Scholar 

  61. Kumar J, Saripalli G, Gahlaut V et al (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 214:219

    Article  CAS  Google Scholar 

  62. Alomari DZ, Eggert K, von Wirén N et al (2019) Whole-genome association mapping and genomic prediction for iron concentration in wheat grains. Int J Mol Sci 20:76

    Article  CAS  Google Scholar 

  63. Chen W, Wang W, Peng M et al (2016) Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nat Commun 7:12767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Riaz A, Kanwal F, Börner A et al (2021) Advances in genomics-based breeding of barley: molecular tools and genomic databases. Agronomy 11(5):894

    Article  CAS  Google Scholar 

  65. Benavente E, Giménez E (2021) Modern Approaches for the Genetic Improvement of Rice, Wheat and Maize for Abiotic Constraints-Related Traits: A Comparative Overview. Agronomy 11(2):376

    Article  CAS  Google Scholar 

  66. Walkowiak S, Pozniak CJ, Nilsen KT (2022) Recent advances in sequencing of cereal genomes. In: Bilichak A, Laurie JD (eds) Accelerated breeding of cereal crops, Springer protocols handbooks. Humana, New York, NY

    Google Scholar 

  67. Nakano Y, Kobayashi Y (2020) Genome-wide association studies of agronomic traits consisting of field-and molecular-based phenotypes. Rev Agric Sci 8:28–45

    Article  Google Scholar 

  68. Edward D, Batley J (2010) Plant genome sequencing: applications for crop improvement. Plant Biotechnol J 8(1):2–9

    Article  CAS  Google Scholar 

  69. Egan AN, Schlueter J, Spooner DM (2012) Applications of next-generation sequencing in plant biology. Am J Bot 99(2):175–185

    Article  CAS  PubMed  Google Scholar 

  70. Barabaschi D, Guerra D, Lacrima K et al (2012) Emerging knowledge from genome sequencing of crop species. Mol Biotechnol 50(3):250–266

    Article  CAS  PubMed  Google Scholar 

  71. International Rice Genome Sequencing Project, Sasaki T [2005] The map-based sequence of the rice genome. Nature 436:793–800

    Google Scholar 

  72. Lee HC, Lai K, Lorenc MT et al (2012) Bioinformatics tools and databases for analysis of next-generation sequence data. Brief Funct Genomics 11(1):12–24

    Article  CAS  PubMed  Google Scholar 

  73. Yu J, Hu S, Wang J et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296(5565):79–92

    Article  CAS  PubMed  Google Scholar 

  74. Varshney RK, Hoisington DA, Tyagi AK (2006) Advances in cereal genomics and applications in crop breeding. Trends Biotechnol 24(11):490–499

    Article  CAS  PubMed  Google Scholar 

  75. Jiang GL (2013) Molecular markers and marker-assisted breeding in plants. In: Andersen SB (ed) Plant breeding from laboratories to fields. InTech, pp 45–83

    Google Scholar 

  76. Choudhary K, Choudhary OP, Shekhawat NS (2008) Marker assisted selection: a novel approach for crop improvement. Am Eurasian J Agric 1:26–30

    Google Scholar 

  77. Kamboj D, Kumar S, Mishra CN et al (2020) Marker assisted breeding in cereals: progress made and challenges in India. J Cereal Res 12(2):85–102

    Article  Google Scholar 

  78. Langridge P, Lagudah ES, Holton TA et al (2001) Trends in genetic and genome analyses in wheat: a review. Aust J Agric Res 52(12):1043–1077

    Article  CAS  Google Scholar 

  79. Perumalsamy S, Bharani M, Sudha M et al (2010) Functional marker-assisted selection for bacterial leaf blight resistance genes in rice (Oryza sativa L.). Plant Breed 129(4):400–406

    CAS  Google Scholar 

  80. Araus JL, Slafer GA, Royo C et al (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27(6):377–412

    Article  Google Scholar 

  81. Holland B (2004) Implementation of molecular markers for quantitative traits in breeding programs-challenges and opportunities. In: New directions for a diverse planet: proceedings for the 4th international crop science congress. Regional Institute, Gosford, Australia, www.cropscience.org.au/icsc2004

