MAGIC populations in crops: current status and future prospects

Abstract

Key message

MAGIC populations present novel challenges and opportunities in crops due to their complex pedigree structure. They offer great potential both for dissecting genomic structure and for improving breeding populations.

Abstract

The past decade has seen the rise of multiparental populations as a study design offering great advantages for genetic studies in plants. The genetic diversity of multiple parents, recombined over several generations, generates a genetic resource population with large phenotypic diversity suitable for high-resolution trait mapping. While there are many variations on the general design, this review focuses on populations where the parents have all been inter-mated, typically termed Multi-parent Advanced Generation Intercrosses (MAGIC). Such populations have already been created in model animals and plants, and are emerging in many crop species. However, there has been little consideration of the full range of factors which create novel challenges for design and analysis in these populations. We will present brief descriptions of large MAGIC crop studies currently in progress to motivate discussion of population construction, efficient experimental design, and genetic analysis in these populations. In addition, we will highlight some recent achievements and discuss the opportunities and advantages to exploit the unique structure of these resources post-QTL analysis for gene discovery.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Ahfock D, Wood I, Stephen S, Cavanagh CR, Huang BE (2014) Characterizing uncertainty in high-density maps from multiparental populations. Genetics 198:117–128

    Article  PubMed  Google Scholar 

  2. Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61

    Article  CAS  PubMed  Google Scholar 

  3. Aylor DL, Valdar W, Foulds-Mathes W, Buus RJ, Verdugo RA et al (2011) Genetic analysis of complex traits in the emerging collaborative cross. Genome Res 21:1213–1222

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Bailey DW (1971) Recombinant-inbred strains: an aid to finding identity, linkage, and function of histocompatibility and other genes. Transplantation 11:325–327

    Article  CAS  PubMed  Google Scholar 

  5. Bandillo N, Raghavan C, Muyco PA, Sevilla MAL, Lobina IT et al (2013) Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 6:11

    Article  PubMed  Google Scholar 

  6. Bardol N, Ventelon M, Mangin B, Jasson S, Loywick V et al (2013) Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism. Theor Appl Genet 126:2717–2736

    Article  CAS  PubMed  Google Scholar 

  7. Bink MCAM, Boer MP, Ter Braak CJF, Jansen J, Voorrips RE et al (2008) Bayesian analysis of complex traits in pedigreed plant populations. Euphytica 161:85–96

    Article  Google Scholar 

  8. Blakeslee AF, Belling J, Farnham ME, Bergner AD (1922) A haploid mutant in the Jimson weed, “Datura Stramonium”. Science 55:646–647

    Article  CAS  PubMed  Google Scholar 

  9. Bottomly D, Ferris MT, Aicher LD, Rosenzweig E, Whitmore A et al (2012) Expression quantitative trait loci for extreme host response to influenza A in pre-Collaborative Cross mice. G3 2:213–221

    Article  PubMed Central  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  11. Brim CA (1966) A modified pedigree method of selection in soybeans. Crop Sci 6:220

    Article  Google Scholar 

  12. Broman K (2005) The genomes of recombinant inbred lines. Genetics 169:1133–1146

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Broman KW (2012) Genotype probabilities at intermediate generations in the construction of recombinant inbred lines. Genetics 190:403–412

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  15. Buet C, Dubreuil P, Tixier M-H, Durantin K, Praud S et al (2013) The molecular characterization of a MAGIC population reveals high potential for gene discovery. MaizeGDB proceedings

  16. Butler D (2009) asreml: asreml() fits the linear mixed model. R package version 3.0. http://www.vsni.co.uk

  17. Cavanagh C, Morell M, Mackay I, Powell W (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Op Plant Biol 11:215–221

    Article  Google Scholar 

  18. Cavanagh C, Chao S, Wang S, Huang BE, Stephen S et al (2013) Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proc Natl Acad Sci 110:8057–8062

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Collaborative Cross Consortium (2012) The genome architecture of the collaborative cross mouse genetic reference population. Genetics 190:389–401

    Article  PubMed Central  Google Scholar 

  20. Complex Trait Consortium (2004) The collaborative cross, a community resource for the genetic analysis of complex traits. Nat Genet 36:1133–1137

    Article  Google Scholar 

  21. Corbett-Detig RB, Zhou J, Clark AG, Hartl DL, Ayroles JF (2013) Genetic incompatibilities are widespread within species. Nature 504:135–137

    Article  CAS  PubMed  Google Scholar 

  22. Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381–393

    Article  Google Scholar 

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

    PubMed Central  CAS  PubMed  Google Scholar 

  24. Das S, Zijdenbos AP, Harlap J, Vins D, Evans AC (2011) LORIS: a web-based data management system for multi-center studies. Front Neuroinform 5:37

