Integrating copy-number analysis with structural-variation detection in 50 ALS patients with two extreme survival phenotypes
published: July 21, 2017, recorded: May 2017, views: 797
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Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease predominantly of motor neurons, characterized by progressive weakness of voluntary muscles and death from respiratory failure due to diaphragmatic paralysis, typically within 3 years of onset. Despite the very poor prognosis, there is considerable variation in the survival rate, and up to 10% of people with ALS live more than 8 years from first symptoms. There is a strong genetic contribution to ALS risk. In 5% of cases or more, a family history of ALS or frontotemporal dementia is obtained, and the Mendelian genes responsible for ALS in such families have now been identified in about 70% of cases. Even in apparently sporadic cases, twin and population studies show the heritability is about 60%. Although risk genes reveal information about the mechanism of causation of ALS, it is also important to identify gene variants that modify survival. Survival genes could potentially be targeted directly, or their product augmented to improve ALS survival. A number of common gene variants associated with ALS survival have been identified through genome-wide association studies or other genome-wide approaches like studying structural variants. Aim: Is to know the structural variations difference between different ALS survival groups. Methods: Analysis of copy-number variation using whole-genome sequencing data of 50 ALS patients with two extreme phenotypes, 25 short lived patients against 25 long survived ALS patients using Copy Number Segmentation by Regression Tree in Next Generation Sequencing (CONSERTING). Samples: The top and bottom 1.5% of ALS patients by survival were identified (25 patients from each tail of the distribution). All patients were classified as definite or probable ALS according to the El Escorial criteria and had no family history of ALS. Sample ancestry and relatedness were evaluated by principal components analysis and relationship matrices.
Download slides: encals2017_al_khleifat_extreme_survival_phenotypes_01.pdf (2.4 MB)
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