The researchers identified non-coding gene variants linked to biological processes like blood sugar regulation.
The protein-coding fraction of our genome is like the cast of a Broadway production. Their performance is critical to the success of Biology: The Musical, but a much larger production crew helps coordinate their onstage activities and ensure things go smoothly. These backstage participants are the genome’s regulatory elements, which make up 98 percent of the genome.
Without the ability to comprehensively map these regulatory chunks of DNA, geneticists have struggled to estimate how mutations in these areas of the genome affect people’s disease risk. Previous efforts have had to consider many potential variants one-by-one, leading to time-consuming experimentation.
A new study has changed this and put a spotlight on how gene mutations affect complex diseases.1 Researchers at The Jackson Laboratory, the Broad Institute, and Yale University cracked the problem at scale by testing more than 220,000 DNA alterations simultaneously to build maps linking genes to complex processes such as cholesterol regulation and blood sugar control. Their work was published in Nature.
Parallel Processing Speeds up Gene Analysis
“For nearly two decades, genetic studies have identified where in the genome we need to look for disease risk, but not which specific DNA changes are responsible,” said Ryan Tewhey, a geneticist at The Jackson Laboratory and study coauthor, in a statement. “Our study helps close this gap by working at the scale needed to confidently pinpoint the specific DNA changes that matter across thousands of regions all at once, rather than one by one.”
Large-scale analysis has been critical to most of the recent advances in genomics. Huge genome-wide association studies have provided researchers with a trove of information about genomic regions linked to specific diseases and health conditions. But when these hotspots lie in non-coding regulatory regions, researchers have been unable to explain how single-letter DNA changes influence gene activity and, ultimately, disease risk.
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Tewhey and his colleagues tackled this task using a massively parallel reporter assay (MPRA). This allowed them to assess the genetic contributions of over 220,000 variants, each just one letter of DNA, simultaneously across five different cell types. In an MPRA, each genomic variant is linked to a reporter gene, allowing researchers to determine how non-coding variants alter gene activity.
Gene Variant Combinations Alter Health Outcomes
This mass analysis revealed 13,000 variations in these non-coding regions that could alter gene expression. The team examined how these different single-letter variants interacted. Roughly 11 percent contributed an effect greater than the sum of their parts when combined with other variants. These combinations included a pair that linked to gene activity associated with lower levels of harmful low-density lipoprotein cholesterol.
Another identified variant was linked to blood sugar control in people with European ancestry. The team’s analysis of this variant’s molecular action suggested that other understudied variants found in people of African ancestry would have a similar connection. Subsequent analysis proved this theory, demonstrating the MPRA technique’s power as a predictor of genetic mechanisms in diverse groups.
The study is not the final act in the show. More work will be needed to understand the mechanisms by which the identified changes in gene activity alter disease risk. There are millions of other gene variants that have not yet been explored, and many other cell types that have not been examined. Only by understanding how the genes act in all of the body’s many cell types will researchers see the full performance of gene regulation.
“What excites me is that this is a bridge from association to biology,” said Layla Siraj, a physician at Columbia University/New York Presbyterian who coauthored the paper during her time at the Broad Institute, in a statement. “By uncovering the patterns underlying how single-letter changes affect gene regulation, we can start mechanistically connecting genetic risk to the pathways therapies could target.”
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