SynthesisBioMed research international2018
A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity.
Synthesis in BioMed research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 23 citations in OpenAlex.
- Unraveling the Genetic Architecture of Obesity: A Path to Personalized Medicine.Diagnostics (Basel, Switzerland) · 2025Review
- TUFM in health and disease: exploring its multifaceted roles.Frontiers in immunology · 2024Review
- tRNA Biology in the Pathogenesis of Diabetes: Role of Genetic and Environmental Factors.International journal of molecular sciences · 2021Review
- Obesity, Insulin Resistance, and Colorectal Cancer: Could miRNA Dysregulation Play A Role?International journal of molecular sciences · 2019Review
- Integrating Multi-Omics Data to Identify Novel Disease Genes and Single-Neucleotide Polymorphisms.Frontiers in genetics · 2019Article
- Integrate GWAS, eQTL, and mQTL Data to Identify Alzheimer's Disease-Related Genes.Frontiers in genetics · 2019Article
- Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method.Frontiers in genetics · 2018Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To identify novel susceptibility genes and gene sets for obesity, we conducted a genomewide expression association analysis of obesity via integrating genomewide association study (GWAS) and expression quantitative trait loci (eQTLs) data. GWAS summary data of body mass index (BMI) and waist-to-hip ratio (WHR) was driven from a published study, totally involving 339,224 individuals. The eQTLs dataset (containing 927,753 eQTLs) was obtained from eQTLs meta-analysis of 5,311 subjects. Integrative analysis of GWAS and eQTLs data was conducted by SMR software. The SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA) for identifying obesity associated gene sets. A total of 13,311 annotated gene sets were analyzed in this study. SMR single gene analysis identified 20 BMI associated genes (
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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 OpenQuestion graph.