DNA variation and brain region-specific expression profiles exhibit different relationships between inbred mouse strains: implications for eQTL mapping studies
- Equal contributors
1 The Salk Institute for Biological Studies, Laboratory of Genetics, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA
2 National Public Health Institute, Department of Molecular Medicine, Haartmaninkatu 8, 00290 Helsinki, Finland
3 INSERM U513, Neurobiology and Psychiatry, Faculté de Médecine, 8 rue du Général Sarrail, Créteil 94010 cedex, France
4 Biomedical Sciences Graduate Program, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
5 Polymorphism Research Laboratory, Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
6 Neurome Inc., 11149 North Torrey Pines Road, La Jolla, CA 92037, USA
7 Rosetta Inpharmatics Inc., 401 Terry Avenue North, Seattle, WA 98109, USA
8 Amicus Therapeutics, 6 Ceder Brook Drive, Cranbury, NJ 08512, USA
9 BrainCells Inc., 10835 Road to the Cure, San Diego, CA 92121, USA
Genome Biology 2007, 8:R25 doi:10.1186/gb-2007-8-2-r25Published: 26 February 2007
Expression quantitative trait locus (eQTL) mapping is used to find loci that are responsible for the transcriptional activity of a particular gene. In recent eQTL studies, expression profiles were derived from either homogenized whole brain or collections of large brain regions. However, the brain is a very heterogeneous organ, and expression profiles of different brain regions vary significantly. Because of the importance and potential power of eQTL studies in identifying regulatory networks, we analyzed gene expression patterns in different brain regions from multiple inbred mouse strains and investigated the implications for the design and analysis of eQTL studies.
Gene expression profiles of five brain regions in six inbred mouse strains were studied. Few genes exhibited a significant strain-specific expression pattern, whereas a large number of genes exhibited brain region-specific patterns. We constructed phylogenetic trees based on the expression relationships between the strains and compared them with a DNA-level relationship tree. The trees based on the expression of strain-specific genes were constant across brain regions and mirrored DNA-level variation. However, the trees based on region-specific genes exhibited a different set of strain relationships, depending on the brain region. An eQTL analysis showed enrichment of cis-acting regulators among strain-specific genes, whereas brain region-specific genes appear to be mainly regulated by trans-acting elements.
Our results suggest that many regulatory networks are highly brain region specific and indicate the importance of conducting eQTL mapping studies using data from brain regions or tissues that are physiologically and phenotypically relevant to the trait of interest.