    PubMed Central  PubMed  Google Scholar 

  25. Demarest K, Koyner J, McCaughran J Jr, Cipp L, Hitzemann R (2001) Further characterization and high- resolution mapping of quantitative trait loci for ethanol-induced locomotor activity. Behav Genet 31:79–91

    Article  CAS  PubMed  Google Scholar 

  26. Durrant C, Mott R (2010) Bayesian quantitative trait locus mapping using inferred haplotypes. Genetics 184:839–852

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Durrant C, Swertz MA, Alberts R, Arends D, Moller S et al (2012) Bioinformatics tools and database resources for systems genetics analysis in mice—a short review and an evaluation of future needs. Brief Bioinform 13:135–142

    Article  PubMed Central  PubMed  Google Scholar 

  28. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K et al (2011) A robust, simple Genotyping-by-Sequencing (GBS) approach for high diversity species. PLoS One 6(5):e19379

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  29. Esch E, Szymaniak JM, Yates H, Pawlowski WP, Buckler ES (2007) Using crossover breakpoints in recombinant inbred lines to identify quantitative trait loci controlling the global recombination frequency. Genetics 177:1851–1858

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Forster BP, Bors-Heberle E, Kasha KJ, Touraev A (2007) The resurgence of haploids in higher plants. Trends Plant Sci 12:368–375

    Article  CAS  PubMed  Google Scholar 

  31. Furbank RT, Tester M (2011) Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644

    Article  CAS  PubMed  Google Scholar 

  32. Gan X, Stegle O, Behr J, Steffen JG, Drewe P et al (2011) Multiple reference genomes and transcriptomes for Arabidopsis thaliana. Nature 477:419–423

    Article  CAS  PubMed  Google Scholar 

  33. Gaur PM, Jukanti AK, Varshney RK (2012) Impact of genomic technologies on chickpea breeding strategies. Agronomy 2:199–221

    Article  Google Scholar 

  34. Giraud H, Lehermeier C, Bauer E, Falque M, Segura V et al (2014) Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the Flint and Dent heterotic groups for maize. Genetics 198:1717–1734

    Article  PubMed  Google Scholar 

  35. Goulden CH (1939) Problems in plant selection. In: Proceedings of the Seventh International Genetics Congress. Cambridge University Press, pp 132–133

  36. Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82:711–732

    Article  Google Scholar 

  37. Gyenesei A, Moody J, Semple CAM, Haley CS, Wei W-H (2012) High throughput analysis of epistasis in genome-wide association studies with BiForce. Bioinformatics 28:1957–1964

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  38. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324

    Article  CAS  PubMed  Google Scholar 

  39. Harushima Y, Yano M, Shomura A, Sato M, Shimano T et al (1998) A high-density rice genetic linkage map with 2275 markers using a single F2 population. Genetics 148:479–494

    PubMed Central  CAS  PubMed  Google Scholar 

  40. Hemani G, Theocharidis A, Wei W, Haley C (2011) EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards. Bioinformatics 27:1462–1465

    Article  CAS  PubMed  Google Scholar 

  41. Hickey JM, Gorjanc G, Hearne S, Huang BE (2014) AlphaMPSim: flexible simulation of multi-parent crosses. Bioinformatics 30:2686–2688

    Article  CAS  PubMed  Google Scholar 

  42. Howe D, Costanzo M, Fey P, Gojobori T, Hannick L et al (2008) Big data: the future of biocuration. Nature 455:47–50

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  44. Huang X, Feng Q, Qian Q, Zhao Q, Wang L et al (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  45. Huang X, Paulo M-J, Boer M, Effgen S, Keizer P et al (2011) Analysis of natural allelic variation in Arabidopsis using a multiparent recombinant inbred line population. PNAS 108(11):4488–4493

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Huang BE, George AW, Forrest KL, Kilian A, Hayden MJ et al (2012) A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotechnol J 10:826–839

    Article  CAS  PubMed  Google Scholar 

  47. Huang BE, Clifford D, Cavanagh C (2013) Selecting subsets of genotyped experimental populations for phenotyping to maximize genetic diversity. Theor Appl Genet 126:379–388

    Article  Google Scholar 

  48. Huang BE, Raghavan C, Mauleon R, Broman KW, Leung H (2014) Imputation of low-coverage genotyping-by-sequencing in multi-parental crosses. Genetics 197:401–404

    Article  PubMed Central  PubMed  Google Scholar 

  49. Jansen RC (1994) Controlling the type I and type II errors in mapping quantitative trait loci. Genetics 138:871–881

    PubMed Central  CAS  PubMed  Google Scholar 

  50. Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152:1203–1216

    PubMed Central  CAS  PubMed  Google Scholar 

  51. Kass RE (1993) Bayes factors in practice. Statistician 42:551–560

    Article  Google Scholar 

  52. Kass RE, Raftery AE (1995) Bayes factors. JASA 90:773–795

    Article  Google Scholar 

  53. King EG, Macdonald SJ, Long AD (2012a) Properties and power of the Drosophila Synthetic Population Resource for the routine dissection of complex traits. Genetics 191:935–949

