2021 Volume 71 Issue 2 Pages 229-239
Ascochyta blight (AB) and botrytis grey mould (BGM) are the most devastating fungal diseases of chickpea worldwide. The wild relative of chickpea, C. reticulatum acc. ILWC 292 was found resistant to BGM whereas, GPF2 (Cicer arietinum L.) is resistant to AB. A total of 187 F8 Recombinant Inbred Lines (RILs) developed from an inter-specific cross of GPF2 × C. reticulatum acc. ILWC 292 were used to identify quantitative trait loci (QTLs) responsible for resistance to AB and BGM. RILs along with parents were evaluated under artificial epiphytotic field/laboratory conditions for two years. Highly significant differences (P < 0.001) were observed for reaction to both pathogens in both years. Parents and RILs were genotyped-by-sequencing to identify genome wide single nucleotide polymorphism (SNPs). A total of 1365 filtered and parental polymorphic SNPs were used for linkage map construction, of which, 673 SNPs were arranged on eight linkage groups. Composite interval mapping revealed three QTLs for AB and four QTLs for BGM resistance. Out of which, two QTLs for AB and three QTLs for BGM were consistent in both years. These QTLs can be targeted for further fine mapping for deployment of resistance to AB and BGM in elite chickpea cultivars using marker-assisted-selection.
Chickpea (Cicer arietinum L.) or Garbanzo beans is a self-pollinated diploid (2n = 2x = 16) crop with genome size of 738 Mb (Varshney et al. 2013). It is the second most consumed grain legume after dry bean grown worldwide and nutrient rich pulse crop that contains 17–31% protein, significant amount of essential amino acids, vitamins and minerals. Chickpea production and productivity is adversely affected by various biotic and abiotic stresses (Thudi et al. 2014). Among the biotic constraints, ascochyta blight (AB; caused by Ascochyta rabiei (Pass.) Lab.) and botrytis grey mould (BGM; caused by Botrytis cinerea Pers. ex. Fr.) are the most devastating fungal diseases of chickpea worldwide. AB can infect chickpea plants at any growth stage from plant emergence to seed maturity; however, the crop is more prone to disease at flowering and podding stages which results in substantial yield loss and poor seed quality (Sharma et al. 2010). All the aerial parts of chickpea are susceptible to the BGM with growing tips and flowers being the most vulnerable (Bakr and Ahmed 1992). For effective control of both the diseases, fungicide applications are used, which resulted in insensitivity of pathogen isolates against several fungicides, besides causing environmental pollution (Chang et al. 2007, Wise et al. 2009). Therefore, development of chickpea cultivars resistant to AB and BGM is the most effective and sustainable approach. Resistance to both the pathogens has complex genetic nature and complete resistance against them have not been reported so far. Globally, several germplasm lines with moderate resistance to these pathogens have been identified and successfully used in chickpea breeding (Sharma and Ghosh 2016). Moreover, rapid evolution of pathogen and breakdown of resistance are major challenges.
In case of AB, the exact genetic and molecular mechanism of partial resistance against A. rabiei infection is still unknown. Depending on the isolates of the pathogen and the method of disease scoring, both qualitative and quantitative modes of inheritance for resistance against AB have been reported in chickpea. Initially, it was characterized as monogenic with additional modifier genes. Single dominant or recessive gene were found to impart AB resistance in both desi and kabuli types (Dey and Singh 1993, Tewari and Pandey 1986). However, recent studies using RILs demonstrated continuous distribution of disease response and suggested a polygenic inheritance (Deokar et al. 2019, Garg et al. 2018). For BGM, a few reports on genetics of resistance suggested that the resistance is controlled by few genes such as single dominant gene ‘Bor1’ for resistance (Tiwari et al. 1985); two genes with epistasis interaction (13:3 ratio) (Rewal and Grewal 1989); and two duplicate dominant genes with epistasis interaction (15:1 ratio) (Chaturvedi et al. 1995). However, complexity of disease and non-availability of high level of resistance suggests the polygenic inheritance of disease.
