Abstract
Genetic screens are powerful methods for the discovery of gene–phenotype associations. However, a systems biology approach to genetics must leverage the massive amount of “omics” data to enhance the power and speed of functional gene discovery in vivo. Thus far, few computational methods for gene function prediction have been rigorously tested for their performance on a genome-wide scale in vivo. In this work, we demonstrate that integrating genome-wide computational gene prioritization with large-scale genetic screening is a powerful tool for functional gene discovery. To discover genes involved in neural development in Drosophila, we extend our strategy for the prioritization of human candidate disease genes to functional prioritization in Drosophila. We then integrate this prioritization strategy with a large-scale genetic screen for interactors of the proneural transcription factor Atonal using genomic deficiencies and mutant and RNAi collections. Using the prioritized genes validated in our genetic screen, we describe a novel genetic interaction network for Atonal. Lastly, we prioritize the whole Drosophila genome and identify candidate gene associations for ten receptor-signaling pathways. This novel database of prioritized pathway candidates, as well as a web application for functional prioritization in Drosophila, called Endeavour-HighFly, and the Atonal network, are publicly available resources. A systems genetics approach that combines the power of computational predictions with in vivo genetic screens strongly enhances the process of gene function and gene–gene association discovery.
Highlights
The demand by systems biology for bona fide, in vivo validated, biochemical interaction data and high quality functional annotations is much higher than the supply that geneticists are able to provide, principally because genetic approaches mainly focus on generating data on a gene-by-gene basis
Expression of the ‘‘Atonalized’’ form of mouse Neurogenin 1 (Ngn1), NgnbATO (Figure 1A) under the control of dpp-Gal4 induces an average of,30 ectopic sensory bristles on the adult wing vein (n = 30; Figure 1B, C). This is in contrast to an average of only,7 bristles induced by Ngn1 itself (n = 26; p,0.001), but is similar to the number induced by Ato (n = 26, n.s.; Figure 1C)
Unlike Ato, NgnbATO induces significantly less lethality and many fewer wing deformities making it much easier to use in a large scale, quantitative genetic screen
Summary
The demand by systems biology for bona fide, in vivo validated, biochemical interaction data and high quality functional annotations is much higher than the supply that geneticists are able to provide, principally because genetic approaches mainly focus on generating data on a gene-by-gene basis. Computational predictions of gene function alone remain far from being accurate enough to be considered high-quality biological data. Integrated solutions, that combine the advantages of several approaches, should in theory provide both fast and physiologically relevant genetic data, while simultaneously increasing our understanding of biological processes. Genetic interactions in model organisms constitute a potentially invaluable source of in vivo interaction data for systems biology provided that throughput and speed can be increased. The number of known genetic interactions remains much smaller than the number of annotated physical interactions. The BioGRID [1] database currently contains approximately 53,000 genetic interactions compared to almost 100,000 physical interactions
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