Asthma is a heterogeneous, complex syndrome that undoubtedly represents an amalgam of multiple distinct diseases, each stemming from a different constellation of gene variations and environmental exposure histories that results in a generally similar diathesis of episodic airflow obstruction, constrictor hyperresponsiveness, mucus hypersecretion, and airway wall inflammation and remodeling. Here, we propose to identify differential genetic contributions to asthma subgroups using a novel approach in which Mendelian diseases (MDs) are used as surrogate genetic markers. Asthma, much more common than MDs, occurs both in patients with or without any particular MD. However, if asthma is overrepresented among patients with a MD relative to those without that disease, then having that MD constitutes a genetic risk factor for asthma, and the association fur- ther implicates non-MD-causing variations in one of the MD-causing genes in the pathogenesis of asthma. Even though MDs are rare, examination of extremely large (administrative/billing or electronic medical record- derived) datasets that document both common and Mendelian diseases allows for such odds-ratio calculation.
In Specific Aim #1, we will apply an in silico pipeline to data from >100 million individuals that identifies asthma subgroup specifically-associated MDs, then prioritizes their underlying genes for biological evaluation in Specific Aim #2. These studies will provide new insights into the distinguishing molecular mechanisms underlying pathogenetically distinct asthma subgroups. Moreover, successful completion of these studies will establish an entirely new paradigm for unraveling complex common diseases, based on their co-occurrence with Mendelian diseases as documented in extremely large databases.
Asthma is a complex syndrome thought to represent an amalgam of multiple different 'diseases' with similar clinical manifestations. In order to unravel the genetic variations that lead to one or another subtype of asthma, we will analyze the co-occurrence of genetically simple Mendelian diseases with asthma subgroups using administrative/billing and/or electronic medical record data from over 100 million individuals, on the premise that overrepresentation of one or another asthma subgroup in subjects with a given Mendelian disease implicates the latter's causal gene in that asthma subgroup; we will narrow our focus to the most promising genes, then analyze their biological functions to infer how they might contribute to the pathogenesis of different subtypes of asthma. This novel approach relies heavily on huge collections of existing electronic data, thereby leveraging the vast investments made their collection.
