Transcriptome-wide association study (TWAS) of the plasma proteome

s described many times in these GEITP email blogs — to discover a gene or genetic loci that is/are correlated with a simple or complex trait (phenotype) of your choosing, genome-wide association studies (GWASs) have been commonly used as a means to search the haploid genomes of large cohorts (human or other populations). GWAS studies have been increasingly popular over the past two decades of genetic studies. In these studies, one looks for one or more single-nucleotide variants (SNVs) that are highly statistically significantly (e.g., P <5.0 x 10–8, also written as <5.0e–08) associated with a phenotype (e.g., heart disease, schizophrenia, autism spectrum disorder, type-2 diabetes, adverse drug effect, increased risk of cancer, etc.) Today’s topic is about transcriptome-wide association studies (TWASs), which offer a whole new level of complexity. Whereas GWASs search for SNVs among every DNA nucleotide in one’s haploid genome, TWASs explore differences in transcription of only those genes that are being actively transcribed. Also, whereas each cell of the human body (except for red cells that have no nuclei) has a copy of the same DNA, transcription (gene expression) varies widely, dependent on cell type; the human body has between 208 and 212 distinct cell-types, which can exhibit altered transcription — dependent on age, circadian rhythm, metabolism, and various critical-life functions. Regulation of gene expression and protein abundance are important mechanisms through which many noncoding SNVs affect traits. Expression quantitative trait loci (eQTL) mapping in multiple human tissues has discovered a variety of both distal (trans) and proximal (cis) variants associated with gene expression. Whereas cis-eQTLs tend to have larger effect-sizes than trans-eQTLs — studies have shown that trans-eQTLs account for a larger portion of the heritability of gene expression. One study of twins found that, on average, trans-eQTLs explained more than three times the variance in gene expression than cis-eQTLs. Furthermore, trans-eQTLs tend to be more tissue-specific, suggesting that they might play a role in cell-type differentiation. Despite the importance of trans-eQTLs in regulating gene expression, QTL-mapping studies have been limited in their ability to detect trans-acting effects due in part to the high multiple-testing burden, as well as their comparatively low effect-sizes. Methods that minimize the multiple-testing burden (by prioritizing subsets of variants, or grouping trans-genes) have proven more successful at identifying trans-eQTLs. For example, one study that tested the cis-component of gene expression for an association with the observed expression of distant genes — identified more replicable trans-acting genes than a comparable trans-eQTL study. This is because many trans-eQTLs co-localize with cis-eQTLs, and they affect expression of distant genes through cis-mediators such as a nearby transcription factor (TF) gene. The advent of advanced assay technologies that capture and measure protein abundances — has enabled protein quantitative trait locus (pQTL)-mapping studies to identify variants associated with the abundance of proteins. Trans-pQTLs, like eQTLs, tend to have lower effect-sizes and be tissue-specific. Parallelling methods for detecting trans-eQTLs, the authors [see attached] hypothesized that cis-prioritization will improve detection of trans-pQTLs. Herein, authors applied a transcriptome-wide association study (TWAS) framework to proteomic data, testing the genetically-predicted expression of genes for any associations with the observed abundance of plasma proteins. [Q: Which tissue(s) did authors study? A: 49 tissues combined.] Figure 1A [see attached file] shows an overview of TWAS analysis. Genotype data from both the INTERVAL and TOPMed MESA cohorts were used to impute genetically-regulated expression levels (GReX) in 49 different GTEx tissues. GReX was tested for associations with measured plasma protein levels for all proteins tested in both studies. Authors concluded that TWAS for protein levels is an effective method for identifying replicable trans-acting associations between predicted transcripts and proteins. Authors also found a high expected proportion of true positives for associations between the predicted transcripts and protein products of the same underlying gene. Lastly, using RNA-sequencing (RNA-Seq) data, authors also show that predicted gene expression correlates better with protein levels than with the observed gene expression. DwN Am J Hum Genet 7 Mar 2024; 111: 445-455