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Plant vigour QTLs co-map with an earlier reported QTL hotspot for drought tolerance while water saving QTLs map in other regions of the chickpea genome

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BMC Plant BiologyBMC series – open, inclusive and trusted201818:29

https://doi.org/10.1186/s12870-018-1245-1

Received: 13 July 2017

Accepted: 21 January 2018

Published: 6 February 2018

Abstract

Background

Terminal drought stress leads to substantial annual yield losses in chickpea (Cicer arietinum L.). Adaptation to water limitation is a matter of matching water supply to water demand by the crop. Therefore, harnessing the genetics of traits contributing to plant water use, i.e. transpiration rate and canopy development dynamics, is important to design crop ideotypes suited to a varying range of water limited environments. With an aim of identifying genomic regions for plant vigour (growth and canopy size) and canopy conductance traits, 232 recombinant inbred lines derived from a cross between ICC 4958 and ICC 1882, were phenotyped at vegetative stage under well-watered conditions using a high throughput phenotyping platform (LeasyScan).

Results

Twenty one major quantitative trait loci (M-QTLs) were identified for plant vigour and canopy conductance traits using an ultra-high density bin map. Plant vigour traits had 13 M-QTLs on CaLG04, with favourable alleles from high vigour parent ICC 4958. Most of them co-mapped with a previously fine mapped major drought tolerance “QTL-hotspot” region on CaLG04. One M-QTL was found for canopy conductance on CaLG03 with the ultra-high density bin map. Comparative analysis of the QTLs found across different density genetic maps revealed that QTL size reduced considerably and % of phenotypic variation increased as marker density increased.

Conclusion

Earlier reported drought tolerance hotspot is a vigour locus. The fact that canopy conductance traits, i.e. the other important determinant of plant water use, mapped on CaLG03 provides an opportunity to manipulate these loci to tailor recombinants having low/high transpiration rate and plant vigour, fitted to specific drought stress scenarios in chickpea.

Keywords

  • Phenotyping
  • Plant vigour
  • Transpiration rate
  • Quantitative trait loci (QTL)
  • QTL-hotspot
  • Drought stress

Background

Chickpea (Cicer arietinum L.), the second most important grain legume crops in the world [1], is widely cultivated on residual soil moisture in the arid and semi-arid agricultural systems of the world. Terminal water deficit is one of the major constraints limiting the chickpea crop productivity [2] and has been reported to cause yield losses upto 50% in chickpea [3].

Deeper and more profuse rooting has been hypothesized to be the major factor contributing to yield increase under water limited environments in chickpea, where the assumption was made that these root traits would increase water extraction [48]. However it was also shown that chickpea genotypes with deeper and more profuse rooting did not extract more water from the soil profile [9]. Rather, tolerant chickpea genotypes turned out to be those able to somewhat limit water use at vegetative stage and making more water available for the grain filling period [9, 10]. Similar results have been reported in other crops (e.g. in pearl millet [11], in sorghum [12]). Therefore, the central hypothesis of the present study is that, given the limited seasonal water budget, any trait allowing water conservation during vegetative growth (e.g. canopy conductivity, canopy size and development) extends the duration of water extraction during pod filling and so increases productivity of chickpea crop under terminal water stress [911, 1315].

In chickpea, the availability of large scale genomic resources has paved the way to dissect the mechanisms underlying various stresses adaptations [16, 17]. A recent mapping study in chickpea reported a genomic region on CaLG04 referred as a “QTL hotspot” that harbours several drought tolerance traits including rooting depth [18]. Introgression of this region into elite variety JG 11 improved yield under drought [19]. This reported “QTL hotspot” region (spanning ~ 29 cM) was originally associated with seven SSR markers [20]. Further, this “QTL-hotspot” region was refined to~ 14 cM, with additional 49 SNP markers, [20] using genotyping-by-sequencing (GBS). Skim sequencing (with bin used as markers, based on recombination break points) approach then allowed to fine map this region to ~ 300 Kb [21]. An intriguing feature of the preliminary steps of this research was also the mapping of a major QTL for shoot weight on CaLG04, which co-mapped with a QTLs for root traits (depth and length density), from a study where these traits were assessed in PVC tubes [22]. Interestingly, the percentage of phenotypic variation explained by this QTL was more for the shoot dry weight than for the root traits, suggesting that this QTL region was a QTL for vigour, but this hypothesis was not followed further.

In chickpea, the studies of physiological traits allowing water conservation (e.g. canopy conductivity, canopy size & development; [9, 10]) are very scarce, partially because an accurate assessment of leaf area is a rate limiting step. Recognizing this obstacle, a high throughput phenotyping platform was developed to measure canopy development traits [23]. The high throughput platform was used to phenotype the RIL population (ICC 4958 × ICC 1882), from which the “QTL-hotspot” was reported, for plant vigour traits (leaf area, plant height, rate of leaf area increase) and water saving traits (conductance), as a mean to re-investigate the map location of these traits with regards to the QTL hotspot earlier identified [18].

Therefore, the overall objective of this study was: i) to assess the phenotypic variation in traits involved in the control of plant water use either from canopy development or canopy conductance, and explore their functional associations in a RIL mapping population previously used for mapping the “drought tolerance QTL” (ICC 4958 × ICC 1882), ii) to map these drought adaptive traits and assess their interactions, iii) to conduct comparative mapping study using differently saturated genetic maps.

Results

Based on trait functionality, these were clustered (Clustering analysis) and grouped into two major clusters: (i) a cluster of plant vigour traits [Plant vigour score (VIG), 3D-leaf area (3D-L), projected leaf area (PL), plant height (PH), 3D-leaf area growth rate (3D-LG), projected leaf area growth rate (PLG), plant height growth rate (PHG), shoot dry weight (SDW), leaf area index (LAI), specific leaf weight (SLW) and specific leaf area (SLA)]; and (ii) a cluster of traits related to canopy conductance [Transpiration (T), evapotranspiration (eT), transpiration rate (TR), evapotranspiration rate (eTR) and the residuals between 3D and projected leaf area (R-3D/PLA, a trait that was interpreted to represent the canopy structure)] (Table 1; Additional file 1).
Table 1

Summary on traits phenotyped using high throughput plant phenotyping platform (LeasyScan). Summary include trait name, trait code, trait type, year of phenotyping, replication, and measurement methods

No. of traits

Trait name

Trait code

Trait type

Year of phenotyping

Replication

Measurement method

1

Plant vigour

VIG

Plant vigour

Nov-Dec-2015

4

Visual eye scoring

2

Projected Leaf area (cm2)

PL

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan-Plant eye camera

3

Projected Leaf area growth rate (cm2 day− 1)

PLG

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan data derived

4

3-Dimentional (3D) Leaf area (mm2)

3DL

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan-Plant eye camera

5

3-Dimentional (3D) Leaf area growth rate (mm2)

3DLG

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan data derived

6

Leaf area index

LAI

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan data derived

7

Shoot dry weight (g)

SDW

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan & gravimetric data derived

8

Specific leaf area (g mm2)

SLA

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan & gravimetric data derived

9

Specific leaf weight (mg mm2)

SLW

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan & gravimetric data derived

10

Residuals from 3-D & projected Leaf area (cm2)

R-3D/PLA

Canopy structure

Nov-Dec-2014 & 2015

3 & 4

LeasyScan data derived

11

Plant height (cm)

