ArticleFrontiers in genetics2012
Mapping Novel Pathways in Cardiovascular Disease Using eQTL Data: The Past, Present, and Future of Gene Expression Analysis.
Article in Frontiers in genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
10 citing papers in PubMed.
- Integration of dilated cardiomyopathy genomics with transcriptomics from the human heart implicates regulatory molecular mechanisms.medRxiv : the preprint server for health sciences · 2026Article
- Secondary analyses for genome-wide association studies using expression quantitative trait loci.Genetic epidemiology · 2022Article
- Systems biology in cardiovascular disease: a multiomics approach.Nature reviews. Cardiology · 2021Review
- Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence.Journal of personalized medicine · 2021Review
- hiPSCs in cardio-oncology: deciphering the genomics.Cardiovascular research · 2019Review
- SNPs and Somatic Mutation on Long Non-Coding RNA: New Frontier in the Cancer Studies?High-throughput · 2018Review
- Association of twelve polymorphisms in three onco-lncRNA genes with hepatocellular cancer risk and prognosis: A case-control study.World journal of gastroenterology · 2018Article
- Through the Looking Glass:Applied in vitro toxicology · 2018Review
- Identification of a Sjögren's syndrome susceptibility locus at OAS1 that influences isoform switching, protein expression, and responsiveness to type I interferons.PLoS genetics · 2017Article
- Proceedings: consideration of genetics in the design of induced pluripotent stem cell-based models of complex disease.Stem cells translational medicine · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWAS) have identified genetic variants associated with numerous cardiovascular and metabolic diseases. Newly identified polymorphisms associated with myocardial infarction, dyslipidemia, hypertension, diabetes, and insulin resistance suggest novel mechanistic pathways that underlie these and other complex diseases. Working out the connections between the polymorphisms identified in GWAS and their biological mechanisms has been especially challenging given the number of non-coding variants identified thus far. In this review, we discuss the utility of expression quantitative trait locus (eQTL) databases in the study of non-coding variants with respect to cardiovascular and metabolic phenotypes. Recent successes in using eQTL data to link variants with functional candidate genes will be reviewed, and the shortcomings of this approach will be outlined. Finally, we discuss the emerging next generation of eQTL studies that take advantage of the ability to generate induced pluripotent stem cell lines from population cohorts.
Indexed as
Identifiers
What OpenQuestion holds
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.