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Dr. Sunil S Gangurde
PhD candidate

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0 Plant Breeding
0 agricultural sciences
0 Marker Assisted Breeding
0 transcriptome analysis
0 Genomics, Genes, Plants

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transcriptome analysis

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Journal article
Published: 17 June 2021 in Agronomy
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The groundnut breeding program at International Crops Research Institute for the Semi-Arid Tropics routinely performs marker-based early generation selection (MEGS) in thousands of segregating populations. The existing MEGS includes planting of segregating populations in fields or glasshouses, label tagging, and sample collection using leaf-punch from 20–25 day old plants followed by genotyping with 10 single nucleotide polymorphisms based early generation selection marker panels in a high throughput genotyping (HTPG) platform. The entire process is laborious, time consuming, and costly. Therefore, in order to save the time of the breeder and to reduce the cost during MEGS, we optimized a single seed chipping (SSC) process based MEGS protocol and deployed on large scale by genotyping >3000 samples from ongoing groundnut breeding program. In SSC-based MEGS, we used a small portion of cotyledon by slicing-off the posterior end of the single seed and transferred to the 96-deep well plate for DNA isolation and genotyping at HTPG platform. The chipped seeds were placed in 96-well seed-box in the same order of 96-well DNA sampling plate to enable tracking back to the selected individual seed. A high germination rate of 95–99% from the chipped seeds indicated that slicing of seeds from posterior end does not significantly affect germination percentage. In addition, we could successfully advance 3.5 generations in a year using a low-cost rapid generation turnover glass-house facility as compared to routine practice of two generations in field conditions. The integration of SSC based genotyping and rapid generation advancement (RGA) could significantly reduce the operational requirement of person-hours and expenses, and save a period of 6–8 months in groundnut genetics and breeding research.

ACS Style

Sejal Parmar; Dnyaneshwar Deshmukh; Rakesh Kumar; Surendra Manohar; Pushpesh Joshi; Vinay Sharma; Sunil Chaudhari; Murali Variath; Sunil Gangurde; Rajaguru Bohar; Prashant Singam; Rajeev Varshney; Pasupuleti Janila; Manish Pandey. Single Seed-Based High-Throughput Genotyping and Rapid Generation Advancement for Accelerated Groundnut Genetics and Breeding Research. Agronomy 2021, 11, 1226 .

AMA Style

Sejal Parmar, Dnyaneshwar Deshmukh, Rakesh Kumar, Surendra Manohar, Pushpesh Joshi, Vinay Sharma, Sunil Chaudhari, Murali Variath, Sunil Gangurde, Rajaguru Bohar, Prashant Singam, Rajeev Varshney, Pasupuleti Janila, Manish Pandey. Single Seed-Based High-Throughput Genotyping and Rapid Generation Advancement for Accelerated Groundnut Genetics and Breeding Research. Agronomy. 2021; 11 (6):1226.

Chicago/Turabian Style

Sejal Parmar; Dnyaneshwar Deshmukh; Rakesh Kumar; Surendra Manohar; Pushpesh Joshi; Vinay Sharma; Sunil Chaudhari; Murali Variath; Sunil Gangurde; Rajaguru Bohar; Prashant Singam; Rajeev Varshney; Pasupuleti Janila; Manish Pandey. 2021. "Single Seed-Based High-Throughput Genotyping and Rapid Generation Advancement for Accelerated Groundnut Genetics and Breeding Research." Agronomy 11, no. 6: 1226.

Article
Published: 09 June 2021 in Journal of Biosciences
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Rice grain shape and nutritional quality traits have high economic value for commercial production of rice and largely determine the market price, besides influencing the global food demand for high-quality rice. Detection, mapping and exploitation of quantitative trait loci (QTL) associated with kernel elongation and grain quality in Basmati rice is considered as an efficient strategy for improving the kernel elongation and grain quality trait in rice varieties. Genetic information in rice for most of these traits is scanty and needed interventions through the use of molecular markers. A recombinant inbred lines (RIL) population consisting of 130 lines generated from the cross involving Basmati 370, a superior quality Basmati variety and Pusa Basmati 1121, a Basmati derived variety were used to map the QTLs for 9 important grain quality and yield related traits. Correlation studies showed that various components of yield show a significant positive relationship with grain yield. A genetic map was constructed using 70 polymorphic simple sequence repeat (SSR) markers spanning a genetic distance of 689.3 cM distributed over 12 rice chromosomes. Significant variation was observed and showed transgressive segregation for grain quality traits in RIL population. A total of 20 QTLs were identified associated with nine yield and quality traits. Epistatic interactions were also identified for grain quality related traits indicating complex genetic nature inheritance. Therefore, the identified QTLs and flanking marker information could be utilized in the marker-assisted selection to improve kernel elongation and nutritional grain quality traits in rice varieties.

