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Abstract
Over the past decade, hundreds of genome-wide association studies (GWAS) have implicated genetic variants in various diseases, including cancer. However, only a few of these variants have been functionally characterized to date, mainly because the majority of the variants reside in non-coding regions of the human genome with unknown function. A comprehensive functional annotation of the candidate variants is thus necessary to fill the gap between the correlative findings of GWAS and the development of therapeutic strategies. By integrating large-scale multi-omics datasets such as the Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE), we performed multivariate linear regression analysis of expression quantitative trait loci, sequence permutation test of transcription factor binding perturbation, and modeling of three-dimensional chromatin interactions to analyze the potential molecular functions of 2,813 single nucleotide variants in 93 genomic loci associated with estrogen receptor-positive breast cancer. To facilitate rapid progress in functional genomics of breast cancer, we have created “Analysis of Breast Cancer GWAS” (ABC-GWAS), an interactive database of functional annotation of estrogen receptor-positive breast cancer GWAS variants. Our resource includes expression quantitative trait loci, long-range chromatin interaction predictions, and transcription factor binding motif analyses to prioritize putative target genes, causal variants, and transcription factors. An embedded genome browser also facilitates convenient visualization of the GWAS loci in genomic and epigenomic context. ABC-GWAS provides an interactive visual summary of comprehensive functional characterization of estrogen receptor-positive breast cancer variants. The web resource will be useful to both computational and experimental biologists who wish to generate and test their hypotheses regarding the genetic susceptibility, etiology, and carcinogenesis of breast cancer. ABC-GWAS can also be used as a user-friendly educational resource for teaching functional genomics. ABC-GWAS is available at http://education.knoweng.org/abc-gwas/.
Highlights
Genome-wide association studies (GWAS) have implicated thousands of genetic variants in various complex traits, including diseases (MacArthur et al, 2017)
The basic framework performs various genomic analyses outlined below to infer how a genome-wide association studies (GWAS) variant or a linked single nucleotide polymorphisms (SNPs) changes the binding affinity of a transcription factors (TFs) in a regulatory region, which in turn alters the transcription of a target gene
We demonstrated the capability of ABC-GWAS to find known, as well as novel, functional mechanisms of breast cancer GWAS
Summary
Genome-wide association studies (GWAS) have implicated thousands of genetic variants in various complex traits, including diseases (MacArthur et al, 2017). GWAS variants may indirectly correlate with a phenotype through a complex gene regulatory network involving multiple target genes, unknown causal variants, and transcription factors (TFs). A reported GWAS variant may be genetically linked to another proximal variant that itself directly perturbs the binding affinity of a TF and changes the expression of a distal target oncogene or tumor suppressor forming a chromatin loop with the causal variant. In such cases, there is the additional complexity of having to dissect how different components of a gene regulatory network are altered and function together to modulate a trait. There is an urgent need for comprehensive and accessible resources that integrate information from heterogeneous large-scale datasets to facilitate rapid functional characterization of GWAS findings and contribute toward the development of therapeutic preventions and interventions
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