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Huang D, Yi X, Zhang S, Zheng Z, Wang P, Xuan C, Sham PC, Wang J, Li MJ. GWAS4D: multidimensional analysis of context-specific regulatory variant for human complex diseases and traits. Nucleic Acids Res 2019; 46:W114-W120. [PMID: 29771388 PMCID: PMC6030885 DOI: 10.1093/nar/gky407] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 05/03/2018] [Indexed: 01/04/2023] Open
Abstract
Genome-wide association studies have generated over thousands of susceptibility loci for many human complex traits, and yet for most of these associations the true causal variants remain unknown. Tissue/cell type-specific prediction and prioritization of non-coding regulatory variants will facilitate the identification of causal variants and underlying pathogenic mechanisms for particular complex diseases and traits. By leveraging recent large-scale functional genomics/epigenomics data, we develop an intuitive web server, GWAS4D (http://mulinlab.tmu.edu.cn/gwas4d or http://mulinlab.org/gwas4d), that systematically evaluates GWAS signals and identifies context-specific regulatory variants. The updated web server includes six major features: (i) updates the regulatory variant prioritization method with our new algorithm; (ii) incorporates 127 tissue/cell type-specific epigenomes data; (iii) integrates motifs of 1480 transcriptional regulators from 13 public resources; (iv) uniformly processes Hi-C data and generates significant interactions at 5 kb resolution across 60 tissues/cell types; (v) adds comprehensive non-coding variant functional annotations; (vi) equips a highly interactive visualization function for SNP-target interaction. Using a GWAS fine-mapped set for 161 coronary artery disease risk loci, we demonstrate that GWAS4D is able to efficiently prioritize disease-causal regulatory variants.
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Affiliation(s)
- Dandan Huang
- Department of Pharmacology, Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xianfu Yi
- School of Biomedical Engineering, Tianjin Medical University, Tianjin, China
| | - Shijie Zhang
- Department of Pharmacology, Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Zhanye Zheng
- Department of Pharmacology, Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Panwen Wang
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, USA
| | - Chenghao Xuan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Pak Chung Sham
- Center for Genomic Sciences, The University of Hong Kong, Hong Kong SAR, China.,Departments of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Junwen Wang
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, USA.,Department of Biomedical Informatics, Arizona State University, Scottsdale, USA
| | - Mulin Jun Li
- Department of Pharmacology, Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
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2
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Li MJ, Yao H, Huang D, Liu H, Liu Z, Xu H, Qin Y, Prinz J, Xia W, Wang P, Yan B, Tran NL, Kocher JP, Sham PC, Wang J. mTCTScan: a comprehensive platform for annotation and prioritization of mutations affecting drug sensitivity in cancers. Nucleic Acids Res 2019; 45:W215-W221. [PMID: 28482068 PMCID: PMC5793836 DOI: 10.1093/nar/gkx400] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 04/27/2017] [Indexed: 12/25/2022] Open
Abstract
Cancer therapies have experienced rapid progress in recent years, with a number of novel small-molecule kinase inhibitors and monoclonal antibodies now being widely used to treat various types of human cancers. During cancer treatments, mutations can have important effects on drug sensitivity. However, the relationship between tumor genomic profiles and the effectiveness of cancer drugs remains elusive. We introduce Mutation To Cancer Therapy Scan (mTCTScan) web server (http://jjwanglab.org/mTCTScan) that can systematically analyze mutations affecting cancer drug sensitivity based on individual genomic profiles. The platform was developed by leveraging the latest knowledge on mutation-cancer drug sensitivity associations and the results from large-scale chemical screening using human cancer cell lines. Using an evidence-based scoring scheme based on current integrative evidences, mTCTScan is able to prioritize mutations according to their associations with cancer drugs and preclinical compounds. It can also show related drugs/compounds with sensitivity classification by considering the context of the entire genomic profile. In addition, mTCTScan incorporates comprehensive filtering functions and cancer-related annotations to better interpret mutation effects and their association with cancer drugs. This platform will greatly benefit both researchers and clinicians for interrogating mechanisms of mutation-dependent drug response, which will have a significant impact on cancer precision medicine.
