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Genetic Mapping Populations for Conducting High-Resolution Trait Mapping in Plants

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Plant Genetics and Molecular Biology

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 164))

Abstract

Fine mapping of quantitative trait loci (QTL) is the route to more detailed molecular characterization and functional studies of the relationship between polymorphism and trait variation. It is also of direct relevance to breeding since it makes QTL more easily integrated into marker-assisted breeding and into genomic selection. Fine mapping requires that marker-trait associations are tested in populations in which large numbers of recombinations have occurred. This can be achieved by increasing the size of mapping populations or by increasing the number of generations of crossing required to create the population. We review the factors affecting the precision and power of fine mapping experiments and describe some contemporary experimental approaches, focusing on the use of multi-parental or multi-founder populations such as the multi-parent advanced generation intercross (MAGIC) and nested association mapping (NAM). We favor approaches such as MAGIC since these focus explicitly on increasing the amount of recombination that occurs within the population. Whatever approaches are used, we believe the days of mapping QTL in small populations must come to an end. In our own work in MAGIC wheat populations, we started with a target of developing 1,000 lines per population: that number now looks to be on the low side.

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References

  1. Sax K (1923) The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8:552

    PubMed  PubMed Central  CAS  Google Scholar 

  2. Beckmann JS, Soller M (1983) Restriction fragment length polymorphisms in genetic improvement: methodologies, mapping and costs. Theor Appl Genet 67:35–43

    Article  CAS  PubMed  Google Scholar 

  3. Paterson AH, Lander ES, Hewitt JD, Peterson S, Lincoln SE, Tanksley SD (1988) Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphisms. Nature 335:721–726

    Article  CAS  PubMed  Google Scholar 

  4. Paux E, Sourdille P, Mackay I, Feuillet C (2012) Sequence-based marker development in wheat: advances and applications to breeding. Biotechnol Adv 30:1071–1088

    Article  CAS  PubMed  Google Scholar 

  5. Broman KW, Wu H, Sen Ś, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890

    Article  CAS  PubMed  Google Scholar 

  6. Collard BC, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B Biol Sci 363:557–572

    Article  CAS  PubMed  Google Scholar 

  7. Beavis WD (1998) QTL analysis: power, precision, and accuracy. In: Paterson AH (ed) Molecular dissection of complex traits. CRC Press, Boca Raton, pp 145–173

    Google Scholar 

  8. Kearsey MJ, Farquhar AG (1998) QTL analysis in plants; where are we now? Heredity 80:137–142

    Article  PubMed  Google Scholar 

  9. Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664

    Article  Google Scholar 

  10. Monna L, Lin HX, Kojima S, Sasaki T, Yano M (2002) Genetic dissection of a genomic region for a quantitative trait locus, Hd3, into two loci, Hd3a and Hd3b, controlling heading date in rice. Theor Appl Genet 104:722–778

    Google Scholar 

  11. Heslot N, Jannink J-L, Sorrells ME (2015) Perspectives for genomic selection applications and research in plants. Crop Sci 55(1):12

    Article  Google Scholar 

  12. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    PubMed  PubMed Central  CAS  Google Scholar 

  13. Mackay I, Ober E, Hickey J (2015) GplusE: beyond genomic selection. Food Energy Secur 4:25–35

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bortesi L, Fischer R (2015) The CRISPR/Cas9 system for plant genome editing and beyond. Biotechnol Adv 33:41–52

    Article  CAS  PubMed  Google Scholar 

  15. Hickey JM, Bruce C, Whitelaw A, Gorjanc G (2016) Promotion of alleles by genome editing in livestock breeding programmes. J Anim Breed Genet 133:83–84

    Article  CAS  PubMed  Google Scholar 

  16. Law CN, Worland AJ, Giorgi B (1976) The genetic control of ear-emergence time by chromosome 5A and 5D of wheat. Heredity 36:49–58

    Article  Google Scholar 

  17. Price AH (2006) Believe it or not, QTLs are accurate! Trends Plant Sci 11:213–216

    Article  CAS  PubMed  Google Scholar 

  18. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, Munafò MR (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14:365–376

    Article  CAS  PubMed  Google Scholar 

  19. Nature Genetics Editorial Board (2005) Framework for a fully powered risk engine. Nat Genet 37:1153

    Article  CAS  Google Scholar 

  20. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9:356–369

    Article  CAS  PubMed  Google Scholar 

  21. Hall D, Tegström C, Ingvarsson PK (2010) Using association mapping to dissect the genetic basis of complex traits in plants. Brief Funct Genomics 9:157–165

