In the past, new diseases were delineated when clinicians brought together a cohort of individuals with similar phenotypic characteristics. They then performed genetic testing and looked for shared genetic changes among the affected cohort. While this method proved successful in some cases, discovery was limited to disorders with relatively few genes and highly distinctive presentation. Since the advent of exome sequencing, physicians have become increasingly aware of the wide spectrum of variability of clinical features associated with previously known genes, with the actual manifestations attributable to a genetic variation being far greater than what was previously anticipated--the proverbial, ?iceberg effect.? Overcoming these challenges requires a new approach. Instead of starting with the defined phenotype, we propose starting at the gene and working our way forward to identify the full spectrum of phenotypes that can arise from these genetic variations. Such work requires high numbers of well-phenotyped and genotyped samples, as well as the expertise to appropriately evaluate patients of interest. This application brings together two institutions with the experience and capability to do just that. The Geisinger Health System maintains extensive medical records and genotype and sequence data on more than 141,000 participants enrolled in the MyCode Community Health Initiative and has the expertise to mine those records and genomic sequences for meaningful and medically relevant associations. The NIH has expertise in the deep phenotyping and discovery in rare disease. For proof of principle that this gene-first strategy works, we are beginning our analysis into elastic fiber mediated connective tissue disease, an area with which our NIH collaborators have significant expertise. Previous literature linked changes in elastic fiber genes to defects in aortic diameter and tortuosity, lung changes such as emphysema and skin changes including laxity with more recent work suggesting connections to more common phenotypes such as hypertension, intracranial aneurysm, and chronic obstructive pulmonary disease. Our goal is to define all phenotypes, rare and common, associated with elastic fiber disease and to investigate the mechanism by which variation in these genes produces phenotypes in order to develop novel treatment strategies. In order to achieve this goal, we have developed two specific aims combining the strengths of Geisinger and the National Institutes of Health investigators.
Aim 1. A) Screen an unselected population for variants in elastin and other elastic fiber genes and B) correlate with phenotypic features mined from the electronic health record.
Aim 2 : Identify previously unidentified phenotypes in patients with known and novel damaging variants in elastic fiber genes through deep phenotyping and disease modeling in vitro functional analysis. Collaboration between the two institution's diverse and multidisciplinary teams will increase understanding about the impact of elastic fiber disease and provide insight leading to treatment of human disease.
In the past, the medical community identified human diseases by combining patients with similar phenotypes and looking for shared genetic changes. This approach is limited by our clinical ability to see appropriate connections between phenotypic features in patients. This application uses a gene-first approach in a large research cohort with associated exome sequences to identify the full range of medically relevant human phenotypes (common and rare) associated with variants in genes coding for elastic fiber proteins followed by deep phenotyping in individual patients to learn more about the mechanism of disease and its clinical impact.