PH

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan-Plant eye camera

12

Plant height growth rate (cm day−1)

PHG

Plant vigour

Nov-Dec-2014 & 2015

3 & 4

LeasyScan data derived

13

Evapotranspiration (g)

eT

Canopy conductance

Nov-Dec-2014 & 2015

3 & 4

Gravimetric pot weighing

14

Evapotranspiration rate (mg mm 2 day−1)

eTR

Canopy conductance

Nov-Dec-2014 & 2015

3 & 4

Gravimetric & LeasyScan-data derived

15

Transpiration (g)

T

Canopy conductance

Nov-Dec-2014 & 2015

3 & 4

Gravimetric data derived

16

Transpiration rate (g cm 2 day−1)

TR

Canopy conductance

Nov-Dec-2014 & 2015

3 & 4

Gravimetric & LeasyScan-data derived

Phenotypic analysis

Plant vigour related traits

Summary statistics

The two parental genotypes (ICC 4958 and ICC 1882), as well as RILs, showed significant differences in plant vigour traits in both years (Table 2). For example, 3D-Leaf area (3D-L) was among those showing the largest variation, i.e. a 5-fold range variation in both years (Fig. 1a & Table 2). Continuous variation and normal frequency distribution were found for plant vigour traits (Additional file 2 A, B, C &D). Additional file 3 A & B showed 3D-leaf area & plant height development dynamics of parental lines. The high vigour parent ICC 4958 had faster leaf area and plant height development (canopy development) than low vigour parent ICC 1882.
Table 2

ANOVA results for the 16 traits phenotyped using high throughput plant phenotyping platform (LeasyScan). F represents probability; SE represents the standard error; LSD represents least significant difference and h2 represents the heritability values

   

Parents

Progenies

Trait No.

Traits code

Year

ICC 4958

ICC 1882

Significance

LSD

Variation in RILs

Grand mean

Significance

S.E

LSD

h2 (%)

1

VIG

2015

5

2.0

0.01

1.0

2.0 - 5.00

3.718

<.001

0.50

0.97

73

2

3DL

2014

46,497

25,389

0.01

16,147

14,237 -71,290

35,549.7

<.001

5575.00

10,956

76

2

3DL

2015

54,684

33,353

0.01

19,884

14,292 - 68,103

40,299

<.001

6285.00

12,339.5

89

3

3DG

2014

3079

2031

0.05

674

1207 - 4461

2397

<.001

311.80

612.7

72

3

3DG

2015

2298

1774

0.05

407

310.5 - 4487

2146

<.001

328.00

643

85

4

PL

2014

435

252

0.01

127

175 - 561

323

<.001

34.50

68.4

50

4

PL

2015

515

354

0.01

174

260 - 649.3

447

<.001

43.50

85.3

70

5

PLG

2014

68

38

0.01

27

−0.079 - 6.7

2.191

<.001

0.73

1.42

37

5

PLG

2015

19

13

0.01

7.4

4.14 - 43.15

18.43

<.001

4.80

9.4

41

6

PH

2014

110

76

0.01

29

54 - 150

96.87

<.001

4.41

8.66

96

6

PH

2015

126

72

0.01

26

47.41 - 198.3

102.7

<.001

9.20

18.1

88

7

PHG

2014

3.1

1.47

0.05

1.2

12.15 - 99.45

57.96

<.001

11.40

22.4

62

7

PHG

2015

2.1

0.97

0.01

1.1

−6.12 - 4.30

1.45

<.001

0.58

1.14

57

8

LAI

2014

0.60

0.42

0.05

0.1

0.18 - 0.79

0.383

<.001

0.0566

0.1113

59

8

LAI

2015

1.21

0.79

0.01

0.4

0.5882 - 1.399

0.988

<.001

0.11

0.21

45

9

R-3D/PLA

2014

0.19

−8.18

ns

22

−95.54

−0.401

<.001

8.16

16.04

68

9

R-3D/PLA

2015

44

68

0.05

17

−26.4 - 160.6

62.54

<.001

16.55

32.53

51

10

SDW

2014

20

12.9

0.01

6.2

8.66 - 28.97

15.78

<.001

1.21

2.38

86

10

SDW

2015

18

11.3

0.01

4.5

6.523 - 25.09

14.24

<.001

2.60

5.12

60

11

SLA

2014

4651

3975

0.05

628

1403 - 9774

4221

<.001

951.80

1870.4

66

11

SLA

2015

3287

2559

0.01

369

543.7 - 7116

2471

<.001

622.00

1222.3

64

12

SLW

2014

0.21

0.25

0.05

0.04

0.102 - 0.7126

0.2616

<.001

0.06

0.12

70

12

SLW

2015

0.73

0.33

0.01

0.25

0.1525 - 1.549

0.4849

<.001

1.22

0.24

72

13

Et

2014

37

24

0.05

4.89

13.92 - 37

22.01

<.001

2.65

5.208

65

13

eT

2015

74.46

58.46

0.01

10.44

39.83 - 108

73.21

<.001

7.9

15.6

53

14

eTR

2014

0.537

1.156

0.01

0.267

0.306 - 1.532

0.771

<.001

0.119

0.233

25

14

eTR

2015

1.278

3.00

0.01

0.539

0.918 - 3.473

1.611

<.001

0.264

0.519

25

15

T

2014

20.33

13.00

0.01

3.069

5.074 - 34.42

16.84

<.001

3.11

6.12

62

15

T

2015

50.26

34

0.01

6.75

17.13 - 88.78

52.28

<.001

6.63

13.02

70

16

TR

2014

0.00047

0.00090

0.01

0.00031

0.000289 - 0.00089

0.00058

0.004

0.000083

0.000163

41

16

TR

2015

0.00046

0.00086

0.01

0.00021

0.00034 - 0.00189

0.00111

<.001

0.000167

0.000328

57

Figure 1
Fig. 1

Range of variation for plant vigour and canopy conductance related traits from LeasyScan. Range of variation in a) 3D-Leaf area (mm− 2) and b) transpiration rate (TR; mg H2O mm− 2 min− 1) in 232 RILs and parents (ICC 4958 & ICC 1882) at 28 DAS under well watered conditions

According to Rabinson et al. [24], heritability (h 2 %) is classified as low (0-30%), moderate (30-60%) and high (> 60%). Most of the plant vigour related traits had h 2 % in the range of 60- 90% (e.g. PH, 3DL, 3D-LG, SLW and SDW; Table 2). Among these, plant height (PH) and 3DL showed highest heritability [PH (87.5 and 88%) and 3DL (76 and 89%) in 2014 and 2015 respectively].

