Theoretical and Applied Genetics

, Volume 129, Issue 2, pp 431–444 | Cite as

Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize

  • Sen Han
  • H. Friedrich Utz
  • Wenxin Liu
  • Tobias A. Schrag
  • Michael Stange
  • Tobias Würschum
  • Thomas Miedaner
  • Eva Bauer
  • Chris-Carolin Schön
  • Albrecht E. Melchinger
Original Article

Abstract

Key message

QTL analysis for Fusarium resistance traits with multiple connected families detected more QTL than single-family analysis. Prediction accuracy was tightly associated with the kinship of the validation and training set.

Abstract

QTL mapping has recently shifted from analysis of single families to multiple, connected families and several biometric models have been suggested. Using a high-density consensus map with 2472 marker loci, we performed QTL mapping with five connected bi-parental families with 639 doubled-haploid (DH) lines in maize for ear rot resistance and analyzed traits DON, Gibberella ear rot severity (GER), and days to silking (DS). Five biometric models differing in the assumption about the number and effects of alleles at QTL were compared. Model 2 to 5 performing joint analyses across all families and using linkage and/or linkage disequilibrium (LD) information identified all and even further QTL than Model 1 (single-family analyses) and generally explained a higher proportion p G of the genotypic variance for all three traits. QTL for DON and GER were mostly family specific, but several QTL for DS occurred in multiple families. Many QTL displayed large additive effects and most alleles increasing resistance originated from a resistant parent. Interactions between detected QTL and genetic background (family) occurred rarely and were comparatively small. Detailed analysis of three fully connected families yielded higher p G values for Model 3 or 4 than for Model 2 and 5, irrespective of the size N TS of the training set (TS). In conclusion, Model 3 and 4 can be recommended for QTL-based prediction with larger families. Including a sufficiently large number of full sibs in the TS helped to increase QTL-based prediction accuracy (r VS) for various scenarios differing in the composition of the TS.

Keywords

Quantitative Trait Locus Quantitative Trait Locus Mapping Quantitative Trait Locus Effect Genomic Prediction Quantitative Trait Locus Detection 

Notes

Acknowledgments

This research was supported by Deutsche Forschungsgemeinschaft (DFG) grant no. ME 2260/6-1. The DH lines used in this study were produced by KWS SAAT SE (Einbeck, Germany). We are indebted to M. Martin and W. Schipprack and the staff of the Agricultural Research Station at Eckartsweier and Hohenheim for conducting the field trials for this study. We acknowledge the support of T. Wimmer in providing the software for cross-validation. We are grateful to S. Jasson and B. Mangin for generously providing technical assistance with software MCQTL_LD and D. Leroux with the “clusthaplo” R package; MCAM Bink and F. van Eeuwijk for giving constructive suggestions for our analyses; J. Li, L. Moreau and H. Giraud for answering questions about multiple regression analysis.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

The experiments reported in this study comply with the current laws of Germany.

Supplementary material

122_2015_2637_MOESM1_ESM.pdf (129 kb)
Supplementary material 1 (PDF 129 kb)
122_2015_2637_MOESM2_ESM.pdf (1.9 mb)
Supplementary material 2 (PDF 1903 kb)