ACS Style

Madhvi Sharma; Sunil S Gangurde; Romesh K Salgotra; Bupesh Kumar; Anil K Singh; Manish K Pandey. Genetic mapping for grain quality and yield-attributed traits in Basmati rice using SSR-based genetic map. Journal of Biosciences 2021, 46, 1 -14.

AMA Style

Madhvi Sharma, Sunil S Gangurde, Romesh K Salgotra, Bupesh Kumar, Anil K Singh, Manish K Pandey. Genetic mapping for grain quality and yield-attributed traits in Basmati rice using SSR-based genetic map. Journal of Biosciences. 2021; 46 (3):1-14.

Chicago/Turabian Style

Madhvi Sharma; Sunil S Gangurde; Romesh K Salgotra; Bupesh Kumar; Anil K Singh; Manish K Pandey. 2021. "Genetic mapping for grain quality and yield-attributed traits in Basmati rice using SSR-based genetic map." Journal of Biosciences 46, no. 3: 1-14.

Journal article
Published: 26 April 2021 in International Journal of Molecular Sciences
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Late leaf spot (LLS) caused by fungus Nothopassalora personata in groundnut is responsible for up to 50% yield loss. To dissect the complex nature of LLS resistance, comparative transcriptome analysis was performed using resistant (GPBD 4), susceptible (TAG 24) and a resistant introgression line (ICGV 13208) and identified a total of 12,164 and 9954 DEGs (differentially expressed genes) respectively in A- and B-subgenomes of tetraploid groundnut. There were 135 and 136 unique pathways triggered in A- and B-subgenomes, respectively, upon N. personata infection. Highly upregulated putative disease resistance genes, an RPP-13 like (Aradu.P20JR) and a NBS-LRR (Aradu.Z87JB) were identified on chromosome A02 and A03, respectively, for LLS resistance. Mildew resistance Locus (MLOs)-like proteins, heavy metal transport proteins, and ubiquitin protein ligase showed trend of upregulation in susceptible genotypes, while tetratricopeptide repeats (TPR), pentatricopeptide repeat (PPR), chitinases, glutathione S-transferases, purple acid phosphatases showed upregulation in resistant genotypes. However, the highly expressed ethylene responsive factor (ERF) and ethylene responsive nuclear protein (ERF2), and early responsive dehydration gene (ERD) might be related to the possible causes of defoliation in susceptible genotypes. The identified disease resistance genes can be deployed in genomics-assisted breeding for development of LLS resistant cultivars to reduce the yield loss in groundnut.

ACS Style

Sunil Gangurde; Spurthi Nayak; Pushpesh Joshi; Shilp Purohit; Hari Sudini; Annapurna Chitikineni; Yanbin Hong; Baozhu Guo; Xiaoping Chen; Manish Pandey; Rajeev Varshney. Comparative Transcriptome Analysis Identified Candidate Genes for Late Leaf Spot Resistance and Cause of Defoliation in Groundnut. International Journal of Molecular Sciences 2021, 22, 4491 .

AMA Style

Sunil Gangurde, Spurthi Nayak, Pushpesh Joshi, Shilp Purohit, Hari Sudini, Annapurna Chitikineni, Yanbin Hong, Baozhu Guo, Xiaoping Chen, Manish Pandey, Rajeev Varshney. Comparative Transcriptome Analysis Identified Candidate Genes for Late Leaf Spot Resistance and Cause of Defoliation in Groundnut. International Journal of Molecular Sciences. 2021; 22 (9):4491.

Chicago/Turabian Style

Sunil Gangurde; Spurthi Nayak; Pushpesh Joshi; Shilp Purohit; Hari Sudini; Annapurna Chitikineni; Yanbin Hong; Baozhu Guo; Xiaoping Chen; Manish Pandey; Rajeev Varshney. 2021. "Comparative Transcriptome Analysis Identified Candidate Genes for Late Leaf Spot Resistance and Cause of Defoliation in Groundnut." International Journal of Molecular Sciences 22, no. 9: 4491.