Canopy conductance traits

Summary statistics

The two parental genotypes (ICC 4958 and ICC 1882) and RILs (progenies) showed significant difference for all canopy conductance traits (T, TR, eT, eTR & R-3D/PLA) in both years (2014-2015; Table 2). For example, T was one among those showing the largest phenotypic variation, i.e. a 5-fold range variation in both years (Table 2). In addition, TR also showed 2-fold range of variation (Fig. 1b). Continuous variation and normal frequency distribution was found for all traits (Additional file 2 E, F, G & H - data not shown for R-3D/PLA). Transpiration and 3D-leaf area were tightly correlated (r2 = 0.68) until the LAI reached a value of 1 (25 DAS). Thereafter, this relationship became much weaker (r2 = 0.22) when the plants reached an LAI between 1 and 2 (38 DAS; see Fig. 2 a & b). At this stage, TR became much more closely related to T (r2 = 0.92), whereas this relationship was weaker (r2 = 0.62) when the LAI was less than 1 (25 DAS; see Fig. 2 c & d). Hence it was interpreted that at a low LAI, leaf area was the main driver of T. By contrast, at a high LAI, TR was the main driver of T. Since the average VPD during the transpiration measurement was high (3.76 kPa), this was interpreted to be caused by TR differences under high VPD.
Figure 2
Fig. 2

Relationship between plant vigour (3D-L) and canopy conductance related traits (T &TR) from LeasyScan. a represents the relationship between transpiration and 3D-leaf area at 25 DAS (Leaf area index > 1). b represents the relationship between transpiration and 3D-leaf area at 38 DAS (Leaf area index = 1). c represents the relationship between transpiration and transpiration rate at 25 DAS (Leaf area index > 1). The (d) represents the relationship between transpiration and transpiration rate at 38 DAS (Leaf area index = 1)

Most of the canopy conductance traits had low to medium (25 to 68%) heritability (e.g. TR, eT, eTR and R-3D/PLA), except T (high h 2 range; 62 and 70% in 2014 and 2015 respectively) (Table 2).

Trait correlation and their relationships

Simple Pearson correlation analysis

Phenotypic correlation coefficients of ICC 4958 x ICC 1882 population are presented in Additional file 4. As expected there were strong relationships within both groups of traits, but also between traits across groups. As expected, 3DL, LAI and SDW (plant vigour traits) were positively correlated with T and eT (canopy conductance traits), whereas 3DL, PL, LAI and SLA were negatively correlated with TR (see Additional file 4). Interestingly, most plant vigour traits were negatively correlated with R-3D/PLA (Canopy structure). By contrast, R-3D/PLA was positively correlated with TR and eTR. A significant correlation was observed among plant vigour traits. For example, plant vigour score (VIG) was significantly correlated with PH (0.73, P = 0.0001), PHG (0.62, P = 0.0001), 3DL (0.46, P = 0.0001), LAI (0.47, P = 0.0001), SDW (0.44, P = 0.0001) (Additional file 4). In addition, a significant correlation was observed among canopy conductance traits. For example, TR was well correlated with eT (0.63, P = 0.0001), eTR (0.61, P = 0.0001) and T (0.44, P = 0.0001) (Additional file 4).

Principal component analysis (PCA)

A principal component analysis was used to identify the relationships between parameters, and group these in a more comprehensive manner. Three principal components (PC) explained 62% of the total variation observed among the RIL population, using BLUPs phenotypic data across years (Additional files 5 and 6). PC1 (34%) had a strong positive loading from SLA and a strong negative loading from 3DL (Additional file 6), which agrees well with the strong negative correlations between these traits (Additional file 4). PC2 (17%) had a strong positive loading from PL and 3DL (plant vigour traits), whereas most canopy conductance traits had a strong negative loading. This also agreed well with the strong negative correlations between plant vigour (PL and 3DL) and canopy conductance (TR and eTR) traits (Additional file 4). PC3 (13%) had a strong positive loading from VIG, PH, PHG, SLA and T, whereas growth rate traits (3D-LG. PLG), canopy structure (R-3D/PLA) and SLW had strong negative loading (Additional file 6). This agreed well with the strong negative correlations between most of the plant vigour and canopy structure (R-3D/PLA) traits (Additional file 4).

Genomic analysis

Plant vigour traits

QTL analysis for single locus

Plant vigour related traits mapped predominantly on CaLG04. One M-QTL [LOD 36.7 & PVE 53%] for VIG was identified in CaLG04 within the reported refined “QTL-hotspot” region (~ 300 Kb; Additional file 7). For 3DL, three M-QTLs were identified, all (LOD 6-10 & PVE 11-18%) being found in both years within the earlier reported refined “QTL-hotspot” region with the favourable allele from ICC 4958 (Additional file 7). For PL, three QTLs were identified within the “QTL-hotspot”, with the favourable allele from ICC 4958 (Additional file 7). Among these, one was a M-QTL (LOD 6 & PVE 11%) and the remaining two were minor QTLs with PVE 8-9% (Additional file 7). For LAI, one M-QTL (LOD 6 & PVE 11%) was identified within the “QTL-hotspot” with the favourable allele from ICC 4958 (Additional file 7). Two minor QTLs were identified in CaLG04 (2 QTLs) (Additional file 7). For SDW, one M-QTL (LOD 9 & PVE 18%) was found with favourable allele from ICC 4958 within the “QTL-hotspot” (Additional file 7), and one minor QTL was identified in CaLG04 (Additional file 7). For PH, three M-QTLs (LOD 20-22 & PVE 34-39%) were identified in CaLG04 within the “QTL-hotspot” with favourable allele from ICC 4958, and one minor QTL was identified in CaLG04 (Additional file 7). For PHG, three M-QTLs (LOD 7-14 & PVE 13-23%) were found in CaLG04 within the “QTL-hotspot” with favourable allele from ICC 4958, and one minor QTL was identified in CaLG04 (Additional file 7). For SLA, three minor QTL were found in CaLG04 (PVE 4-8%; Additional file 7). Few major and minor QTLs from other CaLGs of plant vigour related traits were presented in Additional file 7.

Interaction QTL analysis for multiple loci

Plant vigour and canopy conductance related traits, epistatic QTL (E-QTLs) interactions were analyzed using genotype matrix mapping (GMM). In this section, only selected strongest epistatic QTL (E-QTLs) interactions and high F values with RILs number higher than 10 are discussed (Additional file 8). Additional E-QTLs interactions (Lower F values and RIL number and PVE %) for plant vigour and canopy conductance related traits were found and are shown in Additional file 9. Many E-QTLs interactions were identified for plant vigour traits (VIG, 3D-L, PL, PH, 3D-LG, PLG, PHG, SDW, LAI, SLW and SLA) and these are listed in Additional file 8.

Single locus region explained from − 23% to 17% of the phenotypic variation (Additional file 8). For most of the plant vigour traits, the favourable allele was contributed by the high vigour parent ICC 4958, for instance a single locus QTL [13.5% by LG04, 68.09 (AA)] increased PH by 13.5%. Two loci interactions explained from − 23% to 15% of the phenotypic variations (Additional file 8). For instance, two loci interactions [LG07, 63.45 (AA) + LG04, 68.09 (AA)] increased PH by 15% with favourable alleles from ICC 4958. By contrast, two loci interactions [LG04, 99.17 (BB) + LG04, 68.09 (BB)] strongly decreased PHG by − 25% with favourable allele from ICC 1882 (Additional file 8). Three loci interactions explained from − 25% to 17% of the phenotypic variation (Additional file 8). For instance, three loci interactions [LG04, 68.09 (AA) + LG03, 13.00 (AA) + LG03, 3.08 (AA)] increased PH by 17% with favourable allele from high vigour parent ICC 4958. By contrast, three loci interactions [LG08, 51.27 (−) + LG06, 91.97 (BB) + LG04, 24.82 (BB)] increased SLA by 10% with favourable allele from low vigour parent ICC 1882 (Additional file 8 and Fig. 3).
Figure 3
Fig. 3

