Analyses of copy number variants (CNVs) for candidate genes in complex diseases are currently a promising research field. CNVs of C-C chemokine ligand 3-like 1 (CCL3L1) gene are candidate genomic factors in rheumatoid arthritis (RA). We investigated CCL3L1 CNVs association with a case-control study in Tunisians and a transmission analysis in French trio families. Relative copy number (rCN) of CCL3L1 gene was quantified by droplet digital PCR (ddPCR) in 100 French trio families (RA patients and their two parents) and in 166 RA cases and 102 healthy controls from Tunisia. We calculated odds ratio (OR) to investigate association risk for CCL3L1 CNVs in RA. rCN identified varied from 0 to 4 in the French population and from 0 to 7 in the Tunisian population. A significant difference was observed in the distribution of these rCNs between the two populations (p = 2.34 × 10(-10)), as when rCN from French and Tunisian RA patients were compared (p = 2.83 × 10(-5)). CNVs transmission in French RA trios allowed the characterization of genotypes with the presence of tandem duplication and triplication on the same chromosome. RA association tests highlighted a protective effect of rCN = 5 for CCL3L1 gene in the Tunisian population (OR = 0.056; CI 95 % [0.01-0.46]). Characterization of CCL3L1 CNVs with ddPCR methodology highlighted specific CN genotypes in a French family sample. A copy number polymorphism of a RA candidate gene was quantified, and its significant association with RA was revealed in a Tunisian sample.

This study highlights the powerful accuracy of ddPCR for the quantification of CNVs and suggests that the variation in the CN of GSTM1 is associated with anti-CCP positivity in RA. However, it does not indicate a specific role in the susceptibility to the disease in our Tunisian sample.

BackgroundCurrently, the general understanding of the RA development is that the RA biological initiation precedes the clinical onset of the disease by up to several years. To identify new risk factors and pre-RA biomarkers that would detect specific disease development of pre-clinical phases and eventually predict disease onset and outcome, sampling in new large cohorts of asymptomatic individuals was initiated. Characterization of such individuals at high risk of developing RA will provide helpful strategies for early preventive treatment of RA.ObjectivesBenefiting of such a cohort collected in Switzerland, we aimed to characterize whole expression profile using blood samples from healthy first degree relatives of RA. A differential expression study was performed after stratification regarding clinical symptoms onset during follow-up.MethodsWhole transcriptome analysis was performed with Illumina HumanHT-12 v4 Expression BeadChip using RNAs extracted from blood samples of 69 healthy first degree RA relatives. During the follow-up, 15 out of 69 became symptomatic with at least one swollen or tender joint. After differential expression analysis, bioinformatics tools were used to identify pathways and/or biological process enrichment.ResultsControlling the false discovery rate at 0.5 level to optimize detection of putative signatures, we identified a list of 201 differentially expressed genes. Several pathways and biological process have been highlighted through this gene list.ConclusionsThis preliminary study provides candidate gene expression signatures which could be relevant for the development of RA. These candidate signatures have to be tested for their RA predictive value in a new sample set from first degree RA relatives from a French similar cohort and molecular validation of genes differentially expressed has to be performed. Follow-up of individuals will allow stratification of symptomatic relatives considering rheumatoid arthritis development, to refine expression signatures.Disclosure of InterestNone declared