dbBIP

dbBIP provides an integrated database of genetic and multi-omics data to support identification and functional interpretation of risk variants and candidate genes for bipolar disorder (BIP).

Key Features:

• Genetic summary statistics (SNP Module): Includes genome-wide association study (GWAS) summary statistics aggregated for bipolar disorder variants.
• Functional SNP annotation: Provides functional annotation information linking single nucleotide polymorphisms (SNPs) to biological relevance for BIP risk variants.
• Candidate risk genes (Gene Module): Aggregates candidate BIP risk genes from multiple sources to consolidate potential genetic contributors.
• Data integration (SMR and TWAS): Implements summary-data-based Mendelian randomization and transcriptome-wide association studies for integrating genetic and transcriptomic evidence.
• Co-expression and gene expression analyses: Offers co-expression metrics and gene expression profiles across tissues to characterize molecular relationships.
• Protein-protein interaction data: Includes protein-protein interaction information to support network-based interpretation of implicated genes.
• Brain-specific eQTL analyses: Provides brain expression quantitative trait loci (eQTL) analyses to identify variants affecting gene expression in neural tissues.
• Analysis module for external datasets: Supports analysis of user-provided datasets using the database's summary statistics and annotation resources.

Scientific Applications:

• Functional interpretation of variants: Annotates SNPs with functional data to assess potential mechanisms linking variants to BIP.
• Candidate gene prioritization: Consolidates evidence to prioritize genes implicated in BIP pathobiology.
• Causal inference between genotype and expression: Uses summary-data-based Mendelian randomization and TWAS to explore potential causal relationships between genetic variants and transcriptomic changes.
• Molecular network analysis: Employs co-expression and protein-protein interaction data to construct networks that contextualize implicated genes.
• Neural tissue-specific regulatory mapping: Uses brain eQTL analyses to identify genetic variants that modulate gene expression in neural tissues relevant to BIP.

Methodology:

Integrates GWAS summary statistics with functional SNP annotation, summary-data-based Mendelian randomization (SMR), transcriptome-wide association studies (TWAS), co-expression and gene expression analyses, protein-protein interaction analyses, and brain-specific eQTL analyses.