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

By integrating high throughput eQTL and pQTL data generated using different platforms, in this study, the relationship between transcriptome and proteome, as well as, the efficacy of platforms in measuring transcript and protein levels in blood were investigated. eQTL data were obtained from the eQTLGen study that used Microarray and INTERVAL study that relied on RNASeq platform to measure transcripts. pQTL data were obtained from UK Biobank study that used Olink and deCODE study that used SomaScan platform to measure proteins. A total of 1,162 genes that were shared between the four platforms were selected and investigated.The outcome of Mendelian randomization analysis identified 211 genes that their transcript levels significantly (P<5e-8) predicted their protein levels across the panels. Similarly, genetic correlation analysis identified 67 genes that share significant correlation. %12(N=25) of genes identified through Mendelian randomization and 7% of those identified through genetic correlation showed negative associations. Cross-platform analysis revealed in INTERVAL-UKBB panel the effect size of SNPs on eQTLs and pQTLs show the highest correlation; while in eQTLGen-deCODE panel this value was the lowest. Co-localization analysis further confirmed these findings and indicated genes with strong evidence of colocalization in their eQTLs and pQTLs encode intracellular proteins while those with trivial evidence of colocalization encode secretory proteins that undergo glycosylationIntegrating both transcriptome and proteome for biomarker discovery and locus annotation is important, as overall genetics of transcriptome and proteome are not the same. RNASeq and Olink platforms provide more accurate measures of RNA and protein levels.