Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Integrating GWAS and eQTL to predict genes and pathways for non-syndromic cleft lip with or without palate

OBJECTIVE

To explore susceptibility genes and pathways for non-syndromic cleft lip with or without cleft palate (NSCL/P).

MATERIALS AND METHODS

Two genome-wide association studies (GWAS) datasets, including 858 NSCL/P cases and 1,248 controls, were integrated with expression quantitative trait loci (eQTL) dataset identified by Genotype-Tissue Expression (GTEx) project in whole-blood samples. The expression of the candidate genes in mouse orofacial development was inquired from FaceBase. Protein-protein interaction (PPI) network was visualized to identify protein functions. Go and KEGG pathway analyses were performed to explore the underlying risk pathways.

RESULTS

A total of 233 eQTL single-nucleotide polymorphisms (SNPs) in 432 candidate genes were identified to be associated with the risk of NSCL/P. One hundred and eighty-three susceptible genes were expressed in mouse orofacial development according to FaceBase. PPI network analysis highlighted that these genes involved in ubiquitin-mediated proteolysis (KCTD7, ASB1, UBOX5, ANAPC4) and DNA synthesis (XRCC3, RFC3, KAT5, RHNO1) were associated with the risk of NSCL/P. GO and KEGG pathway analyses revealed that the fatty acid metabolism pathway (ACADL, HSD17B12, ACSL5, PPT1, MCAT) played an important role in the development of NSCL/P.

CONCLUSIONS

Our results identified novel susceptibility genes and pathways associated with the development of NSCL/P.

An update on clonality: what smooth muscle cell type makes up the atherosclerotic plaque?

Almost 50 years ago, Earl Benditt and his son John described the clonality of the atherosclerotic plaque. This led Benditt to propose that the atherosclerotic lesion was a smooth muscle neoplasm, similar to the leiomyomata seen in the uterus of most women. Although the observation of clonality has been confirmed many times, interest in the idea that atherosclerosis might be a form of neoplasia waned because of the clinical success of treatments for hyperlipemia and because animal models have made great progress in understanding how lipid accumulates in the plaque and may lead to plaque rupture. Four advances have made it important to reconsider Benditt's observations. First, we now know that clonality is a property of normal tissue development. Second, this is even true in the vessel wall, where we now know that formation of clonal patches in that wall is part of the development of smooth muscle cells that make up the tunica media of arteries. Third, we know that the intima, the "soil" for development of the human atherosclerotic lesion, develops before the fatty lesions appear. Fourth, while the cells comprising this intima have been called "smooth muscle cells", we do not have a clear definition of cell type nor do we know if the initial accumulation is clonal. As a result, Benditt's hypothesis needs to be revisited in terms of changes in how we define smooth muscle cells and the quite distinct developmental origins of the cells that comprise the muscular coats of all arterial walls. Finally, since clonality of the lesions is real, the obvious questions are do these human tumors precede the development of atherosclerosis, how do the clones develop, what cell type gives rise to the clones, and in what ways do the clones provide the soil for development and natural history of atherosclerosis?

Robust Linear Models for Cis-eQTL Analysis

Expression Quantitative Trait Loci (eQTL) analysis enables characterisation of functional genetic variation influencing expression levels of individual genes. In outbread populations, including humans, eQTLs are commonly analysed using the conventional linear model, adjusting for relevant covariates, assuming an allelic dosage model and a Gaussian error term. However, gene expression data generally have noise that induces heavy-tailed errors relative to the Gaussian distribution and often include atypical observations, or outliers. Such departures from modelling assumptions can lead to an increased rate of type II errors (false negatives), and to some extent also type I errors (false positives). Careful model checking can reduce the risk of type-I errors but often not type II errors, since it is generally too time-consuming to carefully check all models with a non-significant effect in large-scale and genome-wide studies. Here we propose the application of a robust linear model for eQTL analysis to reduce adverse effects of deviations from the assumption of Gaussian residuals. We present results from a simulation study as well as results from the analysis of real eQTL data sets. Our findings suggest that in many situations robust models have the potential to provide more reliable eQTL results compared to conventional linear models, particularly in respect to reducing type II errors due to non-Gaussian noise. Post-genomic data, such as that generated in genome-wide eQTL studies, are often noisy and frequently contain atypical observations. Robust statistical models have the potential to provide more reliable results and increased statistical power under non-Gaussian conditions. The results presented here suggest that robust models should be considered routinely alongside other commonly used methodologies for eQTL analysis.

Exploiting Differential Gene Expression and Epistasis to Discover Candidate Genes for Drought-Associated QTLs in Arabidopsis thaliana

Soil water availability represents one of the most important selective agents for plants in nature and the single greatest abiotic determinant of agricultural productivity, yet the genetic bases of drought acclimation responses remain poorly understood. Here, we developed a systems-genetic approach to characterize quantitative trait loci (QTLs), physiological traits and genes that affect responses to soil moisture deficit in the TSUxKAS mapping population of Arabidopsis thaliana. To determine the effects of candidate genes underlying QTLs, we analyzed gene expression as a covariate within the QTL model in an effort to mechanistically link markers, RNA expression, and the phenotype. This strategy produced ranked lists of candidate genes for several drought-associated traits, including water use efficiency, growth, abscisic acid concentration (ABA), and proline concentration. As a proof of concept, we recovered known causal loci for several QTLs. For other traits, including ABA, we identified novel loci not previously associated with drought. Furthermore, we documented natural variation at two key steps in proline metabolism and demonstrated that the mitochondrial genome differentially affects genomic QTLs to influence proline accumulation. These findings demonstrate that linking genome, transcriptome, and phenotype data holds great promise to extend the utility of genetic mapping, even when QTL effects are modest or complex.

