ArticleHuman genetics2024
A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study.
Article in Human genetics, 2024. 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.
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.
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Who cites it
7 citing papers in PubMed.
- Finding Significant Hits in Networks: a network-based tool for analyzing gene-level P-values to identify significant genes missed by standard methods.Briefings in bioinformatics · 2026Article
- Whole blood transcriptional signatures of age and survival identified in long life family and integrative longevity omics studies.GeroScience · 2026Article
- Immuno-inflammatory-metabolic interactions in cardiovascular diseases: a review from basic mechanisms to clinical translation.Frontiers in immunology · 2026Review
- A Robust Serum Proteomic Signature of the E2 Allele of Apolipoprotein E.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Gene-Embedded Multi-Modal Networks for Population-Scale Multi-Omics Discovery.bioRxiv : the preprint server for biology · 2025Article
- Identification of novel protective loci for executive function using the trail making test part B in the Long Life Family Study.bioRxiv : the preprint server for biology · 2025Article
- Novel Loci (medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
The Long Life Family Study (LLFS) enrolled 4953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8 × 10
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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 Socratic graph.