The objective of the Genomic Variability 2018 cross-cutting program is to understand the role of genes and their variants in the development of disease. This program, based on longitudinal monitoring of cohorts of individuals and their phenotyping, aims to promote the development of new methods for analyzing longitudinal data. The results obtained will contribute to improving interpretation of the association between genetic variants detected in an individual and a disease or endophenotype, and better understanding of the role of genes in phenotypic variability.
Inserm runs cross-cutting scientific programs in order to accelerate scientific progress, support integrated and multidisciplinary research, and ensure a continuum between fundamental and clinical research. These unifying programs aim to create a new dynamic in innovative fields by developing complementary skills for exploring research niches that have as yet been little studied.
The Genomic Variability cross-cutting program offers three workstreams:
- The first must lead to the formation, based on the resources of the Constances cohort and the General Population project of the French Plan for Genomic Medicine 2025, of a cohort of individuals contributing to the identification of genetic variants shared by the general population, and for which different phenotypes and biomarkers will be measured and monitored over time.
- The second workstream will aim to develop innovative methods for integrating heterogeneous data types.
- The third and final workstream will seek to interpret these results in order to better understand the role played by genes in phenotypes.
In order for this cross-cutting program to truly add value to Inserm groups, it must spur the formation of a multidisciplinary consortium combining groups of clinicians, geneticists, epidemiologists, bioinformaticians, biostatisticians, and biologists invested in the central question of the interpretation and impact of genomic variations on the phenotype, as well as mathematicians and statisticians who will develop innovative methods of data analysis.