QTL interactions of plant vigour and canopy conductance related traits using genotype matrix mapping analysis. Solid lines represent the positive allele from high vigour parent ICC 4958 and dashed lines represents positive allele from low vigour parent ICC 1882. The fine dotted line from specific linkage group (LG) does not distinguish any parents

Canopy conductance traits

QTL analysis for single locus

For TR, one M-QTL (LOD 5 & PVE 10%) was identified in CaLG03 (Additional file 7), and four minor QTL were distributed on CaLG01 (2 QTLs and PVE 5%), CaLG02 (1 QTL and PVE 5%), CaLG07 (1 QTL and PVE 5%; Additional file 7). For eTR, one M-QTL (LOD 6 & PVE 11%) was identified on CaLG04 with favourable allele from ICC 1882. This QTL was located just outside the “QTL hotspot” region (Additional file 7). Along with this, four minor QTL were distributed on CaLG04 (2 QTLs & PVE 5-8%) CaLG03 (1 QTL & PVE 8%) and CaLG07 (1 QTL & PVE 5%; see Additional file 7). For T, three minor QTLs were identified, two of these explaining 8-9% phenotypic variation on CaLG04 (“QTL-hotspot” region) with favourable allele from ICC 4958. Another one QTL for T was present in CaLG05 (PVE 5%) with favourable allele from ICC 1882 (Additional file 7). For eTR, four minor QTLs were distributed on CaLG04 (2 QTLs & PVE 6-7%), CaLG05 (1 QTL & PVE 5%) and CaLG06 (1 QTL & PVE 5%; Additional file 7).

Interaction QTL analysis for multiple loci

E-QTLs interactions identified for canopy conductance (T, TR, eT, eTR & R-3D/PLA) are listed in Additional file 8. Single locus region explained 2.2% to 20% of the phenotypic variation (Additional file 8). For instance, single locus [LG07, 39.00 (BB)] increased R-3D/PLA by 20% with the favourable allele from ICC 1882. Two loci interactions explained from − 43% to 8% of the phenotypic variations (Additional file 8). For instance, two loci interactions [LG07, 37.57 (AA) + LG04, 68.09 (AA)] decreased R-3D/PLA by 43% with favourable allele from ICC 4958. By contrast, two loci interactions [LG07, 12.46 (BB) + LG06, 56.51 (BB)] increased TR by 3.25% with favourable alleles from ICC 1882. Three loci interactions explained from 4% to 31% of the phenotypic variations (Additional file 8). For instance, three loci interactions [LG04, 39.08 (BB) + LG03, 09.09 (BB) + LG01, 16.65 (BB)] increased R-3D/PLA (canopy structure) by 31% with favourable alleles from ICC 1882 (Additional file 8 and Fig. 3). Similarly, a three loci interactions (LG06, 12.48 (BB) + LG04, 44.46 (BB) + LG01, 25.00 (BB)] increased TR by 6.5% with all favourable alleles from ICC 1882 (Additional file 8 and Fig. 3).

Co-localization of plant vigour and drought tolerance related traits

Map position of plant vigour traits reported here was compared to map position of roots and drought tolerance traits reported earlier [18, 20, 21]. With the low density marker map, plant vigour traits co-localized with several root traits [eg. root length density, root dry weight/total plant dry weight ratio); see [18] from the previously reported “QTL-hotspot” region (Fig. 4-I-A, B & C; Additional file 10).
Figure 4
Fig. 4

QTL co-localization of plant vigour and drought tolerance related traits using different density markers. Comparison of genomic region with harboring QTLs for various plant vigour and canopy conductance related traits (present study) and drought tolerance traits using 241 SSR-low density marker (Varshney et al. 2014), 1007 SSR + SNP high density marker (Jaganathan et al. 2015) and 1557-SNPs Ultra-high density marker (Kale et al. 2015) identified on CaLG04. The graph 4-I-A, 4-II-A & 4-III-A represent the QTLs identified for various plant vigour and canopy conductance related traits. The graph 4-I-B represent CaLG04 of consensus genetic map; 4-II-B represent CaLG04 of the fine genetic map (Genotype by sequence, GBS approach) and 4-III-B represent CaLG04 of fine bin map (Skim sequencing approach). The graph 4-I-C, 4-II-C & 4-III-C represent QTLs identified for various drought tolerance traits from previous studies. Common QTL regions for both plant vigour and canopy conductance (Present study) and drought tolerance related traits (Varshney et al. 2014; Jaganathan et al. 2015 and Kale et al. 2015) were highlighted in red/pink

Similarly, mapping with high density markers data (GBS) showed that plant vigour traits (VIG, 3DL, PL, PH, PHG, LAI and SDW) co-localized with previously identified drought tolerance related traits [roots traits (RLD, RSA, RTR), morphological traits (SDW, PHT, PBS), phenological traits (DF, DM), yield related traits (100 SDW, BM, HI, POD, SPD, YLD) and drought indices (DSI, DTI)] (see, [20]) on CaLG04, which gave also a refined “QTL hotspot” region (Fig. 4-II-A, B&C; Additional file 11).

Further co-mapping work was done with the ultra-high density bin marker data (skim sequencing approach). Here, plant vigour related traits (VIG, 3DL, PL, PH, PHG, LAI, SDW) co-localized with previously identified drought tolerance related traits [RLD, RTR%, SDW, PHT, DM, POD, 100SDW, HI and DC; see, [21]] on CaLG04 within the “QTL- hotspot” region (Additional file 7; Fig. 4-III-A, B & C).

Bin-map “QTL-hotspot” region a & b

PH, PHG &VIG had several M-QTLs (LOD 6-37 and PVE 11-53%), and these were identified in the fine mapped “QTL-hotspot”-“a region (0.23 cM) on CaLG04. In the same region, PHT, POD, 100-SDW, RLD and DC traits were previously mapped by Kale et al. [21]. Similarly, 3DL, LAI and SDW had several M-QTLs (LOD 5-10 and PVE 11-18%) that were mapped in another “QTL-hotspot”-“b region (0.22 cM). In the same region, RTR and SDW traits were previously mapped [21].

Asserting QTL location and size in different genetic maps

Different density genetic maps showed QTLs for plant vigour traits on CaLG04 and co-located with the “QTL-hotspot” region. Their size within the “QTL- hotspot” region using the low density (29 cM size), high density (~ 15 cM size) and ultra-high density maps (“QTL-hotspot” a & b (see more details on [21]) together ~300Kb size) on CaLG04 is discussed in this section.