Journal article
Published: 30 December 2020 in Genes
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A deep understanding of the genetic control of drought tolerance and iron deficiency tolerance is essential to hasten the process of developing improved varieties with higher tolerance through genomics-assisted breeding. In this context, an improved genetic map with 1205 loci was developed spanning 2598.3 cM with an average 2.2 cM distance between loci in the recombinant inbred line (TAG 24 × ICGV 86031) population using high-density 58K single nucleotide polymorphism (SNP) “Axiom_Arachis” array. Quantitative trait locus (QTL) analysis was performed using extensive phenotyping data generated for 20 drought tolerance- and two iron deficiency tolerance-related traits from eight seasons (2004–2015) at two locations in India, one in Niger, and one in Senegal. The genome-wide QTL discovery analysis identified 19 major main-effect QTLs with 10.0–33.9% phenotypic variation explained (PVE) for drought tolerance- and iron deficiency tolerance- related traits. Major main-effect QTLs were detected for haulm weight (20.1% PVE), SCMR (soil plant analytical development (SPAD) chlorophyll meter reading, 22.4% PVE), and visual chlorosis rate (33.9% PVE). Several important candidate genes encoding glycosyl hydrolases; malate dehydrogenases; microtubule-associated proteins; and transcription factors such as MADS-box, basic helix-loop-helix (bHLH), NAM, ATAF, and CUC (NAC), and myeloblastosis (MYB) were identified underlying these QTL regions. The putative function of these genes indicated their possible involvement in plant growth, development of seed and pod, and photosynthesis under drought or iron deficiency conditions in groundnut. These genomic regions and candidate genes, after validation, may be useful to develop molecular markers for deploying genomics-assisted breeding for enhancing groundnut yield under drought stress and iron-deficient soil conditions.

ACS Style

Manish K. Pandey; Sunil S. Gangurde; Vinay Sharma; Santosh K. Pattanashetti; Gopalakrishna K. Naidu; Issa Faye; Falalou Hamidou; Haile Desmae; Ndjido Ardo Kane; Mei Yuan; Vincent Vadez; Shyam N. Nigam; Rajeev K. Varshney. Improved Genetic Map Identified Major QTLs for Drought Tolerance- and Iron Deficiency Tolerance-Related Traits in Groundnut. Genes 2020, 12, 37 .

AMA Style

Manish K. Pandey, Sunil S. Gangurde, Vinay Sharma, Santosh K. Pattanashetti, Gopalakrishna K. Naidu, Issa Faye, Falalou Hamidou, Haile Desmae, Ndjido Ardo Kane, Mei Yuan, Vincent Vadez, Shyam N. Nigam, Rajeev K. Varshney. Improved Genetic Map Identified Major QTLs for Drought Tolerance- and Iron Deficiency Tolerance-Related Traits in Groundnut. Genes. 2020; 12 (1):37.

Chicago/Turabian Style

Manish K. Pandey; Sunil S. Gangurde; Vinay Sharma; Santosh K. Pattanashetti; Gopalakrishna K. Naidu; Issa Faye; Falalou Hamidou; Haile Desmae; Ndjido Ardo Kane; Mei Yuan; Vincent Vadez; Shyam N. Nigam; Rajeev K. Varshney. 2020. "Improved Genetic Map Identified Major QTLs for Drought Tolerance- and Iron Deficiency Tolerance-Related Traits in Groundnut." Genes 12, no. 1: 37.

Journal article
Published: 13 August 2020 in Scientific Reports
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The study was undertaken to identify the quantitative trait loci (QTLs) governing yield and its related traits using a recombinant inbred line (RIL) population derived from the popular rice hybrid, KRH-2 (IR58025A/KMR3R). A genetic map spanning 294.2 cM was constructed with 126 simple sequence repeats (SSR) loci uniformly distributed across the rice genome. QTL analysis using phenotyping and genotyping information identified a total of 22 QTLs. Of these, five major effect QTLs were identified for the following traits: total grain yield/plant (qYLD3-1), panicle weight (qPW3-1), plant height (qPH12-1), flag leaf width (qFLW4-1) and panicle length (qPL3-1), explaining 20.23–22.76% of the phenotypic variance with LOD scores range of 6.5–10.59. Few genomic regions controlling several traits (QTL hotspot) were identified on chromosome 3 for total grain yield/plant (qYLD3-1) and panicle length (qPL3-1). Significant epistatic interactions were also observed for total grain yield per plant (YLD) and panicle length (PL). While most of these QTLs were observed to be co-localized with the previously reported QTL regions, a novel, major QTL associated with panicle length (qPL3-1) was also identified. SNP genotyping of selected high and low yielding RILs and their QTL mapping with 1,082 SNPs validated most of the QTLs identified through SSR genotyping. This facilitated the identification of novel major effect QTLs with much better resolution and precision. In-silico analysis of novel QTLs revealed the biological functions of the putative candidate gene (s) associated with selected traits. Most of the high-yielding RILs possessing the major yield related QTLs were identified to be complete restorers, indicating their possible utilization in development of superior rice hybrids.