Candidate genes in quantitative trait loci associated with absolute and relative kidney weight in rats with Inherited Stress Induced Arterial Hypertension

BACKGROUND

The kidney mass is significantly increased in hypertensive ISIAH rats with Inherited Stress Induced Arterial Hypertension as compared with normotensive WAG rats. The QTL/microarray approach was carried out to determine the positional candidate genes in the QTL for absolute and relative kidney weight.

RESULTS

Several known and predicted genes differentially expressed in ISIAH and WAG kidney were mapped to genetic loci associated with the absolute and relative kidney weight in 6-month old F2 hybrid (ISIAHxWAG) males. The knowledge-driven filtering of the list of candidates helped to suggest several positional candidate genes, which may be related to the structural and mass changes in hypertensive ISIAH kidney.

CONCLUSIONS

The further experimental validation of causative genes and detection of polymorphisms will provide opportunities to advance our understanding of the underlying nature of structural and mass changes in hypertensive ISIAH kidney.

Application of quantitative trait locus mapping and transcriptomics to studies of the senescence-accelerated phenotype in rats

Background

Etiology of complex disorders, such as cataract and neurodegenerative diseases including age-related macular degeneration (AMD), remains poorly understood due to the paucity of animal models, fully replicating the human disease. Previously, two quantitative trait loci (QTLs) associated with early cataract, AMD-like retinopathy, and some behavioral aberrations in senescence-accelerated OXYS rats were uncovered on chromosome 1 in a cross between OXYS and WAG rats. To confirm the findings, we generated interval-specific congenic strains, WAG/OXYS-1.1 and WAG/OXYS-1.2, carrying OXYS-derived loci of chromosome 1 in the WAG strain. Both congenic strains displayed early cataract and retinopathy but differed clinically from OXYS rats. Here we applied a high-throughput RNA sequencing (RNA-Seq) strategy to facilitate nomination of the candidate genes and functional pathways that may be responsible for these differences and can contribute to the development of the senescence-accelerated phenotype of OXYS rats.

Results

First, the size and map position of QTL-derived congenic segments were determined by comparative analysis of coding single-nucleotide polymorphisms (SNPs), which were identified for OXYS, WAG, and congenic retinal RNAs after sequencing. The transferred locus was not what we expected in WAG/OXYS-1.1 rats. In rat retina, 15442 genes were expressed. Coherent sets of differentially expressed genes were identified when we compared RNA-Seq retinal profiles of 20-day-old WAG/OXYS-1.1, WAG/OXYS-1.2, and OXYS rats. The genes most different in the average expression level between the congenic strains included those generally associated with the Wnt, integrin, and TGF-β signaling pathways, widely involved in neurodegenerative processes. Several candidate genes (including Arhgap33, Cebpg, Gtf3c1, Snurf, Tnfaip3, Yme1l1, Cbs, Car9 and Fn1) were found to be either polymorphic in the congenic loci or differentially expressed between the strains. These genes may contribute to the development of cataract and retinopathy.

Conclusions

This study is the first RNA-Seq analysis of the rat retinal transcriptome generated with 40 mln sequencing read depth. The integration of QTL and transcriptomic analyses in our study forms the basis of future research into the relationship between the candidate genes within the congenic regions and specific changes in the retinal transcriptome as possible causal mechanisms that underlie age-associated disorders.

Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data

Posttraumatic stress disorder (PTSD) and other deployment-related outcomes originate from a complex interplay between constellations of changes in DNA, environmental traumatic exposures, and other biological risk factors. These factors affect not only individual genes or bio-molecules but also the entire biological networks that in turn increase or decrease the risk of illness or affect illness severity. This review focuses on recent developments in the field of systems biology which use multidimensional data to discover biological networks affected by combat exposure and post-deployment disease states. By integrating large-scale, high-dimensional molecular, physiological, clinical, and behavioral data, the molecular networks that directly respond to perturbations that can lead to PTSD can be identified and causally associated with PTSD, providing a path to identify key drivers. Reprogrammed neural progenitor cells from fibroblasts from PTSD patients could be established as an in vitro assay for high throughput screening of approved drugs to determine which drugs reverse the abnormal expression of the pathogenic biomarkers or neuronal properties.

Genetic variation within the Chrna7 gene modulates nicotine reward-like phenotypes in mice

Mortality from tobacco smoking remains the leading cause of preventable death in the world, yet current cessation therapies are only modestly successful, suggesting new molecular targets are needed. Genetic analysis of gene expression and behavior identified Chrna7 as potentially modulating nicotine place conditioning in the BXD panel of inbred mice. We used gene targeting and pharmacological tools to confirm the role of Chrna7 in nicotine conditioned place preference (CPP). To identify molecular events downstream of Chrna7 that may modulate nicotine preference, we performed microarray analysis of α7 knock-out (KO) and wild-type (WT) nucleus accumbens (NAc) tissue, followed by confirmation with quantitative polymerase chain reaction (PCR) and immunoblotting. In the BXD panel, we found a putative cis expression quantitative trait loci (eQTL) for Chrna7 in NAc that correlated inversely to nicotine CPP. We observed that gain-of-function α7 mice did not display nicotine preference at any dose tested, whereas conversely, α7 KO mice demonstrated nicotine place preference at a dose below that routinely required to produce preference. In B6 mice, the α7 nicotinic acetylcholine receptor (nAChR)-selective agonist, PHA-543613, dose-dependently blocked nicotine CPP, which was restored using the α7 nAChR-selective antagonist, methyllycaconitine citrate (MLA). Our genomic studies implicated a messenger RNA (mRNA) co-expression network regulated by Chrna7 in NAc. Mice lacking Chrna7 demonstrate increased insulin signaling in the NAc, which may modulate nicotine place preference. Our studies provide novel targets for future work on development of more effective therapeutic approaches to counteract the rewarding properties of nicotine for smoking cessation.

Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease

The burgeoning interest in the field of epigenetics has precipitated the need to develop approaches to strengthen causal inference when considering the role of epigenetic mediators of environmental exposures on disease risk. Epigenetic markers, like any other molecular biomarker, are vulnerable to confounding and reverse causation. Here, we present a strategy, based on the well-established framework of Mendelian randomization, to interrogate the causal relationships between exposure, DNA methylation and outcome. The two-step approach first uses a genetic proxy for the exposure of interest to assess the causal relationship between exposure and methylation. A second step then utilizes a genetic proxy for DNA methylation to interrogate the causal relationship between DNA methylation and outcome. The rationale, origins, methodology, advantages and limitations of this novel strategy are presented.

A hierarchical bayesian approach to multi-trait clinical quantitative trait locus modeling

Recent advances in high-throughput genotyping and transcript profiling technologies have enabled the inexpensive production of genome-wide dense marker maps in tandem with huge amounts of expression profiles. These large-scale data encompass valuable information about the genetic architecture of important phenotypic traits. Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits. The simultaneous utilization of marker and gene expression data to explain the variation in clinical quantitative trait, known as clinical quantitative trait locus (cQTL) mapping, poses challenges that are both conceptual and computational. Nonetheless, the hierarchical Bayesian (HB) modeling approach, in combination with modern computational tools such as Markov chain Monte Carlo (MCMC) simulation techniques, provides much versatility for cQTL analysis. Sillanpää and Noykova (2008) developed a HB model for single-trait cQTL analysis in inbred line cross-data using molecular markers, gene expressions, and marker-gene expression pairs. However, clinical traits generally relate to one another through environmental correlations and/or pleiotropy. A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision. A multi-trait model also provides a framework for examining a number of biologically interesting hypotheses. In this paper we extend the HB cQTL model for inbred line crosses proposed by Sillanpää and Noykova to a multi-trait setting. We illustrate the implementation of our new model with simulated data, and evaluate the multi-trait model performance with regard to its single-trait counterpart. The data simulation process was based on the multi-trait cQTL model, assuming three traits with uncorrelated and correlated cQTL residuals, with the simulated data under uncorrelated cQTL residuals serving as our test set for comparing the performances of the multi-trait and single-trait models. The simulated data under correlated cQTL residuals were essentially used to assess how well our new model can estimate the cQTL residual covariance structure. The model fitting to the data was carried out by MCMC simulation through OpenBUGS. The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate. Moreover, the covariance matrix of cQTL residuals was typically estimated to an appreciable degree of precision under the multi-trait cQTL model, making our new model a promising approach to addressing a wide range of issues facing the analysis of correlated clinical traits.

Using genomic tools to study regulatory evolution

Differences in gene regulation are thought to play an important role in speciation and adaptation. Comparative genomic studies of gene expression levels have identified a large number of differentially expressed genes among species, and, in a number of cases, also pointed to connections between interspecies differences in gene regulation and differences in ultimate physiological or morphological phenotypes. The mechanisms underlying changes in gene regulation are also being actively studied using comparative genomic approaches. However, the relative importance of different regulatory mechanisms to interspecies differences in gene expression levels is not yet well understood. In particular, it is often difficult to infer causality between apparent differences in regulatory mechanisms and changes in gene expression levels, a challenge that is compounded by the fact that the link between sequence variation and gene regulation is not clear. Indeed, in certain cases, gene regulation can be conserved even when sequences at associated regulatory elements have changed. In this chapter, I examine different genomic approaches to the study of regulatory evolution and the underlying genetic and epigenetic regulatory mechanisms. I try to distinguish between hypothesis-driven and exploratory studies, and argue that the latter class of studies provides valuable information in its own right as well as necessary context for the former. I discuss issues related to study designs and statistical analyses of genomic studies, and review the evidence for natural selection on gene expression levels and associated regulatory mechanisms. Most of the issues that are discussed pertain to the general nature of multivariate genomic data, and thus are often relevant regardless of the technology that is used to collect high-throughput genomic data (for example, microarrays or massively parallel sequencing).

Systems genetics of the nuclear factor-κB signal transduction network. I. Detection of several quantitative trait loci potentially relevant to aging

A theory of aging holds that senescence is caused by a dysregulated nuclear factor kappa B (NF-κB) signal transduction network (STN). We adopted a systems genetics approach in our study of the NF-κB STN. Ingenuity Pathways Analysis (IPA) was used to identify gene/gene product interactions between NF-κB and the genes in our transcriptional profiling array. Principal components factor analysis (PCFA) was performed on a sub-network of 19 genes, including two initiators of the toll-like receptor (TLR) pathway, myeloid differentiation primary response gene (88) (MyD88) and TIR (Toll/interleukin-1 receptor)-domain-containing adapter-inducing interferon-β (TRIF). TLR pathways are either MyD88-dependent or TRIF-dependent. Therefore, we also performed PCFA on a subset excluding the MyD88 transcript, and on another subset excluding two TRIF transcripts. Using linkage analysis we found that each set gave rise to at least one factor with a logarithm of the odds (LOD) score greater than 3, two on chromosome 15 at 15q12 and 15q22.2, and another two on chromosome 17 at 17p13.3 and 17q25.3. We also found several suggestive signals (2<LOD score<3) at 1q32.1, 1q41, 2q34, 3q23, and 7p15.3. We are currently examining potential associations with single nucleotide polymorphisms within the 1-LOD intervals of our linkage signals.

Next generation analytic tools for large scale genetic epidemiology studies of complex diseases

Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.