Several QTLs for resistance to AB contributing 12–50% of the total phenotypic variation have been detected in different mapping populations from inter- and intra-specific crosses (Cho et al. 2004, Collard et al. 2003, Flandez-Galvez et al. 2003, Sabbavarapu et al. 2013, Stephens et al. 2014, Tar’an et al. 2007, Udupa and Baum 2003). SSR markers linked to these QTLs have been used for marker-assisted backcrossing to introgress the AB resistance into adapted chickpea cultivars (Madrid et al. 2012, Tar’an et al. 2013). In case of BGM, very few reports are available for association of markers with QTL on different linkage groups (Anbessa et al. 2009, Anuradha et al. 2011, Kaur et al. 2013).
During last decade, chickpea research community has decoded the chickpea genome (Jain et al. 2013, Varshney et al. 2013) and developed several genomic (Agarwal et al. 2015, Nayak et al. 2010, Thudi et al. 2016) and transcriptomic resources (Hiremath et al. 2011, Kudapa et al. 2014) that has transformed chickpea from “orphan legume crop” to “genomics resource rich legume crop” (Varshney 2016). Besides, the past decade has witnessed the development of several high-throughput genotyping technologies which can mine thousands of SNPs across the genome. Double Digestion Restriction-site-Associated DNA sequencing (ddRAD-seq) technique is one such approach for high-throughput genotyping (Peterson et al. 2012), that uses two different restriction enzymes and size selection for recovering the appropriate number of regions arbitrarily distributed throughout the genome and maximizing the ability of multiplexing numerous samples in a single experiment.
Chickpea is known to have narrow genetic base as compared to the most other legumes (Kushwah et al. 2020a, Stephens et al. 2014). Due to relatively low levels of polymorphism in cultivated chickpeas, inter-specific crosses between C. arietinum and C. reticulatum have been the primary focus for genetic studies (Singh et al. 2008). The amount of polymorphism in an inter-specific mapping population varies from 16% to 36% and 9.5% in intra-specific mapping population (Nayak et al. 2010). High-resolution genetic linkage maps can also be constructed by exploiting the inter-specific polymorphisms between C. arietinum and C. reticulatum (Thudi et al. 2011). Thus, an inter-specific mapping population from a cross between C. arietinum and C. reticulatum has been used in the present study to identify the key genomic regions providing resistance against AB and BGM using ddRAD-seq based genotyping and phenotyping under artificial epiphytotic conditions.
A total of 187 interspecific RILs in F8 generation were developed from a cross of GPF2 × C. reticulatum acc. ILWC 292 using single seed descent method by Punjab Agricultural University, Ludhiana, Punjab, India in year 2017. The wild accession C. reticulatum acc. ILWC 292 is highly susceptible to AB but resistant to BGM, while the cultivated parent GPF 2 is resistant to AB (Basandrai et al. 2009, Islam et al. 2017) but highly susceptible to BGM. Randomly five plants were taken to record the observations on number of pods per plant, yield and biomass in each plot. The data of 100-seed weight were recorded on plot basis. HI was calculated as:
HI = (seed yield/total shoot biomass) × 100
Field screening technique for ABField screening technique (Gurha et al. 2003) was used to screen the RILs and parents against AB at Ludhiana. High relative humidity (85%) was maintained at an ambient temperature of 25°C for a period of 48 h of incubation with the help of perfo-sprayer system for creating the epiphytotic conditions. The RILs along with parents were planted during two consecutive years 2017–18 and 2018–19 in alpha lattice design (17 × 12) with three replications. Each RIL was planted in paired row of 2 m length at 30 cm × 10 cm spacing. Two highly susceptible checks, L 550 and C 214, were planted as indicator-cum-infector rows alternatively after every 8 test entries to spread and monitor the disease epidemic. At the onset of flowering, plants were inoculated with a spore suspension of A. rabiei isolate 8 of race 6 (3968) (4 × 104 spores ml−1) in February (Singh 1990). The physiological race of this isolate along with nine other races of different isolates of pathogen were collected from North India and identified on the basis of pathogenicity on a set of 12 chickpea cultivars/lines which were used as differentials by Singh (1990). The disease symptoms started appearing 10 days after inoculation and observations were recorded 21 days after inoculation (Gurha et al. 2003).