    Article  PubMed Central  PubMed  Google Scholar 

  54. King EG, Merkes CM, McNeil CL, Hoofer SR, Sen S et al (2012b) Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource. Genome Res 22:1558–1566

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  55. King EG, Sanderson BJ, McNeil CL, Long AD, Macdonald SJ (2014) Genetic dissection of the Drosophila melanogaster female head transcriptome reveals widespread allelic heterogeneity. PLoS Genet 10(5):e1004322

    Article  PubMed Central  PubMed  Google Scholar 

  56. Klasen JR, Piepho H-P, Stich B (2012) QTL detection power of multi-parental RIL populations in Arabidopsis thaliana. Heredity 108:626–632

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM et al (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5(7):e1000551

    Article  PubMed Central  PubMed  Google Scholar 

  58. Lai K, Lorenc MT, Edwards D (2012) Genomic databases for crop improvement. Agronomy 2:62–73

    Article  Google Scholar 

  59. Leroux D, Rahmani A, Jasson S, Ventelon M, Louis F et al (2014) Clusthaplo: a plug-in for MCQTL to enhance QTL detection using ancestral alleles in multi-cross design. Theor Appl Genet 127:921–933

    Article  PubMed Central  PubMed  Google Scholar 

  60. Mace ES, Hunt CH, Jordan DR (2013) Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time. Theor Appl Genet 126:1377–1395

    Article  CAS  PubMed  Google Scholar 

  61. Mackay IJ, Bansept-Basler P, Barber T, Bentley AR, Cockram J et al (2014) An eight-parent Multiparent Advanced Generation Inter-Cross population for winter-sown wheat: creation, properties and validation. G3 4:1603–1610

    Article  PubMed Central  PubMed  Google Scholar 

  62. Malosetti M, van Eeuwijk DA, Boer MP, Casas AM, Elia M et al (2011) Gene and QTL detection in a three-way barley cross under selection by a mixed model with kinship information using SNPs. Theor Appl Genet 122:1605–1616

    Article  PubMed Central  PubMed  Google Scholar 

  63. Malosetti M, Ribaut J-M, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modelling genotype-by-environment interaction and its genetic basis. Front Physiol 4:44

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  64. Maluszynski M, Kasha KJ, Szareiko I (2003) Published doubled haploid protocols in plant species. In: Doubled haploid production in crop plants, a manual. Kluwer Academic Publishers, Dordecht, pp 309–335

  65. McClearn GE, Wilson JR, Meredith W (1970) The use of isogenic and heterogenic mouse stocks in behavioral research. In: Lindzey G, Thiessen D (eds) Contributions to behavior-genetic analysis: the mouse as a prototype. Appleton Century Crofts, New York, pp 3–22

    Google Scholar 

  66. McMullen MD, Kresovich S, Villeda HS, Bradbury PJ, Li H et al (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740

    Article  CAS  PubMed  Google Scholar 

  67. Meuwissen TH, Goddard ME (2001) Prediction of identity by descent probabilities from marker-haplotypes. Genet Sel Evol 33:605

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. Mohring J, Piepho H-P (2009) Comparison of weighting in two-stage analyses of series of experiments. Crop Sci 39:1977–1988

    Article  Google Scholar 

  69. Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12:433–436

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed Central  PubMed  Google Scholar 

  71. Pascual L, Desplat N, Huang BE, Desgroux A, Bruguier L et al (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 (in press)

  72. Pea G, Dell’Acqua M, Hlaing ALL, Pe ME (2013) From mice to maize: a multiparental population for fine mapping in Zea mays. MAGIC Populations Workshop. http://openwetware.org/images/e/e6/MatteoDellAcqua_MaizePoster.pdf

  73. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    PubMed Central  CAS  PubMed  Google Scholar 

  74. Ram R, Mehta M, Balmer L, Gatti DM, Morahan G (2014) Rapid identification of major effect genes using the Collaborative Cross. Genetics 198:75–86

    Article  PubMed Central  PubMed  Google Scholar 

  75. Rebetzke GJ, Verbyla AP, Verbyla KL, Morell MK, Cavanagh CR (2014) Use of a large multiparent wheat mapping population in genomic dissection of coleoptile and seedling growth. Plant Biotechnol J 12:219–230