Bridging global gene expression candidates in first trimester placentas with susceptibility loci from linkage studies of preeclampsia

Preeclampsia is as a leading cause of maternal and perinatal morbidity and mortality. Prevention, early identification, and individualized treatments may become feasible if reliable early biomarkers can be developed. Towards a systems biology framework, this review synthesizes prior linkage studies and genome scans of preeclampsia with candidates identified in a global gene expression microarray analysis of chorionic villus sampling (CVS) specimens from women who subsequently developed preeclampsia. Nearly 40% of these CVS candidate genes occurred in previously identified susceptibility loci for preeclampsia. Integration of genetic epidemiologic and functional gene expression data could help to elucidate preeclampsia as a complex disease resulting from multiple maternal and fetal/placental factors that each contributes a greater or lesser effect. These loci and related candidate genes are set to substantially improve insights into the first trimester pathogenesis of this pregnancy disorder.

Systems biology and heart failure: concepts, methods, and potential research applications

Dramatic advances in molecular biology dominated twentieth century biomedical science and delineated the function of individual genes and molecules in exquisite detail. However, biological processes cannot be fully understood based on the properties of individual genes and molecules alone, since these elements act in concert to enable the specific functions that make for living cells and organisms. The discipline of systems biology provides a novel conceptual framework for understanding biological phenomenon. Systems biology synthesizes information concerning the interactions of genes and molecules and allows characterization of the supramolecular networks and functional modules that represent the most essential aspects of cell organization and physiology.

Genomic loci and candidate genes underlying inflammatory nociception

Heritable genetic factors contribute significantly to inflammatory nociception. To determine candidate genes underlying inflammatory nociception, the current study used a mouse model of abdominal inflammatory pain. BXD recombinant inbred (RI) mouse strains were administered the intraperitoneal acetic acid test, and genome-wide quantitative trait locus (QTL) mapping was performed on the mean number of abdominal contraction and extension movements in 3 distinct groups of BXD RI mouse strains in 2 separate experiments. Combined mapping results detected 2 QTLs on chromosomes (Chr) 3 and 10 across experiments and groups of mice; an additional sex-specific QTL was detected on Chr 16. The results replicate previous findings of a significant QTL, Nociq2, on distal Chr 10 for formalin-induced inflammatory nociception and will aid in identification of the underlying candidate genes. Comparisons of sensitivity to intraperitoneal acetic acid in BXD RI mouse strains with microarray mRNA transcript expression profiles in specific brain areas detected covarying expression of candidate genes that are also found in the detected QTL confidence intervals. The results indicate that common and distinct genetic mechanisms underlie heritable sensitivity to diverse inflammatory insults, and provide a discrete set of high-priority candidate genes to investigate further in rodents and human association studies. Novel genomic regions linked to inflammatory nociception were detected, a previously reported locus was confirmed, and high-priority candidate genes for inflammatory nociception and pain were identified.

Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice

BACKGROUND

Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia.

RESULTS

In order to determine the genetic factors that contribute to these T2D related characteristics in TH mice, we interbred TH mice with C57BL/6J (B6) mice. The parental, F1, and F2 mice were phenotyped at 8, 12, 16, 20, and 24 weeks of age for 4-hour fasting plasma triglyceride, cholesterol, insulin, and glucose levels and body, fat pad and carcass weights. The F2 mice were genotyped genome-wide and used for quantitative trait locus (QTL) mapping. We also applied a genetical genomic approach using a subset of the F2 mice to seek candidate genes underlying the QTLs. Major QTLs were detected on chromosomes (Chrs) 1, 11, 4, and 8 for hypertriglyceridemia, 1 and 3 for hypercholesterolemia, 4 for hyperglycemia, 11 and 1 for body weight, 1 for fat pad weight, and 11 and 14 for carcass weight. Most alleles, except for Chr 3 and 14 QTLs, increased phenotypic values when contributed by the TH strain. Fourteen pairs of interacting loci were detected, none of which overlapped the major QTLs. The QTL interval linked to hypercholesterolemia and hypertriglyceridemia on distal Chr 1 contains Apoa2 gene. Sequencing analysis revealed polymorphisms of Apoa2 in TH mice, suggesting Apoa2 as the candidate gene for the hyperlipidemia QTL. Gene expression analysis added novel information and aided in selection of candidates underlying the QTLs.

CONCLUSIONS

We identified several genetic loci that affect the quantitative variations of plasma lipid and glucose levels and obesity traits in a TH × B6 intercross. Polymorphisms in Apoa2 gene are suggested to be responsible for the Chr 1 QTL linked to hypercholesterolemia and hypertriglyceridemia. Further, genetical genomic analysis led to potential candidate genes for the QTLs.

Mapping transcription mechanisms from multimodal genomic data

BACKGROUND

Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data.

RESULTS

We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate.

CONCLUSIONS

The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

Physiological insights gained from gene expression analysis in obesity and diabetes

Microarray technology permits the interrogation of nearly all expressed genes under a wide range of conditions. Patterns of gene expression in response to obesity and diabetes have yielded important insights into the pathogenesis of diabetes and its relationship to obesity. In muscle, microarray studies have motivated research into mitochondrial function. In adipose tissue, clues have pointed to the importance of inflammation in obesity. New adipocyte-derived hormones involved in insulin resistance have been found; a notable example is retinol binding protein 4. In liver, genes responsive to master regulators of lipid metabolism have been identified. In beta-cells, genes involved in cell survival, cell proliferation, and insulin secretion have been identified. These studies have greatly expanded our understanding of mechanisms underlying the pathogenesis of obesity-induced diabetes. When combined with genetic information, microarray data can be used to construct causal network models linking gene expression with disease.

Genetic mechanisms of susceptibility to ozone-induced lung disease

Environmental oxidants remain a major public health concern in industrialized cities throughout the world. Population and epidemiological studies have associated oxidant air pollutants with morbidity and mortality outcomes, and underscore the important detrimental effects of these pollutants on the lung. Interindividual variation in pulmonary responses to air pollutants suggests that some subpopulations are at increased risk to detrimental effects of pollutant exposure, and it has become clear that genetic background is an important susceptibility factor. A number of genetics and genomics tools have recently emerged to enable identification of genes that contribute to differential responsiveness to oxidants, including ozone (O(3)). Integrative omics approaches have been applied in inbred mice to identify genes that determine differential responsiveness to O(3)-induced injury and inflammation, including Tnf, Tlr4, and MHC Class II genes. Combined investigations across cell models, inbred mice, and humans have provided, and will continue to provide, important insight to understanding genetic factors that contribute to differential susceptibility to oxidants.