Cut twig screening technique for BGMFor screening against BGM, cut twig method was used (Singh 1997). In this method, the tender shoots of the chickpea plants were cut and put in a tray containing water, immediately wrapped in wet cotton plug and placed into a test tube (15 × 100 mm) containing fresh tap water. Three twigs were tested from each RIL along with parents in replications for two consecutive years, i.e., 2017–18 and 2018–19. Two highly susceptible checks, JG 62 and H 208, for botrytis grey mould were used to spread and monitor the disease progress. Twigs were inoculated by spraying spore suspension of B. cinerea (104 spores ml–1) and covered with moist polythene covers. These twigs were kept in moist chambers (polyethylene bags supported by iron cage) for 144 h (6 days) with 8 h dark and 16 h light periods provided through a fluorescent lamp (24ʺ × 1.5ʺ, W 20, 32 lm/W.). Disease incidence were recorded (Gurha et al. 2003) after 6 days of inoculation.
Disease scoring for AB and BGMThe data for AB and BGM were recorded on 1–9 rating scale (Gurha et al. 2003) which was as follows: 1.0 = no infection; 1.1–2.0 = minute water-soaked lesions on leaves and stems; 2.1–3.0 = minute water-soaked lesions seen after careful examination; 3.1–4.0 = few small and few large lesions (>5 mm2); 4.1–5.0 = many small and large lesions; 5.1–6.0 = many small and large lesions, lesions coalescing (50–75% plant area infected); 6.1–7.0 = many small and large lesions, lesions coalescing, stem girdled (75–90% plant area infected); 7.1–8.0 = many small and large lesions, lesions coalescing, girdling stem breakage (>90% plant area infected); and 8.1–9.0 = 100% plants dead. The lines with disease score 3–5 were considered as resistant, 5–7 as moderately susceptible and 7–9 as highly susceptible.
Statistical analysis of phenotyping dataAnalysis of variance was calculated to estimate the contribution made by each factor to the total variation using SAS-software version 9.3 (SAS Institute 2002, Cary NC). The contrast analysis used in SAS is based on t-test for comparison of means of two entries.
SNP genotyping by ddRAD-seqGenomic DNA was extracted from parents and RILs using high throughput mini-DNA extraction method described in Mace et al. (2003). The RIL population and the parents were genotyped with ddRAD-seq (Peterson et al. 2012) using restriction enzymes PstI and MspI (Thermo Fisher Scientific, MA, United States). The ddRAD-seq of RILs was outsourced from SciGenom, India and data was received in the form of paired-end filtered and processed reads. The processed reads were aligned to the chickpea reference genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_000331145.1) using BWA (Li and Durbin 2009) program with default parameters. The SNPs were called using freebayes (https://github.com/freebayes/freebayes) which is a bayesian variant detector that finds variants by haplotype based alignment. High-confidence biallelic SNP candidates were selected using VCFtools (Danecek et al. 2011) with the following criteria: (i) depth of coverage ≥5 for each data point, (ii) SNP quality score of ≥30 for each locus, and (iii) proportion of missing data of <20% for each locus.
Linkage map construction and QTL analysisLinkage map was constructed using the OneMap (version 2.0.4) package in R (https://www.r-project.org/) via the “group” command with a minimum LOD (logarithm of the odds) of 3 and maximum recombination fraction of 0.45 (Margarido et al. 2007). The map distances were drawn using MapChart 2.2 software (Voorrips 2002). QTL analysis was performed with the composite interval mapping (CIM) executed in the Windows QTL Cartographer V2.5 software package (Wang et al. 2007) using genotypic and phenotypic data. The CIM analysis was performed using forward and backward stepwise regression. For each trait, experiment-wise significance thresholds (p = ≤0.05) were determined with 1000 permutations for QTL detection. The position of the QTLs was identified based on LOD peak location with 95% confidence interval. The percentage of phenotypic variance and additive effect described by QTLs was also estimated. The phenotypic contribution (R2) was estimated as the percentage of variance explained by each QTL in proportion to the total phenotypic variance, while additive effect was estimated to find the positive or negative effect for the target trait.