    Article  CAS  PubMed  Google Scholar 

  22. Bentley AR, Scutari M, Gosman N, Faure S, Bedford F, Howell P, Cockram J, Rose GA, Barber T, Irigoyen J, Horsnell R, Pumfrey C, Winnie E, Schacht J, Beauchêne K, Praud S, Greenland A, Balding D, Mackay IJ (2014) Applying association mapping and genomic selection to the dissection of key traits in elite European wheat. Theor Appl Genet 127:2619–2633

    Article  CAS  PubMed  Google Scholar 

  23. Cockram J, White J, Zuluaga DL, Smith D, Comadran J et al (2010) Genome-wide association mapping to candidate polymorphism resolution in the un-sequenced barley genome. Proc Natl Acad Sci U S A 107:21611–21616

    Article  PubMed  PubMed Central  Google Scholar 

  24. Waugh R, Marshall D, Thomas B, Comadran J, Russell J, Close T, Stein N, Hayes P, Muehlbauer G, Cockram J, O’Sullivan D, Mackay I, Flavell A, Agoueb A, Barley CAP, Ramsay L (2010) Whole-genome association mapping in elite inbred crop varieties. Genome 53:967–972

    Article  CAS  PubMed  Google Scholar 

  25. Mackay IJ, Powell W (2007) Methods for linkage disequilibrium mapping in crops. Trends Plant Sci 12:57–63

    Article  CAS  PubMed  Google Scholar 

  26. MacArthur D (2012) Methods: face up to false positives. Nature 487:427–428

    Article  CAS  PubMed  Google Scholar 

  27. Highfill CA, Reeves GA, Macdonald SJ (2016) Genetic analysis of variation in lifespan using a multiparental advanced intercross Drosophila mapping population. BMC Genet 17(1):113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cockram J, White J, Leigh FJ, Lea VJ et al (2008) Association mapping of partitioning loci in barley (Hordeum vulgare ssp. vulgare L.) BMC Genet 9:16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Darvasi A, Soller M (1995) Advanced intercross lines, and experimental population for fine genetic mapping. Genetics 141:1199–1207

    PubMed  PubMed Central  CAS  Google Scholar 

  30. Ma J, Wingen LU, Orford S, Fenwick P, Wang J, Griffiths S (2015) Using the UK reference population Avalon x Cadenza as a platform to compare breeding strategies in elite Western European bread wheat. Mol Breed 35:70

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bentley AR, Jensen EF, Mackay IJ, Hönicka H, Fladung M, Hori K, Yano M, Mullet JE, Armstead IP, Hayes C, Thorogood D, Lovatt A, Morris R, Pullen N, Mutasa-Göttgens E, Cockram J (2013) Genomics and breeding for climate-resilient crops (ed Kole C) volume II target traits chapter 1. Flowering time. Springer, Berlin

    Google Scholar 

  32. Bentley A, Mackay I (2016) Advances in wheat breeding techniques. In: Langridge P (ed) Achieving sustainable cultivation of wheat. Burleigh Dodds Science Publishing Ltd., Cambridge

    Google Scholar 

  33. Zou C, Wang P, Xu Y (2016) Bulked sample analysis in genetics, genomics and crop improvement. Plant Biotechnol J 14:1941–1955

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Routaboul J-M, Dubos C, Beck G, Marquis C, Bidzinski P, Loudet O, Lepiniec L (2012) Metabolite profiling and quantitative genetics of natural variation for flavonoids in Arabidopsis. J Exp Bot 63:3749–3764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ries D, Holtgräwe D, Viehöver P, Weisshaar B (2016) Rapid gene identification in sugar beet using deep sequencing of DNA from phenotypic pools selected from breeding panels. BMC Genomics 17:236

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hill WG (1998) A note on the theory of artificial selection in finite populations and application to QTL detection by bulk segregant analysis. Genet Res 72:55–58

    Article  Google Scholar 

  37. Mackay IJ, Caligari PDS (2000) Efficiencies in F2 and backcross generations for bulked segregant analysis using dominant markers. Crop Sci 40:626–630

    Article  Google Scholar 

  38. Fitz Gerald JN, Carlson AL, Smith E, Maloof JN, Weigel D, Chory J, Borevitz JO, Swanson RJ (2014) New Arabidopsis advanced intercross recombinant inbred lines reveal female control of nonrandom mating. Plant Physiol 165:175–185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Balint-Kurti PJ, Wisser R, Zwonitzer JC (2008) Use of an advanced intercross line population for precise mapping of quantitative trait loci for gray leaf spot resistance in maize. Crop Sci 48:1696–1704