Transcriptome profiling and network analysis of genetically hypertensive mice identifies potential pharmacological targets of hypertension

Hypertension is a condition with major cardiovascular and renal complications, affecting nearly a billion patients worldwide. Few validated gene targets are available for pharmacological intervention, so there is a need to identify new biological pathways regulating blood pressure and containing novel targets for treatment. The genetically hypertensive “blood pressure high” (BPH), normotensive “blood pressure normal” (BPN), and hypotensive “blood pressure low” (BPL) inbred mouse strains are an ideal system to study differences in gene expression patterns that may represent such biological pathways. We profiled gene expression in liver, heart, kidney, and aorta from BPH, BPN, and BPL mice and determined which biological processes are enriched in observed organ-specific signatures. As a result, we identified multiple biological pathways linked to blood pressure phenotype that could serve as a source of candidate genes causal for hypertension. To distinguish in the kidney signature genes whose differential expression pattern may cause changes in blood pressure from those genes whose differential expression pattern results from changes in blood pressure, we integrated phenotype-associated genes into Genetic Bayesian networks. The integration of data from gene expression profiling and genetics networks is a valuable approach to identify novel potential targets for the pharmacological treatment of hypertension.

Serious limitations of the QTL/microarray approach for QTL gene discovery

BACKGROUND

It has been proposed that the use of gene expression microarrays in nonrecombinant parental or congenic strains can accelerate the process of isolating individual genes underlying quantitative trait loci (QTL). However, the effectiveness of this approach has not been assessed.

RESULTS

Thirty-seven studies that have implemented the QTL/microarray approach in rodents were reviewed. About 30% of studies showed enrichment for QTL candidates, mostly in comparisons between congenic and background strains. Three studies led to the identification of an underlying QTL gene. To complement the literature results, a microarray experiment was performed using three mouse congenic strains isolating the effects of at least 25 biometric QTL. Results show that genes in the congenic donor regions were preferentially selected. However, within donor regions, the distribution of differentially expressed genes was homogeneous once gene density was accounted for. Genes within identical-by-descent (IBD) regions were less likely to be differentially expressed in chromosome 2, but not in chromosomes 11 and 17. Furthermore, expression of QTL regulated in cis (cis eQTL) showed higher expression in the background genotype, which was partially explained by the presence of single nucleotide polymorphisms (SNP).

CONCLUSIONS

The literature shows limited successes from the QTL/microarray approach to identify QTL genes. Our own results from microarray profiling of three congenic strains revealed a strong tendency to select cis-eQTL over trans-eQTL. IBD regions had little effect on rate of differential expression, and we provide several reasons why IBD should not be used to discard eQTL candidates. In addition, mismatch probes produced false cis-eQTL that could not be completely removed with the current strains genotypes and low probe density microarrays. The reviewed studies did not account for lack of coverage from the platforms used and therefore removed genes that were not tested. Together, our results explain the tendency to report QTL candidates as differentially expressed and indicate that the utility of the QTL/microarray as currently implemented is limited. Alternatives are proposed that make use of microarray data from multiple experiments to overcome the outlined limitations.

Nutritional genomics era: opportunities toward a genome-tailored nutritional regimen

There is increasing evidence indicating that nutritional genomics represents a promise to improve public health. This goal will be reached by highlighting the mechanisms through which diet can reduce the risk of monogenic and common polygenic diseases. Indeed, nutrition is a very relevant environmental factor involved in the development and progression of metabolic disorders, as well as other kind of diseases. The revolutionary changes in the field of genomics have led to the development and implementation of new technologies and molecular tools. These technologies have a useful application in the nutritional sciences, since they allow a more precise and accurate analysis of biochemical alterations, in addition to filling fundamental gaps in the knowledge of nutrient-genome interactions in both health and disease. Overall, these advances will open undiscovered ways in genome-customized diets for disease prevention and therapy. This review summarizes the recent knowledge concerning this novel nutritional approach, paying attention to the human genome variations, such as single-nucleotide polymorphisms and copy number variations, gene expression and innovative molecular tools to reveal them.

Thermoregulation in mice exhibits genetic variability early in senescence

Aging leads to a loss of thermoregulation that can be readily monitored in laboratory mice. However, it is unclear from previous studies-we provide a tabular summary of 15 articles-whether significant loss occurs by midlife ( approximately 15 months of age). In this study, we examined 34 females from 22 LSXSS strains starting at 4 and 8 months of age (17 mice per age group). We used transponders inserted just under the loose skin of the pelt and calibrated against rectal body temperature to measure temperatures quickly without restraint. We found that the mean body temperatures measured 5 months later (9 and 13 months of age) had dropped significantly below normal in both groups: 0.6 masculineC lower in the younger cohort and 1.0 masculineC lower in the older cohort. These drops were not associated with weight loss or signs of pathology. Notably, the loss of thermoregulation between 8 and 13 months of age also exhibited genetic variation that was highly significant (P = 0.004). Such variation is potentially a powerful tool for determining the cause of thermoregulatory loss with age and whether this loss predicts senescence changes later in life, including the force of mortality.

Systems biology in heart diseases

Systems biology based on integrative computational analysis and high technology is in a position to construct networks, to study the interactions between molecular components and to develop models of cardiac function and anatomy. Clinical cardiology gets an integrated picture of parameters that are addressed to ventricular and vessel mechanics, cardiac metabolism and electrical activation. The achievement of clinical objectives is based on the interaction between modern technology and clinical phenotype. In this review the need for more sophisticated realization of the structure and function of the cardiovascular system is emphasized while the incorporation of the systems biology concept in predicting clinical phenotypes is a promising strategy that optimize diagnosis and treatment in cardiovascular disease.

