ABC-GWAS

ABC-GWAS analyzes and functionally annotates genetic variants from estrogen receptor-positive breast cancer GWAS to link variants to gene expression, transcription factor binding, and three-dimensional chromatin interactions.

Key Features:

• Integration of Multi-Omics Data: Integrates large-scale datasets including The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE) to annotate variants, including non-coding regions.
• eQTL Analysis: Performs multivariate linear regression to assess relationships between genetic variants and gene expression levels.
• Transcription Factor Binding Perturbation: Conducts sequence permutation tests to evaluate how variants may affect transcription factor binding.
• Three-Dimensional Chromatin Interaction Modeling: Predicts long-range chromatin interactions to identify potential target genes and causal variants.
• Variant Catalog: Contains annotations for 2,813 single nucleotide variants across 93 genomic loci linked to estrogen receptor-positive breast cancer.
• Variant and Gene Prioritization: Prioritizes putative target genes, causal variants, and transcription factors based on integrated analyses.

Scientific Applications:

• Hypothesis Generation: Enable generation and testing of hypotheses about genetic susceptibility and etiology of breast cancer.
• Functional Study Prioritization: Prioritize variants for experimental functional studies to investigate disease mechanisms.
• Therapeutic Targeting: Inform development of targeted therapeutic strategies by linking variants to regulatory mechanisms and candidate genes.

Methodology:

Integrates TCGA and ENCODE datasets, applies multivariate linear regression for eQTL analysis, performs sequence permutation tests for transcription factor binding perturbation, uses three-dimensional chromatin interaction modeling to predict long-range interactions, and employs statistical and computational analyses to predict functional consequences and prioritize variants.