DIG-ALS – Studio della eredità oligogenica nella SLA cercando geni digenici di malattia attraverso metodiche di machine learning

RATIONALE. Although over 40 ALS genes have been identified, in about 85% of cases the genetic cause is unknown, even if studies estimate that genetics accounts for up to 60% of ALS pathogenesis. Thus, an oligogenic inheritance was suggested and supported by a few studies reporting 1-13% of patients carrying 2 pathogenic variants in ALS genes. Further studies are thus necessary, focused on large cohorts and computational models to perform a genome-wide analysis. BROAD OBJECTIVES DIG-ALS is a multidisciplinary project (3 Units) aimed to systematically investigate the role of the oligogenic/digenic inheritance in ALS using different machine learning tools in a large cohort with available Whole Genome Sequencing (WGS) data, and to functionally validate candidate digenic disease gene pairs. PRELIMINARY DATA DIG-ALS feasibility rely on: a) WGS of 4090 Italian ALS patients and 775 matched controls including a populationbased cohort of the Piemonte and Valle d’Aosta ALS Register, and 64 trios (Unit 2,1,3). b) pilot investigation of 70 ALS WGS with DIVAs, a machine learning tool for the prediction of digenic disease gene pairs (Unit 1) c) expertise and available tools for functional validation of novel disease gene pairs in human motoneurons from iPSC (Unit 3,1). PROJECT DESIGN AND METHODS DISCOVERY. From each WGS (4090 ALS, 775 controls and 64 trios), the identified variants (SNV, CNVs and tandem repeat), filtered for pathogenicity, will be analysed with different machine learning tools (DIVAs, VarCoPP, DiGePred, OligoPVP) for identifying candidate digenic disease gene pairs. REPLICATION. The digenic disease gene pairs with the highest prediction score for pathogenicity will be replicated in WGS from the database of Project Mine ALS . All findings will be correlated with clinical phenotype. FUNCTIONAL VALIDATION. A subset of these candidate gene pairs will be validated with ad hoc designed functional assay in human motoneurons derived from iPSC or immortalized neuronal cell lines. CRISPR/Cas9 system will be utilized to mutagenize or revert each of the gene pairs in iPSC to model digenic conditions to be compared to isogenic wild-type and monogenic conditions. EXPECTED RESULTS. DIG-ALS will provide a comprehensive overview of the contribution of digenic/oligogenic inheritance in ALS . The functionally validated candidate gene pairs will potentially identify new pathogenic mechanisms in ALS and will be a basis for novel therapies.