The French Exome Project

Novel high throughput sequencing technologies enable the characterization of all the genetic variations in the coding sequence (exome) of individuals and the discovery of novel variants involved in monogenic and complex diseases. The study of these two kinds of diseases require different methods and raises specific problems however, in both situations, the major challenge is to understand the functional role of the identified variants to filter out the neutral ones. Indeed, in each human exome, about 20,000 nucleotide variants are found among which a significant proportion is rare and not described in public databases. Filtering the candidate variants based on the information contained in public databases is difficult as these databases contain no data from France. Genome-wide association studies have shown that population stratification is a concern in Europe and even within countries in Europe. Indeed, significant allele frequency differences are seen at common SNPs and the problem is expected to be even worse at rare variants that have appeared more recently in populations and have therefore not spread over large geographic areas. Association studies on exome data could therefore considerably benefit from the setting-up of a specific database containing the exome data of a reference control panel of individuals of French ancestry. The FREX project is a federative project that aims at building such a database and at giving access to the scientific community to exome data that can serve as controls in association studies. A total of 600 individuals sampled in 6 French regions have been sequenced using the Agilent V5+UTR exome capture kit and genotyped on Illumina Core Exome SNP-chip.
The FREX consortium includes 6 partner teams:
(1) Inserm UMR 1078, UBO, EFS, CHU Brest
(2) Inserm UMR 1087, CNRS UMR 6291, l’institut du Thorax, CHU Nantes and Université de Nantes
(3) Centre National de Génotypage, Evry
(4) Inserm UMR 1079, CNR-MAJ, Rouen University Hospital, Rouen University, IRIB, Normandy University, Rouen, France
(5) Inserm UMR 1167, Institut Pasteur de Lille, Université Lille-Nord de France
(6) Inserm UMR 897, Université de Bordeaux

Publications :

• Nicolas G. et al. SORL1 rare variants: a major risk factor for familial early-onset Alzheimer’s disease. Mol Psychiatry. 2016, vol. 21, issue 6
• Le Guennec K. et al. 17q21.31 duplication causes prominent tau-related dementia with increased MAPT expression. Mol Psychiatry. 2016, vol. 13
• Le Guennec K. et al. ABCA7 rare variants and Alzheimer disease risk. Neurology 2016, vol. 86, issue 23
• Bellenguez C. et al. Contribution to Alzheimer’s disease risk of rare variants in TREM2, SORL1, and ABCA7 in 1779 cases and 1273 controls. Neurobiol. Aging 2017,  pii: S0197-4580 issue 17
• Juge P-A. et al. Shared genetic predisposition in rheumatoid arthritis-interstitial lung disease and familial pulmonary fibrosis. European Respiratory Journal 2017, vol. 49, issue 5
• Guey S. et al. Rare RNF213 variants in the C-terminal region encompassing the RING-finger domain are associated with moyamoya angiopathy in Caucasians. European Journal of Human Genetics 2017, vol. 25, issue 8
• Khan W et al. MACARON: a python framework to identify and re-annotate multi-base affected codons in whole genome/exome sequence data. Bioinformatics 2018 vol. 34, issue 19
• Kim A. et al. Integrated clinical and omics approach to rare diseases: novel genes and oligogenic inheritance in holoprosencephaly. Brain 2018, vol. 142, issue 1
• Emmanuelle Boscher et al. Copy Number Variants in miR-138 as a Potential Risk Factor for Early-Onset Alzheimer’s Disease. Journal of Alzheimer’s Disease 2019, vol. 68, issue 3
• Ma Y. et al. Analysis of Whole-Exome Sequencing Data for Alzheimer Disease Stratified by APOE Genotype. JAMA Neurology 2019, vol. 76, issue 9
• Quenez O. et al. Detection of copy-number variations from NGS data using read depth information: a diagnostic performance evaluation. European Journal of Human Genetics 2021, vol.29
• Bocher O. et al. Extension of SKAT to multi-category phenotypes through a geometrical interpretation. European Journal of Human Genetics 2021, vol. 29