A ‘Swiss Army knife’ for finding bad mutations
The new approach to determining the pathogenicity of genetic variants could be broadly applied to many human diseases.
Geneticists have a new and versatile tool to scan for bad mutations across genes related to human disease.
While previous experiments were tailored to understand protein-coding mutations in a single gene and a particular disease, this new method could be applied to proteins encoded by many of the 4,000 disease-related genes in the human genome.
CIFAR Azrieli Global Scholar Douglas Fowler and his colleagues developed the method, known as variant abundance by massively parallel sequencing (VAMP-seq). Their findings were published in Nature Genetics.
“It’s a Swiss Army knife kind of idea as opposed to hand-crafting an assay from each gene or protein which could take months or even years,” says Fowler, an assistant professor of genome sciences at the University of Washington.
The key to his method’s generalizable success is that, despite their diversity, most proteins must be abundant enough to function. VAMP-seq identifies which mutations have a negative effect by measuring the abundance of a protein with that variant. If the variant protein has decreased abundance then it could lead to disease or alter drug response. VAMP-seq simultaneously scans every possible mutation and produces a list of the pathogenic ones that reduce abundance.
Fowler and his colleagues tested VAMP-seq on two disease-related proteins. They revealed 1,138 mutations that disrupt a tumor suppressor linked to cancer and 777 variants in an enzyme that inactivate thiopurine drugs, which treat leukemia and autoimmune diseases. VAMP-seq could be applied to proteins for many other disease and drug-related genes, allowing scientists to find dangerous mutations at an unprecedented scale.
“It’s a general way forward. It takes what has been a cool niche and it looks at how it could be applied at scale,” Fowler says.
From the lab to the clinic
With billions of possible mutations in the human genome, an approach to interpreting which ones are important is crucial. In 2017, geneticists – including Fowler and Genetic Networks Senior Fellow Maitreya Dunham and Program Co-Director Fritz Roth – outlined their vision for addressing the “variant-interpretation crisis.” One of the first goals was to create methods to harness massive amount of functional data as VAMP-seq can.
The next step is to take this information from the lab to the clinic.
“It’s all fine and good for me to collect this data as a researcher but presenting it in a way that clinicians can digest and use is a really hard problem,” says Fowler.
As researchers begin to collect and compile more data, this can be combined with clinical knowledge and machine learning for more accurate patient diagnosis and treatment. This data will also help scientists to map out the interactions that give way to more complex human diseases.
“The Genetic Networks program has had a significant impact on my thinking, in particular my thinking about the scale and scope of the impact we might have.”
Conversations and collaborations with CIFAR fellows played a role in this paper and future research.
“The Genetic Networks program has had a significant impact on my thinking, in particular my thinking about the scale and scope of the impact we might have,” Fowler says.
“It’s really pushed me to think a bit bigger.”
‘Multiplex assessment of protein variant abundance by massively parallel sequencing’ was published in Nature Genetics on May 21, 2018.
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