    Article  Google Scholar 

  40. Balint-Kurti PJ, Zwonitzer J, Wisser R (2008) Use of an advanced intercross line population for precise mapping of quantitative trait loci for grey leaf spot resistance in maize. Crop Sci 48:1696–1703

    Article  Google Scholar 

  41. Kooke R, Wijnker E, Keurentjes JJ (2012) Backcross populations and near isogenic lines. Methods Mol Biol 871:3–16

    Article  CAS  PubMed  Google Scholar 

  42. Fletcher RS, Mullen JL, Yoder S, Bauerle WL, Reuning G, Sen S, Meyer E, Juenger TE, McKay JK (2013) Development of a next-generation NIL library in Arabidopsis thaliana for dissecting complex traits. BMC Genomics 14:655

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gale JS (1980) Population genetics. Blackie and Son, Glasgow and London

    Book  Google Scholar 

  44. Tuinstra MR, Ejeta G, Goldsbrough PB (1997) Heterogeneous inbred family (HIF) analysis: a method for developing near-isogenic lines that differ at quantitative trait loci. Theor Appl Genet 95:1005–1011

    Article  CAS  Google Scholar 

  45. Yamanaka N, Watanabe S, Toda K, Hayashi M, Fuchigami H, Takahashi R, Harada K (2005) Fine mapping of the FT1 locus for soybean flowering time using a residual heterozygous line derived from a recombinant inbred line. Theor Appl Genet 110:634–639

    Article  CAS  PubMed  Google Scholar 

  46. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551

    Article  PubMed  PubMed Central  Google Scholar 

  47. Abecasis GR, Cardon LR, Cookson WOC (2000) A general test of association for quantitative traits in nuclear families. Am J Hum Genet 66:279–292

    Article  CAS  PubMed  Google Scholar 

  48. Guo B, Sleper DA, Beavis WD (2010) Nested association mapping for identification of functional markers. Genetics 186:373–383

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Lu H et al (2009) Genetic properties of a maize nested association mapping population. Science 178:539–551

    Google Scholar 

  50. Brown PJ, Upadyayula N, Mahone GS, Tian F, Bradbury PJ et al (2011) Distinct genetic architectures for male and female inflorescence traits of maize. PLoS Genet 7:e1002383

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ et al (2009) The genetic architecture of maize flowering time. Science 325:714–718

    Article  CAS  PubMed  Google Scholar 

  52. Hung H-Y, Shannon LM, Tian F, Bradbury PJ, Chen C et al (2012) ZmCCT and the genetic basis of day-length adaptation underlying the postdomestication spread of maize. Proc Natl Acad Sci U S A 109:E1913–E1921

    Article  PubMed  PubMed Central  Google Scholar 

  53. Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR et al (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:163–168

    Article  CAS  PubMed  Google Scholar 

  54. Peiffer JA, Flint-Garcia SA, De Leon N, McMullen MD, Kaeppler SM et al (2013) The genetic architecture of maize stalk strength. PLoS One 8:e67066

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Peiffer JA, Romay MC, Gore MA, Flint-Garcia SA, Zhang Z et al (2014) The genetic architecture of maize height. Genetics 196:1337–1356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Poland JA, Bradbury PJ, Buckler ES, Nelson RJ (2011) Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci U S A 108:6893–6898

    Article  PubMed  PubMed Central  Google Scholar 

  57. Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q et al (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162

    Article  CAS  PubMed  Google Scholar 

  58. Wallace JG, Bradbury PJ, Zhang N, Gibon Y, Stitt M, Buckler ES (2014) Association mapping across numerous traits reveals patterns of functional variation in maize. PLoS Genet 10:e1004845

    Article  PubMed  PubMed Central  Google Scholar 

  59. Jordan D, Mace E, Cruickshank A, Hunt C, Henzell R (2011) Exploring and exploiting genetic variation from unadapted sorghum germplasm in a breeding program. Crop Sci 51:1444–1457

    Article  Google Scholar 

  60. Maurer A, Draba V, Jiang Y, Schnaithmann F, Sharma R, Schumann E, Killian B, Reif JC, Pillen K (2015) Modelling the genetic architecture of flowering time control in barley through nested association mapping. BMC Genomics 16:290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Bajgain P, Rouse MN, Tsilo TJ, Macharia GK, Bhavani S, Jin Y, Anderson JA (2016) Nested association mapping of stem rust resistance in wheat using genotyping by sequencing. PLoS One 11:e0155760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wingen LU, West C, Leverington-Waite M, Collier S, Orford S et al (2017) Wheat landrace genome diversity. Genetics 205:1657–1676