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Affiliation(s)
- Mulin Jun Li
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Center for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Hongcheng Yao
- Center for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Dandan Huang
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Huanhuan Liu
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Zipeng Liu
- Center for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Hang Xu
- Center for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Yiming Qin
- Center for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Jeanette Prinz
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Weiyi Xia
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Panwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Bin Yan
- Center for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Nhan L Tran
- Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Jean-Pierre Kocher
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Pak C Sham
- Center for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,Departments of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Junwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA.,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA
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3
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Whole exome sequencing for the identification of CYP3A7 variants associated with tacrolimus concentrations in kidney transplant patients. Sci Rep 2018; 8:18064. [PMID: 30584253 PMCID: PMC6305386 DOI: 10.1038/s41598-018-36085-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 11/09/2018] [Indexed: 02/06/2023] Open
Abstract
The purpose of this study was to identify genotypes associated with dose-adjusted tacrolimus trough concentrations (C0/D) in kidney transplant recipients using whole-exome sequencing (WES). This study included 147 patients administered tacrolimus, including seventy-five patients in the discovery set and seventy-two patients in the replication set. The patient genomes in the discovery set were sequenced using WES. Also, known tacrolimus pharmacokinetics-related intron variants were genotyped. Tacrolimus C0/D was log-transformed. Sixteen variants were identified including novel CYP3A7 rs12360 and rs10211 by ANOVA. CYP3A7 rs2257401 was found to be the most significant variant among the periods by ANOVA. Seven variants including CYP3A7 rs2257401, rs12360, and rs10211 were analyzed by SNaPshot in the replication set and the effects on tacrolimus C0/D were verified. A linear mixed model (LMM) was further performed to account for the effects of the variants and clinical factors. The combined set LMM showed that only CYP3A7 rs2257401 was associated with tacrolimus C0/D after adjusting for patient age, albumin, and creatinine. The CYP3A7 rs2257401 genotype variant showed a significant difference on the tacrolimus C0/D in those expressing CYP3A5, showing its own effect. The results suggest that CYP3A7 rs2257401 may serve as a significant genetic marker for tacrolimus pharmacokinetics in kidney transplantation.
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Lee PH, Lee C, Li X, Wee B, Dwivedi T, Daly M. Principles and methods of in-silico prioritization of non-coding regulatory variants. Hum Genet 2018; 137:15-30. [PMID: 29288389 PMCID: PMC5892192 DOI: 10.1007/s00439-017-1861-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 12/14/2017] [Indexed: 12/13/2022]
Abstract
Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.
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Affiliation(s)
- Phil H Lee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA.
- Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Christian Lee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- Department of Life Sciences, Harvard University, Cambridge, MA, USA
| | - Xihao Li
- Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brian Wee
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
| | - Tushar Dwivedi
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Mark Daly
- Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
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5
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Li MJ, Zhang J, Liang Q, Xuan C, Wu J, Jiang P, Li W, Zhu Y, Wang P, Fernandez D, Shen Y, Chen Y, Kocher JPA, Yu Y, Sham PC, Wang J, Liu JS, Liu XS. Exploring genetic associations with ceRNA regulation in the human genome. Nucleic Acids Res 2017; 45:5653-5665. [PMID: 28472449 PMCID: PMC5449616 DOI: 10.1093/nar/gkx331] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 04/26/2017] [Indexed: 01/01/2023] Open
Abstract
Competing endogenous RNAs (ceRNAs) are RNA molecules that sequester shared microRNAs (miRNAs) thereby affecting the expression of other targets of the miRNAs. Whether genetic variants in ceRNA can affect its biological function and disease development is still an open question. Here we identified a large number of genetic variants that are associated with ceRNA's function using Geuvaids RNA-seq data for 462 individuals from the 1000 Genomes Project. We call these loci competing endogenous RNA expression quantitative trait loci or 'cerQTL', and found that a large number of them were unexplored in conventional eQTL mapping. We identified many cerQTLs that have undergone recent positive selection in different human populations, and showed that single nucleotide polymorphisms in gene 3΄UTRs at the miRNA seed binding regions can simultaneously regulate gene expression changes in both cis and trans by the ceRNA mechanism. We also discovered that cerQTLs are significantly enriched in traits/diseases associated variants reported from genome-wide association studies in the miRNA binding sites, suggesting that disease susceptibilities could be attributed to ceRNA regulation. Further in vitro functional experiments demonstrated that a cerQTL rs11540855 can regulate ceRNA function. These results provide a comprehensive catalog of functional non-coding regulatory variants that may be responsible for ceRNA crosstalk at the post-transcriptional level.
