This proposal addresses the utility of Drosophila as a model for the genetic dissection of the polygenic basis for the etiology of organ failure. Specifically, we will test the hypothesis that segregating variants in genes that regulate the physiology, endocrinology, and morphogenesis of the heart contribute to age-dependent loss of heart function in adult flies. In the process, we expect to identify novel genes that affect the onset of heart disease. There are two distinct classes of models for the genetic determination of complex diseases, namely the """"""""common disease-common variant"""""""" and """"""""preponderance of rare alleles of major effect"""""""" models. The former has been the dominant paradigm, but failure of many linkage studies and inconsistent replication of association with complex diseases is challenging its suitability. On the other hand, there is a clear need for new methodological approaches to the detection of rare alleles that contribute to polygenic disease. We propose here that dysfunction of the Drosophila heart, manifested as pacing-induced heart failure and arrhythmia is an ideal model. Careful visualization of heart function in inbred wild-type lines indicates that there are extreme genotypes that exhibit a range of abnormalities. After detailed assessment of the incidence of elevated or reduced heart failure and of arrhythmias in young and old flies we will carry out quantitative genetic analyses designed to (i) assess the capacity of the genetic background to modify the penetrance and expressivity of disease-promoting allelic combinations;(ii) document the capacity of naturally occurring alleles that affect different aspects of heart performance to complement and/or interact with one another;and (iii) map and clone ten or so rare major-effect heart disease susceptibility alleles. Transcriptional and metabolic profiling will also be performed to develop cardiac biomarkers. Project Narrative: This proposal uses a model organism to study how heart performance in old age may be affected by rare mutations. It combines classical genetic analysis with population genetics to find new genes involved in various aspects of cardiac dysfunction, and asks whether there are common features of susceptibility that might be detected with genomic tools.
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