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Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function

Abstract

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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Acknowledgements

We thank T. Sutter and the Feinstone Center for array support, J. Hogenesch and R. Edwards for helping to map probe sets using BLAT and J. Crabbe and J. Belknap for assistance in updating and compiling the many traits they have contributed to the Published Phenotypes database. Most arrays were processed at Genome Explorations Inc. by D. Patel. The authors acknowledge support of a Human Brain Project funded by the National Institute of Mental Health, the National Institute of Drug Abuse and the National Science Foundation, and an Integrative Neuroscience Initiative on Alcoholism grant from the National Institute of Alcohol Abuse and Addiction. Array costs were covered by the Dunavant Chair of Excellence, University of Tennessee Health Science Center, Department of Pediatrics. Additional support was provided by the National Institute of Aging, the National Science Foundation, Veterans Affairs and the Office of Naval Research.

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Correspondence to Robert W Williams.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Genetic variation and fold range of strain means for transcript abundance. (DOC 241 kb)

Supplementary Table 2

Locations of best cis- and trans-QTLs based on genetic variance >30% and FDR (q-value) < 25%. (DOC 244 kb)

Supplementary Table 3

Location and SNP density by location in the major trans-QTL bands. (DOC 44 kb)

Supplementary Table 4

Tissue specificity and number of single nucleotide polymorphisms (SNPs) by location in cis-QTLs. (DOC 209 kb)

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Figure 1: Genome-wide interval mapping for several transcripts from WebQTL, including the cis-regulatory QTL for Kcnj9 (98322_at), Per3 (102242_at) and Grin2b (101312_at) and trans-regulatory QTLs for Pitpnb (102696_s_at) and Neurod2 (98808_at).
Figure 2: Physical map of chromosome 16 showing cis-regulatory locus for expression of a pyridoxil-dependent decarboxylase (160163_at).
Figure 3: Transcriptome maps for 12,422 transcripts.
Figure 4: Frequency of transcript abundances with LRS peaks mapping to 5-Mb QTL location bins identify approximately seven key trans-regulatory QTLs.
Figure 5: Genetic correlation of Drd2 expression with several behavioral phenotypes in WebQTL's BXD Published Phenotypes database.
Figure 6: Cluster map showing the polygenic and pleiotropic regulation of the synaptic vesicle cycling mechanisms.