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.

Advances in behavioral genetics: mouse models of autism

“…Sets of recombinant inbred lines are publicly available with associated mapping data, such as the BXD lines, leaving the investigator to phenotype for the behavior of interest. 350 Congenic strains, also derived from two inbred backgrounds, provide a different resource to analyze genetic contribution to behavior. The congenic lines carry a small, homozygous portion of one chromosome from one strain on the background of another.…”

The Diasporin Pathway: a tumor progression-related transcriptional network that predicts breast cancer survival

“…We identified three loci on chromosomes 7, 17 and 18 that appear to be linked to metastasis-predictive ECM gene expression. Subsequently, correlation analysis was performed to facilitate candidate gene identification since the current prevailing hypothesis is that most modifiers are likely to result from modest variations in gene expression levels or mRNA stability [23,24]. This revealed a correlation between expression of a variety of genes within the peak region of linkage of each eQTL and the expression of various metastasis-predictive ECM genes (Supplementary Table 4 & Supplementary Table 5 and Crawford et al [10]).…”

“…Correlation analysis was performed to identify potential candidates for the chromosomes 7, 17 and 18 ECM modifiers since the current prevailing hypothesis that most modifiers are likely to result from modest variations in gene expression levels or mRNA stability [23,24]. Whole genome correlation analysis of the microarray data was performed using the Trait Correlation function of the WebQTL database within the GeneNetwork web service [18] to identify probe sets with high correlation coefficients with each of the ECM genes of interest.…”

Multiple sclerosis genetics: leaving no stone unturned

“…110 More recently, genetical genomics yielded the identification of regulatory networks affecting stem cell function, 111 blood pressure, 112 and some neural phenotypes. 113 In humans, analysis of genomically controlled variation in gene expression has also been reported 114 opening a promising venue for unraveling biological pathways in complex diseases.…”