Multiple-line cross QTL mapping for biomass yield and plant height in triticale (× Triticosecale Wittmack)
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QTL mapping in multiple families identifies trait-specific and pleiotropic QTL for biomass yield and plant height in triticale.
Triticale shows a broad genetic variation for biomass yield which is of interest for a range of purposes, including bioenergy. Plant height is a major contributor to biomass yield and in this study, we investigated the genetic architecture underlying biomass yield and plant height by multiple-line cross QTL mapping. We employed 647 doubled haploid lines from four mapping populations that have been evaluated in four environments and genotyped with 1710 DArT markers. Twelve QTL were identified for plant height and nine for biomass yield which cross-validated explained 59.6 and 38.2 % of the genotypic variance, respectively. A major QTL for both traits was identified on chromosome 5R which likely corresponds to the dominant dwarfing gene Ddw1. In addition, we detected epistatic QTL for plant height and biomass yield which, however, contributed only little to the genetic architecture of the traits. In conclusion, our results demonstrate the potential of genomic approaches for a knowledge-based improvement of biomass yield in triticale.
KeywordsPlant Height Biomass Yield Genotypic Variance Double Haploid Genetic Architecture
This research was funded by the German Federal Ministry of Education and Research (BMBF) under the promotional reference 0315414A. This publication reflects the views only of the authors. We acknowledge the handling of the funding by the Project Management Organisation Jülich (PtJ). We thank Angela Harmsen for excellent technical assistance in the laboratory and Agnes Rölfing-Finze, Hans Häge, Jacek Till and Justus von Kittlitz for their outstanding work in the greenhouse and field.
Conflict of interest
The authors declare that they have no conflict of interest.
The authors declare that the experiments comply with the current laws of Germany.
- Banaszak Z (2011) Breeding of triticale in DANKO. 61 Tagung der Vereinigung der Pflanzenzüchter und Saatgutkaufleute Österreichs 2010, pp 65–68Google Scholar
- Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC, Goodman MM, Harjes C, Guill K, Kroon DE, Larsson S, Lepak NK, Li H, Mitchell SE, Pressoir G, Peiffer JA, Rosas MO, Rocheford TR, Romay MC, Romero S, Salvo S, Villeda HS, Da Silva HS, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen MD (2009) The genetic architecture of maize flowering time. Science 325:714–718PubMedCrossRefGoogle Scholar
- Cochran WG, Cox GM (1957) Experimental designs. Wiley, New YorkGoogle Scholar
- Gilmour AR, Gogel BG, Cullis BR, Thompson R (2009) ASReml user guide release 3.0. VSN International Ltd, Hemel Hempstead, HP1 1ES, UKGoogle Scholar
- Miedaner T, Müller BU, Piepho H-P, Falke KC (2011) Genetic architecture of plant height in winter rye introgression libraries. Plant Breed 130:209–216Google Scholar
- R Development Core Team (2005) R: a language and environment for statistical computing, reference index version 2.x.x. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org
- SAS Institute (2008) SAS/STAT 9.2 User’s guide. SAS Institute Inc., Cary, NC, USAGoogle Scholar
- Verhoeven KJF, Jannink J-L, McIntyre LM (2006) Using mating designs to uncover QTL and the genetic architecture of complex traits. Heredity 96:139–149Google Scholar
- Williams E, Piepho H-P, Whitaker D (2011) Augmented p-rep designs. Biometr J 53:19–27Google Scholar