Person:
Varshney, R.K.

Loading...
Profile Picture
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Varshney
First Name
R.K.
Name
Varshney, R.K.

Search Results

Now showing 1 - 9 of 9
  • Author Correction: A chickpea genetic variation map based on the sequencing of 3,366 genomes (Nature, (2021), 599, 7886, (622-627), 10.1038/s41586-021-04066-1)
    (Nature Publishing Group, 2022) Varshney, R.K.; Roorkiwal, M.; Shuai Sun; Bajaj, P.; Annapurna Chitikineni; Thudi, M.; Singh, N.P.; Xiao Du; Upadhyaya, H.D.; Khan, A.W.; Yue Wang; Garg, V.; Guangyi Fan; Cowling, W.A.; Crossa, J.; Gentzbittel, L.; Voss-Fels, K.P.; Valluri, V.K.; Sinha, P.; Singh, V.K.; Ben, C.; Abhishek Rathore; Punna, R.; Muneendra K. Singh; Tar’an, B.; Chellapilla Bharadwaj; Yasin, M.; Pithia, M.S.; Singh, S.; Soren, K.R.; Kudapa, H.; Jarquin, D.; Cubry, P.; Hickey, L.; Dixit, G.P.; Thuillet, A.C.; Hamwieh, A.; Kumar, S.; Deokar, A.; Chaturvedi, S.K.; Francis, A.; Howard, R.; Chattopadhyay, D.; Edwards, D.; Lyons, E.; Vigouroux, Y.; Hayes, B.J.; Von Wettberg, E.; Datta, S.; Huanming Yang; Nguyen, H.T.; Jian Wang; Siddique, K.H.M.; Mohapatra, T.; Bennetzen, J.L.; Xun Xu; Xin Liu
    Publication
  • Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding
    (Crop Science Society of America, 2022) Montesinos-Lopez, O.A.; Montesinos-López, A.; Acosta, R.; Varshney, R.K.; Bentley, A.R.; Crossa, J.
    Publication
  • Bayesian multitrait kernel methods improve multienvironment genome-based prediction
    (Oxford University Press, 2022) Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Montesinos-López, A.; Ramirez-Alcaraz, J.M.; Poland, J.; Singh, R.P.; Dreisigacker, S.; Crespo Herrera, L.A.; Mondal, S.; Velu, G.; Juliana, P.; Huerta-Espino, J.; Shrestha, S.; Varshney, R.K.; Crossa, J.
    Publication
  • A new deep learning calibration method enhances genome-based prediction of continuous crop traits
    (Frontiers, 2021) Montesinos-Lopez, O.A.; Montesinos-López, A.; Mosqueda-Gonzalez, B.A.; Bentley, A.R.; Lillemo, M.; Varshney, R.K.; Crossa, J.
    Publication
  • A chickpea genetic variation map based on the sequencing of 3,366 genomes
    (Nature Publishing Group, 2021) Varshney, R.K.; Roorkiwal, M.; Shuai Sun; Bajaj, P.; Annapurna Chitikineni; Thudi, M.; Singh, N.P.; Xiao Du; Upadhyaya, H.D.; Khan, A.W.; Yue Wang; Garg, V.; Guangyi Fan; Cowling, W.A.; Crossa, J.; Gentzbittel, L.; Voss-Fels, K.P.; Valluri, V.K.; Sinha, P.; Singh, V.K.; Ben, C.; Abhishek Rathore; Punna, R.; Muneendra K. Singh; Tar’an, B.; Chellapilla Bharadwaj; Yasin, M.; Pithia, M.S.; Singh, S.; Soren, K.R.; Kudapa, H.; Jarquin, D.; Cubry, P.; Hickey, L.; Dixit, G.P.; Thuillet, A.C.; Hamwieh, A.; Kumar, S.; Deokar, A.; Chaturvedi, S.K.; Francis, A.; Howard, R.; Chattopadhyay, D.; Edwards, D.; Lyons, E.; Vigouroux, Y.; Hayes, B.J.; Von Wettberg, E.; Datta, S.; Huanming Yang; Nguyen, H.T.; Jian Wang; Siddique, K.H.M.; Mohapatra, T.; Bennetzen, J.L.; Xun Xu; Xin Liu
    Publication
  • Breeding custom-designed crops for improved drought adaptation
    (Wiley, 2021) Varshney, R.K.; Barmukh, R.; Roorkiwal, M.; Yiping Qi; Kholova, J.; Tuberosa, R.; Reynolds, M.P.; Tardieu, F.; Siddique, K.H.M.
    Publication
  • Genomic resources in plant breeding for sustainable agriculture
    (Elsevier, 2021) Thudi, M.; Palakurthi, R.; Schnable, J.C.; Annapurna Chitikineni; Dreisigacker, S.; Mace, E.; Srivastava, R.K.; Satyavathi, C.T.; Odeny, D.A.; Vijay Tiwari; Hon-Ming Lam; Yan-Bin Hong; Singh, V.K.; Guowei Li; Yunbi Xu; Xiao-Ping Chen; Kaila, S.; Nguyen, H.T.; Sivasankar, S.; Jackson, S.A.; Close, T.; Wan Shubo; Varshney, R.K.
    Publication
  • Strategies for effective use of genomic information in crop breeding programs serving Africa and South Asia
    (Frontiers, 2020) Santantonio, N.; Atanda, A.S.; Beyene, Y.; Varshney, R.K.; Olsen, M.; Jones, E.; Roorkiwal, M.; Gowda, M.; Chellapilla Bharadwaj; Gaur, P.; Xuecai Zhang; Dreher, K.; Ayala Hernández, C.; Crossa, J.; Pérez-Rodríguez, P.; Abhishek Rathore; Yanxin Gao; Mccouch, S.; Robbins, K.
    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