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1 McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; McGill Centre for Bioinformatics, McGill University, Montreal, Quebec, Canada
2 McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada; Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
3 Program in Genetics and Genomic Biology, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada
4 McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada
5 Emerillon Therapeutics, Montreal, Quebec, Canada
6 McGill Centre for Bioinformatics, McGill University, Montreal, Quebec, Canada
7 Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada; Emerillon Therapeutics, Montreal, Quebec, Canada
8 McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada; Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
* To whom correspondence should be addressed. E-mail: rob.sladek{at}mail.mcgill.ca.
We present an integrated approach for the enriched detection of genes subject to cisacting variation in the mouse genome. Gene expression profiling was performed using lung tissue from a panel of recombinant congenic strains (RCS) derived from A/J and C57BL/6J inbred mouse strains. A multiple regression model measuring the association between gene expression level, donor strain of origin (DSO) and predominant strain background identified over 1500 genes (P<0.05) whose expression profiles differ according to the DSO. This model also identified over 1200 genes whose expression showed dependence on background (P<0.05) indicating the influence of background genetic context on transcription levels. Sequences obtained from 1kb segments of 3' UTRs identified SNPs in 64% of genes whose expression levels correlated with DSO status, compared to 29% of genes that displayed no association (P<0.01, Fisher Exact Test). Allelic imbalance (AI) was identified in 50% of genes positive for expression-DSO association, compared to 22% of negative genes (P<0.05, Fisher Exact Test). Taken together, these results demonstrate the utility of RCS mice for identifying the roles of proximal genetic determinants and background genetic context in determining gene expression levels. We propose the use of this integrated experimental approach in multiple tissues from this and other RCS panels as a means for genome-wide cataloguing of genetic regulatory mechanisms in laboratory strains of mice.
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