PLoS GeneticsGeneticsPeer-reviewed journal article
A transcription-centric model of SNP-Age interaction
Kun WangiD, Mahashweta BasuiD, Justin MaliniD, Sridhar S. HannenhalliiD
Abstract
Complex age-associated phenotypes are caused, in part, by an interaction between an individuals 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 genes expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 interacting TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the genes 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.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: GWAS / association mapping
- research focus: eQTL / molecular QTL mapping
- approach: statistical model
- Citations
- 9
- FWCI
- 0.85
- Field percentile
- top 31%
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