SynthesisHuman molecular genetics2024
Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data.
Synthesis in Human molecular genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Management of Childhood Obesity.International journal of molecular sciences · 2026Review
- Multiomics of Human Obesity and Related Multisystem Diseases.The Journal of clinical endocrinology and metabolism · 2026Article
- Beyond BMI: Rethinking Obesity Metrics and Cardiovascular Risk in the Era of Precision Medicine.Diagnostics (Basel, Switzerland) · 2025Review
- Unraveling the Genetic Architecture of Obesity: A Path to Personalized Medicine.Diagnostics (Basel, Switzerland) · 2025Review
- Exploring the Shared Genetic Architecture Between Obstructive Sleep Apnea and Body Mass Index.Nature and science of sleep · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThis study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data.
methodsWe conducted a meta-analysis to leverage information from a genome-wide association study (n = 339 224), a transcriptome-wide association study (n = 5619), and an epigenome-wide association study (n = 3743). We prioritized the significant genes with a machine learning-based method, netWAS, which borrows information from adipose tissue-specific interaction networks. We also used the brain-specific network in netWAS to investigate genes potentially involved in brain-adipose interaction.
resultsWe identified 195 genes that were significantly associated with BMI through meta-analysis. The netWAS analysis narrowed down the list to 21 genes in adipose tissue. Among these 21 genes, six genes, including FUS, STX4, CCNT2, FUBP1, NDUFS3, and RAPSN, were not reported to be BMI-associated in PubMed or GWAS Catalog. We also identified 11 genes that were significantly associated with BMI in both adipose and whole brain tissues.
conclusionThis study integrated three types of omics data and identified a group of genes that have not previously been reported to be associated with BMI. This strategy could provide new insights for future studies to identify molecular mechanisms contributing to BMI regulation.
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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.