References

  1. Bardol N, Ventelon M, Mangin B 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. doi: 10.1007/s00122-013-2167-9 PubMedCrossRefGoogle Scholar
  2. Bauer E, Falque M, Walter H et al (2013) Intraspecific variation of recombination rate in maize. Genome Biol 14:R103. doi: 10.1186/gb-2013-14-9-r103 PubMedPubMedCentralCrossRefGoogle Scholar
  3. Beavis WD (1998) QTL analyses: power, precision, and accuracy. In: Paterson AH (ed) Molecular dissection of complex traits. CRC press, New York, pp 145–162Google Scholar
  4. Bink MCAM, Totir LR, ter Braak CJF et al (2012) QTL linkage analysis of connected populations using ancestral marker and pedigree information. Theor Appl Genet 124:1097–1113. doi: 10.1007/s00122-011-1772-8 PubMedPubMedCentralCrossRefGoogle Scholar
  5. Blanc G, Charcosset A, Mangin B et al (2006) Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 113:206–224. doi: 10.1007/s00122-006-0287-1 PubMedCrossRefGoogle Scholar
  6. Bolduan C, Miedaner T, Schipprack W et al (2009) Genetic variation for resistance to ear rots and mycotoxins contamination in early European maize inbred lines. Crop Sci 49:2019–2028. doi: 10.2135/cropsci2008.12.0701 CrossRefGoogle Scholar
  7. Buckler ES, Holland JB, Bradbury PJ et al (2009) The genetic architecture of maize flowering time. Science 325:714–718. doi: 10.1126/science.1174276 PubMedCrossRefGoogle Scholar
  8. Charcosset A, Mangin B, Moreau L, Combes L, Jourjon MF et al (2000) Heterosis in maize investigated using connected RIL populations. In: Quantitative genetics and breeding methods: the way ahead. INRA, Paris, pp 89–98Google Scholar
  9. de Givry S, Bouchez M, Chabrier P et al (2005) CARTHA GENE: multipopulation integrated genetic and radiation hybrid mapping. Bioinformatics 21:1703–1704. doi: 10.1093/bioinformatics/bti222 PubMedCrossRefGoogle Scholar
  10. Edwards MD, Stuber CW, Wendel JF (1987) Molecular-marker-facilitated investigations of quantitative trait loci in maize. I. Numbers, genomic distribution and types of gene action. Genetics 116:113–125PubMedPubMedCentralGoogle Scholar
  11. Foiada F, Westermeier P, Kessel B et al (2015) Improving resistance to the European corn borer: a comprehensive study in elite maize using QTL mapping and genome-wide prediction. Theor Appl Genet 128:875–891. doi: 10.1007/s00122-015-2477-1 PubMedCrossRefGoogle Scholar
  12. Ganal MW, Durstewitz G, Polley A et al (2011) A large maize (zea mays L.) SNP genotyping array: Development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS one. doi: 10.1371/journal.pone.0028334 PubMedPubMedCentralGoogle Scholar
  13. Giraud H, Lehermeier C, Bauer E 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 of maize. Genetics 198:1717–1734. doi: 10.1534/genetics.114.169367 PubMedPubMedCentralCrossRefGoogle Scholar
  14. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324PubMedCrossRefGoogle Scholar
  15. Hill WC, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38:226–231PubMedCrossRefGoogle Scholar
  16. Hill WG, Weir BS (1988) Variances and covariances of squared linkage disequilibria in finite populations. Theor Popul Biol 33:54–78. doi: 10.1016/0040-5809(88)90004-4 PubMedCrossRefGoogle Scholar
  17. Holland JB (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156–161. doi: 10.1016/j.pbi.2007.01.003 PubMedCrossRefGoogle Scholar
  18. 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 108:4488–4493PubMedPubMedCentralCrossRefGoogle Scholar
  19. Huang BE, Verbyla KL, Verbyla AP et al (2015) MAGIC populations in crops: current status and future prospects. Theor Appl Genet 128:999–1017PubMedCrossRefGoogle Scholar
  20. Jannink JL, Jansen R (2001) Mapping epistatic quantitative trait loci with one-dimensional genome searches. Genetics 157:445–454PubMedPubMedCentralGoogle Scholar
  21. Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genom 9:166–177. doi: 10.1093/bfgp/elq001 CrossRefGoogle Scholar
  22. Jansen RC, Jannink JL, Beavis WD (2003) Mapping quantitative trait loci in plant breeding populations. Crop Sci 43:829. doi: 10.2135/cropsci2003.0829 CrossRefGoogle Scholar
