Translational Genomics Program

Improving Genetic Testing

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.

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    Larval transgenic zebrafish expressing green fluorescent protein in the vasculature and red fluorescent protein in the blood. Tg(fli:eGFP/gata-2:dsRed)

Projects

Improved Analytics

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.

Functional Studies

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.

Faculty

Post Docs

  • Margot A. Cousin, Ph.D.

  • Alejandro Ferrer, Ph.D.

  • Aditi Gupta, Ph.D.

  • Joel A. Morales Rosado, M.D.

  • Filippo Pinto e Vairo, M.D., Ph.D.

  • Laura E. Schultz-Rogers, Ph.D.

Former Post Docs

  • Nicole J. Boczek, Ph.D.

  • Patrick R. Blackburn, Ph.D.

  • Charu Kaiwar, M.D., Ph.D.

Lab Team

  • Karl J. Clark, Ph.D. – Associate Consultant I

  • Tanya L. Schwab – SR Research Technologist

  • Ashley N. Sigafoos – Research Technologist

Bioinformatics Team

  • Pritha Chanana, M.S. – Informatics Specialist

  • Gavin R. Oliver – Informatics Specialist LD

  • Naresh Prodduturi – Informatics Specialist II

  • Krutika Satish Gaonkar – 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

Multimedia

Publications

Collectively, Mayo authors publish more than 5,000 articles a year in biomedical journals.

Publishing in medical journals is an expected scholarly activity of professional practice and aligns with our value of sharing expertise and best practices to facilitate the advancement of medical practice worldwide.

Featured publication:

Bi-allelic Alterations in AEBP1 Lead to Defective Collagen Assembly and Connective Tissue Structure Resulting in a Variant of Ehlers-Danlos Syndrome

Citations are from PubMed, a service of the U.S. National Library of Medicine. PubMed is comprised of references and abstracts from MEDLINE, life science journals and online books:

Publications authored by Mayo Clinic experts in the area of translational genomics.