Integrating regulatory features data for prediction of functional disease-associated SNPs
Integrating regulatory features data for prediction of functional disease-associated SNPs
复制标题
整合监管特征数据以预测功能性疾病相关的 SNP
DOI：
10.1093/bib/bbx094
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发表时间：
2019-01-01
影响因子：
9.5
通讯作者：
Yang, Tie-Lin
中科院分区：
文献类型：
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作者：
Dong, Shan-Shan;Guo, Yan;Yang, Tie-Lin
全基因组关联研究（GWAS）是识别人类复杂疾病易感位点的有效策略。然而，遗传性缺失仍然是一个大问题。大多数 GWAS 单核苷酸多态性 (SNP) 位于非编码区域，该区域被认为是基因组中尚未探索的领域。最近，来自 DNA 元件百科全书 (ENCODE) 和表观基因组路线图项目的数据表明，非编码区域中的许多 GWAS SNP 属于调控元件范围。在这项研究中，我们开发了一个名为功能性疾病相关 SNP 预测 (FDSP) 的流程，通过机器学习对已知疾病相关变异的功能特征进行解释，来识别复杂疾病的新易感位点。我们应用我们的流程来预测 2 型糖尿病 (T2D) 和高血压的新易感性 SNP。预测的 SNP 可以解释超出 GWAS 相关 SNP 解释的遗传力。通过表达数量性状位点分析进行的功能注释表明，预测的 SNP 的靶基因在多个组织中的 T2D 或高血压相关通路中显着富集。我们的结果表明，结合 GWAS 和监管特征数据可以识别复杂疾病的其他功能易感性 SNP。我们希望FDSP能够帮助识别复杂疾病的新易感位点并解决缺失的遗传问题。
Genome-wide association studies (GWASs) are an effective strategy to identify susceptibility loci for human complex diseases. However, missing heritability is still a big problem. Most GWASs single-nucleotide polymorphisms (SNPs) are located in noncoding regions, which has been considered to be the unexplored territory of the genome. Recently, data from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects have shown that many GWASs SNPs in the noncoding regions fall within regulatory elements. In this study, we developed a pipeline named functional disease-associated SNPs prediction (FDSP), to identify novel susceptibility loci for complex diseases based on the interpretation of the functional features for known disease-associated variants with machine learning. We applied our pipeline to predict novel susceptibility SNPs for type 2 diabetes (T2D) and hypertension. The predicted SNPs could explain heritability beyond that explained by GWAS-associated SNPs. Functional annotation by expression quantitative trait loci analyses showed that the target genes of the predicted SNPs were significantly enriched in T2D or hypertension-related pathways in multiple tissues. Our results suggest that combining GWASs and regulatory features data could identify additional functional susceptibility SNPs for complex diseases. We hope FDSP could help to identify novel susceptibility loci for complex diseases and solve the missing heritability problem.