During the screening against AB, GPF2 found to be resistant, while C. reticulatum acc. ILWC 292 found to be highly susceptible during both the years (Fig. 1). Characteristic symptoms like concentric rings on pods (Fig. 1a) and brown lesions on stem and leaves (Fig. 1b) appeared during AB incidence. In case of BGM screening, C. reticulatum acc. ILWC 292 was found to be resistant, while GPF2 was found highly susceptible during both the years (Fig. 2). The contrast analysis of parents for screening of AB and BGM depicted that there were highly significant differences between parents in both years (Table 1).
Screening of recombinant inbred lines along with parents for ascochyta blight resistance. Symptoms of ascochyta blight (a) on pods and (b) on leaves. (c) and (d) Field screening technique of ascochyta blight showing resistant and susceptible lines and susceptible checks at Ludhiana, Punjab, India.
Screening of recombinant inbred lines along with parents and susceptible checks for botrytis grey mould resistance. Cut twig screening technique of botrytis grey mould resistance (a) for parents and (b) for susceptible checks. (c) and (d) controlled environmental condition for botrytis grey mould screening using cut-twig screening technique.
| Variable | ILWC 292 | GPF 2 | Contrast analysis between parents | Mean (RILs) | St Dev | CV | Genotypic variance |
|---|---|---|---|---|---|---|---|
| AB 2017–18 | 8.33 | 3.67 | 32.67** | 6.32 | 1.32 | 20.81 | 5.06** |
| AB 2018–19 | 9.00 | 3.33 | 48.17** | 6.27 | 1.31 | 20.90 | 5.32** |
| BGM 2017–18 | 3.00 | 8.67 | 48.17** | 6.36 | 1.28 | 20.11 | 1.94** |
| BGM 2018–19 | 4.00 | 8.33 | 28.17** | 6.28 | 1.27 | 20.22 | 1.82** |
** = significant at 1% probability level, St Dev = standard deviation, CV = coefficient of variation.
Significant variation was also observed for both AB and BGM pathogens in the RILs in both years (Fig. 3). Out of the 187 RILs evaluated for AB disease, 24 RILs were resistant having AB score of 3.0–5.0, 95 RILs were moderately susceptible having AB score of 5.1–7.0, while 68 RILs were highly susceptible having AB score of 7.1–9.0 during 2017–18 (Fig. 3a). During 2018–19, 25 RILs were found to be resistant having AB score of 3.0–5.0, 101 RILs were moderately susceptible having AB score of 5.1–7.0, while 61 RILs were highly susceptible having AB score of 7.1–9.0 (Fig. 3b). A total of 23 common RILs were observed to be resistant having AB score of 3.0–5.0 in both years.
Graphical representations of reaction of parents and recombinant inbred lines for ascochyta blight and botrytis grey mould screening for two consecutive seasons (2017–18 and 2018–19).
For BGM, out of the total 187 RILs evaluated, 22 RILs were resistant having BGM score of 3.0–5.0, 104 RILs were moderately susceptible with score of 5.1–7.0, while 61 RILs were highly susceptible with score of 7.1–9.0 during 2017–18 (Fig. 3c). During 2018–19, 22 RILs were found to be resistant having BGM score of 3.0–5.0, 99 RILs were moderately susceptible with score of 5.1–7.0, while 66 RILs were found to be highly susceptible having BGM score of 7.1–9.0 (Fig. 3d).
The ANOVA showed highly significant differences (P < 0.001) in RILs for genotypic variance for reaction to both AB and BGM pathogens in both the years (Table 1). Out of the 187 RILs, six lines showing resistance against AB and eight lines showing resistance against BGM during both the years were also promising for yield and yield related traits like number of pods per plant, biomass, 100-seed weight and harvest index (Table 2). These lines are being evaluated in multi-location trials. One line, RIL 41, having resistance to both AB and BGM during both the years, was also found promising for yield and yield related traits (Table 2).
