It has been found that the majority of disease-associated genetic variants identified by genome-wide association studies are located outside of protein-coding regions, where they seem to affect regions that control transcription (promoters, enhancers) and non-coding RNAs that also can influence gene expression. In this review, we focus on two classes of non-coding RNAs that are currently a major focus of interest: micro-RNAs and long non-coding RNAs. We describe their biogenesis, suggested mechanism of action, and discuss how these non-coding RNAs might be affected by disease-associated genetic alterations. The discovery of these alterations has already contributed to a better understanding of the etiopathology of human diseases and yielded insight into the function of these non-coding RNAs. We also provide an overview of available databases, bioinformatics tools, and high-throughput techniques that can be used to study the mechanism of action of individual non-coding RNAs. This article is part of a Special Issue entitled: From Genome to Function.
Genome-wide association and fine-mapping studies in 14 autoimmune diseases (AID) have implicated more than 250 loci in one or more of these diseases. As more than 90% of AID-associated SNPs are intergenic or intronic, pinpointing the causal genes is challenging. We performed a systematic analysis to link 460 SNPs that are associated with 14 AID to causal genes using transcriptomic data from 629 blood samples. We were able to link 71 (39%) of the AID-SNPs to two or more nearby genes, providing evidence that for part of the AID loci multiple causal genes exist. While 54 of the AID loci are shared by one or more AID, 17% of them do not share candidate causal genes. In addition to finding novel genes such as ULK3, we also implicate novel disease mechanisms and pathways like autophagy in celiac disease pathogenesis. Furthermore, 42 of the AID SNPs specifically affected the expression of 53 non-coding RNA genes. To further understand how the non-coding genome contributes to AID, the SNPs were linked to functional regulatory elements, which suggest a model where AID genes are regulated by network of chromatin looping/non-coding RNAs interactions. The looping model also explains how a causal candidate gene is not necessarily the gene closest to the AID SNP, which was the case in nearly 50% of cases.
Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, the current methods are labor-intensive and expensive. Here we introduce a new method, Decon2, a statistical framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) and consecutive deconvolution of cell type eQTLs (Decon-eQTL). The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell we can predict the proportions of 34 circulating cell types for 3,194 samples from a population-based cohort. Next we identified 16,362 whole blood eQTLs and assign them to a cell type with Decon-eQTL using the predicted cell proportions from Decon-cell. Deconvoluted eQTLs show excellent allelic directional concordance with those of eQTL(≥ 96%) and chromatin mark QTL (≥87%) studies that used either purified cell subpopulations or single-cell RNA-seq.Our new method provides a way to assign cell type effects to eQTLs from bulk blood, which is useful in pinpointing the most relevant cell type for a certain complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2), and as a web tool ( www.molgenis.org/deconvolution ).
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