A Transcription-Centric Model Of Snp-Age Interaction

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 similar to 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.Author summaryNumerous traits, such as cardiovascular diseases and cancer, are associated with age. However, these associations vary across races and ethnicities, suggesting an interplay between age and the genetic background in determining the trait. Although previously studies have attempted to detect Age-Genotype interactions based on statistical models, they are mostly devoid of mechanism, thus limiting their efficacy and scope in informing therapeutic strategies. Here, we propose a novel framework to investigate such interactions, by incorporating a specific transcription-based mechanism in the model. More specifically, our model is based on the mechanistic scenario that an age-associated transcription factor (TF) binds to a regulatory polymorphism (SNP) in an allele-specific manner to regulate the transcription of the downstream gene in an Age- and Genotype-specific fashion. By analyzing 25 tissues in the GTEx consortium, we detected tissue specific SNP-TF-Gene interaction triplets and functionally validated the detected SNP based on epigenomic and functional data. What's more, multiple lines of evidence link detected interactions to aging and to age-associated diseases. We expect our new methodological framework and the detected functionally relevant interactions will enhance understanding of the underlying mechanism of SNP-Age interaction and its contribution to age-associated diseases.