Modularity & Gene Interactions & Networks 09

Modularity & Gene Interactions & Networks...

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Unformatted text preview: Modularity and interactions in the genetics of gene expression Oren Litvin a,b , Helen C. Causton a , Bo-Juen Chen a,c , and Dana Pe’er a,b,1 a Department of Biological Sciences, Columbia University, New York, NY 10027; b Center for Computational Biology and Bioinformatics, Columbia University, New York, NY 10032; and c Department of Biomedical Informatics, Columbia University, New York, NY 10032 Edited by John Ross, Stanford University, Stanford, CA, and approved January 12, 2009 (received for review October 13, 2008) Understanding the effect of genetic sequence variation on phe- notype is a major challenge that lies at the heart of genetics. We developed GOLPH (GenOmic Linkage to PHenotype), a statistical method to identify genetic interactions, and used it to characterize the landscape of genetic interactions between gene expression quantitative trait loci. Our results reveal that allele-specific inter- actions, in which a gene only exerts an influence on the phenotype in the presence of a particular allele at the primary locus, are widespread and that genetic interactions are predominantly non- additive. The data portray a complex picture in which interacting loci influence the expression of modules of coexpressed genes involved in coherent biological processes and pathways. We show that genetic variation at a single gene can have a major impact on the global transcriptional response, altering interactions between genes through shutdown or activation of pathways. Thus, differ- ent cellular states occur not only in response to the external environment but also result from intrinsic genetic variation. computational biology u gene regulation u molecular networks u systems biology U nderstanding the effect of genetic sequence variation on phenotype is a major challenge that lies at the heart of genetics. Recent technological advances in genotyping have now made it possible to obtain a comprehensive view of genomewide variation in a large number of individuals. However, association studies involving tens of thousands of individuals (1) have, for the most part, only been able to detect loci that collectively account for 3% of the heritable phenotype. This finding suggests that the connection between genotype and phenotype is more complex than previously assumed and that more sophisticated approaches are needed to interpret the data. Quantitative trait mapping of gene expression abundances [expression quantitative trait locus (eQTL)] has proved a pow- erful model system for studying genetic traits in a number of organisms (2–5). To study gene–gene interactions between QTL, we use gene expression and genotype data on segregants generated in a cross between a laboratory strain (BY) and a wild strain (RM) of Saccharomyces cerevisiae (6, 7). We developed GOLPH (GenOmic Linkage to PHenotype), a statistical algo- rithm to identify multiple genetic factors influencing gene expression abundance. Our premise is that the modular organi- zation of gene regulation can be used to enhance the statistical...
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This note was uploaded on 08/05/2010 for the course BIO 5750 taught by Professor Arking during the Winter '09 term at Wayne State University.

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Modularity & Gene Interactions & Networks...

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