Evidence map›Paper›PMID 33770080›Full record

ArticlePLoS genetics2021

A transcription-centric model of SNP-age interaction.

Kun Wang, Mahashweta Basu, Justin Malin, Sridhar Hannenhalli

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 36% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. VEGF-A related SNPs: a cardiovascular context.Frontiers in cardiovascular medicine · 2023
    Review
  5. Article
  6. Genetic variants in promoter region ofGastroenterology report · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Kun WangCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID 0000-0001-8923-8557
Mahashweta BasuCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0002-3539-5677
Justin MalinLaboratory of Genome Integrity, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
Sridhar HannenhalliCancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID 0000-0001-9603-7569
National Institutes of Health · USUniversity of Maryland, Baltimore · US

Funding

Gene interactions in cancerZIABC011984 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI HANNENHALLI, SRIDHAR · 2020 to 2025
$1.4M
6 · The paper itself

Abstract

Complex age-associated phenotypes are caused, in part, by an interaction between an individual's genotype and age. The mechanisms governing such interactions are however not entirely understood. Here, we provide a novel transcriptional mechanism-based framework-SNiPage, to investigate such interactions, whereby a transcription factor (TF) whose expression changes with age (age-associated TF), binds to a polymorphic regulatory element in an allele-dependent fashion, rendering the target gene's expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 significant TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the gene's promoter or putative enhancer and potentially regulates its expression in an age- and allele-dependent fashion. The detected SNPs are enriched for epigenomic marks indicative of regulatory activity, exhibit allele-specific chromatin accessibility, and spatial proximity to their putative gene targets. Furthermore, the TF-SNP interaction-dependent target genes have established links to aging and to age-associated diseases. In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. Lastly, the age-interacting SNPs exhibit a greater proximity to the reported phenotype/diseases-associated SNPs than eSNPs identified in an interaction-independent fashion. Overall, we present a novel mechanism-based model, and a novel framework SNiPage, to identify functionally relevant SNP-age interactions in transcriptional control and illustrate their potential utility in understanding complex age-associated phenotypes.

Indexed as

Gene Expression RegulationModels, BiologicalPhenotypePolymorphism, Single NucleotideTranscription, GeneticAgingAlgorithmsAllelesHumansTranscription FactorsTranscription Factors

Identifiers

PMID33770080
PMCPMC7997000
OpenAlexW3136484527

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

None linked

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.