A QTL on Chromosome 3B in Bread Wheat (Triticum aestivum) Is Associated with Leaf Width Under Well-Watered and Water-Deficit Conditions
At an early stage of crop development, the rate of growth is largely determined by leaf characteristics. Plants with rapid leaf area development could save more water for transpiration and crop growth. In our study, a recombinant inbred family was used to identify quantitative trait loci (QTL) controlling leaf length (LL), leaf width (LW), and leaf area (LA) in wheat seedlings under well-watered (WW) and PEG-induced water-deficit (WD) conditions. A total of five QTL for LW, LL, and LA were detected, most of which were reported for the first time. A “constitutive” QTL for LW (Qheb.LW-3B), located on the long arm of chromosome 3B, was consistently detected under two water conditions, explaining 17.7 % of the phenotypic variance with a LOD value of 7.20 under WW condition and 13.3 % of the phenotypic variance with a LOD value of 4.87 under WD condition. The other four “adaptive” QTL were detected under a single water condition only. These QTL include the following: Qheb.LW-5B for LW (WW condition), Qheb.LL-3A, and Qheb.LL-5B for LL (WD condition) and Qheb.LA-3B for LA (WW condition). Four pairs of near isogenic lines (NILs) were developed to validate the effects of Qheb.LW-3B. The allele from the parent “CSCR6” increased the LW by an average of 8.2 % under WW condition and 13.8 % under WD condition, respectively. The position and effects of Qheb.LW-3B was confirmed. Qheb.LW-3B would be a valuable genetic resource to improve wheat seedling early establishment. The NILs we have generated would be useful for further characterization of Qheb.LW-3B, in studying its interaction with other traits of agronomic importance and in developing markers that can be reliably used to follow this major locus.
KeywordsWheat Quantitative trait loci Drought Leaf width
The authors are grateful to Dr. Chunji Liu from CSIRO Agriculture Flagship for providing the seeds of the QTL mapping population used in this experiment and also for his valuable suggestions during the preparation of the manuscript. This research was partially funded by an Australian Research Council (ARC) grant LP120200830.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no competing interests.
This article does not contain any studies with human participants performed by any of the authors.
- Dingkuhn M, Johnson DE, Sow A, Audebert AY (1999) Relationship between upland rice canopy characteristics and weed competitiveness. Field Crops Res 61:71–95Google Scholar
- El SM, Malosetti M, Zwaan BJ, Koornneef M, Aarts MG (2014) Genotype × environment interaction QTL mapping in plants: lessons from Arabidopsis. Trends Plant Sci 19:390–398Google Scholar
- Goudriaan J, Van LHH (1994) Modelling potential crop growth processes: textbook with exercises (Vol. 2). Springer Science & Business MediaGoogle Scholar
- Rebetzke GJ, Richards RA (1999) Genetic improvement of early vigour in wheat. Aust J Agric Res 53:41–50Google Scholar
- Van Ooijen JW (2006) JointMap 4, software for the calculation of genetic linkage maps in experimental populations. Kyazma BV, WageningenGoogle Scholar
- Wang S, Basten CJ, Zeng ZB (2012) Windows QTL Cartogra- pher 2.5. Department of Statistics, North Carolina State University, RaleighGoogle Scholar
- Zhang L, Richards RA, Condon AG, Liu DC, Rebetzke GJ (2014) Recurrent selection for wider seedling leaves increases early biomass and leaf area in wheat (Triticum aestivum L) J Exp Bot:eru468Google Scholar
- Zhou XG, Jing RL, Hao ZF, Chang XP, Zhang ZB (2005) Mapping QTL for seedling root traits in common wheat. J Integr Agric 38:1951–1957Google Scholar