ACS Style

Swapnil Ravindra Kulkarni; S. M. Balachandran; K. Ulaganathan; Divya Balakrishnan; M. Praveen; A. S. Hari Prasad; R. A. Fiyaz; P. Senguttuvel; Pragya Sinha; Ravindra R. Kale; G. Rekha; M. B. V. N. Kousik; G. Harika; M. Anila; E. Punniakoti; T. Dilip; S. K. Hajira; K. Pranathi; M. Ayyappa Das; Mastanbee Shaik; K. Chaitra; P. Koteswara Rao; Sunil S Gangurde; Manish K. Pandey; R. M. Sundaram. Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping. Scientific Reports 2020, 10, 1 -21.

AMA Style

Swapnil Ravindra Kulkarni, S. M. Balachandran, K. Ulaganathan, Divya Balakrishnan, M. Praveen, A. S. Hari Prasad, R. A. Fiyaz, P. Senguttuvel, Pragya Sinha, Ravindra R. Kale, G. Rekha, M. B. V. N. Kousik, G. Harika, M. Anila, E. Punniakoti, T. Dilip, S. K. Hajira, K. Pranathi, M. Ayyappa Das, Mastanbee Shaik, K. Chaitra, P. Koteswara Rao, Sunil S Gangurde, Manish K. Pandey, R. M. Sundaram. Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping. Scientific Reports. 2020; 10 (1):1-21.

Chicago/Turabian Style

Swapnil Ravindra Kulkarni; S. M. Balachandran; K. Ulaganathan; Divya Balakrishnan; M. Praveen; A. S. Hari Prasad; R. A. Fiyaz; P. Senguttuvel; Pragya Sinha; Ravindra R. Kale; G. Rekha; M. B. V. N. Kousik; G. Harika; M. Anila; E. Punniakoti; T. Dilip; S. K. Hajira; K. Pranathi; M. Ayyappa Das; Mastanbee Shaik; K. Chaitra; P. Koteswara Rao; Sunil S Gangurde; Manish K. Pandey; R. M. Sundaram. 2020. "Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping." Scientific Reports 10, no. 1: 1-21.

Journal article
Published: 01 February 2020 in The Crop Journal
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ACS Style

Yaduru Shasidhar; Murali T. Variath; Manish K. Vishwakarma; Surendra S. Manohar; Sunil S Gangurde; Manda Sriswathi; Hari Kishan Sudini; Keshavji L. Dobariya; Sandip K. Bera; Thankappan Radhakrishnan; Manish K. Pandey; Pasupuleti Janila; Rajeev K. Varshney. Improvement of three popular Indian groundnut varieties for foliar disease resistance and high oleic acid using SSR markers and SNP array in marker-assisted backcrossing. The Crop Journal 2020, 8, 1 -15.

AMA Style

Yaduru Shasidhar, Murali T. Variath, Manish K. Vishwakarma, Surendra S. Manohar, Sunil S Gangurde, Manda Sriswathi, Hari Kishan Sudini, Keshavji L. Dobariya, Sandip K. Bera, Thankappan Radhakrishnan, Manish K. Pandey, Pasupuleti Janila, Rajeev K. Varshney. Improvement of three popular Indian groundnut varieties for foliar disease resistance and high oleic acid using SSR markers and SNP array in marker-assisted backcrossing. The Crop Journal. 2020; 8 (1):1-15.

Chicago/Turabian Style

Yaduru Shasidhar; Murali T. Variath; Manish K. Vishwakarma; Surendra S. Manohar; Sunil S Gangurde; Manda Sriswathi; Hari Kishan Sudini; Keshavji L. Dobariya; Sandip K. Bera; Thankappan Radhakrishnan; Manish K. Pandey; Pasupuleti Janila; Rajeev K. Varshney. 2020. "Improvement of three popular Indian groundnut varieties for foliar disease resistance and high oleic acid using SSR markers and SNP array in marker-assisted backcrossing." The Crop Journal 8, no. 1: 1-15.

