Familial vs. sporadic sarcoidosis: BTNL2 polymorphisms, clinical presentations, and outcomes in a French cohort.

BACKGROUND: The occurrence of familial forms of sarcoidosis (OMIM 181100) suggests a genetic predisposition. The involvement of butyrophilin-like 2 (BTNL2) gene (rs2076530 variant) has to be investigated. RESULTS: The study performed independent analyses of BTNL2 polymorphism, clinical phenotypes, and outcomes in familial vs. sporadic presentations in 256 sporadic and 207 familial cases from 140 families. The logistic multivariate model showed that a young age at diagnosis and the combination of lung and skin involvement at diagnosis may distinguish sporadic from familial sarcoidosis (p = 0.016 and p = 0.041). We observed also that Sarcoid Clinical Activity Classification (SCAC) profiles were significantly different between familial and sporadic cases (p = 0.0497). Variant rs2076530 was more frequent in patients than in controls (OR = 2.02; 95% CI: [1.32-3.09]) but showed no difference between sporadic and familial cases and no difference according to the clinical phenotype or the outcome. CONCLUSION: Despite a significant difference in BTNL2 polymorphism between sarcoid patients and controls, there was no such difference between familial and sporadic sarcoidosis cases and no correlation between BTNL2 polymorphism and disease severity or outcome. Thus, BTNL2 difference cannot be considered as a key marker for disease classification or patient management.

Functional variants of POC5 identified in patients with idiopathic scoliosis.

Idiopathic scoliosis (IS) is a spine deformity that affects approximately 3% of the population. The underlying causes of IS are not well understood, although there is clear evidence that there is a genetic component to the disease. Genetic mapping studies suggest high genetic heterogeneity, but no IS disease-causing gene has yet been identified. Here, genetic linkage analyses combined with exome sequencing identified a rare missense variant (p.A446T) in the centriolar protein gene POC5 that cosegregated with the disease in a large family with multiple members affected with IS. Subsequently, the p.A446T variant was found in an additional set of families with IS and in an additional 3 cases of IS. Moreover, POC5 variant p.A455P was present and linked to IS in one family and another rare POC5 variant (p.A429V) was identified in an additional 5 cases of IS. In a zebrafish model, expression of any of the 3 human IS-associated POC5 variant mRNAs resulted in spine deformity, without affecting other skeletal structures. Together, these findings indicate that mutations in the POC5 gene contribute to the occurrence of IS.

Comparison of two next-generation sequencing kits for diagnosis of epileptic disorders with a user-friendly tool for displaying gene coverage, DeCovA.

In recent years, molecular genetics has been playing an increasing role in the diagnostic process of monogenic epilepsies. Knowing the genetic basis of one patient's epilepsy provides accurate genetic counseling and may guide therapeutic options. Genetic diagnosis of epilepsy syndromes has long been based on Sanger sequencing and search for large rearrangements using MLPA or DNA arrays (array-CGH or SNP-array). Recently, next-generation sequencing (NGS) was demonstrated to be a powerful approach to overcome the wide clinical and genetic heterogeneity of epileptic disorders. Coverage is critical for assessing the quality and accuracy of results from NGS. However, it is often a difficult parameter to display in practice. The aim of the study was to compare two library-building methods (Haloplex, Agilent and SeqCap EZ, Roche) for a targeted panel of 41 genes causing monogenic epileptic disorders. We included 24 patients, 20 of whom had known disease-causing mutations. For each patient both libraries were built in parallel and sequenced on an Ion Torrent Personal Genome Machine (PGM). To compare coverage and depth, we developed a simple homemade tool, named DeCovA (Depth and Coverage Analysis). DeCovA displays the sequencing depth of each base and the coverage of target genes for each genomic position. The fraction of each gene covered at different thresholds could be easily estimated. None of the two methods used, namely NextGene and Ion Reporter, were able to identify all the known mutations/CNVs displayed by the 20 patients. Variant detection rate was globally similar for the two techniques and DeCovA showed that failure to detect a mutation was mainly related to insufficient coverage.