| RIL No. | AB score 2017–18 | AB score 2018–19 | BGM score 2017–18 | BGM score 2018–19 | Number of pods/plant | Biomass/plant (g) | Yield/plant (g) | 100-seed weight (g) | Harvest index (%) |
|---|---|---|---|---|---|---|---|---|---|
| 41 | 4.67 | 5.00 | 4.67 | 4.00 | 61.11 | 100.03 | 36.80 | 16.10 | 36.62 |
| RILs having high yield as well as resistance against AB for both of the seasons | |||||||||
| 44 | 3.00 | 4.33 | 5.00 | 5.67 | 52.55 | 87.57 | 34.77 | 15.97 | 39.72 |
| 48 | 4.00 | 4.33 | 6.33 | 6.00 | 55.89 | 89.13 | 36.70 | 15.27 | 41.07 |
| 56 | 4.00 | 4.67 | 5.67 | 5.33 | 55.55 | 96.83 | 42.93 | 16.30 | 44.25 |
| 58 | 4.00 | 3.33 | 5.00 | 5.00 | 60.67 | 98.07 | 47.70 | 18.17 | 49.22 |
| 61 | 4.67 | 3.67 | 5.00 | 5.33 | 49.55 | 87.20 | 35.67 | 14.90 | 41.02 |
| 127 | 4.33 | 4.00 | 6.33 | 6.00 | 66.89 | 98.33 | 46.63 | 17.30 | 47.45 |
| RILs having high yield as well as resistance against BGM for both of the seasons | |||||||||
| 1 | 8.33 | 9.00 | 3.00 | 3.33 | 63.11 | 97.30 | 38.73 | 17.53 | 39.86 |
| 2 | 6.33 | 5.00 | 3.33 | 3.67 | 76.44 | 105.80 | 45.17 | 17.40 | 42.76 |
| 7 | 7.67 | 6.33 | 3.33 | 3.00 | 62.45 | 87.03 | 40.10 | 13.90 | 45.79 |
| 12 | 5.33 | 6.67 | 3.33 | 3.00 | 50.33 | 92.43 | 34.70 | 14.93 | 37.53 |
| 14 | 8.67 | 8.33 | 4.67 | 4.33 | 53.33 | 101.60 | 42.37 | 16.43 | 41.72 |
| 42 | 6.33 | 7.33 | 4.67 | 4.00 | 40.22 | 81.07 | 37.30 | 15.70 | 46.17 |
| 46 | 5.33 | 5.00 | 4.67 | 4.67 | 53.22 | 83.27 | 35.03 | 16.37 | 41.97 |
| 51 | 9.00 | 8.33 | 4.00 | 3.67 | 66.44 | 102.03 | 40.10 | 15.23 | 39.37 |
The RILs along with parents (GPF2 and C. reticulatum acc. ILWC 292) were genotyped by sequencing following ddRAD-seq approach. A total of 16.75 million reads for C. reticulatum acc. ILWC 292 and 3.74 million reads for GPF2 were generated. In addition, a total of 550.74 million reads were generated for 187 RILs with an average of 2.94 million reads per line. The number of reads generated varied from 0.42 million reads (RIL171) to 9.78 million reads (RIL119). The reads were aligned to the chickpea reference genome and overall, 83.74% of total reads mapped to the reference genome. SNP calling and filtering identified 8519 high quality SNPs. Further, based on criteria to identify homozygous polymorphic SNPs between parents, a total of 1365 informative SNPs were extracted, which were used in linkage map construction and QTL mapping. These informative SNPs can be located on coding as well as non-coding regions.
Linkage map constructionOut of 1365 informative SNPs, 673 SNPs could be arranged on eight chromosomal linkage groups (Fig. 4). Rest of the SNPs showed linkage in several smaller sized chromosomal linkage groups which were not used for construction of linkage maps. The average linkage map distance constructed from the RIL population was 4569.09 cM with an average of 6.79 cM between the markers (Supplemental Table 1). The maximum inter-marker distance was observed on chromosome 6 with the value of 7.83, while the minimum inter-marker distance was observed on chromosome 8 with the value of 5.80. The maximum number of markers (200) was on chromosome 4, while the minimum number of markers (8) was on chromosome 8. On an average, the highest marker density was observed on chromosome 8 with 0.172 markers per cM, while the lowest marker density was observed on chromosome 6 with 0.128 markers per cM. Overall, the genetic linkage map had a density of 0.15 markers per cM on an average.