Research article
Published: 05 December 2019 in Plant Biotechnology Journal
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Multiparental genetic mapping populations such as nested‐association mapping (NAM) have great potential for investigating quantitative traits and associated genomic regions leading to rapid discovery of candidate genes and markers. To demonstrate the utility and power of this approach, two NAM populations, NAM_Tifrunner and NAM_Florida‐07, were used for dissecting genetic control of 100‐pod weight (PW) and 100‐seed weight (SW) in peanut. Two high‐density SNP‐based genetic maps were constructed with 3341 loci and 2668 loci for NAM_Tifrunner and NAM_Florida‐07, respectively. The quantitative trait locus (QTL) analysis identified 12 and 8 major effect QTLs for PW and SW, respectively, in NAM_Tifrunner, and 13 and 11 major effect QTLs for PW and SW, respectively, in NAM_Florida‐07. Most of the QTLs associated with PW and SW were mapped on the chromosomes A05, A06, B05 and B06. A genomewide association study (GWAS) analysis identified 19 and 28 highly significant SNP–trait associations (STAs) in NAM_Tifrunner and 11 and 17 STAs in NAM_Florida‐07 for PW and SW, respectively. These significant STAs were co‐localized, suggesting that PW and SW are co‐regulated by several candidate genes identified on chromosomes A05, A06, B05, and B06. This study demonstrates the utility of NAM population for genetic dissection of complex traits and performing high‐resolution trait mapping in peanut.

ACS Style

Sunil S. Gangurde; Hui Wang; Shasidhar Yaduru; Manish K. Pandey; Jake C. Fountain; Ye Chu; Thomas Isleib; C. Corley Holbrook; Alencar Xavier; Albert K. Culbreath; Peggy Ozias‐Akins; Rajeev K. Varshney; Baozhu Guo. Nested‐association mapping (NAM)‐based genetic dissection uncovers candidate genes for seed and pod weights in peanut ( Arachis hypogaea ). Plant Biotechnology Journal 2019, 18, 1457 -1471.

AMA Style

Sunil S. Gangurde, Hui Wang, Shasidhar Yaduru, Manish K. Pandey, Jake C. Fountain, Ye Chu, Thomas Isleib, C. Corley Holbrook, Alencar Xavier, Albert K. Culbreath, Peggy Ozias‐Akins, Rajeev K. Varshney, Baozhu Guo. Nested‐association mapping (NAM)‐based genetic dissection uncovers candidate genes for seed and pod weights in peanut ( Arachis hypogaea ). Plant Biotechnology Journal. 2019; 18 (6):1457-1471.

Chicago/Turabian Style

Sunil S. Gangurde; Hui Wang; Shasidhar Yaduru; Manish K. Pandey; Jake C. Fountain; Ye Chu; Thomas Isleib; C. Corley Holbrook; Alencar Xavier; Albert K. Culbreath; Peggy Ozias‐Akins; Rajeev K. Varshney; Baozhu Guo. 2019. "Nested‐association mapping (NAM)‐based genetic dissection uncovers candidate genes for seed and pod weights in peanut ( Arachis hypogaea )." Plant Biotechnology Journal 18, no. 6: 1457-1471.

Research article
Published: 25 September 2019 in Plant Biotechnology Journal
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The subspecies fastigiata of cultivated groundnut lost fresh seed dormancy (FSD) during domestication and human‐made selection. Groundnut varieties lacking FSD experience precocious seed germination during harvest imposing severe losses. Development of easy‐to‐use genetic markers enables early‐generation selection in different molecular breeding approaches. In this context, one recombinant inbred lines (RIL) population (ICGV 00350 × ICGV 97045) segregating for FSD was used for deploying QTL‐seq approach for identification of key genomic regions and candidate genes. Whole‐genome sequencing (WGS) data (87.93 Gbp) were generated and analysed for the dormant parent (ICGV 97045) and two DNA pools (dormant and nondormant). After analysis of resequenced data from the pooled samples with dormant parent (reference genome), we calculated delta‐SNP index and identified a total of 10,759 genomewide high‐confidence SNPs. Two candidate genomic regions spanning 2.4 Mb and 0.74 Mb on the B05 and A09 pseudomolecules, respectively, were identified controlling FSD. Two candidate genes—RING‐H2 finger protein and zeaxanthin epoxidase—were identified in these two regions, which significantly express during seed development and control abscisic acid (ABA) accumulation. QTL‐seq study presented here laid out development of a marker, GMFSD1, which was validated on a diverse panel and could be used in molecular breeding to improve dormancy in groundnut.

