Joint Analysis of QTL Data Provided Insights into the Connection of Transcriptome and Proteome and the Impact of...

This study investigated the connection between the transcriptome and proteome by integrating eQTL (Expression Quantitative Trait Locus) and pQTL (Protein Quantitative Trait Locus) datasets generated using different technologies. eQTL data were obtained from the eQTLGen (microarray-based) and INTERVAL (RNA-Seq-based) studies, while pQTL data were derived from the UK Biobank (Olink platform) and deCODE (SomaScan platform) studies. A total of 1162 genes common to all four datasets were analyzed. Mendelian randomization (MR) identified 211 genes whose transcript levels significantly (p < 5e-8) predicted protein levels, whereas genetic correlation analysis detected 67 genes with shared genetic regulation. Negative transcript-protein associations were observed for 12% of genes identified by MR and 7% of those identified through genetic correlation. Cross-platform comparisons showed the strongest concordance between eQTL and pQTL effect sizes in the INTERVAL-UK Biobank panel and the weakest in the eQTLGen-deCODE panel. Colocalization analysis further confirmed these findings and indicated genes with strong eQTL-pQTL overlap predominantly encode intracellular proteins, whereas genes with weak overlap tend to encode glycosylated secreted proteins. Integrating both the transcriptome and proteome for biomarker discovery and locus annotation is important, as the overall genetic architectures of the blood transcriptome and proteome are not the same. RNA-Seq and Olink platforms provide more accurate measurements of RNA and protein levels.