Inter-specific genetic linkage map constructed using the RIL mapping population from a cross between GPF2 and C. reticulatum acc. ILWC 292.
Three QTLs were identified for resistance to AB on chromosomes 4 (qab-4.1, qab-4.2) and 7 (qab-7.1) in both the years (Table 3, Fig. 5). The QTLs, qab-4.2 and qab-7.1 were identified in same genomic region in 2017–18 and 2018–19. The QTL qab-4.1 was detected in a 22.3 cM interval on chromosomes 4. The QTL qab-4.2 was mapped 8.1 cM distal to a group of right flanking SNP markers and 4.4 cM proximal to a group of left flanking SNP markers. The qab-7.1 QTL was spanned by 8.3 cM between left and right flanking SNP markers that explained 6.91% and 8.26% of the phenotypic variation in 2017–18 and 2018–19, respectively. QTLs having positive or negative additive effect for a particular trait implied that the increase in the proportion of the phenotypic variation of that particular trait is contributed by the allele from GPF2 or C. reticulatum acc. ILWC 292, respectively. The QTL, qab-4.2 explained 10.69% and 7.35% phenotypic variance in 2017–18 and 2018–19. Similarly, QTL, qab-7.1 explained 6.91% and 7.41% phenotypic variance in 2017–18 and 2018–19.
| Disease/Year | Ch | QTL name | LOD | Additive effect | R2 (%) | TR2 | Position (cM) | Contributing alleles | Left flanking marker position (cM) | Right flanking marker position (cM) | Left flanking marker | Right flanking marker |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AB 2017–18 | 4 | qab-4.1 | 3.53 | 0.58 | 15.47 | 0.378 | 260.9 | GPF2 | 248.9 | 271.2 | CNC_021163.1.32280291 | CNC_021163.1.37933917 |
| 4 | qab-4.2 | 4.33 | –0.55 | 10.69 | 0.312 | 979.0 | C. reticulatum acc ILWC 292 | 970.6 | 983.1 | CNC_021163.1.23799836 | CNC_021163.1.24184658 | |
| 7 | qab-7.1 | 3.57 | 0.49 | 6.91 | 0.282 | 491.5 | GPF2 | 487.8 | 496.1 | CNC_021166.1.34330294 | CNC_021166.1.34330283 | |
| AB 2018–19 | 4 | qab-4.1 | – | – | – | – | – | – | – | – | – | – |
| 4 | qab-4.1 | 3.13 | –0.45 | 7.35 | 0.278 | 979.0 | C. reticulatum acc ILWC 292 | 970.6 | 983.1 | CNC_021163.1.23799836 | CNC_021163.1.24184658 | |
| 7 | qab-7.1 | 3.26 | 0.51 | 8.26 | 0.282 | 491.5 | GPF2 | 487.8 | 496.1 | CNC_021166.1.34330294 | CNC_021166.1.34330283 | |
| BGM 2017–18 | 3 | qbgm-3.1 | 3.88 | –0.48 | 8.53 | 0.355 | 215.2 | C. reticulatum acc ILWC 292 | 201.2 | 216.9 | CNC_021162.1.25569161 | CNC_021162.1.27507201 |
| 4 | qbgm-4.1 | 6.03 | 0.59 | 10.89 | 0.335 | 73.7 | GPF2 | 63.7 | 75.6 | CNC_021163.1.11476712 | CNC_021163.1.7883450 | |
| 4 | qbgm-4.2 | 3.50 | –0.60 | 9.06 | 0.336 | 193.5 | C. reticulatum acc ILWC 292 | 191.5 | 195.9 | CNC_021163.1.38343874 | CNC_021163.1.28025601 | |
| 5 | qbgm-5.1 | 3.16 | 0.51 | 7.24 | 0.407 | 37.8 | GPF2 | 29.8 | 39.3 | CNC_021164.1.23539887 | CNC_021164.1.19923058 | |
| BGM 2018–19 | 4 | qbgm-4.1 | 7.08 | 0.70 | 17.19 | 0.358 | 73.7 | GPF2 | 63.7 | 75.6 | CNC_021163.1.11476712 | CNC_021163.1.7883450 |
| 4 | qbgm-4.3 | 3.71 | –0.59 | 7.75 | 0.317 | 193.5 | C. reticulatum acc ILWC 292 | 191.5 | 195.9 | CNC_021163.1.38343874 | CNC_021163.1.28025601 | |
| 5 | qbgm-5.1 | 3.83 | 0.64 | 11.77 | 0.378 | 37.8 | GPF2 | 29.8 | 39.3 | CNC_021164.1.23539887 | CNC_021164.1.19923058 | |
| 6 | qbgm-6.1 | 3.33 | –0.46 | 6.20 | 0.321 | 0.0 | C. reticulatum acc ILWC 292 | 0.0 | 18.7 | CNC_021165.1.1002514 | CNC_021165.1.8008006 |
Ch.- chromosome number, LOD- logarithm of odds, R2 = proportion of the variance explained by genetic effect, TR2 = proportion of the total variance explained by the model including covariates. Bold characters show QTLs which were common for both of the consecutive years (2017–18 and 2018–19).