For plant vigour related traits (VIG, 3DL, PL, PH, PHG, LAI, SDW), 28 and 32 M-QTLs were mapped on the low and high density maps, and their size ranged from 1 cM to 8.0 cM on the low density map and 0.8 cM to 5.6 cM on the high density map. For the same traits, the 15 M-QTLs that were mapped using the ultra-high density marker map (Table 3) had a size ranging from 0.14 cM to 0.15 cM. For instance, Fig. 5-I, II, III, IV-A, B&C showed plant vigour traits (VIG, 3DL, PH and SDW) in three different genetic maps. It showed that gradually LOD and PVE increased with marker density and simultaneously QTL size decreased, being fine-tuned down to 300Kb with the ultra- high density marker map. More details on major and minor QTLs for plant vigour in different density genetic maps are presented in Additional files 7, 10 and 11. In addition, low density genetic map along with plant vigour traits QTLs position are shown in Additional file 12A.
Table 3

Summary of Major-QTLs (M-QTLs) for plant vigour and canopy conductance related traits using different genetic map. Low density (241 SSR marker-Varshney et al. 2014); high density (1007 SSR + SNP marker- Jaganathan et al. 2015) and ultra-high density (1557 SNP markers- Kale et al. 2015) markers were used for identification of QTLs. The trait on only measured at 2015 indicates (+) and newly identified additional QTLs with high density markers were indicated by (*). Details of traits code were mentioned in Table 1

Marker used

Trait code

Linkage groups (LGs)

Total QTLs

No. of QTLs in the QTL hotspot

Consistent QTLs

Genetic Size (cM)

Logarithm of the odds ratio (LOD)

Phenotypic variation explained (PVE, %)

Low density-SSR

VIG

4

2

2

+

2.00

7.0-32

13-44

High density-SSR + SNPs

VIG

4

2

2

+

0.4-2.7

36-39

47-51

Ultra-high density-SNPs

VIG

4

1

1

+

0.14

36.7

53.00

Low density-SSR

3DL

4

5

5

2

1.0-6.0

5.0-12

10-23

High density-SSR + SNPs

3DL

4

5

5

3

0.4-3.6

6.0-13

11-20

Ultra-high density-SNPs

3DL

4&6

4

3

1

0.15-13

2.3-9.8

11-19

Low density-SSR

PL

4

3

3

1

5.0-7.0

6.0-6.0

12-13

High density-SSR + SNPs

PL

4

3

3

1

1.3-5.6

6.0-9.0

10-14

Ultra-high density-SNPs

PL

4

1

1

1

0.05

5.6

11

Low density-SSR

SDW

4

5

5

3

3.0-7.0

4.0-10

10-20

High density-SSR + SNPs

SDW

4

6*

6

3

0.9-2.8

5.0-11

11-18

Ultra-high density-SNPs

SDW

4

1

1

1

0.15

9.3

18

Low density-SSR

LAI

4

2

2

2

4.0-7.0

5.0-7.0

10-16

High density-SSR + SNPs

LAI

4

1

1

0.8

6.0

10

Ultra-high density-SNPs

LAI

4

1

1

1

0.15

5.7

11

Low density-SSR

PH

4

6

6

2

2.0-8.0

6.0-23

10-32

High density-SSR + SNPs

PH

4

6

6

2

0.8-2.9

8.0-29

14-37

Ultra-high density-SNPs

PH

2,4&7

7*

5

3

3.4-0.14-0.10

4.9-21.7

10-39

Low density-SSR

PHG

4

5

5

2

3.0-4.0

5.0-13

11-25

High density-SSR + SNPs

PHG

4

9*

9

3

1.1-4.6

7.0-17

 

Ultra-high density-SNPs

PHG

4&7

4

3

1

0.14-0.07

4.8-13.6

10-23

Low density-SSR

eT

4

1

1

1

8.0

5.0

11

High density-SSR + SNPs

eT

4

1

1

1

0.21

4.0

12

Ultra-high density-SNPs

eT

Low density-SSR

eTR

4

2

1

7.0-10

6.0-8.0

10-11

High density-SSR + SNPs

eTR

3&4

4*

3

2

2.0-5.0

3.0-6.0

11-14

Ultra-high density-SNPs

eTR

4

1

1

0.48

5.7

11

Low density-SSR

T

4&8

2

1

1

6.0-8.0

5.0

12-14

High density-SSR + SNPs

T

5&8

2

2.9-3.5

3.0-6.0

10-14

Ultra-high density-SNPs

T

Low density-SSR

TR

7

3

1

5.0-13

3.0-5.0

10-17

High density-SSR + SNPs

TR

Ultra-high density-SNPs

TR

3

1

0.08

5.1

10

Low density-SSR

R-3D/PLA

4, 6 &7

10

5

1.0-15.0

6.0-13

10-15

High density-SSR + SNPs

R-3D/PLA

1,4, 6&7

13*

1

4

0.3-4.2

7.0-14

10-16

Ultra-high density-SNPs

R-3D/PLA

 

Figure 5
Fig. 5

Comparison of M-QTL size for plant vigour related traits using different density markers. Evaluation of M-QTL size performed by using different density markers [A) 241-SSR-Low density marker (Varshney et al. 2014), 1007-SSR + SNPs-high density marker (Jaganathan et al. 2015) and C) 1557-SNPs-Ultra high density (Kale et al. 2015)] on derived mapping population ICC 4958 x ICC 1882. Figure 5-I represent plant vigour QTL peak; 5-II represent 3D-leaf area peak; 5-III represent plant height QTL peak and 5-IV represent shoot dry weight QTL peak

For canopy conductance traits (TR, eTR, T, eT and R-3D/PLA), several QTLs were identified on different linkage groups (CaLG01, LG03, LG04, LG05, LG06, LG07 & LG08) across the genome. A total of 18 and 20 M-QTLs were mapped on different LGs using low and high density maps, respectively (see more details in Additional files 10 and 11). The QTL size ranged from 1 cM to 15.0 cM size in low density map and high density ranged from 0.3 cM to 5.0 cM size (Table 3). Two M-QTLs were mapped on CaLG03 (TR) and just outside the CaLG04 “QTL-hotspot” region (eTR) using ultra-high density map. The QTL size ranged from 0.08 cM (TR) to 0.48 cM (eTR) size (Table 3). For TR, three M-QTLs with 5-13 cM were identified on CaLG07 using low density marker (Table 3). In the high density marker, no M-QTL was detected for TR. But, six minor QTLs were identified on CaLG03 (2QTLs; 4.9-5.1 cM), CaLG07 (1QTL; 2.0 cM), CaLG06 (1QTL; 10.3 cM) and CaLG04 (2.3-11.9 cM; Additional file 11). On the ultra-high density map, one M-QTL for TR was mapped on CaLG03 (0.08 cM). For TR, mapping position varied between low and ultra-high density markers. This might be most of the similar alleles between CaLG03 and CaLG07 (Table 3). Similarly for R-3D/PLA (canopy structure), 10 M-QTLs were identified on different linkage groups [CaLG04 (7 M-QTLs; 1.0-8.0 cM), CaLG06 (2 M-QTLs; 11-15 cM) & CaLG07 (1 M-QTL; 2.0 cM)] using low density markers (Table 3 & see more details in Additional file 10). For high density markers, 13 M-QTLs were identified on different linkage groups [CaLG04 (7 M-QTLs; 0.3-1.3 cM), CaLG06 (2 M-QTLs; 2.6-4.2 cM), CaLG07 (2 M-QTLs; 2.4-2.6 cM) and CaLG01 (2 M-QTLs; 4.1-4.2 cM)] (Table 3 & see more details in Additional file 11). There was no QTL was detected with the ultra-high density markers map (Table 3). More details of canopy conductance traits major and minor QTLs in different density genetic maps are presented in Additional files 7, 10 and 11. In addition, low density genetic map along with canopy conductance traits QTLs position is shown in Additional file 12B, C & D.

