Prioritization of candidate genes in “QTL-hotspot” region for drought tolerance in chickpea (Cicer arietinum L.)

Kale, Sandip M.; Jaganathan, Deepa; Ruperao, Pradeep; Chen, Charles; Ramu, Punna; Kudapa, Himabindu; Thudi, Mahendar; Roorkiwal, Manish; Katta, Mohan A. V. S. K.; Doddamani, Dadakhalandar; Garg, Vanika; Kishor, P. B. Kavi; Gaur, Pooran M.; Nguyen, Henry T.; Batley, Jacqueline; Edwards, David; Sutton, Tim; Varshney, Rajeev K.

doi:10.1038/srep15296

Cited by 135 publications

(177 citation statements)

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“…For instance using linkage mapping a genomic region “ QTL-hotspot ” was identified on CaLG04 of chickpea that harbor several QTLs for controlling the drought tolerance related root traits and several other yield related traits (Varshney et al, 2014b). Following studies have indicated the role of several small effect QTLs for conferring drought tolerance in chickpea (Jaganathan et al, 2015; Kale et al, 2015). Successful identification and mapping of several drought responsive gene(s)/genomic region(s) (Roorkiwal et al, 2014; Thudi et al, 2014) further widen the scope of selection of genomic regions for breeding purposes.…”

Section: Discussionmentioning

confidence: 99%

Genome-Enabled Prediction Models for Yield Related Traits in Chickpea

et al. 2016

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Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations (Delhi and Patancheru, India) during the crop seasons 2011–12 and 2012–13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits viz. seed yield, 100 seed weight, days to 50% flowering and days to maturity. Prediction accuracy for the models tested varied from 0.138 (seed yield) to 0.912 (100 seed weight), whereas performance of models did not show any significant difference for estimating prediction accuracy within traits. Kinship matrix calculated using genotyping data reaffirmed existence of two different groups within selected lines. There was not much effect of population structure on prediction accuracy. In brief, present study establishes the necessary resources for deployment of GS in chickpea breeding.

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Section: Discussionmentioning

confidence: 99%

Genome-Enabled Prediction Models for Yield Related Traits in Chickpea

et al. 2016

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“…Owing to the large size of this genomic region, the region was refined using a genotyping‐by‐sequencing (GBS) approach (Jaganathan et al ., ). As a result, the ‘ QTL‐hotspot ’ region was narrowed to 14 cM (~3 Mb) and candidate genes prioritized for analysis (Kale et al ., ).…”

Section: Introductionmentioning

confidence: 97%

QTL‐seq for rapid identification of candidate genes for 100‐seed weight and root/total plant dry weight ratio under rainfed conditions in chickpea

Singh

Khan

Jaganathan

et al. 2016

Plant Biotechnology Journal

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SummaryTerminal drought is a major constraint to chickpea productivity. Two component traits responsible for reduction in yield under drought stress include reduction in seeds size and root length/root density. QTL‐seq approach, therefore, was used to identify candidate genomic regions for 100‐seed weight (100SDW) and total dry root weight to total plant dry weight ratio (RTR) under rainfed conditions. Genomewide SNP profiling of extreme phenotypic bulks from the ICC 4958 × ICC 1882 population identified two significant genomic regions, one on CaLG01 (1.08 Mb) and another on CaLG04 (2.7 Mb) linkage groups for 100SDW. Similarly, one significant genomic region on CaLG04 (1.10 Mb) was identified for RTR. Comprehensive analysis revealed four and five putative candidate genes associated with 100SDW and RTR, respectively. Subsequently, two genes (Ca_04364 and Ca_04607) for 100SDW and one gene (Ca_04586) for RTR were validated using CAPS/dCAPS markers. Identified candidate genomic regions and genes may be useful for molecular breeding for chickpea improvement.

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“…In chickpea, many genomic studies focus on biotic fungal, bacterial, viral, nematode and insect stress affecting the crop. Recently QTLs related to resistance to Ascochyta blight, fusarium wilt, drought tolerance and pod borer diseases were studied (Kale et al, 2015;Millan et al, 2006;Rajesh et al, 2007). In recent years, the incidence of disease is getting less like, Fusarium wilt has been reported in the US, Spain and India (Jimenez-Gasco et al, 2004) and genes linked to fusarium wilt resistance have also been reported for various races (Millan et al, 2006;Sharma et al, 2004).…”

Section: Genetic Variationmentioning

confidence: 99%

“…However, the knowledge generated by the gene action and genetic variation for crop improvement efforts has enabled to transfer useful alleles from the desi to kabuli varieties such as ICCV2 and KAK 2. There are numerous known molecular markers linked to stress tolerance (Dita et al, 2006;Millan et al, 2006), salt tolerance (Samineni, 2010), drought tolerance (Azam et al, 2014;Kale et al, 2015), heat tolerance (Thudi et al, 2014), yield (Rehman et al, 2011) and biotic stress tolerance (Flandez-Galvez et al, 2003) in chickpea. With availability of a reference genome, the potential to map different QTL and identify the linked molecular markers facilitates the transfer of several QTL in one improved cultivar simultaneously.…”

Section: Gene Discovery and Qtl Analysismentioning

confidence: 99%

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Analysis of the Chickpea genome using next generation sequencing data

Ruperao¹

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Chickpea is an annual, self-pollinating, diploid (2n=2x=16) and the second most important food legume crop with major production areas in the Indian sub-continent, West Asia and North Africa. Australia is the largest exporter of chickpea and yield for 2015 has been forecasted at 990,000 tonnes (http://www.agriculture.gov.au/abares/media-releases/2015). The main constraint for increasing yield is the susceptibility of the plant to foliar disease, ascochyta blight, fusarium wilt. Chickpea breeding aims at high yielding cultivars that combine longlasting resistance against both biotic and abiotic stress. Emerging sequencing technologies have enhanced the availability of crop genomic resources. Analysis of huge amounts of genomic data comes with major computational challenges.This thesis describes a new method for assessing the quality of reference genome assembly by flow sorting and isolating chromosomes based on size. This chromosomal DNA was sequenced and mapped to the reference genome assembly.This approach will reduce the genome DNA complexity to a chromosome level and is expect to correspond to only one chromosome assembly. Appling this approach on both released desi and kabuli reference genomes has shown miss-assembly. The desi genome was of poor quality compared to the kabuli genome. To fix these misassemblies, we developed a novel method called skimGBS for rearranging the fragmented sequences. In this approach, genotypes were called from population individuals to construct haplotype blocks. Based on the haplotype block signature, contigs/fragmented sequences were reordered as new assemblies. Using this approach, both desi and kabuli genomes were improved by placing unplaced contig sequences into chromosomes. Furthermore, these improved reference assemblieswere assessed and annotated.This thesis also reports identification of more than 800,000 high quality SNPs by sequencing 69 diverse Australian chickpea accessions. Gene loss, genetic relatedness, population structure and diversity analysis was also performed. The public accessibility of the data and above results provides a valuable resource to support chickpea research.iiThe developed methodology can also apply to other genomics studies, and will therefore be a valuable approach to assist crop improvement and further breeding approaches.iii

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Prioritization of candidate genes in “QTL-hotspot” region for drought tolerance in chickpea (Cicer arietinum L.)

Cited by 135 publications

References 56 publications

Genome-Enabled Prediction Models for Yield Related Traits in Chickpea

Genome-Enabled Prediction Models for Yield Related Traits in Chickpea

QTL‐seq for rapid identification of candidate genes for 100‐seed weight and root/total plant dry weight ratio under rainfed conditions in chickpea

Analysis of the Chickpea genome using next generation sequencing data

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