  23. Jourjon MF, Jasson S, Marcel J et al (2005) MCQTL: multi-allelic QTL mapping in multi-cross design. Bioinformatics 21:128–130. doi: 10.1093/bioinformatics/bth481 PubMedCrossRefGoogle Scholar
  24. Lander ES, Botstein D (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps (published erratum appears in Genetics 1994 Feb; 136(2):705). Genetics 121:185–199PubMedPubMedCentralGoogle Scholar
  25. Lehermeier C, Krämer N, Bauer E et al (2014) Usefulness of multi-parental populations of maize (Zea mays L.) for genome-based prediction. Genetics 198:3–16. doi: 10.1534/genetics.114.161943 PubMedPubMedCentralCrossRefGoogle Scholar
  26. Leroux D, Rahmani A, Jasson S 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. doi: 10.1007/s00122-014-2267-1 PubMedPubMedCentralCrossRefGoogle Scholar
  27. Li H, Bradbury P, Ersoz E et al (2011) Joint QTL linkage mapping for multiple-cross mating design sharing one common parent. PLoS One. doi: 10.1371/journal.pone.0017573 Google Scholar
  28. Liu Y, Zeng ZB (2000) A general mixture model approach for mapping quantitative trait loci from diverse cross designs involving multiple inbred lines. Genet Res 75:345–355. doi: 10.1017/S0016672300004493 PubMedCrossRefGoogle Scholar
  29. Lu Y, Xu J, Yuan Z et al (2012) Comparative LD mapping using single SNPs and haplotypes identifies QTL for plant height and biomass as secondary traits of drought tolerance in maize. Mol Breed 30:407–418. doi: 10.1007/s11032-011-9631-5 CrossRefGoogle Scholar
  30. Martin M, Miedaner T, Dhillon BS et al (2011) Colocalization of QTL for gibberella ear rot resistance and low mycotoxin contamination in early European maize. Crop Sci 51:1935–1945. doi: 10.2135/cropsci2010.11.0664 CrossRefGoogle Scholar
  31. Martin M, Miedaner T, Schwegler DD et al (2012) Comparative quantitative trait loci mapping for Gibberella ear rot resistance and reduced deoxynivalenol contamination across connected maize populations. Crop Sci 52:32–43. doi: 10.2135/cropsci2011.04.0214 CrossRefGoogle Scholar
  32. Melchinger AE, Utz HF, Schön CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149:383–403. doi: 10.1016/1369-5266(88)80015-3 PubMedPubMedCentralGoogle Scholar
  33. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  34. Miedaner T, Han S, Kessel B, et al (2015) Prediction of deoxynivalenol and zearalenone concentrations in Fusarium graminearum inoculated backcross populations of maize by symptom rating and near-infrared spectroscopy. Plant Breed 009:n/a–n/a. doi:  10.1111/pbr.12297
  35. Mode CJ, Robinson HF (1959) Pleitropism and the genetic variance and covariance. Biometrics 15:518–537. doi: 10.2307/2527650 CrossRefGoogle Scholar
  36. Ogut F, Bian Y, Bradbury PJ, Holland JB (2015) Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population. Hered (Edinb) 114:552–563. doi: 10.1038/hdy.2014.123 CrossRefGoogle Scholar
  37. Peleman JD, Wye C, Zethof J, Sorensen AP, Verbakel H, van Oeveren J, Gerats T, van der Voort JR (2005) Quantitative trait locus (QTL) isogenic recombinant analysis: a method for high-resolution mapping of QTL within a single population. Genetics 171(3):1341–1352. doi: 10.1534/genetics.105.045963 PubMedPubMedCentralCrossRefGoogle Scholar
  38. Prigge V, Melchinger AE (2012) Production of haploids and doubled haploids in maize. Methods Mol Biol 877:161–172PubMedCrossRefGoogle Scholar
  39. 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. doi: 10.1007/BF00211055 PubMedCrossRefGoogle Scholar
  40. Rebai A, Goffinet B (2000) More about quantitative trait locus mapping with diallel designs. Genet Res 75:243–247PubMedCrossRefGoogle Scholar
  41. Reif JC, Melchinger AE, Frisch M (2005) Genetical and mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management. Crop Sci 45:1–7. doi: 10.2135/cropsci2005.0001 CrossRefGoogle Scholar
  42. Riedelsheimer C, Endelman JB, Stange M et al (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194:493–503. doi: 10.1534/genetics.113.150227 PubMedPubMedCentralCrossRefGoogle Scholar
