Cap analysis of gene expression (CAGE) and noncoding regulatory elements

Cap analysis of gene expression (CAGE) was developed to detect the 5' end of RNA. Trapping of the RNA 5'-cap structure enables the enrichment and selective sequencing of complete transcripts. Upscaled high-throughput versions of CAGE have enabled the genome-wide identification of transcription start sites, including transcriptionally active promoters and enhancers. CAGE sequencing can be exploited to draw comprehensive maps of active genomic regulatory elements in a cell type- and activation-specific manner. The cells of the immune system are among the best candidates to be analyzed in humans, since they are easily accessible. In this review, we discuss how CAGE data are instrumental for integrative analyses with quantitative trait loci and omics data, and their usefulness in the mechanistic interpretation of the effects of genetic variations over the entire human genome. Integrating CAGE data with the currently available omics information will contribute to better understanding of the genome-wide association study variants that lie outside of annotated genes, deepening our knowledge on human diseases, and enabling the targeted design of more specific therapeutic interventions.

Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification

The integration of transcriptomic studies and genome-wide association studies (GWAS) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA-sequencing samples from 948 post-mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine-mapping (dap-g) and borrowing information across tissues (mashr) can lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes identified as indicated by precision-recall and receiver operating characteristic curves. All prediction models are made publicly available at predictdb.org.