Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America.
PLoS Genet. 2021 Mar 26;17(3):e1009427. doi: 10.1371/journal.pgen.1009427. eCollection 2021 Mar.
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.
复杂的与年龄相关的表型部分是由个体基因型和年龄之间的相互作用引起的。然而，控制这种相互作用的机制并不完全清楚。在这里，我们提供了一个新的基于转录机制的框架-SNiPage，用于研究这种相互作用，其中一个随着年龄变化而表达的转录因子（age-associated TF）以依赖等位基因的方式与一个多态性调节元件结合，使靶基因的表达既依赖于年龄又依赖于基因型。我们将 SNiPage 应用于 GTEx，在 25 种组织中平均检测到约 637 个显著的 TF-SNP-基因三联体，其中 TF 结合到基因启动子或推定增强子中的调节 SNP，并且以年龄和等位基因依赖的方式潜在地调节其表达。检测到的 SNP 富集了表明调节活性的表观遗传标记，表现出等位基因特异性染色质可及性，并与它们潜在的基因靶标空间接近。此外，TF-SNP 相互作用依赖性靶基因与衰老和与年龄相关的疾病有明确的联系。在六个与高血压相关的组织中，检测到的相互作用显著影响个体的高血压状态。最后，与年龄相互作用的 SNP 比以独立于相互作用的方式识别的 eSNP 更接近报告的表型/疾病相关 SNP。总体而言，我们提出了一种新的基于机制的模型和一个新的 SNiPage 框架，用于识别转录控制中功能相关的 SNP-年龄相互作用，并说明其在理解复杂的与年龄相关的表型中的潜在用途。
