Eleventh ICRISAT CEG Course
Participants Trained: 32 from 13 countries (Bangladesh, Burkina Faso, Ethiopia, India, Kenya, Malaysia, Mali, Nigeria, Sri Lanka, Syria, Tanzania, Uganda and Uzbekistan)
The ICRISAT ‘s Center of Excellence in Genomics (CEG) hosted its Eleventh training course on “Next Generation Sequencing Technologies for Crop Improvement” during Nov 24 – Dec 05, 2014 at ICRISAT Campus, Patancheru, under ICRISAT-CEG Phase II. A total of 32 scientists from 13 countries (Bangladesh, Burkina Faso, Ethiopia, India, Kenya, Malaysia, Mali, Nigeria, Sri Lanka, Syria, Tanzania, Uganda and Uzbekistan) were trained. It was jointly sponsored by ICRISAT for its partners in the CGIAR Research Program on Dryland Cereals and CGIAR Research Program on Grain Legumes, and by the Department of Biotechnology, Government of India for NARS partners from India.
Rajeev Varshney, Research Program Director- Grain Legumes and Director, Center of Excellence in Genomics, welcomed the participants and provided a brief overview, essence of the training course. He informed the participants though the country made some progress in some pulse crops in use of molecular markers in crop improvement but lot more need to be done in the coming days keeping the challenges that agricultural research community would face in the coming years. Further, he emphasized that in the context of recent advances in next-generation sequencing (NGS) technologies, it is now possible to undertake re-sequencing or high throughput genotyping (HTPG) approaches such as genotyping-by-sequencing (GBS) in any crop. However, the main challenge is to undertake appropriate data analysis in a given time frame (within the window of crossing) by using appropriate decision support tools. These technologies are being successfully utilized at CGIAR centers or the advanced research institutes (ARI) across the world. To serve the national partners to have access this technology through capacity building and the genotyping services, ICRISAT established the Center of Excellence of Genomics supported by the Department of Biotechnology, Government of India. This is the second training of under the second phase of Department of Biotechnology funded Project for organizing this training course. Rajeev Varshney informed the participants that learning is a continuous and evolutionary process and requested the participants institute also can impart the knowledge from the participants.
During the inaugural session Deputy Director General Dr. CLL Gowda welcomed the participants and congratulated the course participants. “There is a need to deploy the emerging technologies for crop improvement, we have been designing and offering training courses for making effective use of modern technologies in breeding. Recent advances in next generation technologies enabled the availability of draft genomes of several crop plants and the question before us is making best of the sequence information. This course provides insights into data analysis and effective use of the information for crop improvement” said Dr. Gowda.
Dr. Varshney mentioned “Since 2008, we have organized 10 training courses and trained 257 scientists including 195 from India and 62 from developing countries in the field of modern genomics and molecular breeding. We are pleased to make a mention that scientists trained in these courses are deploying the genomics tools in their breeding programs” said the course coordinator. This training course has been specially designed to handle the NGS data for deploying in crop improvement programs.
The participants learnt more about NGS data and omics approaches for crop improvement, genotyping-by-sequencing, genome-wide association studies (GWAS), marker-assisted recurrent selection (MARS), marker-assisted backcrossing and genomic selection (GS). In addition, the participants had hands on training on genetic mapping, QTL mapping, GWAS, GS, R- software and decision support tools. The participants learnt more about the advance methodologies of molecular plant breeding and its applications, rather generating the data.