In 2007, when the first genome-wide association studies (GWAS) got underway, researchers began to realize just how poorly they had previously been able to predict which genes might be related to certain diseases.
Height is the “poster child” of complex genetic traits, meaning that it’s influenced by multiple genetic variants working together. Because height is easy to measure, it’s a relatively simple model for understanding traits produced by not one gene, but many.
“Mastering the complex genetics of height may give us a blueprint for studying multifactorial disorders that have eluded our complete understanding, such as diabetes and heart disease,” says Joel Hirschhorn, MD, PhD, a pediatric endocrinologist and researcher at Boston Children’s Hospital and the Broad Institute of MIT and Harvard.
Genome-wide association studies are huge undertakings that compare the genomes of large populations. They can turn up thousands to tens of thousands of genetic variants associated with disease. But which GWAS variants really matter?
That question becomes exponentially harder when the variants lie in the vast stretches of DNA that don’t encode proteins, but instead have regulatory functions.
Reporting in Cell, Sankaran’s team and two other groups at the Broad Institute describe a new tool that can looks at hundreds of thousands of genetic elements at once to pinpoint variants that truly affect gene expression or function. Called the massively parallel reporter assay (MPRA), it could help reveal subtle genetic influences on diseases and traits.
In Sankaran’s case, the MPRA is helping him understand how common variants contribute to blood disorders in children. “Most of the common variation is just tuning genetic function,” he says. “Just slightly, not turning it on or off, but actually just tuning it like a dimmer switch.”
Part of the problem may be that, until now, the right tools haven’t been available to exploit GWAS data. But a few recent studies—including two out of Dana-Farber/Boston Children’s Cancer and Blood Disorders Center—have used GWAS data to identify therapeutically promising targets, and then manipulated those targets using the growing arsenal of gene editing methods.
While it’s not yet clear what this means for patients with either disease, the findings help untangle some very perplexing data about human genetics and diabetes risk, and could change doctors’ thinking about the treatment of both conditions down the road.
Scientists have long known that cancerous and healthy cells don’t use sugar in the same ways. …