ACS Style

Rakesh Kumar; Pasupuleti Janila; Manish K. Vishwakarma; Aamir W. Khan; Surendra S. Manohar; Sunil S. Gangurde; Murali T. Variath; Yaduru Shasidhar; Manish K. Pandey; Rajeev K. Varshney. Whole‐genome resequencing‐based QTL‐seq identified candidate genes and molecular markers for fresh seed dormancy in groundnut. Plant Biotechnology Journal 2019, 18, 992 -1003.

AMA Style

Rakesh Kumar, Pasupuleti Janila, Manish K. Vishwakarma, Aamir W. Khan, Surendra S. Manohar, Sunil S. Gangurde, Murali T. Variath, Yaduru Shasidhar, Manish K. Pandey, Rajeev K. Varshney. Whole‐genome resequencing‐based QTL‐seq identified candidate genes and molecular markers for fresh seed dormancy in groundnut. Plant Biotechnology Journal. 2019; 18 (4):992-1003.

Chicago/Turabian Style

Rakesh Kumar; Pasupuleti Janila; Manish K. Vishwakarma; Aamir W. Khan; Surendra S. Manohar; Sunil S. Gangurde; Murali T. Variath; Yaduru Shasidhar; Manish K. Pandey; Rajeev K. Varshney. 2019. "Whole‐genome resequencing‐based QTL‐seq identified candidate genes and molecular markers for fresh seed dormancy in groundnut." Plant Biotechnology Journal 18, no. 4: 992-1003.

Review
Published: 03 June 2019 in Toxins
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Aflatoxin is considered a “hidden poison” due to its slow and adverse effect on various biological pathways in humans, particularly among children, in whom it leads to delayed development, stunted growth, liver damage, and liver cancer. Unfortunately, the unpredictable behavior of the fungus as well as climatic conditions pose serious challenges in precise phenotyping, genetic prediction and genetic improvement, leaving the complete onus of preventing aflatoxin contamination in crops on post-harvest management. Equipping popular crop varieties with genetic resistance to aflatoxin is key to effective lowering of infection in farmer’s fields. A combination of genetic resistance for in vitro seed colonization (IVSC), pre-harvest aflatoxin contamination (PAC) and aflatoxin production together with pre- and post-harvest management may provide a sustainable solution to aflatoxin contamination. In this context, modern “omics” approaches, including next-generation genomics technologies, can provide improved and decisive information and genetic solutions. Preventing contamination will not only drastically boost the consumption and trade of the crops and products across nations/regions, but more importantly, stave off deleterious health problems among consumers across the globe.

ACS Style

Manish K. Pandey; Rakesh Kumar; Arun K. Pandey; Pooja Soni; Sunil S. Gangurde; Hari K. Sudini; Jake C. Fountain; Boshou Liao; Haile Desmae; Patrick Okori; Xiaoping Chen; Huifang Jiang; Venugopal Mendu; Hamidou Falalou; Samuel Njoroge; James Mwololo; Baozhu Guo; Weijian Zhuang; Xingjun Wang; Xuanqiang Liang; Rajeev K. Varshney. Mitigating Aflatoxin Contamination in Groundnut through A Combination of Genetic Resistance and Post-Harvest Management Practices. Toxins 2019, 11, 315 .

AMA Style

Manish K. Pandey, Rakesh Kumar, Arun K. Pandey, Pooja Soni, Sunil S. Gangurde, Hari K. Sudini, Jake C. Fountain, Boshou Liao, Haile Desmae, Patrick Okori, Xiaoping Chen, Huifang Jiang, Venugopal Mendu, Hamidou Falalou, Samuel Njoroge, James Mwololo, Baozhu Guo, Weijian Zhuang, Xingjun Wang, Xuanqiang Liang, Rajeev K. Varshney. Mitigating Aflatoxin Contamination in Groundnut through A Combination of Genetic Resistance and Post-Harvest Management Practices. Toxins. 2019; 11 (6):315.