Discussion

The summary of the main results is as follows: i) Genetic variation of 16 phenotypic traits revealed two clusters of plant vigour and canopy conductance traits and their association was clarified with PCA analysis and correlation. ii) Using the ultra-high density map, M-QTLs for plant vigour traits predominantly mapped on CaLG04 and these co-mapped with a previously refined “QTL-hotspot” region (~300Kb) for drought tolerance traits. Canopy conductance traits were mapped in CaLG03 (TR) and CaLG04 (eTR). iii) The refined “QTL-hotspot” region (Bin-Map) was further sub-divided into a “QTL-hotspot- a and “QTL-hotspot- b regions. While both “QTL-hotspot” sub-regions co-mapped with previous study [21], the phenotyping data at a lower level of plant organization gathered here led us to interpret that region ‘a’ (139.22Kb or 0.23 cM) could be a locus for branching and tissue/organ expansive processes while region ‘b’ (153.36Kb or 0.22 cM) could be interpreted as a locus for physiological processes related to biomass accumulation. iv) As marker density increased QTL number and size decreased (~ 29 cM to 0.22 cM); and LOD and PVE (%) increased for most of the QTLs. v) Most of plant vigour traits had alleles from high vigour parent ICC 4958 whereas in the case of canopy conductance traits (eTR and TR) the favourable alleles were contributed by the low vigour parent ICC 1882. vi) Plant vigour traits mapped mostly on CaLG04 whereas canopy conductance traits mapped on CaLG03, providing an opportunity to manipulate these loci to tailor recombinants having lower transpiration rate and high plant vigour desirable for water limited environments.

Phenotyping at different level of plant organization

The vigour traits (3DL, PL, SDW, PH, and 3D-LG) were tightly linked to plant water use traits. These traits were reported to be linked to crop biomass production and then crop yield [25, 26]. The co-localization study clearly demonstrated the close relationship between traits from the present study at a lower level of plant organization (eg. 3D-Leaf area, growth rate) and the agronomic traits (eg.shoot biomass, harvest index) studied previously by Varshney et al. [18]. Canopy development traits had also a clear effect on crop production [25]. Although phenotyping of traits at a lower level of plant organization is usually laborious and time-consuming process, it was facilitated by the use of a high throughput phenotyping platform (LeasyScan). Most of the plant vigour traits had high heritabilities, making them suitable for breeding applications. The high vigour parent ICC 4958 had higher biomass and water use (absolute T) than the low vigour parent ICC 1882. By contrast, the high vigour parent had lower transpiration rate (TR; g of water transpired per unit of leaf area) than low vigour parent. Hence, the cause for such response in water-use was the difference in leaf area (vigour/canopy development). The effect of such combination, having high vigour and lower TR would be then of high value to test across time and geographical scale using crop simulation analysis. Crop simulation modelling of water saving traits (eg. limited transpiration rate) has indeed shown a clear yield advantage under terminal drought stress conditions (Soybean-[27], Maize-[28] and Sorghum-[15]).

Co-localization of plant vigour traits and previously identified drought tolerance traits in different genetic maps

Early plant vigour is an important trait for water limited environments. It may contribute to shading of the soil surface, thereby reducing evaporation of water from the soil and leaving more water available for the crop [25, 29, 30]. In the present study, most of the plant vigour traits had several M-QTLs on CaLG04 and co-mapped with the earlier reported fine mapped “QTL hotspot” region [18, 20, 21] with QTLs for root traits. The alleles for these vigour traits were contributed by high vigour parent ICC 4958. Here is a first detailed study reporting the co-mapping of plant vigour traits with root and so-called drought tolerance traits. This is also a confirmation of the earlier observations that shoot dry weight and root length density QTL co-mapping in preliminary results [22]. This result, therefore, suggests that the drought tolerance reported earlier to be associated with that QTL in the hotspot region (241 Low density SSR marker-[18]; 1007-High density GBS markers-[20] and Ultra-high density Bin maps-[21]) would actually be conferred by plant vigour aspects. Such result was also predicted by a crop simulation study [31] that concluded that in the short duration environments where chickpea cultivation is now mostly cultivated, a high plant vigour associated with faster rooting would be necessary to reach the water available deep in the soil profile. Similarly, in recent pearl millet mapping studies [32, 33] it was reported that plant vigour traits also co-localized with agronomic traits related to terminal drought tolerance [34]; drought index of stover yield, grain yield, biomass yield and harvest index [3537]. Similarly, another study in a high- resolution cross (HRC) population of pearl millet showed that plant vigour traits (3D-leaf area, plant growth rate, plant height) measured from LeasyScan co-localized with yield traits measured in the field under different water stress treatments (Tharanya et al-unpublished data. The present study suggests that high root length density obtained earlier [18] could be more easily proxied by vigour traits at the canopy level, which would then ease the phenotyping of that particular trait. Overall, plant vigour traits might lead to high biomass, which would then link to higher yield potential. Therefore, the genotypes that have alleles from ICC 4958 would be beneficial for water limited conditions.

Binmap QTL hotspot region

With the ultra-high density marker, the refined QTL-hotspot was sub-divided into two sub-regions “QTL-hotspot”-“a” & “QTL-hotspot”-“b”. Our interpretation, on the basis of the phenotyping at a lower level of plant organization done in the present study, is that these two regions could control two domains of physiological processes. “QTL-hotspot”-“a” region, which had QTL for traits related to vigour and growth rate (PH and VIG), could be interpreted as a region coding for branching and expansive processes. We interpret the possible effect on the branching from the two fairly opposite phenotypes of the parents of the population used here, i.e. highly branched ICC 1882 with low height versus less branched but taller ICC 4958. More work would be needed to decipher in more details the possible interaction between height and branching. The interpretation of the expansive processes comes from recent genetic work on regions controlling leaf expansion in maize [38], and where vigour could simply be consequences of differences in the expansive processes leading to larger organ sizes and quicker development. Interestingly, this region ‘a’ was earlier reported to harbour QTL for pod number per plant, 100-seed weight and plant height [21], although a finer analysis of the plant processes possibly involved was not done. It was particularly interesting to see that this region led to seed size differences, which then raises the question whether seed size is not itself controlled by expansive processes at the time of embryo development and seed formation. “QTL-hotspot”-“b” region could then simply be a locus controlling physiological processes involved in biomass accumulation, which was corroborated by the QTLs found here for 3DL, LAI and SDW, or for biomass traits SDW and RTR traits in Kale et al. [21].

Twelve candidate genes were reported from this fine mapped “QTL hotspot” region (see, [21]) stating that most of the genes were involved in abiotic stress tolerance. The same genes were also reported to be associated with plant growth and development related functions (e.g. genes of serine threonine-protein kinases, E3 ubiquitin ligases, Leucine-rich repeat extension (LRXs), Protein IQ domain and Vicilin 47 K and Cotyledon vascular pattern (CVP2) genes that were reported to be associated with drought stress adaptation by Kale et al., [21] were also reported to be associated with plant growth and development related process [3948]. These reports additionally suggest that earlier reported “QTL-hotspot” region to be associated more likely with vigour related traits.

Ideotyping of plant vigour and canopy conductance genomic regions

An ideal ideotype for water limited environment would be the one having higher plant vigour (the proxy for higher biomass and yield) potential with restriction of transpiration under high VPD conditions. These combinations would achieve higher water use efficiency, eventually soil moisture conservation, and then ultimately lead to crop production success. The plant vigour traits were mapped on CaLG04 and the canopy conductance (eg. TR) traits were present on CaLG03. These two genomic regions contributed more than 75% QTLs for plant water use (vigour and conductance) traits. Therefore, CaLG04 (plant vigour) and CaLG03 (canopy conductance) provide an opportunity to manipulate these loci to tailor recombinants having alleles with lower transpiration rate along with high plant vigour. This ideotype might be useful in enhancing the water stress adaptation in chickpea. Similar kind of ideotyping was recommended in pearl millet [32, 33]. Recent modelling reports on sorghum [15] showed that alteration of leaf area (plant vigour components) and transpiration rate increased grain yield under severe stress conditions. This study suggests that plant vigour and transpiration rate trait assessed in the current study might also have an effect on crop production success in specific target environments.

