Olivier Quenez (France)
Inserm / University Hospital of Rouen
Inserm U1245 - CNR-MAJ
Olivier Quenez is a bioinformatics engineer at the Inserm unit U1245 and the University Hospital of Rouen, currently doing his PhD at the university of Rouen Normandie. After obtaining his master's degree, he first worked on the development of applications for biologists for data storage and processing, before going back to bioinformatics data analysis. In his team, “genetics of Alzheimer’s disease and dementia”, he is in charge of the bioinformatics analysis of sequencing data performed in the context of neurodegenerative and neuropsychiatric diseases, as well as the implementation of new analysis approaches for high-throughput sequencing data. As part of his thesis work, he is developing an approach to detect copy number variations from exome sequencing data, based on the CANOES software. The aim is to apply this approach to case-control studies in the context of Alzheimer's disease research in order to identify potential new risk factors. He is also involved in several national training courses for biologists, where he gives an overview of the use of bioinformatics in the context of high-throughput sequencing, both in a theoretical and practical way. Finally, he is also a member of the board of BioinfoDiag, the national association for bioinformatics in diagnostics.

Presenter of 1 Presentation

 Conference Calendar

CONTRIBUTION OF RARE COPY NUMBER VARIANTS TO THE RISK OF COMPLEX FORMS OF ALZHEIMER DISEASE

Session Name
0220 - GENETICS OF NEURODEGENERATION 1 (ID 7)
Session Type
SYMPOSIUM
Date
Wed, 16.03.2022
Session Time
08:30 AM - 10:30 AM
Room
ONSITE: 113
Lecture Time
09:15 AM - 09:30 AM
Presenter

Abstract

Aims

Rare missense and truncating nucleotide variants in SORL1, TREM2 and ABCA7 are moderate-to-high Alzheimer disease (AD) risk factors, to which ATP8B4 and ABCA1 were recently added thanks to the aggregation of exome sequencing data from the ADES and ADSP consortia.

Methods

To assess the role of copy number variants (CNVs, deletions and duplications), we applied a highly-sensitive bioinformatics pipeline based on the CNV caller CANOES to 20,661 exomes (8941 controls, 3770 early-onset AD [EOAD], 7950 late-onset AD [LOAD]) from ADES-ADSP. We focused on rare CNVs (frequency<1%) and aggregated CNV counts at the gene level.

Results

We detected 49,460 rare CNVs. Among previously mentioned genes, we identified 6 deletions of ABCA7 in cases and 3 in controls and 3 ABCA1 deletions in three cases (all EOAD, none in controls). To assess the effect of ABCA1 loss of function on EOAD and LOAD risk simultaneously, we gathered data from high-confidence truncating nucleotide variants with CNVs in an ordinal regression model. While the previously reported aggregation of truncating and missense variants (OR=1.9 [1.5-2.5]) hid a heterogeneous effect of missense and truncating variants considered separately (OR=1.7 [1.3-2.2] and OR = 4.7 [2.2-10.3] respectively), the joint analysis of the full spectrum of rare high-confidence loss-of-function ABCA1 variants (p=3.1x10-5) confirmed their strong association with EOAD risk (OR=8.1 [2.9-29.4]).

Conclusions

Our data suggest that extremely rare CNVs may contribute to AD risk, some of them could be associated with a large effect, as the loss of ABCA1 function. Additional genes are currently being analyzed and will be presented.

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