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Theoretical and Applied Genetics

, Volume 126, Issue 11, pp 2717–2736 | Cite as

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

  • N. Bardol
  • M. Ventelon
  • B. Mangin
  • S. Jasson
  • V. Loywick
  • F. Couton
  • C. Derue
  • P. Blanchard
  • A. Charcosset
  • Laurence Moreau
Original Paper

Abstract

Advancements in genotyping are rapidly decreasing marker costs and increasing marker density. This opens new possibilities for mapping quantitative trait loci (QTL), in particular by combining linkage disequilibrium information and linkage analysis (LDLA). In this study, we compared different approaches to detect QTL for four traits of agronomical importance in two large multi-parental datasets of maize (Zea mays L.) of 895 and 928 testcross progenies composed of 7 and 21 biparental families, respectively, and genotyped with 491 markers. We compared to traditional linkage-based methods two LDLA models relying on the dense genotyping of parental lines with 17,728 SNP: one based on a clustering approach of parental line segments into ancestral alleles and one based on single marker information. The two LDLA models generally identified more QTL (60 and 52 QTL in total) than classical linkage models (49 and 44 QTL in total). However, they performed inconsistently over datasets and traits suggesting that a compromise must be found between the reduction of allele number for increasing statistical power and the adequacy of the model to potentially complex allelic variation. For some QTL, the model exclusively based on linkage analysis, which assumed that each parental line carried a different QTL allele, was able to capture remaining variation not explained by LDLA models. These complementarities between models clearly suggest that the different QTL mapping approaches must be considered to capture the different levels of allelic variation at QTL involved in complex traits.

Keywords

Quantitative Trait Locus Quantitative Trait Locus Mapping Quantitative Trait Locus Detection Parental Allele Quantitative Trait Locus Allele 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Part of this work was financed by Euralis Semences; we thank them as well for the phenotypic and genetic material. We also thank the National Agency of French Research (ANR) which financed the MCQTL-LD project. We are grateful to the platform of bioinformatics Toulouse Midi-Pyrénées which partially supported this project. We also thank Frank Gauthier for helpful scripts to deal with the large amount of data. We are grateful to the editor and three anonymous reviewers for insightful comments that improved the manuscript.

Supplementary material

122_2013_2167_MOESM1_ESM.docx (1.5 mb)
Supplementary material 1 (DOCX 1530 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • N. Bardol
    • 1
    • 2
  • M. Ventelon
    • 2
  • B. Mangin
    • 3
  • S. Jasson
    • 3
  • V. Loywick
    • 1
    • 2
  • F. Couton
    • 2
  • C. Derue
    • 2
  • P. Blanchard
    • 2
  • A. Charcosset
    • 1
  • Laurence Moreau
    • 1
  1. 1.UMR0320/UMR8120 de Génétique Végétale, INRA, Université Paris-Sud, CNRS, Ferme du MoulonGif-sur-YvetteFrance
  2. 2.Euralis Semences, Domaine de SandreauMondonvilleFrance
  3. 3.INRA, UR875, Unité de Biométrie et Intelligence ArtificielleCastanet TolosanFrance

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