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Affiliation(s)
- Mulin Jun Li
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Department of Statistics, Harvard University, Cambridge, MA 02138, USA.,Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Jian Zhang
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Qian Liang
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Chenghao Xuan
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jiexing Wu
- Department of Statistics, Harvard University, Cambridge, MA 02138, USA
| | - Peng Jiang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public Health, Boston, MA 02215, USA
| | - Wei Li
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public Health, Boston, MA 02215, USA
| | - Yun Zhu
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Panwen Wang
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Daniel Fernandez
- Department of Statistics, Harvard University, Cambridge, MA 02138, USA
| | - Yujun Shen
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Yiwen Chen
- Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jean-Pierre A Kocher
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Ying Yu
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Pak Chung Sham
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,Department of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Junwen Wang
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA.,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA
| | - Jun S Liu
- Department of Statistics, Harvard University, Cambridge, MA 02138, USA
| | - X Shirley Liu
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public Health, Boston, MA 02215, USA
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Li MJ, Pan Z, Liu Z, Wu J, Wang P, Zhu Y, Xu F, Xia Z, Sham PC, Kocher JPA, Li M, Liu JS, Wang J. Predicting regulatory variants with composite statistic. Bioinformatics 2016; 32:2729-36. [PMID: 27273672 DOI: 10.1093/bioinformatics/btw288] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 04/29/2016] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. RESULTS Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. AVAILABILITY AND IMPLEMENTATION We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS CONTACT: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mulin Jun Li
- Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA, Centre for Genomic Sciences
| | - Zhicheng Pan
- Centre for Genomic Sciences, Department of Psychiatry
| | - Zipeng Liu
- Centre for Genomic Sciences, Department of Anaesthesiology
| | - Jiexing Wu
- Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA
| | | | - Yun Zhu
- Centre for Genomic Sciences, School of Biomedical Sciences
| | | | | | | | - Jean-Pierre A Kocher
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA and
| | - Miaoxin Li
- Centre for Genomic Sciences, Department of Psychiatry, Centre for Reproduction, Development and Growth, LKS Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China
| | - Jun S Liu
- Department of Statistics, Harvard University, Cambridge, Boston, 02138-2901 MA, USA, Center for Statistical Science, Tsinghua University, Beijing 100084, China and
| | - Junwen Wang
- Centre for Genomic Sciences, Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA and Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA
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Li MJ, Liu Z, Wang P, Wong MP, Nelson MR, Kocher JPA, Yeager M, Sham PC, Chanock SJ, Xia Z, Wang J. GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res 2015; 44:D869-76. [PMID: 26615194 PMCID: PMC4702921 DOI: 10.1093/nar/gkv1317] [Citation(s) in RCA: 139] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Accepted: 11/10/2015] [Indexed: 12/19/2022] Open
Abstract
Genome-wide association studies (GWASs), now as a routine approach to study single-nucleotide polymorphism (SNP)-trait association, have uncovered over ten thousand significant trait/disease associated SNPs (TASs). Here, we updated GWASdb (GWASdb v2, http://jjwanglab.org/gwasdb) which provides comprehensive data curation and knowledge integration for GWAS TASs. These updates include: (i) Up to August 2015, we collected 2479 unique publications from PubMed and other resources; (ii) We further curated moderate SNP-trait associations (P-value < 1.0×10−3) from each original publication, and generated a total of 252 530 unique TASs in all GWASdb v2 collected studies; (iii) We manually mapped 1610 GWAS traits to 501 Human Phenotype Ontology (HPO) terms, 435 Disease Ontology (DO) terms and 228 Disease Ontology Lite (DOLite) terms. For each ontology term, we also predicted the putative causal genes; (iv) We curated the detailed sub-populations and related sample size for each study; (v) Importantly, we performed extensive function annotation for each TAS by incorporating gene-based information, ENCODE ChIP-seq assays, eQTL, population haplotype, functional prediction across multiple biological domains, evolutionary signals and disease-related annotation; (vi) Additionally, we compiled a SNP-drug response association dataset for 650 pharmacogenetic studies involving 257 drugs in this update; (vii) Last, we improved the user interface of website.
