Kickoff Meeting – Pilot A2.1- Thessaloniki, Greece, on October 24-25, 2017.
DataBio’s pilot A2.1 “Big data management in greenhouse eco-system”
The DataBio’s pilot A2.1 “Big data management in greenhouse eco-systems” held its kickoff meeting in Thessaloniki, Greece, on October 24-25, 2017. Were represented at this meeting the Italian Council for agricultural research and economics (CREA), EXUS and two Greek entities: the centre for research and technology (CERTH), and Neuropublic (NP). The main objective of the meeting was to outline the roadmap (refer to the below diagram) of the A2.1 pilot over DataBio’s lifecycle. The pilot is run in close collaboration between CREA and CERTH. The two institutions will share complete complementary tasks with CREA handling genomic predictions and selection (GS), while CERTH will be responsible for phenomics, metabolomics, genomics and environmental datasets acquisition.
GS is a new paradigm in breeding and has shown superior results relative to breeding approaches implemented thus far including phenotypic selection and marker aided selection (MAS). Phenotypic selection (PS) allowed early green revolution in the mid-twentieth century; the truth is PS has had its merits but by now, its progress curve reached a plateau. On the other hand, within the framework of MAS, thousands of marker-trait associations have been reported in plant breeding worldwide but, they exploitation has not been routinely done. This is particularly to be blamed on the intrinsic technological limitations of conventional MAS. Indeed too many trait loci have effects too small to detect and hence, escaped conventional MAS. The time for a novel cutting-edge breeding approach has come. The superiority of GS is mostly dependent upon the novel kind of algorithms accommodating the use of large number of marker loci covering the entire genome of interest; accounting for QTLs of small effects becomes possible. On the other hand, in breeding, speed is more important than size. Genomic modelling is well poised on this issue as it allows to drastically reduce the time to develop a new cultivar and hence, offers endless possibilities to significantly increase genetic gain and productivity per unit of time and cost. Some of the GS hallmarks is its speed and the intrinsic high prediction accuracy and the possibility to carry out GS-driven intercrosses.
These GS attributes are expected to have wide-range implications in this pilot as the cost of cultivar development is going to be reduced. Therefore, farmers can grow a better tomato variety sooner due to rapid variety development and release, making more income. GS in greenhouse tomato is expected to exert significant impact on the market potential and industry interests in this crop. Indeed, tomato is among the top cultivated crops in greenhouses, with billions of euros turnover worldwide. Tomato is considered one of the most nutritive solanum vegetables due to its high content in sugars, vitamins and antioxidants and its consumption is steadily increasing.
Integrating quantitative and population genetics, driven by big data streaming from large-scale high-throughput genomics platforms, is going to offer new solutions to classical current problems in quantitative genetics as applied in tomato breading at CERTH.