The dbGaP data browser: a new tool for browsing dbGaP controlled-access genomic data

Background: African American (AA) men have a higher risk of developing prostate cancer than white men. SNPs are known to play an important role in developing prostate cancer. The impact of PVT1 and its neighborhood genes (CASC11 and MYC) on prostate cancer risk are getting more attention recently. The interactions among these three genes associated with prostate cancer risk are understudied, especially for AA men. The objective of this study is to investigate SNP-SNP interactions in the CASC11-MYC-PVT1 region associated with prostate cancer risk in AA men.Methods: We evaluated 205 SNPs using the 2,253 prostate cancer patients and 2,423 controls and applied multiphase (discovery-validation) design. In addition to SNP individual effects, SNP-SNP interactions were evaluated using the SNP Interaction Pattern Identifier, which assesses 45 patterns.Results: Three SNPs (rs9642880, rs16902359, and rs12680047) and 79 SNP-SNP pairs were significantly associated with prostate cancer risk. These two SNPs (rs16902359 and rs9642880) in CASC11 interacted frequently with other SNPs with 56 and 9 pairs, respectively. We identified the novel interaction of CASC11-PVT1, which is the most common gene interaction (70%) in the top 79 pairs. Several top SNP interactions have a moderate to large effect size (OR, 0.27-0.68) and have a higher prediction power to prostate cancer risk than SNP individual effects.Conclusions: Novel SNP-SNP interactions in the CASC11-MYC-PVT1 region have a larger impact than SNP individual effects on prostate cancer risk in AA men.Impact: This gene-gene interaction between CASC11 and PVT1 can provide valuable information to reveal potential biological mechanisms of prostate cancer development. reverse-reverse coding for SNP1 and SNP2. "AA_int" pattern means a monotonic risk (OR > 1) or protective (OR < 1) effect based on the selected alleles. See Supplementary Tables S2 and S3 for details. Lin et al. Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): H.-Y. Lin, H.-Y. Tung Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis):

show abstract

“…In this study, we applied the dbGaP version of the MEC data (dbGaP phs000306.v3.p1; ref. 41), which contains a portion of the large MEC cohort.…”

Section: Study Populationmentioning

confidence: 99%

Interactions ofPVT1andCASC11on Prostate Cancer Risk in African Americans

Lin

Callan

Fang

et al. 2019

Cancer Epidemiology, Biomarkers &Amp; Prevention

View full text Add to dashboard Cite

show abstract

“…Human disease-associated SNPs were obtained from the Database of Genotypes and Phenotypes (dbGaP), which provided large genetic and phenotypic datasets (Wong et al, 2017). A total of 32 samples of disease phenotypes were downloaded.…”

Section: Obtaining and Preprocessing Datamentioning

confidence: 99%

Detection of Allosteric Effects of lncRNA Secondary Structures Altered by SNPs in Human Diseases

Ding

Bai

et al. 2020

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Recent studies have shown that structuralized long non-coding RNAs (lncRNAs) play important roles in genetic and epigenetic processes. The spatial structures of most lncRNAs can be altered by distinct in vivo and in vitro cellular environments, as well as by DNA structural variations, such as single-nucleotide polymorphisms (SNPs) and variants (SNVs). In the present study, we extended candidate SNPs that had linkage disequilibria with those significantly associated with lung diseases in genome-wide association studies in order to investigate potential disease mechanisms originating from SNP structural changes of host lncRNAs. Following accurate alignments, we recognized 115 ternary-relationship pairs among 41 SNPs, 10 lncRNA transcripts, and 1 type of lung disease (adenocarcinoma of the lung). Then, we evaluated the structural heterogeneity induced by SNP alleles by developing a local-RNA-structure alignment algorithm and employing randomized strategies to determine the significance of structural variation. We identified four ternary-relationship pairs that were significantly associated with SNPinduced lncRNA allosteric effects. Moreover, these conformational changes disrupted the interactive regions and binding affinities of lncRNA-HCG23 and TF-E2F6, suggesting that these may represent regulatory mechanisms in lung diseases. Taken together, our findings support that SNP-induced changes in lncRNA conformations regulate many biological processes, providing novel insight into the role of the lncRNA "structurome" in human diseases.

show abstract

“…The data loading process will have oversight by a PDX Data Coordinator who will broker submission of data to established archives in collaboration with the data providers. Data that have the potential to allow identification of individuals will be submitted to relevant secure molecular archives such as the European Genome-Phenome Archive (10) or dbGAP (11). These resources provide secure environments where the approval of a submitter defined Data Access Committee is required for data access.…”

Section: Coordination With Other Informatics Resourcesmentioning

confidence: 99%

PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery

Conte

Mason

Halmagyi

et al. 2018

Preprint

View full text Add to dashboard Cite

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patient's tumors. PDX models are generated and distributed by a diverse group of academic labs, research organizations, multi-institution consortia, and contract research organizations. The distributed nature of PDX repositories and the use of different standards in the associated metadata presents a significant challenge to finding PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for more than 1900 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet, and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the "findability" of their models by participating in the PDX Finder initiative at www.pdxfinder.org.

show abstract

The dbGaP data browser: a new tool for browsing dbGaP controlled-access genomic data

Cited by 47 publications

References 9 publications

Interactions ofPVT1andCASC11on Prostate Cancer Risk in African Americans

Interactions ofPVT1andCASC11on Prostate Cancer Risk in African Americans

Detection of Allosteric Effects of lncRNA Secondary Structures Altered by SNPs in Human Diseases

PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery

Contact Info

Product

Resources

About