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Affiliation(s)
- Mulin Jun Li
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Zipeng Liu
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China Department of Anaesthesiology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Panwen Wang
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Maria P Wong
- Department of Pathology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Matthew R Nelson
- Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, NC, USA
| | - Jean-Pierre A Kocher
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Meredith Yeager
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Pak Chung Sham
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China State Key Laboratory of Brain and Cognitive Sciences and Department of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Zhengyuan Xia
- Department of Anaesthesiology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Junwen Wang
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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8
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Wang J, Batmanov K. BayesPI-BAR: a new biophysical model for characterization of regulatory sequence variations. Nucleic Acids Res 2015. [PMID: 26202972 PMCID: PMC4666384 DOI: 10.1093/nar/gkv733] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Sequence variations in regulatory DNA regions are known to cause functionally important consequences for gene expression. DNA sequence variations may have an essential role in determining phenotypes and may be linked to disease; however, their identification through analysis of massive genome-wide sequencing data is a great challenge. In this work, a new computational pipeline, a Bayesian method for protein–DNA interaction with binding affinity ranking (BayesPI-BAR), is proposed for quantifying the effect of sequence variations on protein binding. BayesPI-BAR uses biophysical modeling of protein–DNA interactions to predict single nucleotide polymorphisms (SNPs) that cause significant changes in the binding affinity of a regulatory region for transcription factors (TFs). The method includes two new parameters (TF chemical potentials or protein concentrations and direct TF binding targets) that are neglected by previous methods. The new method is verified on 67 known human regulatory SNPs, of which 47 (70%) have predicted true TFs ranked in the top 10. Importantly, the performance of BayesPI-BAR, which uses principal component analysis to integrate multiple predictions from various TF chemical potentials, is found to be better than that of existing programs, such as sTRAP and is-rSNP, when evaluated on the same SNPs. BayesPI-BAR is a publicly available tool and is able to carry out parallelized computation, which helps to investigate a large number of TFs or SNPs and to detect disease-associated regulatory sequence variations in the sea of genome-wide noncoding regions.
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Affiliation(s)
- Junbai Wang
- Pathology Department, Oslo University Hospital-Norwegian Radium Hospital, Montebello 0310, Oslo, Norway
| | - Kirill Batmanov
- Pathology Department, Oslo University Hospital-Norwegian Radium Hospital, Montebello 0310, Oslo, Norway
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9
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Tang H, Zhao Z. Bioinformatics drives the applications of next-generation sequencing in translational biomedical research. Methods 2015; 79-80:1-2. [PMID: 25982352 DOI: 10.1016/j.ymeth.2015.04.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Haixu Tang
- School of Informatics and Computing, Indiana University, Bloomington, United States
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, United States
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10
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Li MJ, Deng J, Wang P, Yang W, Ho SL, Sham PC, Wang J, Li M. wKGGSeq: A Comprehensive Strategy-Based and Disease-Targeted Online Framework to Facilitate Exome Sequencing Studies of Inherited Disorders. Hum Mutat 2015; 36:496-503. [PMID: 25676918 DOI: 10.1002/humu.22766] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2014] [Accepted: 02/03/2015] [Indexed: 12/19/2022]
Abstract
With the rapid advances in high-throughput sequencing technologies, exome sequencing and targeted region sequencing have become routine approaches for identifying mutations of inherited disorders in both genetics research and molecular diagnosis. There is an imminent need for comprehensive and easy-to-use downstream analysis tools to isolate causal mutations in exome sequencing studies. We have developed a user-friendly online framework, wKGGSeq, to provide systematic annotation, filtration, prioritization, and visualization functions for characterizing causal mutation(s) in exome sequencing studies of inherited disorders. wKGGSeq provides: (1) a novel strategy-based procedure for downstream analysis of a large amount of exome sequencing data and (2) a disease-targeted analysis procedure to facilitate clinical diagnosis of well-studied genetic diseases. In addition, it is also equipped with abundant online annotation functions for sequence variants. We demonstrate that wKGGSeq either outperforms or is comparable to two popular tools in several real exome sequencing samples. This tool will greatly facilitate the downstream analysis of exome sequencing data and can play a useful role for researchers and clinicians in identifying causal mutations of inherited disorders. The wKGGSeq is freely available at http://statgenpro.psychiatry.hku.hk/wkggseq or http://jjwanglab.org/wkggseq, and will be updated frequently.
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Affiliation(s)
- Mulin Jun Li
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China; Departments of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, Guangdong, 518057, China
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