Gene ExpressionA new study has found that structural variants (SVs) may contribute more to gene expression variation than previously thought.  The research, published in Nature Genetics, used deep whole genome sequencing data to examine the influence SVs have on cis expression quantitative trait loci (eQTLs) in different tissue types.

The team, consisting of researchers at Washington University School of Medicine, used multi-tissue RNA-Seq expression data that had previously been collected by the Genotype-Tissue Expression project to perform eQTL mapping of almost 150 people. In total, they were able to identify 23,602 SVs and 24,884 cis-eQTLs across 13 different tissue types.

Using joint eQTL mapping, the team established that SVs were the causal variants of 3.5% of eQTLs, whereas single nucleotide variants (SNVs) were at 85.5% and insertion and deletion mutations (indels) were at 10.9%. These results went against the previously held conception that SVs were not of particular significance in altering gene expression. In particular, the team found that SVs contributed to 2.2% of eQTLs in whole blood samples, a value that is 4-fold higher than the previous estimate put forward by the 1000 Genomes Project. Overall, they estimated that between 3.5% and 6.8% of eQTLs had SVs as the causal variant, depending on the inference method used.

Moreover, the researchers estimated that SVs have a greater influence on expression than SNVs and indels. They reported that SVs were 28-54 times more likely to alter gene expression and that the median effect size was 1.3-fold larger than SNVs and indels.

“Our results show that SVs comprise a substantial and disproportionately large fraction of expression-altering genetic variants, a large portion of which have been untested in typical association studies,” the authors wrote.

Another observation the team made was that rare variants, especially SVs but also SNVs and indels, were enriched in regions close to gene expression outliers. This might suggest that rare SVs are a common cause of aberrantly expressed genes.

“These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies,” they wrote.

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