ArticleBioData mining2014
Identifying genetic interactions associated with late-onset Alzheimer's disease.
Article in BioData mining, 2014. 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, 22 citations in OpenAlex.
- Cross-tissue graph attention networks for semi-supervised gene expression prediction.Briefings in bioinformatics · 2026Article
- Selective Inactivation of Astrocytic Monoacylglycerol Lipase for Alzheimer's Disease Therapy.bioRxiv : the preprint server for biology · 2025Article
- Navigating the blurred boundary: Neuropathologic changes versus clinical symptoms in Alzheimer's disease, and its consequences for research in genetics.Journal of Alzheimer's disease : JAD · 2025Review
- Deep joint learning diagnosis of Alzheimer's disease based on multimodal feature fusion.BioData mining · 2024Article
- Alzheimer's Disease: An Updated Overview of Its Genetics.International journal of molecular sciences · 2023Review
- Alzheimer's disease-associated U1 snRNP splicing dysfunction causes neuronal hyperexcitability and cognitive impairment.Nature aging · 2022Article
- The interaction of early life factors and depression-associated loci affecting the age at onset of the depression.Translational psychiatry · 2022Article
- Effects of Specific Inhibitors for CaMK1D on a Primary Neuron Model for Alzheimer's Disease.Molecules (Basel, Switzerland) · 2021Article
- GSAP regulates lipid homeostasis and mitochondrial function associated with Alzheimer's disease.The Journal of experimental medicine · 2021Article
- Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease.Scientific reports · 2021Article
- Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data.Mechanisms of ageing and development · 2021Article
- Novel Alzheimer's disease risk variants identified based on whole-genome sequencing of APOE ε4 carriers.Translational psychiatry · 2021Article
- A fast forward 3D connection algorithm for mitochondria and synapse segmentations from serial EM images.BioData mining · 2018Article
- LEAP: biomarker inference through learning and evaluating association patterns.Genetic epidemiology · 2015Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIdentifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease.
resultsWe applied BCM to two late-onset Alzheimer's disease (LOAD) GWAS datasets to identify SNPs that interact with known Alzheimer associated SNPs. We also compared BCM with logistic regression that is implemented in PLINK. Gene Ontology analysis of genes from the top 200 dataset SNPs for both GWAS datasets showed overrepresentation of LOAD-related terms. Four genes were common to both datasets: APOE and APOC1, which have well established associations with LOAD, and CAMK1D and FBXL13, not previously linked to LOAD but having evidence of involvement in LOAD. Supporting evidence was also found for additional genes from the top 30 dataset SNPs.
conclusionBCM performed well in identifying several SNPs having evidence of involvement in the pathogenesis of LOAD that would not have been identified by univariate analysis due to small main effect. These results provide support for applying BCM to identify potential genetic variants such as SNPs from high dimensional GWAS datasets.
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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 OpenQuestion graph.