ArticleScientific reports2023
Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 21 citations in OpenAlex.
- Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.Briefings in bioinformatics · 2026Article
- SASH1 as a Context-Dependent Multi-Docking Scaffold Linking Receptor Signaling to Cytoskeletal Dynamics.International journal of molecular sciences · 2026Review
- Extensive genetic interactions (epistasis) linked to alcohol use disorder in a high-risk population.Communications biology · 2026Article
- Sign Epistasis Can be Absent in Multi-peaked Landscapes With Neutral Mutations.Genome biology and evolution · 2026Article
- Personality Genomics.Annual review of psychology · 2026Review
- Genetic testing predicts appearance but not behavior in dogs.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Transcriptomic and protein analysis of human cortex reveals genes and pathways linked to NPTX2 disruption in Alzheimer's disease.bioRxiv : the preprint server for biology · 2025Article
- Alzheimer's disease transcriptional landscape in ex vivo human microglia.Nature neuroscience · 2025Article
- Complex genetic interactions affect susceptibility to Alzheimer's disease risk in the BIN1 and MS4A6A loci.GeroScience · 2025Article
- UK Biobank-A Unique Resource for Discovery and Translation Research on Genetics and Neurologic Disease.Neurology. Genetics · 2025Review
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- Deciphering novel mitochondrial signatures: multi-omics analysis uncovers cross-disease markers and oligodendrocyte pathways in Alzheimer's disease and glioblastoma.Frontiers in aging neuroscience · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- Genetic forms of tauopathies: inherited causes and implications of Alzheimer's disease-like TAU pathology in primary and secondary tauopathies.Journal of neurology · 2024Review
- The use of artificial intelligence to improve mycetoma management.PLoS neglected tropical diseases · 2024Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.