Abstract
Genome-wide association studies (GWAS) have proven a fundamental tool to identify common variants associated to complex traits, thus contributing to unveil the genetic components of human disease. Besides, the advent of GWAS contributed to expose unexpected findings that urged to redefine the framework of population genetics. First, loci identified by GWAS had small effect sizes and could only explain a fraction of the predicted heritability of the traits under study. Second, the majority of GWAS hits mapped within non-coding regions (such as intergenic or intronic regions) where new functional RNA species (such as lncRNAs or circRNAs) have started to emerge. Bigger cohorts, meta-analysis and technical improvements in genotyping allowed identification of an increased number of genetic variants associated to coronary artery disease (CAD) and cardiometabolic traits. The challenge remains to infer causal mechanisms by which these variants influence cardiovascular disease development. A tendency to assign potential causal variants preferentially to coding genes close to lead variants contributed to disregard the role of non-coding elements. In recent years, in parallel to an increased knowledge of the non-coding genome, new studies started to characterize disease-associated variants located within non-coding RNA regions. The upcoming of databases integrating single-nucleotide polymorphisms (SNPs) and non-coding RNAs together with novel technologies will hopefully facilitate the discovery of causal non-coding variants associated to disease. This review attempts to summarize the current knowledge of genetic variation within non-coding regions with a focus on long non-coding RNAs that have widespread impact in cardiometabolic diseases.
Highlights
Genome-wide association studies (GWAS) have proven a fundamental tool to identify common variants associated to complex traits, contributing to unveil the genetic components of human disease
Further refinement of the human genome by the 1,000 Genomes Project mapped over 88 million variants from 26 populations where ∼20 million correspond to common single-nucleotide polymorphisms (SNPs), a coverage of >95% of all estimated human common SNPs [1, 2]
It seems that the common disease-common variant (CD-CV) model that drove the first decade of GWAS studies is shifting to a complex trait-complex genetics (CT-CG) scenario, where a handful of relevant variants cannot fully explain genetic variation in whole populations
Summary
One of the longest-standing challenges in human genetics is to assign potential causality within a locus to every variant in close linkage disequilibrium (LD) with the lead variant [34]. Despite the potential of lncRNAs as causal factors of disease, GWAS studies had a tendency to explore genetic variant causality preferentially in coding genes, mostly due to our limited knowledge of ncRNAs genomic structure and functionality. Low evolutionary conservation of lncRNAs constitutes a challenge to predict structural domains and how genetic variants induce functional modifications [47]. SNPs may affect lncRNA transcriptional expression by altering its promoter region and may influence expression of proximal or distal protein coding genes through the action of enhancers [19]. Modulation of distant genes by trans-regulation is mediated by lncRNAs-enhancers but the effect of induced chromatin structural changes must be considered. Distal regulatory elements (DRE) can regulate the transcription of lincRNA through chromatin interactions, which can be influenced by GWAS-identified SNPs and define disease association [50]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
