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. 2017 Dec 19;12(12):e0189594.
doi: 10.1371/journal.pone.0189594. eCollection 2017.

Mapping QTLs for grain yield components in wheat under heat stress

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Mapping QTLs for grain yield components in wheat under heat stress

Nabin Bhusal et al. PLoS One. .

Abstract

The current perspective of increasing global temperature makes heat stress as a major threat to wheat production worldwide. In order to identify quantitative trait loci (QTLs) associated with heat tolerance, 251 recombinant inbred lines (RILs) derived from a cross between HD2808 (heat tolerant) and HUW510 (heat susceptible) were evaluated under timely sown (normal) and late sown (heat stress) conditions for two consecutive crop seasons; 2013-14 and 2014-15. Grain yield (GY) and its components namely, grain weight/spike (GWS), grain number/spike (GNS), thousand grain weight (TGW), grain filling rate (GFR) and grain filling duration (GFD) were recorded for both conditions and years. The data collected for both timely and late sown conditions and heat susceptibility index (HSI) of these traits were used as phenotypic data for QTL identification. The frequency distribution of HSI for all the studied traits was continuous during both the years and also included transgressive segregants. Composite interval mapping identified total 24 QTLs viz., 9 (timely sown traits), 6 (late sown traits) and 9 (HSI of traits) mapped on linkage groups 2A, 2B, and 6D during both the crop seasons 2013-14 and 2014-15. The QTLs were detected for GWS (6), GNS (6), GFR (4), TGW (3), GY (3) and GFD (2). The LOD score of identified QTLs varied from 3.03 (Qtgns.iiwbr-6D) to 21.01 (Qhsitgw.iiwbr-2A) during 2014-15, explaining 11.2 and 30.6% phenotypic variance, respectively. Maximum no of QTLs were detected in chromosome 2A followed by 6D and 2B. All the QTL detected under late sown and HSI traits were identified on chromosome 2A except for QTLs associated with GFD. Fifteen out of 17 QTL detected on chromosome 2A were clustered within the marker interval between gwm448 and wmc296 and showed tight linkage with gwm122 and these were localized in 49-52 cM region of Somers consensus map of chromosome 2A i.e. within 18-59.56 cM region of chromosome 2A where no QTL related to heat stress were reported earlier. Besides, three consistent QTLs, Qgws.iiwbr-2A, Qgns.iiwbr-2A and Qgns.iiwbr-2A were also detected in all the environments in this region. The nearest QTL detected in earlier studies, QFv/Fm.cgb-2A was approximately 6cM below the presently identified QTLs region, respectively Additionally, QTLs for physiological and phenological traits and plant height under late sown and HSI of these traits were also detected on chromosome 2A. QTL for HSI of plant height and physiological maturity were located in the same genomic region of chromosome 2Awhereas QTLs for physiological and phonological traits under late sown were located 8cM and 33.5 cM below the genomic location associated with grain traits, respectively in consensus map of Somers. This QTL hot-spot region with consistent QTLs could be used to improve heat tolerance after validation.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. QTLs identified during 2013–14 and 2014–15 crop seasons using HD2808/ HUW 510 RIL population.
Fig 2
Fig 2. Previously identified QTLs for heat tolerance traits in various studies with respect to QTLs identified in the present investigation on chromosome 2A.
Positions of linked markers for the QTL were used as the positions on consensus map of Somers et al. [24], Crossa et al. [35] and consensus map of Cavanagh et al. [36] for SSRs, DArT and SNP markers respectively.

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References

    1. Hertel TW, Burke MB, Lobell DB. The poverty implications of climate-induced crop yield changes by 2030. Glob Environ Change. 2010; 20(4): 577–585.
    1. Lobell DB, Schlenker W, Costa-Roberts J. Climate trends and global crop production since 1980. Science. 2011; 333(6042): 616–620. doi: 10.1126/science.1204531 - DOI - PubMed
    1. IPCC. Summary for policy makers. In field C Policy markers In: Field C, Barros V, Stockeret T, Editors. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Cambridge University Press, Cambridge, UK and New York, NY, USA; 2012. pp. 1–19.
    1. Sareen S, Munjal R, Singh NB, Singh BN, Verma RS, Meena BK, et al. Genotype x environment interaction and ammi analysis for heat tolerance in wheat. Cereal Res Commun. 2012; 40 (2): 267–276.
    1. Sharma P, Sareen S, Saini M, Verma A, Tyagi BS, Sharma I. Assessing genetic variation for heat tolerance in synthetic wheat lines using phenotypic data and molecular markers. Aust J Crop Sci 2014; 8:515–522.