Logarithm of odds ratio (LOD) curves obtained by composite interval mapping for quantitative trait loci (QTLs) mapped for ascochyta blight resistance in RIL population (GPF2 × C. reticulatum acc. ILWC 292) for two years, i.e., 2017–18 and 2018–19.
Five QTLs (qbgm-3.1, qbgm-4.1, qbgm-4.2, qbgm-5.1, qbgm-6.1) for resistance to BGM were identified on chromosomes 3, 4, 5 and 6 in both the years. Of which, QTL, qbgm-4.1, qbgm-4.2 and qbgm-5.1, were found to be consistent during both the years (Table 3, Fig. 6). The proportion of phenotypic variation explained by individual QTLs ranged from 7.24% (qbgm-5.1) to 10.89% (qbgm-4.1) during 2017–18 and from 6.20% (qbgm-6.1) to 17.19% (qbgm-4.1) during 2018–19. Out of these QTLs, three consensus QTLs namely, qbgm-4.1, qbgm-4.2 and qbgm-5.1, have been identified in both the years (2017–18 and 2018–19).
Logarithm of odds ratio (LOD) curves obtained by composite interval mapping for quantitative trait loci (QTLs) mapped for botrytis grey mould resistance in RIL population (GPF2 × C. reticulatum acc. ILWC 292) for two years, i.e., 2017–18 and 2018–19.
AB and BGM are the two most devastating fungal diseases of chickpea that cause substantial yield losses and poor seed quality worldwide (Pande et al. 2005, Udupa and Baum 2003). In chickpea, the wild relative can play an important role in bringing genetic diversity for resistance to complex diseases such as BGM (von Wettberg et al. 2018), where the limited resistance is available in cultivated gene pool. The wild relative of chickpea, C. reticulatum acc. ILWC 292 was found to be moderately resistant to BGM at PAU, India; however, it was susceptible to AB. On the other hand, GPF2, a cultivated chickpea, released by PAU is resistant to AB, but susceptible to BGM. The C. reticulatum acc. ILWC 292 can be directly crossed with cultivated chickpea and showed high level of fertility. The RIL population developed from the cross gave us opportunity to identify QTLs for both the important diseases. Additionally, PAU has well-established system of evaluation of AB and BGM under artificial epiphytotic conditions. Significant variation was observed for both AB and BGM screening in the RILs in both the years. The frequency distribution of RILs for AB and BGM screening in this study depicted normal distribution indicating that AB and BGM resistance is governed by polygene which is in accordance of several previous researchers (Anuradha et al. 2011, Stephens et al. 2014, Tar’an et al. 2013). RILs were also evaluated for yield component traits along with AB and BGM. One RIL (RIL 41) having resistance to both AB and BGM, showed comparable yield traits to elite cultivated parent GPF2 and avowed promise for further evaluation.
