Weighted gene co-expression network analysis identifies biomarkers in glycerol kinase deficient mice.

Symptomatic glycerol kinase deficiency (GKD) is associated with episodic metabolic and central nervous system deterioration. We report here the first application of weighted gene co-expression network analysis (WGCNA) to investigate a knockout (KO) murine model of a human genetic disease. WGCNA identified networks and key hub transcripts from liver mRNA of glycerol kinase (Gyk) KO and wild-type (WT) mice. Day of life 1 (dol1) samples from KO mice contained a network module enriched for organic acid metabolism before Gyk KO mice develop organic acidemia and die on dol3-4. Furthermore, the module containing Gyk was enriched with apoptotic genes. We used causal testing to elucidate the causal relationships between intramodular hub genes Acot, Psat and Plk3. Important causal relationships are confirmed in cell cultures. We provide evidence that GK may have an apoptotic moonlighting role that is lost in GKD. This first application of WGCNA to mouse knockout data provides insights into the molecular mechanisms of GKD pathogenesis. The resulting systems-genetic gene screening method identifies candidate biomarkers for GKD.

Genetic dissection of a major mouse obesity QTL (Carfhg2): integration of gene expression and causality modeling.

HG.CAST-(D9Mit249-D9Mit133) (HG9) congenic mice are homozygous for CAST/EiJ chromosome (Chr) 9 alleles from approximately 9 to 84 Mbp on a C57BL6/J-hg/hg (HG) background. This region contains the carcass fat in high growth mice (Carfhg2) quantitative trait locus (QTL), and while its obesity-promoting effects have been confirmed in HG9 mice, its underlying genetic basis remains elusive. To refine the location of Carfhg2, we preformed a linkage analysis in two congenic F2 intercrosses and progeny-tested a recombinant F2 male. These analyses narrowed Carfhg2 to between 33.0 and 40.8 Mbp on Chr 9. To identify candidate genes we measured the expression of 44 transcripts surrounding the Carfhg2 peak in adipose, brain, liver, and muscle tissues from F2 mice using Biomark 48.48 Dynamic Arrays. In total, 68% (30 of the 44) of genes were regulated by a significant expression QTL (eQTL) in at least one tissue. To prioritize genes with eQTL we used Network Edge Orienting, a causality modeling tool. These analyses advance our goal of identifying the molecular basis of Carfhg2.

An integrative genetics approach to identify candidate genes regulating BMD: combining linkage, gene expression, and association.

Numerous quantitative trait loci (QTLs) affecting bone traits have been identified in the mouse; however, few of the underlying genes have been discovered. To improve the process of transitioning from QTL to gene, we describe an integrative genetics approach, which combines linkage analysis, expression QTL (eQTL) mapping, causality modeling, and genetic association in outbred mice. In C57BL/6J x C3H/HeJ (BXH) F(2) mice, nine QTLs regulating femoral BMD were identified. To select candidate genes from within each QTL region, microarray gene expression profiles from individual F(2) mice were used to identify 148 genes whose expression was correlated with BMD and regulated by local eQTLs. Many of the genes that were the most highly correlated with BMD have been previously shown to modulate bone mass or skeletal development. Candidates were further prioritized by determining whether their expression was predicted to underlie variation in BMD. Using network edge orienting (NEO), a causality modeling algorithm, 18 of the 148 candidates were predicted to be causally related to differences in BMD. To fine-map QTLs, markers in outbred MF1 mice were tested for association with BMD. Three chromosome 11 SNPs were identified that were associated with BMD within the Bmd11 QTL. Finally, our approach provides strong support for Wnt9a, Rasd1, or both underlying Bmd11. Integration of multiple genetic and genomic data sets can substantially improve the efficiency of QTL fine-mapping and candidate gene identification.

Using genetic markers to orient the edges in quantitative trait networks: the NEO software.

BACKGROUND: Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers. RESULTS: We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue. CONCLUSION: The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored to genetic marker data. R software tutorials, data, and supplementary material can be downloaded from: http://www.genetics.ucla.edu/labs/horvath/aten/NEO.

Weighted gene coexpression network analysis strategies applied to mouse weight.

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes-a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F(2) mouse inter-cross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene-trait chasm.

Thematic review series: The pathogenesis of atherosclerosis. Toward a biological network for atherosclerosis.

The goal of systems biology is to define all of the elements present in a given system and to create an interaction network between these components so that the behavior of the system, as a whole and in parts, can be explained under specified conditions. The elements constituting the network that influences the development of atherosclerosis could be genes, pathways, transcript levels, proteins, or physiologic traits. In this review, we discuss how the integration of genetics and technologies such as transcriptomics and proteomics, combined with mathematical modeling, may lead to an understanding of such networks.

A family-based test for correlation between gene expression and trait values.

Advances in microarray technology have made it attractive to combine information on clinical traits, marker genotypes, and comprehensive gene expression from family studies to dissect complex disease genetics. Without accounting for family structure, methods that test for association between a trait and gene-expression levels can be misleading. We demonstrate that the standard unstratified test based on Pearson's correlation coefficient can produce spurious results when applied to family data, and we present a stratified family expression association test (FEXAT). We illustrate the utility of the FEXAT via simulation and an application to gene-expression data from lymphoblastoid cell lines from four CEPH families. The FEXAT has a smaller estimated false-discovery rate than the standard test when within-family correlations are of interest, and it detects biologically plausible correlations between beta catenin and genes in the WNT-activation pathway in humans that the standard test does not.