TARDBP公司
肌萎缩侧索硬化
C9orf72
疾病
遗传关联
SOD1
遗传学
医学
生物信息学
生物
失智症
基因
单核苷酸多态性
内科学
基因型
痴呆
作者
Meysam Ahangaran,Adriano Chiò,Fabrizio D’Ovidio,Umberto Manera,Rosario Vasta,Antonio Canosa,Cristina Moglia,Andrea Calvo,Behrouz Minaei-Bidgoli,Mohammad-Reza Jahed-Motlagh
标识
DOI:10.1016/j.cmpb.2022.106681
摘要
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.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.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.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.
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