Causal associations of genetic factors with clinical progression in amyotrophic lateral sclerosis. (April 2022)

• Record Type: Journal Article
• Authors: Ahangaran, Meysam; Chiò, Adriano; D'Ovidio, Fabrizio; Manera, Umberto; Vasta, Rosario; Canosa, Antonio; Moglia, Cristina; Calvo, Andrea; Minaei-Bidgoli, Behrouz; Jahed-Motlagh, Mohammad-Reza
• Is Part Of: Computer methods and programs in biomedicine. Volume 216(2022)
• Publication Date: 2022-04
• Subjects: Amyotrophic lateral sclerosis -- Longitudinal analysis -- Machine learning -- Prognosis -- Causal discovery; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106681
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A causal learning model named PCDSD was used to extract causal associations between ALS clinical factors in the form of causal graph. There is a meaningful association between genetic factors ( C9ORF72, SOD1, TARDBP and FUS genes) and ALS progression rate. Longitudinal study of ALS dataset is a suitable method for discovering causal factors of this disease. Entropy-based analysis is a reliable approach for determining the degree of certainty of causal associations. Probabilistic causal graph of ALS disease was used for predicting the future states of the patients. Abstract: Background and objective: Recent advances in the genetic causes of ALS reveals that about 10% of ALS patients have a genetic origin and that more than 30 genes are likely to contribute to this disease. However, four genes are more frequently associated with ALS: C9ORF72, TARDBP, SOD1, and FUS . The relationship between genetic factors and ALS progression rate is not clear. In this study, we carried out a causal analysis of ALS disease with a genetics perspective in order to assess the contribution of the four mentioned genes to the progression rate of ALS. Methods: In this work, we applied a novel causal learning model to the CRESLA dataset which is a longitudinal clinical dataset of ALS patients including genetic information of such patients. This study aims to discover the relationship between four mentioned genes and ALS progression rate from a causation perspective using machine learning and probabilistic methods. Results: The results indicate a meaningful association between genetic factors and ALS progression rate with causality viewpoint. Our findings revealed that causal relationships between ALSFRS-R items associated with bulbar regions have the strongest association with genetic factors, especially C9ORF72 ; and other three genes have the greatest contribution to the respiratory ALSFRS-R items with a causation point of view. Conclusions: The findings revealed that genetic factors have a significant causal effect on the rate of ALS progression. Since C9ORF72 patients have higher proportion compared to those carrying other three gene mutations in the CRESLA cohort, we need a large multi-centric study to better analyze SOD1, TARDBP and FUS contribution to the ALS clinical progression. We conclude that causal associations between ALSFRS-R clinical factors is a suitable predictor for designing a prognostic model of ALS. Graphical abstract: Image, graphical abstract