Stable patterns of gene expression regulating carbohydrate metabolism determined by geographic ancestry

Background

Individuals of African descent in the United States suffer disproportionately from diseases with a metabolic etiology (obesity, metabolic syndrome, and diabetes), and from the pathological consequences of these disorders (hypertension and cardiovascular disease).

Methodology/Principal Findings

Using a combination of genetic/genomic and bioinformatics approaches, we identified a large number of genes that were both differentially expressed between American subjects self-identified to be of either African or European ancestry and that also contained single nucleotide polymorphisms that distinguish distantly related ancestral populations. Several of these genes control the metabolism of simple carbohydrates and are direct targets for the SREBP1, a metabolic transcription factor also differentially expressed between our study populations.

Conclusions/Significance

These data support the concept of stable patterns of gene transcription unique to a geographic ancestral lineage. Differences in expression of several carbohydrate metabolism genes suggest both genetic and transcriptional mechanisms contribute to these patterns and may play a role in exacerbating the disproportionate levels of obesity, diabetes, and cardiovascular disease observed in Americans with African ancestry.

Disruption of the aortic elastic lamina and medial calcification share genetic determinants in mice

BACKGROUND

Disruption of the elastic lamina, as an early indicator of aneurysm formation, and vascular calcification frequently occur together in atherosclerotic lesions of humans.

METHODS AND RESULTS

We now report evidence of shared genetic basis for disruption of the elastic lamina (medial disruption) and medial calcification in an F(2) mouse intercross between C57BL/6J and C3H/HeJ on a hyperlipidemic apolipoprotein E (ApoE(-/-)) null

BACKGROUND

gene, known to mediate myocardial calcification. Using transgenic complementation, we show that Abcc6 also contributes to aortic medial calcification.

CONCLUSIONS

Our data indicate that calcification, though possibly contributory, does not always lead to medial disruption and that in addition to aneurysm formation, medial disruption may be the precursor to calcification.

Systems biology asks new questions about sex differences

Females and males differ in physiology and in the incidence and progression of diseases. The sex-biased proximate factors causing sex differences in phenotype include direct effects of gonadal hormones and of genes represented unequally in the genome because of their X- or Y-linkage. Novel systems approaches have begun to assess the magnitude and character of sex differences in organization of gene networks on a genome-wide scale. These studies identify functionally related modules of genes that are coexpressed differently in males and females, and sites in the genome that regulate gene networks in a sex-specific manner. Measurement of the aggregate behavior of genes uncovers novel sex differences that can be related more effectively to susceptibility to disease.

Genetic polymorphisms associated with acute lung injury

Acute lung injury and acute respiratory distress syndrome are the result of intense inflammation in the lungs leading to respiratory failure. The causes of acute lung injury/acute respiratory distress syndrome are numerous (e.g., pneumonia, sepsis and trauma) but the reasons why certain individuals develop lung injury in response to these stimuli and others do not are not well understood. There is ample evidence in the literature that gene-host and gene-environment interactions may play a large role in the morbidity and mortality associated with this syndrome. In this review, we initially discuss methods for identification of candidate acute lung injury/acute respiratory distress syndrome susceptibility genes using a number of model systems including in vitro cell systems and inbred mice. We then describe examples of polymorphisms in genes that have been associated with the pathogenesis of acute lung injury/acute respiratory distress syndrome in human case-control studies. Systematic bench to bedside approaches to understand the genetic contribution to acute lung injury/acute respiratory distress syndrome have provided important insight to this complex disease and continuation of these investigations could lead to the development of novel prevention or intervention strategies.

Genetical genomic determinants of alcohol consumption in rats and humans

BACKGROUND

We have used a genetical genomic approach, in conjunction with phenotypic analysis of alcohol consumption, to identify candidate genes that predispose to varying levels of alcohol intake by HXB/BXH recombinant inbred rat strains. In addition, in two populations of humans, we assessed genetic polymorphisms associated with alcohol consumption using a custom genotyping array for 1,350 single nucleotide polymorphisms (SNPs). Our goal was to ascertain whether our approach, which relies on statistical and informatics techniques, and non-human animal models of alcohol drinking behavior, could inform interpretation of genetic association studies with human populations.

RESULTS

In the HXB/BXH recombinant inbred (RI) rats, correlation analysis of brain gene expression levels with alcohol consumption in a two-bottle choice paradigm, and filtering based on behavioral and gene expression quantitative trait locus (QTL) analyses, generated a list of candidate genes. A literature-based, functional analysis of the interactions of the products of these candidate genes defined pathways linked to presynaptic GABA release, activation of dopamine neurons, and postsynaptic GABA receptor trafficking, in brain regions including the hypothalamus, ventral tegmentum and amygdala. The analysis also implicated energy metabolism and caloric intake control as potential influences on alcohol consumption by the recombinant inbred rats. In the human populations, polymorphisms in genes associated with GABA synthesis and GABA receptors, as well as genes related to dopaminergic transmission, were associated with alcohol consumption.

CONCLUSION

Our results emphasize the importance of the signaling pathways identified using the non-human animal models, rather than single gene products, in identifying factors responsible for complex traits such as alcohol consumption. The results suggest cross-species similarities in pathways that influence predisposition to consume alcohol by rats and humans. The importance of a well-defined phenotype is also illustrated. Our results also suggest that different genetic factors predispose alcohol dependence versus the phenotype of alcohol consumption.

