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Journal Article

Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes


Stegle,  O
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Parts, L., Stegle, O., Winn, J., & Durbin, R. (2011). Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes. PLoS Genetics, 7(1), 1-10. doi:10.1371/journal.pgen.1001276.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BCCE-5
Even within a defined cell type, the expression level of a gene differs in individual samples. The effects of genotype, measured factors such as environmental conditions, and their interactions have been explored in recent studies. Methods have also been developed to identify unmeasured intermediate factors that coherently influence transcript levels of multiple genes. Here, we show how to bring these two approaches together and analyse genetic effects in the context of inferred determinants of gene expression. We use a sparse factor analysis model to infer hidden factors, which we treat as intermediate cellular phenotypes that in turn affect gene expression in a yeast dataset. We find that the inferred phenotypes are associated with locus genotypes and environmental conditions and can explain genetic associations to genes in trans. For the first time, we consider and find interactions between genotype and intermediate phenotypes inferred from gene expression levels, complementing and extending established results.