Human Genetics and Genomics AdvancesGeneticsPeer-reviewed journal article
From GWAS Variant to Function: a Study of ~148,000 Variants for Blood Cell Traits.
Quan SuniD, Cheynna CrowleyiD, Le HuangiD, Jia WeniD, Jiawen Carmen CheniD, Erik L. BaoiD, Paul Livermore AueriD, Guillaume LettreiD, Alex P. ReineriD, Vijay G. SankaraniD, Laura M. RaffieldiD, Yun LiiD
Abstract
Genome-wide association studies (GWAS) have identified hundreds of thousands of genetic variants associated with complex diseases and traits. However, most variants are noncoding and not clearly linked to genes, making it challenging to interpret these GWAS signals. We present a systematic variant-to-function study, prioritizing the most likely functional elements of the genome for experimental follow-up, for >148,000 variants identified for hematological traits. Specifically, we developed VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny (http://shiny.bios.unc.edu/vampire/). This tool efficiently integrates and displays information from multiple complementary sources, including epigenomic signatures from blood cell relevant tissues or cells, functional and conservation summary scores, variant impact on protein and gene expression, chromatin conformation information, as well as publicly available GWAS and phenome-wide association study (PheWAS) results. Leveraging data generated from independently performed functional validation experiments, we demonstrate that our prioritized variants, genes, or variant-gene links are significantly more likely to be experimentally validated. This study not only has important implications for systematic and efficient revelation of functional mechanisms underlying GWAS variants for hematological traits, but also provides a prototype that can be adapted to many other complex traits, paving the path for efficient variant to function (V2F) analyses.
- organism: human
- assay: GWAS summary statistics
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 15
- FWCI
- 1.84
- Field percentile
- top 14%
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