Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 820, Houston, TX, 77030, USA.
Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA.
Genome Med. 2018 Jan 29;10(1):7. doi: 10.1186/s13073-018-0513-x.
Genome-phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.
In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.
We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer's disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).
This study offers powerful tools for exploring the functional consequences of variants generated from genome-phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.
全基因组-表型关联研究已经鉴定出数千个与疾病或表型具有统计学关联的变异，但它们的功能作用在很大程度上仍不清楚。需要对表型全基因组关联研究（PheWAS）和全基因组关联研究（GWAS）之间的调控机制和基因调控网络进行全面研究，以鉴定导致人类疾病风险的新的调控变异。
在这项研究中，我们开发了一种整合的功能基因组学框架，通过整合来自 PheWAS 目录的 215107 个显著的单核苷酸多态性（SNP）表型和来自 GWAS 目录的 28870 个全基因组显著 SNP 表型，将其映射到一个全球人类基因组调控图谱中，整合了各种功能注释数据，包括转录因子（TF）基序、启动子、增强子和来自四个主要功能基因组学数据库的表达定量性状基因座（eQTL）：FANTOM5、ENCODE、NIH 路线图和基因型组织表达（GTEx）。此外，我们通过整合在 GTEx 中 32 个组织的 7051 个样本中鉴定的调控变异和组织特异性基因表达谱，进行了组织特异性调控回路分析。
我们发现，PheWAS 和 GWAS 目录中的疾病相关位点显著富集了功能 SNPs。功能注释的整合显著提高了在 PheWAS 中检测新关联的能力，通过这种方式，我们在 PheWAS 目录中发现了许多具有强调控证据的功能关联。最后，我们通过探索组织特异性 TF-启动子/增强子-靶基因相互作用网络，为几种复杂疾病构建了组织特异性调控回路：精神疾病、自身免疫性疾病和癌症。我们发现了一些有希望的组织特异性调控 TF 或基因，用于阿尔茨海默病（例如 ZIC1 和 STX1B）和哮喘（例如 CSF3 和 IL1RL1）。
本研究提供了强大的工具，用于从全基因组-表型关联研究中探索变异的功能后果，包括它们在影响多种复杂疾病和表型方面的机制。
