Nature Genetics37, 233 - 242 (2005)
Published online: 13 February 2005; | doi:10.1038/ng1518
Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function
Elissa J Chesler1, 5, Lu Lu1, 5, Siming Shou1, Yanhua Qu1, Jing Gu1, Jintao Wang1, Hui Chen Hsu2, John D Mountz2, Nicole E Baldwin3, Michael A Langston3, David W Threadgill4, Kenneth F Manly1
& Robert W Williams1
1
University of Tennessee Health Science Center, 855 Monroe Avenue, Memphis, Tennessee 38163, USA.
2
Department of Medicine, University of Alabama at Birmingham, 701 S. 19th St., Birmingham, Alabama 35294, USA.
3
Department of Computer Science, University of Tennessee-Knoxville, Knoxville, Tennessee 37996-3450, USA.
4
Department of Genetics, CB# 7264, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
Patterns of gene expression in the central nervous system are highly variable and heritable. This genetic variation among normal individuals leads to considerable structural, functional and behavioral differences. We devised a general approach to dissect genetic networks systematically across biological scale, from base pairs to behavior, using a reference population of recombinant inbred strains. We profiled gene expression using Affymetrix oligonucleotide arrays in the BXD recombinant inbred strains, for which we have extensive SNP and haplotype data. We integrated a complementary database comprising 25 years of legacy phenotypic data on these strains. Covariance among gene expression and pharmacological and behavioral traits is often highly significant, corroborates known functional relations and is often generated by common quantitative trait loci. We found that a small number of major-effect quantitative trait loci jointly modulated large sets of transcripts and classical neural phenotypes in patterns specific to each tissue. We developed new analytic and graph theoretical approaches to study shared genetic modulation of networks of traits using gene sets involved in neural synapse function as an example. We built these tools into an open web resource called WebQTL that can be used to test a broad array of hypotheses.
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