Identifying functional noncoding variants from genome-wide association studies for cardiovascular disease and related traits.

Abstract

Genome-wide association studies have identified many novel loci for cardiovascular disease and related traits. Attention is now shifting towards the analysis of these loci for causal variants, with a view to identify the novel mechanisms leading to disease. This review focuses on the approaches to identify causal, noncoding variants for coronary artery disease, lipid traits and other cardiovascular risk factors. Fine-mapping studies are discussed, along with the novel statistical approaches to produce 'credible sets'. The use of combining genome-wide association study datasets with experimental methods such as expression quantitative trait loci and allele-specific chromatin accessibility are explored, with recent examples discussed. Mapping long-range chromatin interactions and evolving genome-editing technologies such as clustered regularly interspaced short palindromic repeats combined with clustered regularly interspaced short palindromic repeats-associated (Cas9) nuclease promise to aid considerably the search for causal variants. Identification of causal variants for cardiovascular disease and related traits is still in the early stages, but with technologies evolving and increasingly relevant tissue samples undergoing analysis, there are favourable prospects that many new mechanisms for disease will be uncovered by the end of this decade.