Chicago/Turabian Style

Manish K. Pandey; Rakesh Kumar; Arun K. Pandey; Pooja Soni; Sunil S. Gangurde; Hari K. Sudini; Jake C. Fountain; Boshou Liao; Haile Desmae; Patrick Okori; Xiaoping Chen; Huifang Jiang; Venugopal Mendu; Hamidou Falalou; Samuel Njoroge; James Mwololo; Baozhu Guo; Weijian Zhuang; Xingjun Wang; Xuanqiang Liang; Rajeev K. Varshney. 2019. "Mitigating Aflatoxin Contamination in Groundnut through A Combination of Genetic Resistance and Post-Harvest Management Practices." Toxins 11, no. 6: 315.

Chapter
Published: 16 February 2019 in Genomic Designing of Climate-Smart Oilseed Crops
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About 90% of total groundnut is cultivated in the semi-arid tropic (SAT) regions of the world as a major oilseed and food crop and provides essential nutrients required by human diet. Climate change is the main threat to yield and quality of the produce in the SAT regions, and effects are already being seen in some temperate areas also. Rising CO2 levels, erratic rainfall, humidity, short episodes of high temperature and salinity hamper the physiology, disease resistance, fertility and yield as well as seed nutrient levels of groundnut. To meet growing demands of the increasing population against the threats of climate change, it is necessary to develop climate-smart varieties with enhanced and stable genetic improvements. Identifying key traits affected by climate change in groundnut will be important for developing an appropriate strategy for developing new varieties. Fast-changing scenarios of product ecologies as a consequence of climate change need faster development and replacement of improved varieties in the farmers’ fields to sustain yield and quality. Use of modern genomics technology is likely to help in improved understanding and efficient breeding for climate-smart traits such as tolerance to drought and heat, and biotic stresses such as foliar diseases, stem rot, peanut bud necrosis disease, and preharvest aflatoxin contamination. The novel promising technologies such as genomic selection and genome editing need to be tested for their potential utility in developing climate-smart groundnut varieties. System modeling may further improve the understanding and characterization of the problems of target ecologies for devising strategies to overcome the problem. The combination of conventional breeding techniques with genomics and system modeling approaches will lead to a new era of system biology assisted breeding for sustainable agricultural production to feed the ever-growing population.

ACS Style

Sunil S. Gangurde; Rakesh Kumar; Arun K. Pandey; Mark Burow; Haydee E. Laza; Spurthi N. Nayak; Baozhu Guo; Boshou Liao; Ramesh S. Bhat; Naga Madhuri; S. Hemalatha; Hari K. Sudini; Pasupuleti Janila; Putta Latha; Hasan Khan; Babu N. Motagi; T. Radhakrishnan; Naveen Puppala; Rajeev K. Varshney; Manish K. Pandey. Climate-Smart Groundnuts for Achieving High Productivity and Improved Quality: Current Status, Challenges, and Opportunities. Genomic Designing of Climate-Smart Oilseed Crops 2019, 133 -172.

AMA Style

Sunil S. Gangurde, Rakesh Kumar, Arun K. Pandey, Mark Burow, Haydee E. Laza, Spurthi N. Nayak, Baozhu Guo, Boshou Liao, Ramesh S. Bhat, Naga Madhuri, S. Hemalatha, Hari K. Sudini, Pasupuleti Janila, Putta Latha, Hasan Khan, Babu N. Motagi, T. Radhakrishnan, Naveen Puppala, Rajeev K. Varshney, Manish K. Pandey. Climate-Smart Groundnuts for Achieving High Productivity and Improved Quality: Current Status, Challenges, and Opportunities. Genomic Designing of Climate-Smart Oilseed Crops. 2019; ():133-172.

Chicago/Turabian Style

Sunil S. Gangurde; Rakesh Kumar; Arun K. Pandey; Mark Burow; Haydee E. Laza; Spurthi N. Nayak; Baozhu Guo; Boshou Liao; Ramesh S. Bhat; Naga Madhuri; S. Hemalatha; Hari K. Sudini; Pasupuleti Janila; Putta Latha; Hasan Khan; Babu N. Motagi; T. Radhakrishnan; Naveen Puppala; Rajeev K. Varshney; Manish K. Pandey. 2019. "Climate-Smart Groundnuts for Achieving High Productivity and Improved Quality: Current Status, Challenges, and Opportunities." Genomic Designing of Climate-Smart Oilseed Crops , no. : 133-172.

