Work

Disease-Specific Integration of Omics Data to Guide Functional Validation of Genetic Associations.

Year: 2017

Type: conference-paper

Abstract: Unbiased genetic association studies, including genome-wide association and whole-genome sequencing studies, have uncovered many novel disease-associated variants. Relatively few of these associated regions, however, have led to insights that are biologically mechanistic or clinically actionable due in part to the difficulty in designing appropriate functional validation studies to understand how variants contribute to disease. Asthma is a complex inflammatory lung disease for which many genetic associations have been identified. Using asthma as a disease model, we designed Reducing Associations by Linking Genes And transcriptomic Results (REALGAR), an app that facilitates the design of functional validation studies by integrating cell- and tissue-specific results of diseaserelevant gene expression and other omics studies. Via specific examples, we demonstrate how integrated gene- centric and disease-specific information leads to asthma insights, and more broadly, can help understand complex diseases.

Source: PubMed

Authors: Maya Shumyatcher, Rui Hong, Jessica Levin, Blanca E Himes

Institution: University of Pennsylvania

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, National Institute of Environmental Health Sciences

Awards: CEBPD-Mediated Mechanisms of Glucocorticoid Insensitivity in Severe Asthma, Center of Excellence in Environmental Toxicology