The organization of the transcriptional network in specific neuronal classes

Genome-wide expression profiling has aided the understanding of the molecular basis of neuronal diversity, but achieving broad functional insight remains a considerable challenge. Here, we perform the first systems-level analysis of microarray data from single neuronal populations using weighted gene co-expression network analysis to examine how neuronal transcriptome organization relates to neuronal function and diversity. We systematically validate network predictions using published proteomic and genomic data. Several network modules of co-expressed genes correspond to interneuron development programs, in which the hub genes are known to be critical for interneuron specification. Other co-expression modules relate to fundamental cellular functions, such as energy production, firing rate, trafficking, and synapses, suggesting that fundamental aspects of neuronal diversity are produced by quantitative variation in basic metabolic processes. We identify two transcriptionally distinct mitochondrial modules and demonstrate that one corresponds to mitochondria enriched in neuronal processes and synapses, whereas the other represents a population restricted to the soma. Finally, we show that galectin-1 is a new interneuron marker, and we validate network predictions in vivo using Rgs4 and Dlx1/2 knockout mice. These analyses provide a basis for understanding how specific aspects of neuronal phenotypic diversity are organized at the transcriptional level.

Methods for the inference of biological pathways and networks

In this chapter, we discuss a number of approaches to network inference from large-scale functional genomics data. Our goal is to describe current methods that can be used to infer predictive networks. At present, one of the most effective methods to produce networks with predictive value is the Bayesian network approach. This approach was initially instantiated by Friedman et al. and further refined by Eric Schadt and his research group. The Bayesian network approach has the virtue of identifying predictive relationships between genes from a combination of expression and eQTL data. However, the approach does not provide a mechanistic bases for predictive relationships and is ultimately hampered by an inability to model feedback. A challenge for the future is to produce networks that are both predictive and provide mechanistic understanding. To do so, the methods described in several chapters of this book will need to be integrated. Other chapters of this book describe a number of methods to identify or predict network components such as physical interactions. At the end of this chapter, we speculate that some of the approaches from other chapters could be integrated and used to "annotate" the edges of the Bayesian networks. This would take the Bayesian networks one step closer to providing mechanistic "explanations" for the relationships between the network nodes.

Single nucleotide polymorphisms affect both cis- and trans-eQTLs

Single nucleotide polymorphisms (SNPs) between microarray probes and RNA targets can affect the performance of expression array by weakening the hybridization. In this paper, we examined the effect of the SNPs on Affymetrix GeneChip probe set summaries and the expression quantitative trait loci (eQTL) mapping results in two eQTL datasets, one from mouse and one from human. We showed that removing SNP-containing probes significantly changed the probe set summaries and the more SNP-containing probes we removed the greater the change. Comparison of the eQTL mapping results between with and without SNP-containing probes showed that less than 70% of the significant eQTL peaks were concordant regardless of the significance threshold. These results indicate that SNPs do affect both probe set summaries and eQTLs (both cis and trans), thus SNP-containing probes should be filtered out to improve the performance of eQTL mapping.

A systems-based framework for understanding complex metabolic and cardiovascular disorders

Common forms of metabolic and cardiovascular diseases involve the interplay of numerous genes as well as important environmental factors. Traditional biochemical and genetic approaches generally attempt to dissect these diseases one gene at a time, for example, by analysis of Mendelian forms or genetically engineered experimental organisms. But, it is also important to understand how the genes interact with each other and the environment, and how these interactions change in disease states. Technological advances, such as the development of expression arrays that allow quantification of all transcript levels in a cell or tissue, have made it feasible to globally monitor molecular phenotypes that underlie disease states. By applying statistical methods, relationships between DNA variation, gene expression patterns, and diseases can be modeled.

Are transgenic mice the 'alkahest' to understanding myocardial hypertrophy and failure?

Murine transgenesis using cardioselective promoters has become increasingly common in studies of cardiac hypertrophy and heart failure, with expression mediated by pronuclear microinjection being the commonest format. Without wishing to decry their usefulness, in our view, such studies are not necessarily as unambiguous as sometimes portrayed and clarity is not always their consequence. We describe broadly the types of approach undertaken in the heart and point out some of the drawbacks. We provide three arbitrarily-chosen examples where, in spite of a number of often-independent studies, no consensus has yet been achieved. These include glycogen synthase kinase 3, the extracellular signal-regulated kinase pathway and the ryanodine receptor 2. We believe that the transgenic approach should not be viewed in an empyreal light and, depending on the questions asked, we suggest that other experimental systems provide equal (or even more) valuable outcomes.

Metabolic syndrome: from epidemiology to systems biology

Metabolic syndrome (MetSyn) is a group of metabolic conditions that occur together and promote the development of cardiovascular disease (CVD) and diabetes. Recent genome-wide association studies have identified several novel susceptibility genes for MetSyn traits, and studies in rodent models have provided important molecular insights. However, as yet, only a small fraction of the genetic component is known. Systems-based approaches that integrate genomic, molecular and physiological data are complementing traditional genetic and biochemical approaches to more fully address the complexity of MetSyn.

Complex genetics of obesity in mouse models

Traits related to energy balance and obesity are exceptionally complex, with varying contributions of genetic susceptibility and interacting environmental factors. The use of mouse models has been a powerful driving force in understanding the genetic architecture of polygenic traits such as obesity. However, the use of mouse models for analysis of complex traits is at an important crossroad. Genome-wide association studies in humans are now leading to direct identification of obesity genes. In this review, we focus on three areas representing the current and future roles of mouse models regarding genetics of complex obesity. First, we summarize increasingly powerful ways to harness the strength of mouse models for discovery of genes affecting polygenic obesity. Second, we examine the status of using a systems biology approach to dissect the genetic architecture of obesity. And third, we explore the effects of recent findings indicating increasing levels of complexity in the nature of variation underlying, and the heritability of, complex traits such as obesity.

