This Faster, Low-tech Test Identifies Rare Disease-causing Genetic Mutations

Not all genetic mutations cause disease. That’s why scientists must sort through many changes in the genome to determine which genetic changes make people sick and which have no effect. These experiments are often expensive and require special equipment that not all labs can afford.

Now, researchers at Yale School of Medicine have developed a low-tech and cost-effective test to search for mutations that cause dystroglycanopathies, a group of rare and sometimes life-threatening genetic disorders that can lead to muscle problems, developmental delays, and seizures.

When experiencing a rare disease, “a lot of patients out there don’t have a definitive diagnosis,” says Monkol Lek, PhD, assistant professor of genetics at Yale School of Medicine. Lek hopes that the test—called saturation mutagenesis-reinforced function assays, or SMuRF—will lower barriers for researchers to tackle this understudied group of diseases. This research was published Nov. 14 in Cell.

Sequencing DNA to determine an individual’s genetic makeup used to be the biggest challenge facing geneticists. But these days, researchers can quickly learn exactly what base pair exists at what location in everyone’s genome. The challenge now is interpreting the meaning of genetic differences between people.

Dystroglycanopathies occur because of issues with the enzyme alpha-DG, which helps connect cell walls to the body’s extracellular membrane. Mutations that impact the enzyme’s function can have major consequences. But “just because you have a rare variant [in a disease-causing gene], it doesn’t mean it will cause disease,” explains Lek.

Researchers traditionally figure out which variants cause disease by comparing large groups of people. However, this approach doesn’t necessarily work for rare diseases—where as few as one or two people might have the causal mutation. High throughput experiments that compare variants can also work, but existing tests use robotics and expensive reagents that make it harder for labs with fewer resources to access.

Aiming to make a more accessible test, Lek and his colleagues decided to develop a novel method for deep-mutational scanning (DMS), which quickly assesses genetic mutational data at scale. The team created every possible single-nucleotide version of two genes involved in the glycosylation of alpha-DG using readily available reagents. They then inserted those variants into cells and used alpha-DG antibodies sensitive to glycosylation to see whether the mutation impacted how well the enzyme functioned.

The team found that their test results lined up with existing knowledge around the conditions—with genetic variants linked to disease showing that the enzymes had reduced function. Their analysis also showed that less than 17% of variants have clinical interpretations regarding their impact on alpha-DG glycolyslation, highlighting challenges in finding a genetic diagnosis in patients with dystroglycanopathies.

Their analysis included variants that have so far not been linked to any disease. This could help clinicians identify causal mutations in future cases, says Lek. Most large-population databases used genetic data from European populations, so variants in other groups may be difficult to interpret. This method, which isn’t based on ancestry, “removes that bias,” he says.

Currently, the method can only be used to interpret variants associated with dystroglycanopathies. But the cost-effective framework could be applied by other labs to different diseases, assuming researchers are able to develop gene-specific functional assays, says Lek.

One of the major goals of the project was to create a method that could be used by other research groups, he says. To that end, Lek decided to go with first author Kaiyue Ma’s suggestion of calling the assay SMuRF. “I wanted to have something that other people would use,” he says. “The best way of getting others to use it is to have a catchy name.”