Responsabile Scientifico

• D'ALFONSO Sandra (DSS)

Partecipanti al progetto

• D'ALFONSO Sandra (DSS)
• CORRADO Lucia (DSS)

Titolo del progetto

• Genetic and environment interaction in determining phenotype and progression of ALS (GENIALS)

Ente finanziatore

• 
  MUR - Ministero dell'Università e Ricerca

Programma di finanziamento

• PRIN - Progetti di Ricerca di Rilevante Interesse Nazionale (dal 2020)

Bando di riferimento

• PRIN 2022

Anno di presentazione del progetto

• 2022

Anno di approvazione del progetto

• 2023

Project ID / numero contratto

• 20228N7573_002

Durata del progetto

• 24 mesi

Inizio del progetto

• 18/10/2023

Fine del progetto

• 18/10/2025

Ente capofila

• UNITO - Università degli studi di Torino

Progetto approvato - Contributo unità di ricerca del Dipartimento

• €85.000,00

Sustainable Development Goals - Agenda 2030

Keyword

• Amyotrophic Lateral Sclerosis
• Motor and cognitive phenoype
• Genetic-environment interaction
• Machine learning
• Disease progression
• Lifelong lifestyle

Stato del progetto

• Approvato

Abstract

• Amyotrophic lateral sclerosis (ALS) is the most common adult-onset motor neuron disease. Despite the considerable advances made over the last decade in identifying the genetic causes of ALS, the etiology of ~85% of cases is unknown, and it is increasingly thought that ALS is a complex disease arising from the interplay of genetic and environmental factors. A key advance in our understanding of ALS over the last decade has been the realization that it is not a single entity, but rather a collection of distinct syndromes whose complexity is reflected in the clinical heterogeneity observed among patients, the disparate neuropathology found at autopsy, and the large number of genes that have been implicated in ALS. This complexity of ALS represents a major obstacle to understanding and predicting the clinical progression of the disease and the identification of effective treatments. GENIALS is an entirely new type of project designed to identify the gene- environment interactions that are associated in predicting the clinical phenotype of ALS, with particular regards to its age at onset and progression. This data-driven project is only feasible because of the Piemonte ALS registry that has collected longitudinal demographical and clinical data and biospecimens on every ALS case in the region over the last two decades, and because of the environmental data that has been independently collected by Regional Agency for the Protection of the Environment (Agenzia Regionale per la Protezione Ambientale - ARPA). Clinical, life-style and environmental data will be merged with the genome sequencing data that were already generated for this cohort. The resulting multi-dimensional dataset will form the ideal structure for machine learning and data mining approaches to pinpoint the networks underlying ALS with particular regard to the factor determining patients’ phenotype, age at onset and disease progression. The discovery of each new gene, environmental factor and network involved in ALS disease expression proves fundamental insights into the cellular mechanisms underlying neuron degeneration, and eventually will facilitate disease modeling, as well as the design of targeted therapeutics. Currently, a total of 1320 whole genome sequences (WGS) have been obtained for ALS patients resident in Piemonte in the 2007-2016 period, together with 770 whole genomes of age-, gender and geographically matched controls. GENIALS is unprecedented in terms of the depth of information available for each patient and for matched control subjects within a defined geographical region. Machine learning has emerged as a powerful technique to identify complex patterns within massive datasets. Combining these resources and our analytical expertise represents an exceptional opportunity to unravel the determinants of heterogeneity of progression and phenotype of ALS, a fatal neurodegenerative disease that kills approximately 11,000 Europeans annually.