The most comprehensive international collaborative analysis to date of the impact of variants on gene expression has revealed thousands of previously unknown regulatory genomic regions controlling disease-linked genes, representing a major advance in genomics-driven precision medicine.

This identification of gene expression quantitative trait loci (eQTL) provides "an entirely new view of genetic regulation by revealing new insights into how genes and disease are linked," said co-senior author Joseph Powell, an associate professor and director of the Garvan Institute of Medical Research in Sydney, Australia.

New insights

Genome-wide association studies (GWAS), which search patients' genomes for variants that are commonly associated with a specific condition, are widely used to study genetic variations affecting human disease risk.

However, interpretation of GWAS results can be difficult, especially as many genetic variants regulate gene activity to influence, for example, how much of a specific protein is produced, rather than directly driving disease.

GWAS results "merely tell us where in the genome a likely disease risk loci resides, but other than that position, no additional information is revealed," said Powell, who is also deputy director of the University of New South Wales Cellular Genomics Futures Institute.

In contrast, "eQTL analyses provide a means of showing how that loci acts and specifically how it changes the amount of RNA transcribed from a gene or genes into proteins, showing the function whereby disease risk loci act."

In other words, by pinpointing gene eQTL regulatory regions, researchers can determine which genes directly contribute to disease risk and could therefore be targeted with precision medical treatments.

In the new gene expression analysis, the authors used specialized machine learning algorithms to analyze genomic data from blood samples of over 31,000 individuals, they reported in the September 2, 2021, edition of Nature Genetics.

The statistical power of this large dataset is sufficient to identify new regulatory regions on the human genome, noted Powell.

"Rather than merely cataloguing adjacent regulatory gene locations, known as cis-eQTLs, we were also able to reveal genes that modulate the activity of more distant genes or trans-eQTLs," he said.

"This provides another layer of information in uncovering how different types of molecular functions underlie disease risk loci, with those acting in cis- typically having a different biological process to those acting in trans-eQTL."

Of the millions of genes investigated, 88% were found to have a cis-eQTL effect, but 32% also had a trans-eQTL effect further away in the genome, more than half of which could be linked to a biological impact, including on immune diseases and cardiovascular disease (CVD).

"We first tested for cis-eQTL, then for those that we identified we tested whether they also had effects acting at trans-eQTL," explained Powell.

"For 32% of them, we identified significant evidence of effects acting at both cis- and trans-eQTL, so it is likely that some of these loci impact other diseases beyond CVD and immune disorders," he told BioWorld Science.

The study's data are publicly available at

New disease links

"While genetic variants are almost invariably a cause of disease, the mechanisms by which they influence that disease are far less clear," noted Powell.

For instance, while a specific disease may be linked to multiple genetic variants, most contribute to disease by regulating gene activity, so understanding which genes this regulation 'converges' on will be invaluable for identifying specific targets for potential new treatments.

New treatments

"This work allows us to identify the 'pathways' of genes that collectively contribute to diseases, with these pathways being important, as they offer more possibilities for identifying new druggable targets," Powell said.

This obviously has important drug development implications, since if a therapy were developed targeting a certain molecular target, "our resource could help identify how its expression is regulated and whether the genetic background of different patients would be likely to impact [that drug's] efficacy," he said.

"We have discovered an entirely new level of genomic information, which provides a deeper understanding of both biology and disease," Powell added.

The study findings may help researchers to identify better biomarkers of which patients would derive most benefit from which treatments, and will assist in tracking disease progression and assessing treatment efficacy.

In addition, these data could help with interpretation of GWAS results, to prioritize presumed trait-related genes for further functional studies, and to develop new methods by which to perform these tasks