Whole genome-based pre-breeding for climate and disease-resilient cucurbits varieties
(contact: Prof. Naceur DJEBALI, naceur.djebali@cbbc.rnrt.tn, Dr. Mokhtar ELBEKKAY, and Prof. Laurent GENTZBITTEL, L.Gentzbittel@skoltech.ru, PhD Thesis "En Co-tutelle" (doctorate in joint-supervision) between Skoltech Agro / Center of Biotechnology of Borj Cedria & Arid Regions Institute, Medénine, Tunisia)
Cucurbits are a valuable source of essential nutrients for millions of people around the world. They are rich in vitamins A and C, fiber, minerals, and antioxidants. Cucurbits are economically important crops for many countries including Russia and Tunisia. They provide a source of income for farmers and contribute significantly to the agricultural sector ensuring food security and dietary diversity for people worldwide. The objective of this PhD thesis is to comprehensively characterize the genetic diversity of three Cucurbits species, namely melon (Cucumis melo), water melon (Citrullus lanatus) and Summer squash (Cucurbita pepo), and their resistance to salinity and emerging diseases in Tunisia and Russia. This will allow optimizing their utilization to create more resilient varieties, to cope with the future environmental challenges in the context of climate change.
The objectives of this project include:
1. To characterize the genetic diversity within cucurbits germplasm by analysing high-throughput genotyping-by-sequencing (GBS) data.
2. To perform statistical analysis of phenotypic data obtained from Multi-Environmental Trials (MET) in Tunisia and Russia.
3. To carry out experiments to analyze the response to salinity and emerging diseases in controlled conditions within the collection of cucurbits.
4. To conduct Genome-wide Association study to reveal the genetic architecture of major agronomical traits of interest for the cucurbits species.
5. To develop molecular markers associated with QTL identified by GWAS to help in breeding for the cucurbits species.
6. To implement genomic prediction for complex traits for the three cucurbits species.
Required skills: Bio-computational analysis of NGS datasets (incl. de novo sequence alignment, SNP calling), Biostatistics (incl. GLM and MLM), Quantitative genetics incl. (GWAS, genomic prediction), molecular biology (for molecular markers development), and knowledge of plant breeding (incl. Multi-environmental trials data analysis and GXE analysis).
Cucurbits are a valuable source of essential nutrients for millions of people around the world. They are rich in vitamins A and C, fiber, minerals, and antioxidants. Cucurbits are economically important crops for many countries including Russia and Tunisia. They provide a source of income for farmers and contribute significantly to the agricultural sector ensuring food security and dietary diversity for people worldwide. The objective of this PhD thesis is to comprehensively characterize the genetic diversity of three Cucurbits species, namely melon (Cucumis melo), water melon (Citrullus lanatus) and Summer squash (Cucurbita pepo), and their resistance to salinity and emerging diseases in Tunisia and Russia. This will allow optimizing their utilization to create more resilient varieties, to cope with the future environmental challenges in the context of climate change.
The objectives of this project include:
1. To characterize the genetic diversity within cucurbits germplasm by analysing high-throughput genotyping-by-sequencing (GBS) data.
2. To perform statistical analysis of phenotypic data obtained from Multi-Environmental Trials (MET) in Tunisia and Russia.
3. To carry out experiments to analyze the response to salinity and emerging diseases in controlled conditions within the collection of cucurbits.
4. To conduct Genome-wide Association study to reveal the genetic architecture of major agronomical traits of interest for the cucurbits species.
5. To develop molecular markers associated with QTL identified by GWAS to help in breeding for the cucurbits species.
6. To implement genomic prediction for complex traits for the three cucurbits species.
Required skills: Bio-computational analysis of NGS datasets (incl. de novo sequence alignment, SNP calling), Biostatistics (incl. GLM and MLM), Quantitative genetics incl. (GWAS, genomic prediction), molecular biology (for molecular markers development), and knowledge of plant breeding (incl. Multi-environmental trials data analysis and GXE analysis).