A transcription-centric model of SNP-age interaction

PLoS Genet. 2021 Mar 26;17(3):e1009427. doi: 10.1371/journal.pgen.1009427. eCollection 2021 Mar.

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.

Publication types

  • Research Support, N.I.H., Intramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aging / genetics*
  • Algorithms
  • Alleles
  • Gene Expression Regulation*
  • Humans
  • Models, Biological*
  • Phenotype*
  • Polymorphism, Single Nucleotide*
  • Transcription Factors / metabolism
  • Transcription, Genetic*

Substances

  • Transcription Factors

Grants and funding

This work is supported in part by NSF (https://www.nsf.gov/) award 1564785 to S.H. and Intramural Research Program of the National Institutes of Health (NIH), (https://irp.nih.gov/), National Cancer Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.