Presented by , Associate Professor, Department of Population Health Sciences, Geisinger

The majority of genetic studies take a phenotype-first approach to identifying disease risk genes. This phenotype-first approach may be subject to ascertainment bias towards high penetrance alleles, miss unexpected features or symptoms of the disease, miss diseases of variable expressivity, or may overestimate disease severity. Large, unselected populations with sequence data tied to electronic health records (EHR) are becoming more common and are available for research. These data support a genome-first strategy to determine genetic effects on the full range of clinical phenotypes. This approach has the potential advantages of reducing selection bias and being more agnostic to what is currently known about phenotypes associated with genetic variation, thus promoting novel gene-phenotype discoveries. The Elastin (ELN) gene encodes a protein that provides recoil to tissues that stretch repetitively, such as the lungs, vasculature, and skin. Its presence across many tissues makes it a good candidate for study as alterations may lead to a range of phenotypic presentations. Thus, this presentation will highlight the potential benefits and limitations of a genotype-first approach as applied to a large, unselected population with exome sequencing data to examine the phenotypic consequences of rare variation predicted to be of high consequence in ELN.