Advancements in biomedical technologies have transformed genetic testing for rare diseases. Health care providers have the ability to sequence every gene in a patient and appropriate family members, and use emerging bioinformatics analytical algorithms and scientific knowledge to hone in on the genetic changes often causal in previously undiagnosed disease.
This testing paradigm is commonly referred to as whole-exome sequencing for patients who are on a Diagnostic Odyssey, and has led to the resolution of a genetic diagnosis in many thousands of patients who were previously left without a clear explanation for their symptoms.
The utility of this testing has become so powerful that institutions across the world, including Mayo Clinic, have adopted this approach to testing patients with suspected rare genetic disease.
Despite the amazing impact this testing has had on some patients, the diagnostic yield from the current approach has been estimated at approximately 25 percent. This means roughly three-fourths of the patients tested fail to receive a genetic diagnosis.
The Mayo Clinic Center for Individualized Medicine has launched a genomics research program specifically focused on improving the diagnostic yield from genetic testing. Using a variety of new analytical methods, additional forms of genetic tests, and employing a variety of laboratory-based functional studies, the Center seeks answers for patients for whom whole-exome sequencing was not sufficient to confidently return a genetic diagnosis.
The success of this program is based not only on the transformative vision of Mayo’s Center for Individualized Medicine, but on the Center's unique ability to integrate cutting-edge analytical processes and laboratory research with world-leading clinical expertise.
At the crux of today’s advanced clinical genetic testing is a massive data analysis need, as an individual patient harbors tens-of-thousands of genetic changes. To understand this data it must be integrated with a detailed account of the patient’s condition or phenotype, and existing scientific knowledge on human health genetics.
To address these needs, the Genomics Program utilizes an intuitive computer system to capture the clinician’s observations about a patient and store them in a structured manner (structured-phenotyping) amenable to automated analysis.
The program also is implementing a process to automatically cross-reference this structured phenotype data with the published scientific literature, existing biological and clinical databases, and certain basic-science data repositories, to quickly identify a set of genes related to the patient’s observed condition.
Finally, the team has deployed a system using machine learning tools to take patient-specific genetic changes identified by the whole-exome sequencing and integrate those with the existing scientific knowledge to identify candidate genetic changes likely related to a patient’s underlying disease.
Expanded Genetic Testing
Current Diagnostic Odyssey testing evaluates a patient’s DNA within those regions of their genome that contain genes, which is where the majority of our current clinical knowledge resides, regarding genetics of disease.
Another type of genomic data, a patient’s RNA, is a measure of which genes are currently active in the patient. By studying both the changes to a patient genome, or DNA, and the activity of the patient’s genes, or RNA, the team is able to better understand the potential ramifications of specific genetic changes. To this end, scientists are evaluating the use of RNA sequencing, along with several other types of genomic testing, to complement the findings obtained from the whole-exome sequencing. The team has discovered, in a subset of patients, this data integration can greatly improve diagnostic capabilities and help identify the underlying cause of a patient’s genetic disease.
A significant challenge with current genetic testing is the large number of genetic variants of uncertain significance (VUSs) that are identified. These are genetic changes that are poorly studied, or are identified in genes for which little is known. Consequently, clinical interpretation of the genetic change is extremely difficult.
To better understand a subset of these VUSs, the program has established a functional studies initiative using protein and animal models to complement laboratory testing. Protein modeling allows researchers to predict and visualize the impact a genetic variant has on a patient’s protein, leading to proposed experimental tests to evaluate its subsequent biological impacts.
The team is also using cutting-edge genome engineering technologies to introduce a patient’s genetic variant into an animal model or lab system, to make observations and carry out tests to better understand the functional impact of the genetic change.
Margot A. Cousin, Ph.D.
Alejandro Ferrer, Ph.D.
Aditi Gupta, Ph.D.
Charu Kaiwar, M.D., Ph.D.
Filippo Pinto e Vairo, M.D., Ph.D.
Former Post Docs
Nicole J. Boczek, Ph.D.
Patrick R. Blackburn, Ph.D.
Karl J. Clark, Ph.D. – Associate Consultant I
Tanya L. Schwab – SR Research Technologist
Ashley N. Sigafoos – Research Technologist
Gavin R. Oliver – Informatics Specialist LD
Naresh Prodduturi – Informatics Specialist II
Krutika Satish Gaonkar – Informatics Specialist
Pritha Chanana, M.S. – Informatics Specialist
Protein Modeling Team
These collaborators work with the Genomics Program through a formal collaboration with Medical College of Wisconsin:
Raul A. Urrutia, M.D. - Director of the Human and Molecular Genetics Center and Professor, Department of Surgery
Michael T. Zimmermann, Ph.D. – Assistant Professor, Clinical and Translational Science Institute
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