    Article  PubMed  PubMed Central  Google Scholar 

  63. Stich B (2009) Comparison of mating designs for establishing nested association mapping populations in maize and Arabidopsis thaliana. Genetics 183:1525–1534

    Article  PubMed  PubMed Central  Google Scholar 

  64. Nice LM, Steffenson BJ, Brown-Guedira GL, Akhunov ED, Liu C, Kono TJY, Morrell PL, Blake TK, Horsley RD, Smith KP, Meuhlbauer GJ (2016) Development and genetic characterization of an advanced backcross-nested association mapping (AB-NAM) population of wild x cultivated barley. Genetics 203:1453–1467

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Moore G (2015) Strategic pre-breeding for wheat improvement. Nat Plants 1:15018

    Article  CAS  PubMed  Google Scholar 

  66. Myles S, Peiffer J, Brown PJ, Ersoz ES, Zhang Z, Costich DE, Buckler ES (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Tversky A, Kahneman D (1971) Belief in the law of small numbers. Psychol Bull 76:105

    Article  Google Scholar 

  68. 3000 Rice Genomes Project (2014) The 3000 rice genomes project. Gigascience 3:7

    Article  CAS  Google Scholar 

  69. Cao J, Schneeberger K, Ossowski S, Gunther T, Bender S, Fitz J, Koenig D, Lanz C, Stegle O, Lippert C, Wang X, Ott F, Müller J, Alonso-Blanco C, Borgwardt K, Schmid KJ, Weigel D (2011) Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nat Genet 43:956–963

    Article  CAS  PubMed  Google Scholar 

  70. Mott R, Talbot CJ, Turri MG, Collins AC, Flint J (2000) A method for fine mapping quantitative trait loci in outbred animal stocks. Proc Natl Acad Sci U S A 97:12649–12654

    Article  PubMed  PubMed Central  Google Scholar 

  71. Valdar W, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson WO, Taylor MS, Rawlins JNP, Mott R, Flint J (2006) Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet 38:879–887

    Article  CAS  PubMed  Google Scholar 

  72. Goldringer I, Enjalbert J, David J, Paillard S, Pham JL et al (2001) Dynamic management of genetic resources: a 13-year experiment on wheat. In: Cooper HD, Spillane C, Hodgkin T (eds) Broadening the genetic base of crop production. CABI, Wallingford, pp 245–260

    Chapter  Google Scholar 

  73. Thépot S, Restoux G, Goldringer I, Gouache D, Mackay I, Enjalbert J (2015) Efficiently tracking selection in a multiparental population: the case of earliness in wheat. Genetics 199:609–623

    Article  CAS  PubMed  Google Scholar 

  74. The Complex Trait Consortium (2002) The collaborative cross, a community resource for the genetic analysis of complex traits. Nat Genet 36:1133–1137

    Article  CAS  Google Scholar 

  75. Huang BE, Verbyla KL, Verbyla AP, Raghavan C, Singh VK, Gaur P, Leung H, Varshney RK, Cavanagh CR (2015) MAGIC populations in crops: current status and future prospects. Theor Appl Genet 128:999–1017

    Article  PubMed  Google Scholar 

  76. Bandillo N, Raghaven C, Muyca PA, Sevilla MAL, Lobina IT (2013) Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetic research and breeding. Rice 6:11

    Article  PubMed  PubMed Central  Google Scholar 

  77. Meng L, Guo L, Ponce K, Zhao X, Ye G (2016) Characterization of three indica rice multiparent advanced generation intercross (MAGIC) populations for quantitative trait loci identification. Plant Genome 9(2).

    Google Scholar 

  78. Huang BE, George AW, Forrest KL, Kilian A, Hayden MJ, Morell MK, Cavanagh CR (2012) A multiparent advanced generation inter-cross population for genetic analysis of wheat. Plant Biotechnol J 10:826–839

    Article  CAS  PubMed  Google Scholar 

  79. Mackay I, Bansept-Basler P, Barber T, Bentley AR, Cockram J et al (2014) An eight-parent multiparent advanced generation intercross population for winter-sown wheat: creation, properties and validation. G3 (Bethesda) 4:1603–1610

    Article  CAS  Google Scholar 

  80. 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 

  81. Verbyla AP, George AW, Cavanagh CR, Verbyla KL (2014) Whole-genome QTL analysis for MAGIC. Theor Appl Genet 127:1753–1770