A large number of polymorphic markers are required for linkage analysis and mapping quantitative traits in chickpea as it shows low levels of genetic polymorphism due to narrow genetic base (Kushwah et al. 2020b, Stephens et al. 2014). GBS overtook the conventional genotyping procedures involving the use of traditional markers such as RAPD, AFLP, SSR and many others in terms of time, labor and cost involved, with additional benefits of more polymorphism. The large length of current genetic map could be due to incorporation of SNP markers that showed segregation distortion in the genetic map. Segregation distortion has been observed in the intra-specific (C. arietinum × C. arietinum) as well as inter-specific (C. arietinum × C. reticulatum) crosses of chickpea by numerous authors (Abbo et al. 2005, Castro et al. 2011, Cobos et al. 2006, Flandez-Galvez et al. 2003, Kazan et al. 1993, Radhika et al. 2007, Tekeoglu et al. 2002, Winter et al. 2000). Various genetic or physiological aspects such as recessive lethal genes, gametic selection, zygotic selection can be the reason of segregation distortion (Castro et al. 2011, Li et al. 2007, Lu et al. 2002, Mano et al. 2005).
Identification of QTLs is an important step for breeding of quantitative traits in plants. Resistance to AB and BGM are quantitative in nature as observed from near normal distribution in RIL population. The disease reaction was evaluated at flowering stage in present study, hence these QTLs can be used for imparting adult plant resistance in chickpea cultivars. Previous studies have identified several QTLs for AB resistance on chromosome 2 (Anbessa et al. 2009, Cobos et al. 2006, Iruela et al. 2007, Madrid et al. 2014), chromosome 3 (Anbessa et al. 2009, Kottapalli et al. 2009, Tar’an et al. 2007), chromosome 4 (Anbessa et al. 2009, Cho et al. 2004, Garg et al. 2018, Iruela et al. 2007, Kottapalli et al. 2009, Lichtenzveig et al. 2006, Madrid et al. 2013, Tar’an et al. 2007), chromosome 6 (Anbessa et al. 2009, Tar’an et al. 2007) and chromosome 8 (Anbessa et al. 2009, Lichtenzveig et al. 2006) of chickpea employing different mapping populations. However, chromosome 4 has been consistently reported in several mapping studies spanning the QTLs (Deokar et al. 2019, Garg et al. 2018, Madrid et al. 2013, Sharma and Ghosh 2016) indicating its importance for imparting resistance to AB.
For resistance to BGM, five QTLs were identified on chromosomes 3, 4, 5 and 6 in both years, of which, QTL, qbgm-4.1, qbgm-4.2 and qbgm-5.1, were found to be consistent. Till now, there is only one report on identification of QTLs for resistance to BGM. Anuradha et al. (2011) identified three QTLs responsible for BGM resistance, out of which, two QTLs were located on chromosome 3 while one QTL was located on chromosome 6. This is the first report on mapping of QTLs for BGM resistance in chickpea using high-throughput SNPs genotyping. Lack of chickpea genotypes having high levels of BGM resistance hampers the genetic studies and precise mapping of BGM. Thus, identification of several genomic regions from different sources of BGM resistance is an urgent requirement for gene pyramiding to obtain high level of resistance.
The present study has helped to identify promising chickpea RILs possessing resistance to AB and BGM that are being evaluated in multi-location trials. The identified QTLs for AB and BGM will help to provide opportunity for fine mapping and cloning these QTLs, and identification of closely linked markers for marker-assisted transfer of resistance to AB and BGM in elite chickpea cultivars. These QTLs were specifically detected using artificial screening technique under epiphytotic field conditions. Thus, these QTLs can be responsible for providing adult plant resistance (APR) as the disease reaction was evaluated during flowering stage.
AK, SS and IS designed and conducted the experiments. AK and UR did screening for AB and BGM in experimental material. DB, AK, CB and ISY performed the data analysis. AK, DB, CB and SS prepared and edited the final manuscript.
The INSPIRE research grant provided to AK by Department of Science and Technology (DST), New Delhi, India and research grant provided under the project ‘Incentivizing Research in Agriculture’ by Indian Council of Agricultural Research and ‘Consortia Research Platform on Molecular Breeding ’, New Delhi to SS for carrying out the research are highly acknowledged.