    Article  CAS  PubMed  Google Scholar 

  76. 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 Breeding 35:86

    Article  Google Scholar 

  77. Schmitt CP, Burchinal M (2011) Data management practices for collaborative research. Front Psychiatry 2:47

    Article  PubMed Central  PubMed  Google Scholar 

  78. Schnaithmann F, Kopahnke D, Pillen K (2014) A first step toward the development of a barley NAM population and its utilization to detect QTLs conferring leaf rust seedling resistance. Theor Appl Genet 127:1513–1525

    Article  PubMed  Google Scholar 

  79. Scutari M, Howell P, Balding DJ, Mackay IJ (2014) Multiple quantitative trait analysis using Bayesian networks. Genetics 198:129–137

    Article  PubMed  Google Scholar 

  80. Smith AB, Lim P, Cullis BR (2006) The design and analysis of multi-phase plant breeding experiments. J Agric Sci Camb 144:393–409

    Article  Google Scholar 

  81. Smith AB, Thompson R, Butler DC, Cullis BR (2011) The design and analysis of variety trials using mixtures of composite and individual plot samples. J Royal Stat Soc C 60:437–455

    Article  Google Scholar 

  82. Smith AB, Butler DG, Cavanagh CR, Cullis BR (2015) Multi-phase variety trials using both composite and individual replicate samples: a model-based design approach. J Agric Sci Camb (in press)

  83. Stich B (2009) Comparison of mating designs for establishing Nested Association Mapping populations in maize and Arabidopsis thaliana. Genetics 183:1525–1534

    Article  PubMed Central  PubMed  Google Scholar 

  84. Svenson KL, Gatti DM, Valdar W, Welsh CE, Cheng R et al (2012) High-resolution genetic mapping using the Mouse Diversity outbred population. Genetics 190:437–447

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  85. Thépot S, Restoux G, Goldringer I, Hospital F, 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  PubMed  Google Scholar 

  86. Valdar W, Flint J, Mott R (2006) Simulating the collaborative cross: power of quantitative trait loci detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics 172:1783–1797

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  87. Valdar W, Holmes CC, Mott R, Flint J (2009) Mapping in structured populations by resample model averaging. Genetics 182:1263–1277

    Article  PubMed Central  PubMed  Google Scholar 

  88. van Eeuwijk FA, Bink MC, Chenu K, Chapman SC (2010) Detection and use of QTL for complex traits in multiple environments. Curr Opin Plant Biol 13:193–205

    Article  PubMed  Google Scholar 

  89. Verbyla AP, Cullis BR (2012) Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments. Theor Appl Genet 125:933–953

    Article  PubMed  Google Scholar 

  90. Verbyla AP, Cullis BR, Thompson R (2007) The analysis of QTL by simultaneous use of the full linkage map. Theor Appl Genet 116:95–111

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  92. Verbyla AP, Cavanagh CR, Verbyla KL (2014b) Whole genome analysis of multi-environment or multi-trait QTL in MAGIC G3(4):1569–1584

    Google Scholar 

  93. Wang J, de Villena FP, Lawson HA, Cheverud JM, Churchill GA et al (2012) Imputation of single-nucleotide polymorphisms in inbred mice using local phylogeny. Genetics 190:449–458

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  94. Wang S, Wong D, Forrest K, Allen A, Chao S et al (2014) Characterization of polyploidy wheat genomic diversity using the high-density 90,000 SNP array. Plant Biotechnol J 12:787–796

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  95. Xu S (1996) Mapping quantitative trait loci using four-way crosses. Genet Res 68:175–181

    Article  Google Scholar 

  96. Yalcin B, Flint J, Mott R (2005) Using progenitor strain information to identify quantitative trait nucleotides in outbred mice. Genetics 171:673–681

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  97. Yamamoto E, Iwata H, Tanabata T, Mizobuchi R, Yonemaru J et al (2014) Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in rice. BMC Genet 15:50

    Article  PubMed Central  PubMed  Google Scholar 

  98. 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 Central  PubMed  Google Scholar 

  99. Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468

    PubMed Central  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Many thanks to three anonymous reviewers for their helpful suggestions. Dr. Huang is the recipient of an Australian Research Council Discovery Early Career Researcher Award (Project Number DE120101127).

Conflict of interest

No authors have any conflicts of interest.

Author information

Affiliations

Authors

Corresponding author

Correspondence to B. Emma Huang.

Additional information

Communicated by H. H. Geiger.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huang, B.E., Verbyla, K.L., Verbyla, A.P. et al. MAGIC populations in crops: current status and future prospects. Theor Appl Genet 128, 999–1017 (2015). https://doi.org/10.1007/s00122-015-2506-0

Download citation

Keywords

  • Quantitative Trait Locus
  • Quantitative Trait Locus Analysis
  • Quantitative Trait Locus Mapping
  • Collaborative Cross
  • Marker Score