Person:
Varshney, R.K.

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Varshney
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R.K.
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Varshney, R.K.

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Now showing 1 - 9 of 9
  • Novel SSR markers from BAC-end sequences, DArT arrays and a comprehensive genetic map with 1,291 marker loci for chickpea (Cicer arietinum L.)
    (Public Library of Science, 2011) Thudi, M.; Abhishek Bohra; Nayak, S.N.; Varghese, N.; Shah, T.; Penmetsa, R.V.; Thirunavukkarasu, N.; Gudipati, S.; Gaur, P.; Kulwal, P.L.; Upadhyaya, H.D.; Kavikishor, P.B.; Winter, P.; Kahl, G.; Town, C.D.; Kilian, A.; Cook, D.; Varshney, R.K.
    Publication
  • Integration of novel SSR and gene-based SNP marker loci in the chickpea genetic map and establishment of new anchor points with Medicago truncatula genome
    (Springer Verlag, 2010) Nayak, S.N.; Zhu, H.; Varghese, N.; Datta, S.; Choi, H.K.; Horres, R.; Jüngling, R.; Singh, J.; Kavikishor, P.B.; Sivaramakrishnan, S.; Hoisington, D.A.; Kahl, G.; Winter, P.; Cook, D.; Varshney, R.K.
    Publication
  • An international reference consensus genetic map with 897 marker loci based on 11 mapping populations for tetraploid groundnut (arachis hypogaea l.)
    (Public Library of Science, 2012) Gautami, B.; Foncéka, D.; Pandey, M.K.; Moretzsohn, M.C.; Sujay, V.; Hongde Qin; Yanbin Hong; Faye, I.; Xiaoping Chen; Bhanuprakash, A.; Shah, T.; Gowda, M.V.C.; Nigam, S.N.; Xuanqiang Liang; Hoisington, D.A.; Baozhu Guo; Bertioli, D.; Rami, J.F.; Varshney, R.K.
    Publication
  • Groundnut improvement: use of genetic and genomic tools
    (Frontiers, 2013) Janila, P.; Nigam, S.N.; Pandey, M.K.; Nagesh, P.; Varshney, R.K.
    Publication
  • Genome-enabled prediction models for yield related traits in chickpea
    (Frontiers, 2016) Roorkiwal, M.; Abhishek Rathore; Das, R.R.; Muneendra K. Singh; Ankit Jain; Samineni Srinivasan; Gaur, P.; Chellapilla Bharadwaj; Tripathi, S.; Yongle Li; Hickey, J.; Lorenz, A.J.; Sutton, T.; Crossa, J.; Jannink, J.L.; Varshney, R.K.
    Publication
  • Large-scale transcriptome analysis in chickpea (Cicer arietinum L.), an orphan legume crop of the semi-arid tropics of Asia and Africa
    (Wiley, 2011) Hiremath, P.J.; Farmer, A.; Cannon, S.B.; Woodward, J.; Kudapa, H.; Tuteja, R.; Kumar, A.; Bhanuprakash, A.; Mulaosmanovic, B.; Gujaria, N.; Krishnamurthy, L.; Gaur, P.; Kavikishor, P.B.; Shah, T.; Srinivasan, R.; Lohse, M.; Yongli Xiao; Town, C.D.; Cook, D.; May, G.D.; Varshney, R.K.
    Publication
  • Development and use of genic molecular markers (GMMs) for construction of a transcript map of chickpea (Cicer arietinum L.)
    (Springer, 2011) Gujaria, N.; Kumar, A.; Dauthal, P.; Dubey, A.; Hiremath, P.J.; Prakash, A.B.; Farmer, A.; Bhide, M.; Shah, T.; Gaur, P.; Upadhyaya, H.D.; Bhatia, S.; Cook, D.; May, G.D.; Varshney, R.K.
    Publication
  • Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea
    (Nature Research, 2018) Roorkiwal, M.; Jarquin, D.; Muneendra K. Singh; Gaur, P.; Chellapilla Bharadwaj; Abhishek Rathore; Howard, R.; Samineni Srinivasan; Ankit Jain; Garg, V.; Kale, S.; Annapurna Chitikineni; Shailesh Tripathi; Jones, E.; Robbins, K.; Crossa, J.; Varshney, R.K.
    Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates.
    Publication
  • Crop breeding chips and genotyping platforms: progress, challenges, and perspectives
    (Elsevier, 2017) Rasheed, A.; Yuanfeng Hao; Xianchun Xia; Khan, Awais; Yunbi Xu; Varshney, R.K.; He Zhonghu
    There is a rapidly rising trend in the development and application of molecular marker assays for gene mapping and discovery in field crops and trees. Thus far, more than 50 SNP arrays and 15 different types of genotyping-by-sequencing (GBS) platforms have been developed in over 25 crop species and perennial trees. However, much less effort has been made on developing ultra-high-throughput and cost-effective genotyping platforms for applied breeding programs. In this review, we discuss the scientific bottlenecks in existing SNP arrays and GBS technologies and the strategies to develop targeted platforms for crop molecular breeding. We propose that future practical breeding platforms should adopt automated genotyping technologies, either array or sequencing based, target functional polymorphisms underpinning economic traits, and provide desirable prediction accuracy for quantitative traits, with universal applications under wide genetic backgrounds in crops. The development of such platforms faces serious challenges at both the technological level due to cost ineffectiveness, and the knowledge level due to large genotype–phenotype gaps in crop plants. It is expected that such genotyping platforms will be achieved in the next ten years in major crops in consideration of (a) rapid development in gene discovery of important traits, (b) deepened understanding of quantitative traits through new analytical models and population designs, (c) integration of multi-layer -omics data leading to identification of genes and pathways responsible for important breeding traits, and (d) improvement in cost effectiveness of large-scale genotyping. Crop breeding chips and genotyping platforms will provide unprecedented opportunities to accelerate the development of cultivars with desired yield potential, quality, and enhanced adaptation to mitigate the effects of climate change.
    Publication