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High throughput phenotyping and advanced genotyping reveals QTLs for plant vigour and water saving traits co-localize in a “QTL-hotspot”: Progress in understanding the drought adaptations in chickpea
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High throughput phenotyping and advanced genotyping reveals QTLs for plant vigour and water saving traits co-localize in a “QTL-hotspot”: Progress in understanding the drought adaptations in chickpea

  1. Feb 2017 High throughput phenotyping and advanced genotyping reveals QTLs for plant vigour and water saving traits co-localize in a “QTL-hotspot”: Progress in understanding the drought adaptations in chickpea Kaliamoorthy Sivasakthi1 ,2 , Mahendar Thudi1 , Murugesan Tharanya1 ,2 , Sandeep M Kale1 , Jana Kholová1 , Mahamat Hissene Halime1 , Deepa Jaganathan1 , Rekha Baddam1 , Thiyagarajan Thirunalasundari2 , Pooran M Gaur1 , Rajeev K Varshney1 , Vincent Vadez1 * 1 International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Greater Hyderabad, Telangana, India 2 Bharathidasan University, Tiruchirappalli, Tamil Nadu, India About ICRISAT: www.icrisat.org ICRISAT’s scientific information: http://EXPLOREit.icrisat.org For more details contact v.vadez@cgiar.org Introduction • Earlier, root traits (depth and density) were hypothesized to improve water extraction and so contribute to yield increase under water limited environments in chickpea (Varshney et al 2013). • Usually across the crop species, enhanced root growth is functionally linked with enhanced shoot growth. • Therefore, here we want to investigate whether enhanced root growth also links to enhanced shoot vigour (e.g. canopy conductivity, canopy size & development) and so functionally explain increased chickpea crop productivity in water limited environments. Figure 1. Phenotyping facility for lower level traits (Canopy development; High throughput crop phenotyping facility-LeasyScan) to higher level (Crop production; Semi-field (Lysimeter) and field). Figure 2. a) Dynamics of 3D-leaf area development in parental lines, b) Transpiration rate (TR; mg H2 O mm-2 min-1 ) in population (232 RILs) and parents (ICC 4958 & ICC 1882) at 28 DAS under WW conditions. Figure 3. Comparison of different density markers in “QTL hotspot” genomic region harbouring QTLs for vigour traits (present study) and drought tolerance / root traits (earlier studies) on CaLG04 using QTL cartographer software. Figure 4. a) Water extraction at pod filling stage, b) Seed yield for selected 40 RILs contrasting for plant vigour and canopy conductivity [HLA-HTR (High leaf area & high transpiration rate); HLA-LTR (High leaf area & low transpiration rate); LLA-HTR (Low leaf area & high transpiration rate) and LLA-LTR (Low leaf area & low transpiration rate)] characteristics were evaluated in lysimeter (semi-field) and field under different water stress treatments [Well water (WW), mild stress (MS), severe stress (SS)]. Aerial view-LeasyScan-Facility Pot Scanning & gravimetric measurements Aerial view-chickpea field evaluation Lysimeter facility • In the semi-field (Lysimeter) and field, lines combining high vigour and lower canopy conductivity attained higher water extraction at pod filling stage and also higher seed yield especially compared to lines combining high vigour and higher canopy conductivity lines under severe water stress conditions. Conclusion • Our study shows that hotspot region on LG4 previously reported to underlies root growth characteristics and yield under water stress also harbours plant vigour-related traits. • This implies that vigour-related root traits reported earlier (Varshney et al 2014) could be assessed by vigour-related shoot traits which considerably ease its phenotyping. • We showed that plant vigour traits on CaLG04 combined with lowered canopy conductivity traits on CaLG03 provide an opportunity to tailor recombinants enhancing the water extraction and crop production in severe water stress situation. Selected references Vadez V et al (2015) J. Exp. Bot. (2015) 66 (18): 5581-5593. Varshney et al (2014) Theor. Appl. Genet. 127:445–462. Acknowledgements The authors are greatly thankful for the funding from ICRISAT, USAID (Feed the Future Innovation Lab—Climate Resilient Chickpea) and CGIAR Research Program on Dryland Cereals (CRP-DC) and Grain Legumes (CRP-GL) towards the establishment of LeasyScan facility. Specific objectives • To identify genomic regions (QTLs) responsible for plant vigour-related traits in recombinant inbred lines (RILs) material • Comparison of QTLs for vigour-related traits with previously reported QTLs for root-related traits. • Validation of water saving and crop production traits in selected 40 RILs contrasting for vigour and water use chrecterists. Refined QTL hotspot region (~15cM) 1007-High density SSR + SNPs (GBS) markers Sivasakthi et al-Present study Fine mapped QTL hotspot region (~300 Kb) 1557-Ultra-high density SNPs (Bin) markers Sivasakthi et al-Present studyKale S.M et al 2015 Materials and methods • Plant materials: 232 recombinant inbred lines (RILs) derived from a cross between ICC 4958 (high vigour) and ICC 1882 (low vigour). • Genotyping data: Two types of genetic maps; i) “High density map” containing 1007 markers (SSR+SNPs; Jaganathan et al 2015) and ii) “Ultra high density map” containing 1557 markers (SNPs; Kale S.M et al 2015). • Software used: QTL Cartographer and ICIM (QTL IciMapping) for high density and Ultra-high density marker analysis • Phenotyping at LeasyScan: 15 plant vigor-related traits were phenotyped under LeasyScan platform in well-watered conditions at Nov-Dec-2014 and 15 (working principle & more information see Vadez et al 2015). • Phenotyping at Lysimeter and Field: Dynamics of plant water extraction and crop production traits were assessed under different water stress treatments [mild stress (MS; 85 mm/season), severe stress (SS; 60 mm/ season) and well water (WW; 150 mm/ season)]. Key results • Using both genetic maps, we identified several major-QTLs underlying vigour-related traits on CaLG04 (~300Kb) where the QTLs for major “drought tolerance” were reported earlier while canopy conductivity traits mapped to locus on CaLG03. • Plant vigour-related traits were underlied by positive allele from ICC 4958, while canopy conductivity- related traits had allele from ICC 1882. 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 228 247 266 285 304 323 342 361 380 399 418 437 456 475 494 513 532 551 570 589 608 627 646 665 680 696 713 730 749 768 787 806 3D-leafarea(mm-2sector-1) Thermal time (degree days) 3D-Leaf area -Parental lines ICC 4958-High vigour parent ICC 1882-Low vigour parent 2 3 4 5 6 7 8 9 10 11 27 5 155 31 70 150 67 17 214 42 47 61 91 40 145 69 201 117 49 230 206 115 81 190 186 110 41 138 192 212 30 24 121 107 85 133 229 191 200 Transpirationrate (mgH2Omm-2 min-1 ) 232 Recombinant inbred lines (RILs) Transpiration rate (TR) ICC 4958 ICC 1882LSD (0.5)=2*** a) b) Water extraction at pod filling stage Seed Yield a) b)
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