Conclusion

The present study has shown that a previously identified “QTL hotspot” region on LG04 of chickpea and harbouring QTL for root traits and so-called terminal drought tolerance in chickpea was a vigor locus, with favourable alleles from high vigour parent ICC 4958. Our phenotypic analysis at a lower level of plant organization led us to interpret that this locus may be divided into two sub-regions, one coding for expansive processes and one for biomass accumulation. Another genomic region on CaLG03 harboured QTL for canopy conductance traits (e.g. TR). Plant vigour and canopy conductance traits were somewhat negatively related but being mapped on different chromosome provides an opportunity to manipulate these loci to tailor recombinants having lower transpiration rate and high plant vigour which would be useful enhancing the drought adaptation in chickpea. In addition, potential genomic region on CaLG04 with simple vigour traits (e.g vigour score) could be used for breeding programs through marker assisted backcross (MAB) to devolep improved variety. Enrichment of the marker density reduced QTL size and increased in LOD and PVE% for all plant vigour and canopy conductance traits.

Methods

Plant material

The genetic material was a set of 232 recombinant inbred lines from a population derived by single seed descent method from the cross between ICC 4958 and ICC 1882 and advanced to F10+ generation [18]. Genotype ICC 4958 is a drought tolerant breeding line developed by Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, and Madhya Pradesh, India. It has a large root system, early vigour is early to reach 50% of flowering (608 cumulative degrees) and maturity (1650 cumulative degrees). The ICC 1882 landrace was collected in India and added to the ICRISAT’s genebank in 1973. It has a small root system, late vigour, is later to reach 50% of flowering (779 cumulative degrees) and maturity (1806 cumulative degrees) compared to ICC 4958 [8, 18, 49]. These two parental lines were contrasting for root traits and plant vigour i.e. were used for mapping population development. Additional detail account on parental lines and mapping population are provided in Varshney et al. [18].

Crop Phenotyping

Plant growth conditions

Phenotyping was performed from November to December 2014 & 2015 in the LeasyScan facility [23]. Plants were sown during the post-rainy chickpea sowing window (November). Plants were grown in 27 cm diameter plastic pots filled initially with 9 kg of dry black soil (Vertisol) collected from ICRISAT farm. Each experimental unit in the LeasyScan platform was composed of 2 pots, each containing 4 healthy plants. These experimental units being of 65 × 40 cm, i.e. approximately 0.25 m2, the sowing density was 32 plant m− 2, which is equivalent to the sowing density in the field. In other words, phenotyping was done on a crop canopy that had close similarities with a field situation. Sowing was done with 6-8 seeds per pot and seedlings were thinned to maintain four homogeneous seedlings per pot at 12 days after sowing (DAS). Fertilizers were provided with single super phosphate (SSP) as basal dose at the rate of 0.3 g/kg of soil. The experimental design was an Alpha lattice with 4 replications and 24 blocks of 10 genotypes in each replication to avoid geographical variations. Plants were maintained under well water conditions the throughout experiment. During the crop grown period, 11/35.8 °C minimum and maximum temperature and 17.2/93.2% relative humidity were observed.

Phenotypic traits evaluated

Sixteen phenotypic traits were measured and categorized into three groups: (i) Canopy traits (measured by LeasyScan) (ii) Transpiration traits (measured by gravimetric balance system) and (iii) Biomass traits.

i) Canopy traits

LeasyScan PlantEye® scanners measured canopy development related traits [3DLeaf area (3D-L), projected leaf area (PL) and plant height (PH)] on the hourly basis during crop growth periods. Using these traits, plant growth rate related traits [3D-Leaf area growth rate (3D-LG), projected leaf area growth rate (PLG), plant height growth rate (PHG)] were calculated. Plant growth rate (3DLG, PLG, PHG) was calculated based on the average difference in respective leaf area and plant height between consecutive days during the exponential growth phase. The leaf area index (LAI) was estimated as the projected leaf area PL divided by the area of the pots in the sector. Plant vigour score was estimated by visual eye basis, on a scale from 1 (low vigour) to 5 (high vigour) at 20 DAS after sowing, all four replications being scored by one person eye visual score. Similar protocal was reported in other crop species such as wheat [50] and maize [51]. Residual (canopy structure) was calculated by using 3D-leaf area and projected leaf area.

ii) Transpiration traits

Transpiration (evapotranspiration (eT)) was measured by a gravimetric method (see [32]). The pots were watered abundantly and drained overnight to attain field capacity. An extra 20 pots without plants were also brought to field capacity and were there to evaluate soil evaporation. Following day, plants were manually weighed (Model FCB 24 K0.2B, KERN & Sohn GmbH, Baligen, Germany.). All four replications were weighed between 6 and 7 am (Initial weight; average VPD~ 0.8 kPa). Pots were weighed again late afternoon between 3 and 4 pm (final weight; average VPD ~ 3.76 kPa), following the same sequence of pot weighing as in the morning. Evapotranspiration was calculated by the difference between initial and final pot weight. Further, plant transpiration (T) was estimated by subtracting an estimate of soil evaporation (pot without plant soil evaporation). Briefly, it was assumed that soil evaporation in planted pot would be maximum with zero plant cover, and would be zero at a leaf area index of 2.

Therefore, the projected leaf area was used to infer a LAI. Briefly,

LAI = PL/area of the pots in the sector.

At the time of eT measurements and transpiration values were estimated from this correction. While this may have induced some error, we made the assumption the method would be correct for genotypic comparison and QTL analysis. Transpiration rate (TR) and evapotranspiration rate (eTR) were calculated by transpiration and evapotranspiration divided by 3D-leaf area and time [52].

iii) Biomass traits

At the end of the experiment (canopy covered maximum in the pot; 35 DAS), shoot samples were harvested and over dried at 65 °C for 48 h. Further, shoot dry weights (SDW) were weighed using gravimetric balance (KERN 3Kg) method. Specific leaf area (SLA) was estimated by leaf area divided by shoot dry weight. Specific leaf weight (SLW) was estimated by 1/SLA (inverse of SLA).

QTL analysis- single locus

QTL analysis was conducted independently using three genetic maps developed earlier [18, 20, 21] and phenotyping data generated in this study. QTL Cartographer version 2.5, composite interval mapping (CIM) method was employed [53]. For ultra-high density bin markers, inclusive composite interval mapping-Additive mapping (ICIM-ADD) method was used for identification of QTLs using IciMapping software (v3.2; [54]). LOD threshold was set by using 1000 permutation and p value ≤0.05. Constructed linkage map was visualized using Mapchart 2.2 [55] software. When the PVE (phenotypic variation explained) was above 10%, QTLs were considered major QTLs (M-QTLs) and PVE below 10% were minor QTLs.

