ArticleFrontiers in genetics2018
Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Multilayer Analysis of RNA Sequencing Data in Alzheimer's Disease to Unravel Molecular Mysteries.Advances in neurobiology · 2024Review
- Evolutionary and genomic perspectives of brain aging and neurodegenerative diseases.Progress in brain research · 2023Article
- Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer's disease.Frontiers in aging neuroscience · 2022Article
- Convergent lines of evidence support BIN1 as a risk gene of Alzheimer's disease.Human genomics · 2021Article
- Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease.Frontiers in physiology · 2021Article
- GWAS-Top Polymorphisms Associated With Late-Onset Alzheimer Disease in Brazil: Pointing Out Possible New Culprits Among Non-Coding RNAs.Frontiers in molecular biosciences · 2021Article
- Considerations for integrative multi-omic approaches to explore Alzheimer's disease mechanisms.Brain pathology (Zurich, Switzerland) · 2020Review
- Deep Reinforcement Learning for Data Association in Cell Tracking.Frontiers in bioengineering and biotechnology · 2020Article
- CHG: A Systematically Integrated Database of Cancer Hallmark Genes.Frontiers in genetics · 2020Article
- Erosion of Gene Co-expression Networks Reveal Deregulation of Immune System Processes in Late-Onset Alzheimer's Disease.Frontiers in neuroscience · 2020Article
- Predicting the Disease Genes of Multiple Sclerosis Based on Network Representation Learning.Frontiers in genetics · 2020Article
- Serum Calcium Levels and Parkinson's Disease: A Mendelian Randomization Study.Frontiers in genetics · 2020Article
- Identification of Gingivitis-Related Genes Across Human Tissues Based on the Summary Mendelian Randomization.Frontiers in cell and developmental biology · 2020Article
- Selecting Essential MicroRNAs Using a Novel Voting Method.Molecular therapy. Nucleic acids · 2019Article
- ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function.BMC bioinformatics · 2019Article
- Attention-based recurrent neural network for influenza epidemic prediction.BMC bioinformatics · 2019Article
- Acceptance Prediction for Answers on Online Health-care Community.BMC bioinformatics · 2019Article
- Using deep reinforcement learning to speed up collective cell migration.BMC bioinformatics · 2019Article
- Identifying Alzheimer's disease-related proteins by LRRGD.BMC bioinformatics · 2019Article
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5 authors.
Funding
Abstract
Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered.
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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.