GWASdb v2: an update database for human genetic variants identified by genome-wide association studies

Plewczynski, Dariusz; Liu, Zipeng; Wang, Panwen; Wong, Maria Pik; Nelson, Matthew R.; Kocher, Jean Pierre A.; Yeager, Meredith; Sham, Pak C.; Chanock, Stephen J.; Xia, Zhengyuan; Wang, Junwen

doi:10.1093/nar/gkv1317

Cited by 174 publications

(168 citation statements)

References 60 publications

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“…We have highlighted several genes that contribute towards this susceptibility using transcription analysis (Table 12), finding some overlap between GWAS variants and differential gene expression. Our present biomarker results overlap with a genetic database of genes implicated in either BD or SZ or both from a database of GWAS-curated genes (GWASdb v2 [69]). We found a significant overlap with our list of 122 differentially expressed genes for BD and SZ that were temporally stable (Table 12).…”

Section: Discussionmentioning

confidence: 54%

“…Regarding genes implicated in BD or SZ from a database of genome-wide association study (GWAS)-curated genes (GWASdb v2 [69]) there were 2,189 unique genes meeting this criterion ( p < 0.001). In our list of 122 differentially expressed genes for BD and SZ that were temporally stable, there was an overlap of 22 genes with the GWASdb genes (Table 12), resulting in a 2-fold enrichment, as only 11 genes were expected ( p = 0.029, one-tailed Pearson).…”

Section: Resultsmentioning

confidence: 99%

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Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

et al. 2017

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This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83–88% accuracy). The results are also consistent with the actual data and the “leave-one-out” analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.

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Section: Discussionmentioning

confidence: 54%

Section: Resultsmentioning

confidence: 99%

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

et al. 2017

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“…This might be caused by the fact that GWAS associations with moderate p values are ignored by many GWAS databases. Hence, we decided to utilize the potential of the comprehensive database GWASdb [30,31] to include manually curated associations at a less stringent p value (0.001). The successful prediction of drug-target interactions reinforces the ability of GWASs to generate insight that is applicable in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

“…We utilized the databases SIDER (Side Effect Resource) [29], and GWASdb [30,31]. For more details on the data integration, please refer to the sections "Drug Side Effects," "Known Drug-Target Interactions," and "GWAS Data" in the Appendix.…”

Section: Methodsmentioning

confidence: 99%

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Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies

et al. 2018

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Aims: We propose a novel machine learning approach to expand the knowledge about drug-target interactions. Our method may help to develop effective, less harmful treatment strategies and to enable the detection of novel indications for existing drugs. Methods: We developed a novel machine learning strategy to predict drug-target interactions based on drug side effects and traits from genome-wide association studies. We integrated data from the databases SIDER and GWASdb and utilized them in a unique way by a neural network approach. Results: We validate our method using drug-target interactions from the STITCH database. In addition, we compare the chemical similarity of the predicted target to known targets of the drug under consideration and present literature-based evidence for predicted interactions. We find drug combination warnings for drugs we predict to target the same protein, hinting to synergistic effects aggravating harmful events. This substantiates the translational value of our approach, because we are able to detect drugs that should be taken together with care due to common mechanisms of action. Conclusion: Taken together, we conclude that our approach is able to generate a novel and clinically applicable insight into the molecular determinants of drug action.

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LBH Gene Transcription Regulation by the Interplay of an Enhancer Risk Allele and DNA Methylation in Rheumatoid Arthritis

Hammaker

Whitaker

Maeshima

et al. 2016

Arthritis & Rheumatology

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Objective To identify non-obvious therapeutic targets for rheumatoid arthritis, we performed an integrative analysis incorporating multiple ‘omics data and the ENCODE database for potential regulatory regions. This analysis identified the Limb Bud and Heart development (LBH) gene, which has risk alleles associated with RA/celiac disease and lupus, and can regulate cell proliferation in RA. We identified a novel LBH transcriptional enhancer with an RA-risk allele (rs906868 G (risk, Ref) /T) 6kb upstream of the LBH gene with a differentially methylated locus. The confluence of three regulatory elements, rs906868, an RA differentially methylated locus and a putative enhancer, led us to investigate their effect on LBH regulation in fibroblast-like synoviocytes (FLS). Methods We cloned the 1.4kb putative enhancer with either the rs906868 Ref allele or SNP variant into reporter constructs. The constructs were methylated in vitro and transfected into cultured FLS by nucleofection. Results We found that both variants increased transcription, thereby confirming the region’s enhancer function. Unexpectedly, the transcriptional activity of the Ref risk allele was significantly lower than the SNP variant and is consistent with low LBH levels as a risk factor for aggressive FLS behavior. Using RA FLS lines with homozygous Ref or SNP allele, we confirmed that homozygous Ref lines expressed lower LBH mRNA levels than the SNP lines. Methylation significantly reduced enhancer activity for both alleles, indicating that enhancer function is dependent on its methylation status. Conclusion This study shows how the interplay between genetics and epigenetics can affect expression of LBH in rheumatoid arthritis.

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GWASdb v2: an update database for human genetic variants identified by genome-wide association studies

Cited by 174 publications

References 60 publications

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder

Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies

LBH Gene Transcription Regulation by the Interplay of an Enhancer Risk Allele and DNA Methylation in Rheumatoid Arthritis

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