Interactions QTL analysis-multi-loci

The QTL interactions influencing the traits were identified using Genotype Matrix Mapping software (GMM; v. 2.1; [56], http://www.kazusa.or.jp/GMM). Using GMM, two and three loci interactions were tested. GMM analysis showed interactions between loci and different linkage groups of plant vigour and canopy conductance related traits. The current study identified allelic interactions that contributed to either a positive (increase) or negative (decrease) effect on the phenotypic value of the trait. In most cases, single locus QTL identified using GMM analysis were similar to those identified with CIM analysis, even though two approaches use different algorithms. In the following text, symbols “AA”, “BB” and “stand for alleles originated from the high vigour parent (AA; ICC 4958) and low vigour parent (BB; ICC 1882)” and not distinguished from any parent (−), respectively.

Statistical analysis

To find the phenotypic variations and their significance in the population, ANOVA was performed for all observed parameters individually using GENSTAT 14.0 (VSN International Ltd., Hemel Hempstead, UK). Similarly, to find the phenotypic variations and their significance in parental lines were analyzed with statistical program package CoStat version 6.204 (Cohort Software, Monterey, CA, USA). One-way ANOVA was carried out to test for genotypic difference between the genotypes. Means were compared using Tukey-Kramer test and Least Significant Difference (at P ≤ 0.05). Normal histograms with frequency distribution analysis for phenotypic traits were done using SPSS 16 desktop version (IBM, SPSS Statistical software). Principal component analysis (PCA) was used to visualize the relationships between traits in a multidimensional space using R software (version 2.11.1). To find the trait correlation of all phenotypic traits, simple Pearson correlation was performed using R software (version 2.11.1). For QTL and PCA analysis, Best Linear Unbiased Predictors (BLUPs) data were estimated by using GENSTAT 14.0 were used. The clustering analysis was performed by PCA loadings using R software (version 2.11.1). Genotypic and residuals mean square components were obtained from ANOVA through GENSTAT 14.0, which was used to calculate the broad sense heritability (h 2 ). The broad-sense heritability (h 2 ) was calculated as h2 = σ 2 G/ (σ 2 G + σ 2 E) [31, 32], where σ 2 G is the genetic variance and σ 2 E is the error variance.

Abbreviations

ANOVA: 

Analysis of variance

BLUPs: 

Best linear unbiased predictions

CIM: 

Composite interval mapping

DAS: 

Days after sowing

E-QTLs: 

Epistatic QTLs

GBS: 

Genotype by sequencing

GMM: 

Genotype matrix mapping

h2

Heritability

ICIM-ADD: 

Inclusive composite interval mapping-Additive mapping

LG: 

Linkage group

LOD: 

Logarithm of odds

LSD: 

Least significant difference

M-QTLs: 

Major QTLs

PCA: 

Principal component analysis

PVE: 

Phenotypic variation explained

QTL: 

Quantitative trait loci

RILs: 

Recombinant inbred lines

SNPs: 

Single nucleotide polymorphisms

SSRs: 

Simple sequence repeats

Declarations

Acknowledgements

The authors acknowledge Dr. Grégoire Hummel and Dr. Uladzimir Zhokavets from Phenospex for co-designing the LeasyScan platform with Dr. Vincent Vadez. Thanks, Mr. P.V.D Maheswar Rao and Mr. M. Anjaiah for their help in planting, irrigation and crop management practices.

Funding

This research was financially supported by ICRISAT for the capital investment in the LeasyScan facility and Blue Sky Research Project on “Developing crops with high productivity at high temperatures”. Additional funds were provided by CGIAR Research Program (CRP) on Grain Legumes.

Availability of data and materials

All data generated or analyzed during this study are included in the supplementary files as well as in the manuscript.

Authors’ contributions

VV and JK - conceived and designed the experiments; KS, MT1 (Murugesan Tharanya), MHH, RB - Performed the experiments; KS, MT1, JK and VV - Analyzed the phenotypic data; RKV, MT2 (Mahendar Thudi), KS, SMK, DJ - Genotypic data sharing and analysis; PMG - Mapping population development and seed materials; KS, VV, JK, MT1, MT2 -wrote the paper; VV, JK, MT2, RKV and TT - Manuscript review; All authors read the MS and provided their consent; All authors read and approved the final manuscript.

Authors’ information

Vincent Vadez is working as Principal Scientist and Theme Leader – System Analysis for Climate Smart Agriculture (for more details see www.gems.icrisat.org) at ICRISAT in India, and now a Principal Scientist at IRD (Institut de Recherche pour le Developpement), Montpellier, France. He focuses mainly on phenotyping for drought adaptive traits in different legume and cereal crops. In addition, he has expertise in crop modelling of various drought adaptive traits and their possible production benefits in SAT agro-ecologies. He has collaborators across disciples from various international institutes.

Kaliamoorthy Sivasakthi is a Research Scholar pursuing his PhD in Crop Physiology Laboratory at ICRISAT in India. His Ph.D. work mainly focuses on Contribution of water saving traits for drought adaptation in chickpea (Cicer arietinum L.) through Physiological, Molecular and Genetics approaches.

Mahendar Thudi is currently working as Senior Scientist (Chickpea Genomics) at ICRISAT in India and leading the genomics and molecular breeding activities of chickpea. He developed large genetic and genomic resources in chickpea. Played a significant role in the generation of genome sequence of chickpea and re-sequencing of ~ 500 chickpea genotypes.

Murugesan Tharanya is a Research Scholar pursuing her PhD in Crop Physiology Laboratory at ICRISAT in India. Her Ph.D. work focuses mainly on “Contribution of water saving traits for drought adaptation in Pearl Millet (Pennisetum glaucum (L.) R. Br.) through Physiological, Molecular and Genetics approaches”.

Sandip M Kale is currently working as Visiting Scientist at Center of Excellence in Genomics at ICRISAT in India.

Jana Kholova` is currently working as Senior Scientist in System Analysis for Climate Smart Agriculture (SACSA) team at ICRISAT in India. She is mainly focuses on phenotyping for drought adaptive traits in semi-arid crops (Sorghum, Pearl millet, Chickpea etc.). In addition, she is expertise in APSIM crop modelling for various drought adaptive traits and their possible productions benefits in SAT agro-ecologies.

Mahamat Hissene Halime is a Research Fellow and completed her PhD in Crop Physiology Laboratory at ICRISAT in India.

Deepa Jaganathan is Research scholar and completed her PhD in Center of Excellence in Genomics at ICRISAT in India.

Rekha Baddum is working as Scientific Officer in Crop Physiology Laboratory at ICRISAT in India.

Thiyagarajan Thirunalasundari is working as Professor and Head, Department of Industrial Biotechnology, Bharathidasan University at Tiruchirappalli in India.

Pooran M. Gaur is Principal Scientist (Chickpea Breeding) at ICRISAT in India. He has extensive expertise on chickpea mapping population development and marker assisted breeding.

Rajeev K Varshney is Research Program Director – Genetic Gains at ICRISAT in India. Heis internationally recognized for his contribution in genome sequencing of pigeonpea, chickpea, peanut, pearl millet, sesame, mungbean and azuki bean and development of molecular breeding products.

Ethics approval and consent to participate

Not applicable – Data were generated from our own trials. The genetic material that was tested was readily available at ICRISAT and did not required us any kind of permit or request.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Greater Hyderabad, Telangana, India
(2)
Bharathidasan University, Tiruchirappalli, India
(3)
Institut de Recherche pour le Developpement (IRD), Université de Montpellier – UMR DIADE, Montpellier cedex 5, France

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© The Author(s). 2018

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