Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize
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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.
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.
KeywordsQuantitative Trait Locus Quantitative Trait Locus Mapping Quantitative Trait Locus Effect Genomic Prediction Quantitative Trait Locus Detection
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.
The experiments reported in this study comply with the current laws of Germany.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Utz HF (2005) PLABSTAT: a computer program for the statistical analysis of plant breeding experiments. University of Hohenheim, GermanyGoogle Scholar
- 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
- 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