![]() |
Transcriptome-QTL analysis of correlated CNS transcripts
in WebQTL
reveals major genetic variation in synaptic vesicle related gene
expression
|
In our ongoing microarray-based anlaysis of genetic regulation of gene transcription in the recombinant inbred strains derived from C57BL/6J and DBA/2J (BXDRI), we have identified a small number of chromosomal loci that regulate mRNA abundance of several hundred transcripts each. These loci were detected using a novel application of clique analysis for dimension reduction of microarray data. Many of these results can be explored using new tools and links in WebQTL (www.webQTL.org ) which allows users to rapidly examine multiple traits and interpret their relations and patterns of covariance. Using a combination of genetic correlation analysis, clique analysis, QTL analysis, Gene Ontology category representation analysis, we have identified loci that modulate expression of components of the synaptic vesicle cycling system. Specific candidate genes which reside at these regulatory loci have been identified. A single locus has been identified that regulates the transcription of well over 1500 transcripts. The synaptic vesicle structure and function is dependent on a large number of cytoskeletal, anchoring, motor and fusion proteins. Vesicle mediated transport, exocytosis and endocytosis is a major means of effecting and controlling cell-cell communication in the CNS. We postulate that the major genetic difference between the C57BL/6J and DBA/2J strains is in synaptic vesicle cycling systems. Other related cellular processes that rely on the same gene networks are similarly varied. The relationship of the cliques of genetically correlated transcript abundances to systems level phenotypes such as pre-pulse inhibition demonstrates the utility of this analytic strategy for biological research across all levels of scale. New clique-related methods are enabling us to identify the overall patterns of genetic regulation of diverse complex phenotypes. With the expansion of high-throughput phenotyping in genetic reference populations, this scalable approach to the extraction of global genetic signal will become increasingly valuable. |
-------------------------------------------------------------------------------
Chesler, E. J., Lu, L., Baldwin, N.E.,
Zhang, B., Kirov, S., Langston, M.A., Manly, K. F., Williams, R.W.
University of Tennessee Health Science Center
Memphis, TN 38163