As genetic sequencing technology becomes more accessible and efficient, researchers have made significant strides in understanding the genetic underpinnings of various diseases. This knowledge has led to a surge in clinical applications of genetic testing, offering hope and improved outcomes for individuals affected by many genetic diseases and disorders. Despite these successes, scientists continue to try to improve genetic testing technologies, because many individuals with rare diseases remain undiagnosed even after current state-of-the-art genomic testing.
Scientists at the HudsonAlpha Institute for Biotechnology were awarded a five-year, $2.9 million National Institutes of Health (NIH) grant to use a newer sequencing tool called long-read genome sequencing to re-sequence hundreds of genomes from individuals who previously had genome sequencing with no diagnostic results. Long-read sequencing technology provides a more comprehensive view of the genome, allowing scientists to identify many genetic variants that may be missed by traditional sequencing methods.
HudsonAlpha Faculty Investigator Greg Cooper, PhD, and his lab are at the forefront of using genome sequencing to revolutionize the diagnosis of genetic disorders in children. Since 2013, Cooper and his lab have sequenced the genomes of nearly 2,000 children, and over 40 percent of these had a genetic finding that may be relevant to their symptoms.
Now, they want to focus on long-read sequencing technology, which they think will afford even more children and families a diagnosis. Early studies show that long-read sequencing can uncover relevant genetic findings in 5-10 percent of previously tested but still undiagnosed children, suggesting its potential to significantly improve diagnostic rates for a wide range of pediatric diseases. Also, as the technology is relatively young, it can be improved and refined and has the potential to increase diagnostic success even beyond what early studies are showing.
“Long-read sequencing holds immense promise for uncovering the genetic causes of diseases,” says Dr. Cooper. “My team and I are passionate about making a difference in the lives of individuals and families affected by rare genetic disorders. By pushing the boundaries of genetic research, we hope to shed light on previously hidden genetic variation and provide more accurate and timely diagnoses. Our goal is to empower families with the knowledge they need to navigate their health challenges and build a better future.”
Dr. Cooper and his team will use long-read sequencing to re-analyze the genomes of more than 500 individuals whose short-read sequencing had previously yielded no results. They will also, in some cases, sequence the genomes of the individuals’ parents, which helps the team identify shared or brand-new variations between the children and their parents.
Long-read sequencing allows researchers to see types of genetic variation they wouldn’t see with short-read sequencing. One type of variation that shows great promise in diagnosing rare diseases is structural variants. These include large deletions, duplications, inversions, translocations, and more complex events that can disrupt gene function, resulting in diseases. Short-read sequencing has a limited ability to detect structural variants. Dr. Cooper and his team feel confident that they will identify more structural variants with long-read sequencing that might be responsible for some individuals’ symptoms.
HudsonAlpha Faculty Investigators Jane Grimwood, PhD, and Jeremy Schmutz will join Dr. Cooper in this project. For nearly two decades, they’ve co-directed the HudsonAlpha Genome Sequencing Center (GSC; formerly the Stanford Human Genome Center). The GSC team are experts at sequencing and assembling complex genomes. In fact, the GSC was one of the first labs in the world to have access to the sequencing technology being used for this proposal, continuing their long tradition of using cutting-edge genomic technologies. They will perform long-read sequencing on the samples and send them to Cooper’s lab, which will analyze the resulting data to identify any potentially medically relevant findings.