  43. Rodgers-Melnick E, Bradbury PJ, Elshire RJ et al (2015) Recombination in diverse maize is stable, predictable, and associated with genetic load. Proc Natl Acad Sci 112:201413864. doi: 10.1073/pnas.1413864112 CrossRefGoogle Scholar
  44. Schnable PS, Ware D, Fulton RS et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115. doi: 10.1126/science.1178534 PubMedCrossRefGoogle Scholar
  45. Schön CC, Lee M, Melchinger AE et al (1993) Mapping and characterization of quantitative trait loci affecting resistance against second-generation European corn borer in maize with the aid of RFLPs. Hered (Edinb) 70:648–659. doi: 10.1038/hdy.1993.93 CrossRefGoogle Scholar
  46. Schön CC, Utz HF, Groh S et al (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167:485–498. doi: 10.1534/genetics.167.1.485 PubMedPubMedCentralCrossRefGoogle Scholar
  47. Steinhoff J, Liu W, Maurer HP et al (2011) Multiple-line cross quantitative trait locus mapping in European elite maize. Crop Sci 51:2505. doi: 10.2135/cropsci2011.03.0181 CrossRefGoogle Scholar
  48. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org
  49. Utz HF (2005) PLABSTAT: a computer program for the statistical analysis of plant breeding experiments. University of Hohenheim, GermanyGoogle Scholar
  50. Utz HF, Melchinger AE (1994) Comparison of different approaches to interval mapping of quantitative trait loci. In: Ooijen JW van, Jansen J (ed), Biometrics plant Breed Appl Mol markers Wageningen: the Netherlands, 6–8 July 1994. 1994, 195–204 STGoogle Scholar
  51. Utz HF, Melchinger AE, Schön CC (2000) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154:1839–1849PubMedPubMedCentralGoogle Scholar
  52. Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. Heredity 93(1):77–78CrossRefGoogle Scholar
  53. Wu XL, Jannink JL (2004) Optimal sampling of a population to determine QTL location, variance, and allelic number. Theor Appl Genet 108:1434–1442. doi: 10.1007/s00122-003-1569-5 PubMedCrossRefGoogle Scholar
  54. Wu Y, Bhat PR, Close TJ, Lonardi S (2008) Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph. PLoS Genet. doi: 10.1371/journal.pgen.1000212 Google Scholar
  55. Würschum T, Kraft T (2015) Evaluation of multi-locus models for genome-wide association studies: a case study in sugar beet. Heredity 114:281–290PubMedCrossRefGoogle Scholar
  56. Würschum T, Liu W, Gowda M et al (2012) Comparison of biometrical models for joint linkage association mapping. Hered (Edinb) 108:332–340. doi: 10.1038/hdy.2011.78 CrossRefGoogle Scholar
  57. Xu S (1998) Mapping quantitative trait loci using multiple families of line crosses. Genetics 148:517–524PubMedPubMedCentralGoogle Scholar
  58. Xu S (2003) Theoretical basis of the Beavis effect. Genetics 165:2259–2268PubMedPubMedCentralGoogle Scholar
  59. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551. doi: 10.1534/genetics.107.074245 PubMedPubMedCentralCrossRefGoogle Scholar
  60. Zila CT, Samayoa LF, Santiago R et al (2013) A Genome-Wide association study reveals genes associated with Fusarium ear rot resistance in a maize core diversity panel. G3: Genes|Genomes|Genet. doi: 10.1534/g3.113.007328 PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Sen Han
    • 1
  • H. Friedrich Utz
    • 1
  • Wenxin Liu
    • 2
  • Tobias A. Schrag
    • 1
  • Michael Stange
    • 1
    • 5
  • Tobias Würschum
    • 3
  • Thomas Miedaner
    • 3
  • Eva Bauer
    • 4
  • Chris-Carolin Schön
    • 4
  • Albrecht E. Melchinger
    • 1
  1. 1.Institute of Plant Breeding, Seed Science and Population Genetics (350a)University of HohenheimStuttgartGermany
  2. 2.Crop Genetics and Breeding DepartmentChina Agricultural UniversityBeijingChina
  3. 3.State Plant Breeding Institute (720)University of HohenheimStuttgartGermany
  4. 4.Department of Plant Breeding, Center of Life and Food Sciences WeihenstephanTechnische Universität MünchenFreisingGermany
  5. 5.Strube Research GmbH and Co. KGSöllingenGermany

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