Original article
Published: 11 December 2018 in Theoretical and Applied Genetics
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Genetic mapping identified large number of epistatic interactions indicating the complex genetic architecture for stem rot disease resistance. Groundnut (Arachis hypogaea) is an important global crop commodity and serves as a major source of cooking oil, diverse confectionery preparations and livestock feed. Stem rot disease caused by Sclerotium rolfsii is the most devastating disease of groundnut and can cause up to 100% yield loss. Genomic-assisted breeding (GAB) has potential for accelerated development of stem rot resistance varieties in short period with more precision. In this context, linkage analysis and quantitative trait locus (QTL) mapping for resistance to stem rot disease was performed in a bi-parental recombinant inbred line population developed from TG37A (susceptible) × NRCG-CS85 (resistant) comprising of 270 individuals. Genotyping-by-sequencing approach was deployed to generate single nucleotide polymorphism (SNP) genotyping data leading to development of a genetic map with 585 SNP loci spanning map distance of 2430 cM. QTL analysis using multi-season phenotyping and genotyping data could not detect any major main-effect QTL but identified 44 major epistatic QTLs with phenotypic variation explained ranging from 14.32 to 67.95%. Large number interactions indicate the complexity of genetic architecture of resistance to stem rot disease. A QTL of physical map length 5.2 Mb identified on B04 comprising 170 different genes especially leucine reach repeats, zinc finger motifs and ethyleneresponsive factors, etc., was identified. The identified genomic regions and candidate genes will further validate and facilitate marker development to deploy GAB for developing stem rot disease resistance groundnut varieties.

ACS Style

Sneha M. Dodia; Binal Joshi; Sunil S. Gangurde; Polavakkalipalayam P. Thirumalaisamy; Gyan P. Mishra; Dayama Narandrakumar; Pooja Soni; Arulthambi L. Rathnakumar; Jentilal R. Dobaria; Chandramohan Sangh; Annapurna Chitikineni; Sumitra V. Chanda; Manish K. Pandey; Rajeev K. Varshney; Radhakrishnan Thankappan. Genotyping-by-sequencing based genetic mapping reveals large number of epistatic interactions for stem rot resistance in groundnut. Theoretical and Applied Genetics 2018, 132, 1001 -1016.

AMA Style

Sneha M. Dodia, Binal Joshi, Sunil S. Gangurde, Polavakkalipalayam P. Thirumalaisamy, Gyan P. Mishra, Dayama Narandrakumar, Pooja Soni, Arulthambi L. Rathnakumar, Jentilal R. Dobaria, Chandramohan Sangh, Annapurna Chitikineni, Sumitra V. Chanda, Manish K. Pandey, Rajeev K. Varshney, Radhakrishnan Thankappan. Genotyping-by-sequencing based genetic mapping reveals large number of epistatic interactions for stem rot resistance in groundnut. Theoretical and Applied Genetics. 2018; 132 (4):1001-1016.

Chicago/Turabian Style

Sneha M. Dodia; Binal Joshi; Sunil S. Gangurde; Polavakkalipalayam P. Thirumalaisamy; Gyan P. Mishra; Dayama Narandrakumar; Pooja Soni; Arulthambi L. Rathnakumar; Jentilal R. Dobaria; Chandramohan Sangh; Annapurna Chitikineni; Sumitra V. Chanda; Manish K. Pandey; Rajeev K. Varshney; Radhakrishnan Thankappan. 2018. "Genotyping-by-sequencing based genetic mapping reveals large number of epistatic interactions for stem rot resistance in groundnut." Theoretical and Applied Genetics 132, no. 4: 1001-1016.

Review
Published: 10 January 2016 in Annual Research & Review in Biology
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Akula Dinesh; Baramappanavara Muralidhara; Sunil S Gangurde; More Yogeshwar. Molecular Response of Plants to Drought, Cold and Heat Stress - A Review. Annual Research & Review in Biology 2016, 10, 1 -8.

AMA Style

Akula Dinesh, Baramappanavara Muralidhara, Sunil S Gangurde, More Yogeshwar. Molecular Response of Plants to Drought, Cold and Heat Stress - A Review. Annual Research & Review in Biology. 2016; 10 (5):1-8.

Chicago/Turabian Style

Akula Dinesh; Baramappanavara Muralidhara; Sunil S Gangurde; More Yogeshwar. 2016. "Molecular Response of Plants to Drought, Cold and Heat Stress - A Review." Annual Research & Review in Biology 10, no. 5: 1-8.