ArticleScientific reports2020
Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Microglial TDP-43 mediates myelin refinement and represses Tyrobp cryptic exon inclusion in mice.Nature neuroscience · 2026Article
- An in silico protocol for predicting genetic biomarkers in rare diseases: a case study in sporadic amyotrophic lateral sclerosis.Frontiers in genetics · 2026Article
- Unveiling the regulatory potential of the non-coding genome: Insights from the human genome project to precision medicine.Genes & diseases · 2025Review
- Loss of MEF2C function by enhancer mutation leads to neuronal mitochondria dysfunction and motor deficits in mice.Molecular neurodegeneration · 2025Article
- Epigenetics in Neurodegenerative Diseases.Sub-cellular biochemistry · 2025Review
- Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.HGG advances · 2024Article
- Exploring Stroke Risk through Mendelian Randomization: A Comprehensive Study Integrating Genetics and Metabolic Traits in the Korean Population.Biomedicines · 2024Article
- Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends.Healthcare (Basel, Switzerland) · 2023Review
- Article
- Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.Frontiers in aging neuroscience · 2022Review
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- NovelFrontiers in aging neuroscience · 2021Article
- What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis?Journal of personalized medicine · 2020Review
- Article
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Authors and funding
6 authors.
Funding
Abstract
Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sclerosis (ALS). However, pinpointing the risk variants in noncoding regions and underlying biological mechanisms remains a major challenge. Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analysis dataset to unravel functional noncoding variants associated with ALS based on their epigenetic features. After filtering and prioritizing of candidates, we fine-mapped two new risk variants, rs2370964 and rs3093720, on chromosome 3 and 17, respectively. Further analysis revealed that these polymorphisms are associated with the expression level of CX3CR1 and TNFAIP1, and affect the transcription factor binding sites for CTCF, NFATc1 and NR3C1. Our results may provide new insights for ALS pathogenesis, and the proposed research methodology can be applied for other complex diseases as well.
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