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Convergent downstream candidate mechanisms of independent intergenic polymorphisms between co-classified diseases implicate epistasis among noncoding elements

机译:共同分类疾病之间独立的基因间多态性的下游候选机制收敛暗示了非编码元件之间的上位性

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摘要

Eighty percent of DNA outside protein coding regions was shown biochemically functional by the ENCODE project, enabling studies of their interactions. Studies have since explored how convergent downstream mechanisms arise from independent genetic risks of one complex disease. However, the cross-talk and epistasis between intergenic risks associated with distinct complex diseases have not been comprehensively characterized. Our recent integrative genomic analysis unveiled downstream biological effectors of disease-specific polymorphisms buried in intergenic regions, and we then validated their genetic synergy and antagonism in distinct GWAS. We extend this approach to characterize convergent downstream candidate mechanisms of distinct intergenic SNPs across distinct diseases within the same clinical classification. We construct a multipartite network consisting of 467 diseases organized in 15 classes, 2,358 disease-associated SNPs, 6,301 SNP-associated mRNAs by eQTL, and mRNA annotations to 4,538 Gene Ontology mechanisms. Functional similarity between two SNPs (similar SNP pairs) is imputed using a nested information theoretic distance model for which p-values are assigned by conservative scale-free permutation of network edges without replacement (node degrees constant). At FDR≤5%, we prioritized 3,870 intergenic SNP pairs associated, among which 755 are associated with distinct diseases sharing the same disease class, implicating 167 intergenic SNPs, 14 classes, 230 mRNAs, and 134 GO terms. Co-classified SNP pairs were more likely to be prioritized as compared to those of distinct classes confirming a noncoding genetic underpinning to clinical classification (odds ratio ~3.8; p≤10−25). The prioritized pairs were also enriched in regions bound to the same/interacting transcription factors and/or interacting in long-range chromatin interactions suggestive of epistasis (odds ratio ~ 2,500; p≤10−25). This prioritized network implicates complex epistasis between intergenic polymorphisms of co-classified diseases and offers a roadmap for a novel therapeutic paradigm: repositioning medications that target proteins within downstream mechanisms of intergenic disease-associated SNPs. and software:
机译:ENCODE项目显示蛋白质编码区以外80%的DNA具有生化功能,从而可以研究它们之间的相互作用。此后,研究探索了一种复杂疾病的独立遗传风险如何引发下游融合机制。然而,与不同复杂疾病相关的基因间风险之间的串扰和上位性尚未得到全面表征。我们最近的综合基因组分析揭示了埋在基因间区域的疾病特异性多态性的下游生物学效应子,然后我们在不同的GWAS中验证了它们的遗传协同作用和拮抗作用。我们扩展这种方法来表征同一临床分类内不同疾病之间不同基因间SNP的收敛下游候选机制。我们构建了一个多部分网络,该网络由15类的467种疾病组成,包括2358个与疾病相关的SNP,6301个通过eQTL与SNP相关的mRNA,以及4538个基因本体机制的mRNA注释。使用嵌套信息理论距离模型来估算两个SNP(相似的SNP对)之间的功能相似性,对于该模型,p值由网络边缘的无标度的无标度保守置换而分配,无需替换(节点度常数)。在FDR≤5%时,我们对3,870个基因间SNP对进行了优先排序,其中755个与共有相同疾病类别的不同疾病相关,涉及167个基因间SNP,14个类别,230个mRNA和134个GO术语。与不同类别的共分类SNP对相比,共分类SNP对更有可能被优先考虑,这证实了非编码遗传基础对临床分类的支持(几率〜3.8;p≤10 −25 )。优先配对的区域也富集于与相同/相互作用的转录因子结合和/或在长距离染色质相互作用中相互作用的区域,提示存在上位性(优势比约为2,500;p≤10 −25 )。该优先网络牵涉到共同分类疾病的基因间多态性之间的复杂上位现象,并为新型治疗范例提供了路线图:在与基因间疾病相关的SNP下游机制内重新定位靶向蛋白质的药物。和软件:

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