    Article  PubMed  Google Scholar 

  82. Ladejobi O, Elderfield J, Gardner KA, Gaynor RC, Hickey J, Hibberd JM, Mackay IJ, Bentley AR (2016) Maximizing the potential of multi-parental crop populations. App Transl Genom 11:9–17

    Google Scholar 

  83. R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. URL: https://www.R-project.org/

    Google Scholar 

  84. Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48

    Article  Google Scholar 

  85. Huang BE, George AW (2011) R/mpMap: a computational platform for the genetic analysis of multiparent recombinant inbred lines. Bioinformatics 27:727–729

    Article  CAS  PubMed  Google Scholar 

  86. Zheng C, Boer MP, van Eeuwijk F (2015) Reconstruction of genome ancestry blocks in multiparental populations. Genetics 200:1073–1087

    Article  PubMed  PubMed Central  Google Scholar 

  87. Huang X, Paulo MJ, Boer M, Effgen S, Keizer P, Koornneef M, van Eeuwijk FA (2011) Analysis of natural allelic variation in Arabidopsis using a multiparent recombinant inbred line population. Proc Natl Acad Sci U S A 108:4488–4493

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Rebai A, Goffinet B (1993) Power of tests for QTL detection using replicated progenies derived from a diallel cross. Theor Appl Genet 86:1014–1022

    Article  CAS  PubMed  Google Scholar 

  89. Han S, Utz HF, Liu W, Schrag TA, Stange M, Würschum T, Miedaner T, Bauer E, Schön CC, Melchinger AE (2016) Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize. Theor Appl Genet 129:431–444

    Article  CAS  PubMed  Google Scholar 

  90. Sannemann W, Huang BE, Mathew B, Léon J (2015) Multi-parent advanced generation inter-cross in barley: high-resolution quantitative trait locus mapping for flowering time as a proof of concept. Mol Breed 35:86

    Article  CAS  Google Scholar 

  91. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, Durrant C, Mott R (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5:e7

    Article  CAS  Google Scholar 

  92. Dell’Acqua M, Gatti DM, Pea G, Cattonaro F, Coppens F, Magris G, Hlaing AL, Aung HH, Nelissen H, Baute J, Frascaroli E, Churchill GA, Inzé D, Morgante M, Pé ME (2015) Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays. Genome Biol 16:167

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

JC and IM were partially funded by grants from the Biotechnology and Biological Sciences Research Council (BB/M008908/1, BB/M011666/1 and BB/L011700/1) and the Agriculture and Horticulture Development Board (RD2200003).

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  1. The John Bingham Laboratory, National Institute of Agricultural Botany (NIAB), Cambridge, UK

    James Cockram & Ian Mackay

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  1. James Cockram
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Correspondence to James Cockram .

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Editors and Affiliations

  1. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Rajeev K. Varshney

  2. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Manish K. Pandey

  3. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India

    Annapurna Chitikineni

Glossary

Advanced inter-cross (AIC)

A bi-parental population, in which founders have been intercrossed for two or more generations prior to the production of inbred lines.

Doubled haploid (DH)

A genotype formed when haploid cells undergo chromosome doubling.

Genomic selection (GS)

A form of marker-assisted selection in which genetic markers are combined with phenotypic data to estimate breeding values in the absence of precise knowledge of where specific genes are located.

Genome wide association scan (GWAS)

Method for genetic mapping using a collection of varieties or landraces with phenotypic and genome-wide genotypic datasets.

Linkage disequilibrium (LD)

The non-random association of alleles at separate loci located on the same chromosome.

Multiparent advanced generation inter cross (MAGIC) population

A multi-founder population created by intercrossing the founders over multiple generations in a balanced crossing scheme, prior to the production of inbred lines.

Nested association mapping (NAM) population

A multi-founder population created by generating multiple bi-parental inbred populations, each of which contains a common founder.

Quantitative trait locus (QTL)

A polymorphic site contributing to the genetic variability of a quantitative trait.

Recombinant inbred line (RIL)

A population developed by single seed descent from the F2 generation.

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Cockram, J., Mackay, I. (2018). Genetic Mapping Populations for Conducting High-Resolution Trait Mapping in Plants. In: Varshney, R., Pandey, M., Chitikineni, A. (eds) Plant Genetics and Molecular Biology. Advances in Biochemical Engineering/Biotechnology, vol 164. Springer, Cham. https://doi.org/10.1007/10_2017_48

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