ArticleFrontiers in genetics2019
Integrate GWAS, eQTL, and mQTL Data to Identify Alzheimer's Disease-Related Genes.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 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
- Post-genome-wide association study variant-to-function challenges in asthma research.The Journal of allergy and clinical immunology · 2026Review
- Blood DNA Methylation Predicts Long-Term Risk of Dementia in Prospective Cohorts.medRxiv : the preprint server for health sciences · 2026Article
- Computational analysis of DNA methylation from long-read sequencing.Nature reviews. Genetics · 2025Review
- Interaction of genetic variants and methylation in transcript-level expression regulation in Alzheimer's disease by multi-omics data analysis.BMC genomics · 2025Article
- DNA Methylation in Alzheimer's Disease.Current topics in behavioral neurosciences · 2025Review
- Identification of novel proteins associated with intelligence by integrating genome-wide association data and human brain proteomics.PloS one · 2025Article
- Identification of novel drug targets through integrative PWAS of brain and plasma proteins with Ulcerative Colitis GWAS.PloS one · 2025Article
- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework.Briefings in bioinformatics · 2024Article
- Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes.Annual review of biomedical data science · 2024Review
- Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.BMC genomics · 2024Article
- Translation of genome-wide association study: from genomic signals to biological insights.Frontiers in genetics · 2024Review
- Genetic impacts on DNA methylation help elucidate regulatory genomic processes.Genome biology · 2023Article
- Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle.Animals : an open access journal from MDPI · 2023Review
- Golgi apparatus, endoplasmic reticulum and mitochondrial function implicated in Alzheimer's disease through polygenic risk and RNA sequencing.Molecular psychiatry · 2023Article
- DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits.Nature genetics · 2023Article
- Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes.Human molecular genetics · 2022Review
- Article
- Identification of novel proteins for lacunar stroke by integrating genome-wide association data and human brain proteomes.BMC medicine · 2022Article
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4 authors.
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Abstract
It is estimated that the impact of related genes on the risk of Alzheimer's disease (AD) is nearly 70%. Identifying candidate causal genes can help treatment and diagnosis. The maturity of sequencing technology and the reduction of cost make genome-wide association study (GWAS) become an important means to find disease-related mutation sites. Because of linkage disequilibrium (LD), neither the gene regulated by SNP nor the specific SNP can be determined. Because GWAS is affected by sample size and interaction, we introduced empirical Bayes (EB) to make a meta-analysis of GWAS to greatly eliminate the bias caused by sample and the interaction of SNP. In addition, most SNPs are in the noncoding region, so it is not clear how they relate to phenotype. In this paper, expression quantitative trait locus (eQTL) studies and methylation quantitative trait locus (mQTL) studies are combined with GWAS to find the genes associated with Alzheimer disease in expression levels by pleiotropy. Summary data-based Mendelian randomization (SMR) is introduced to integrate GWAS and eQTL/mQTL data. Finally, we prioritized 274 significant SNPs, which belong to 20 genes by eQTL analysis and 379 significant SNPs, which belong to seven known genes by mQTL. Among them, 93 SNPs and 2 genes are overlapped. Finally, we did 10 case studies to prove the effectiveness of our method.
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