    Google Scholar 

  82. Charcosset A (1997) Marker-assisted introgression of quantitative trait loci. Genetics 147(3):1469–1485

    Article  PubMed  PubMed Central  Google Scholar 

  83. Shanti ML, George MLC, Cruz CV et al (2001) Identification of resistance genes effective against rice bacterial blight pathogen in eastern India. Plant Dis 85(5):506–512

    Article  CAS  PubMed  Google Scholar 

  84. Xu Y, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48(2):391–407

    Article  Google Scholar 

  85. Gasser CS, Fraley RT (1989) Genetically engineering plants for crop improvement. Science 244(4910):1293–1299

    Article  CAS  PubMed  Google Scholar 

  86. Collinge DB, Sarrocco S (2022) Transgenic approaches for plant disease control: Status and prospects. Plant Pathol 71(1):207–225

    Article  CAS  Google Scholar 

  87. Nepolean T, Kaul J, Mukri G et al (2018) Genomics-enabled next-generation breeding approaches for developing system-specific drought tolerant hybrids in maize. Front Plant Sci 9:361

    Article  PubMed  PubMed Central  Google Scholar 

  88. Bakshi S, Dewan D (2013) Status of transgenic cereal crops: a review. Clon Transgen 3(119):2

    Google Scholar 

  89. Shoeb E, Badar U, Venkataraman S et al (2021) In: Upadhyay SK (ed) Genome engineering and essential mineral enrichment of crops. Genome engineering for crop improvement, pp 210–223

    Google Scholar 

  90. Ziemienowicz A (2014) Agrobacterium-mediated plant transformation: Factors, applications and recent advances. Biocatal Agric Biotechnol 3(4):95–102

    Article  Google Scholar 

  91. Persley GJ, Siedow JN (1999) Applications of biotechnology to crops: benefits and risks. https://www.iatp.org/sites/default/files/Applications_of_Biotechnology_to_Crops_Benefit.htm

  92. Paterson AH, Bowers JE, Peterson DG et al (2003) Structure and evolution of cereal genomes. Curr Opin Genet Dev 13(6):644–650

    Article  CAS  PubMed  Google Scholar 

  93. Muehlbauer FJ, Cho S, Sarker A et al (2006) Application of biotechnology in breeding lentil for resistance to biotic and abiotic stress. Euphytica 147(1):149–165

    Article  Google Scholar 

  94. Gupta SM, Grover A, Nasim M (2013) Transgenic technologies in agriculture: from lab to field to market. CIBtech J Biotechnol 3:20–47

    Google Scholar 

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Authors and Affiliations

  1. Department of Agricultural Biotechnology, CSK Himachal Pradesh Agricultural University, Palampur, Himachal Pradesh, India

    Megha Katoch

  2. Department of Botany and Microbiology, Gurukul Kangri Vishwavidyalaya, Haridwar, Uttarakhand, India

    Ajay Kumar

  3. Department of Botany, Akal University, Bathinda, Punjab, India

    Simranjeet Kaur & Avneesh Kumar

  4. Department of Microbiology, CCS Haryana Agriculture University, Hisar, Haryana, India

    Anuj Rana

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  1. Megha Katoch
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  2. Ajay Kumar
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  3. Simranjeet Kaur
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  4. Anuj Rana
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  5. Avneesh Kumar
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Correspondence to Avneesh Kumar .

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  1. Sher-e-Kashmir University of Agricultura, KHUDWANI ANANTNAG, Jammu and Kashmir, India

    Shabir Hussain Wani

  2. Dalhousie University, Halifax, Canada

    Anuj Kumar

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Katoch, M., Kumar, A., Kaur, S., Rana, A., Kumar, A. (2022). Common Genomic Tools and Their Implementations in Genetic Improvement of Cereals. In: Wani, S.H., Kumar, A. (eds) Genomics of Cereal Crops. Springer Protocols Handbooks. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2533-0_6

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  • DOI: https://doi.org/10.1007/978-1-0716-2533-0_6

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