[Integrating genetic and gene expression data: methods and applications of eQTL mapping]

The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to investigate genetic variations that influence levels of gene expression. Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expression. Similar to traditional QTL studies, the main goal of eQTL is to identify the genomic locations to which the expression traits are linked. Although microarrays provide the expression data of thousands of transcripts, standard QTL mapping methods, which are able to handle at most tens of traits, cannot be applied directly. As a result, it is necessary to consider the statistical principles involved in the design and analysis of these experiments. In this paper, we reviewed individual selection, experimental design of microarray, normalization of gene expression data, mapping methods, and explaining of results and proposed potential methodological problems for such analyses. Finally, we discussed the applications of this integrative genomic approach to estimate heritability of transcripts, identify candidate genes, construct gene networks, and understand interactions between genes, genes and environments.

Innate immune modulation by RNA viruses: emerging insights from functional genomics

By providing a global and integrated view of the host response to infection, functional genomic and systems-biology approaches are contributing to our understanding of RNA virus–host interactions. One area in which these approaches are being put to particularly good use is in shedding new light on the components of innate antiviral defence mechanisms and the viral strategies used to regulate or overcome them.

Genomic analyses have helped to reveal virus-specific differences in the way that viral recognition through pathogen-recognition receptors (PRRs) initiates intracellular signalling cascades. Whereas influenza virus appears to signal primarily through retinoic-acid-inducible gene I (RIG-I), West Nile virus signals through both RIG-I and melanoma differentiation-associated gene 5 (MDA5). Both viruses induce the expression of interferon (IFN)-regulatory factor 3 (IRF3) target genes and IFN-stimulated genes (ISGs).

Genomic analyses have provided a comprehensive view of the transcriptional programmes that are induced by Toll-like receptor (TLR) activation. One transcriptional profile is universally activated by all TLRs and a second profile is specific to TLR3 and TLR4. Nuclear factor-κB (NF-κB) is the key regulator of the universal response, which occurs early after TLR stimulation, and the IFN-stimulated response element (ISRE) is the key component of the TLR3/TLR4 response, which is induced after the NF-κB response.

Some highly virulent viruses, such as Ebola virus and rabies virus, are successful at inhibiting ISG expression, resulting in the marked suppression of genes in key innate antiviral pathways, including those mediated by IRF3. There seems to be a correlation between the antagonism of the IFN response and virulence.

Genomic analyses of the host response to the reconstructed 1918 pandemic influenza virus have revealed similarities and differences to contemporary influenza virus infection. Contemporary and 1918 influenza viruses each trigger an innate immune response that includes the expression of NF-κB and IRF3 target genes, and both viruses trigger a robust cytokine response that attracts immune-cell infiltration to infected tissues. Unlike contemporary virus strains, in which the early response to infection is resolved, the innate immune response triggered by the 1918 influenza virus is characterized by a strong and sustained induction that is associated with massive tissue damage and death.

Global gene-expression profiling has revealed that many effective, attenuated live-virus vaccines transiently induce a stronger type I IFN response than the cognate pathogen, and therefore implicates modulation of this response as an important strategy in rational vaccine design.

By providing a global view of the host response to infection, functional genomic approaches are proving useful in deciphering complex virus–host interactions. Here, the authors reveal how such approaches are being used to better understand viral triggering and regulation of host innate immune responses.

Although often encoding fewer than a dozen genes, RNA viruses can overcome host antiviral responses and wreak havoc on the cells they infect. Some manage to evade host antiviral defences, whereas others elicit an aberrant or disproportional immune response. Both scenarios can result in the disruption of intracellular signalling pathways and significant pathology in the host. Systems-biology approaches are increasingly being used to study the processes of viral triggering and regulation of host immune responses. By providing a global and integrated view of cellular events, these approaches are beginning to unravel some of the complexities of virus–host interactions and provide new insights into how RNA viruses cause disease.

Proteomic insights into cardiac cell death and survival

Cardiovascular disease is the leading cause of death and disability in the developed world. To design novel therapeutic strategies to treat and prevent this disease, better understanding of cardiac cell function is necessary. In addition to (and, indeed, in combination with) genetics, physiology and molecular biology, proteomics plays a critical role in our understanding of cardiovascular systems at multiple scales. The purpose of this review is to examine recent developments in the field of myocardial injury and protection, examining how proteomics has informed investigations into organelles, signaling complexes, and cardiac phenotype.

Distribution and functional impact of DNA copy number variation in the rat

The abundance and dynamics of copy number variants (CNVs) in mammalian genomes poses new challenges in the identification of their impact on natural and disease phenotypes. We used computational and experimental methods to catalog CNVs in rat and found that they share important functional characteristics with those in human. In addition, 113 one-to-one orthologous genes overlap CNVs in both human and rat, 80 of which are implicated in human disease. CNVs are nonrandomly distributed throughout the genome. Chromosome 18 is a cold spot for CNVs as well as evolutionary rearrangements and segmental duplications, suggesting stringent selective mechanisms underlying CNV genesis or maintenance. By exploiting gene expression data available for rat recombinant inbred lines, we established the functional relationship of CNVs underlying 22 expression quantitative trait loci. These characteristics make the rat an excellent model for studying phenotypic effects of structural variation in relation to human complex traits and disease.

Genetic networks of liver metabolism revealed by integration of metabolic and transcriptional profiling

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin^ob/ob and the diabetes-susceptible BTBR leptin^ob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identifying individual genes and their potential roles in molecular pathways leading to disease remains a challenge. In this study, we include transcriptional and metabolic profiling in genomic analyses to address this limitation. We investigated an F2 intercross between the diabetes-resistant C57BL/6 leptin^ob/ob and the diabetes-susceptible BTBR leptin^ob/ob mouse strains that segregates for genotype and diabetes-related physiological traits; blood glucose, plasma insulin and body weight. Our study shows that liver metabolites (comprised of amino acids, organic acids, and acyl-carnitines) map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal, testable networks for control of specific metabolic processes in liver. We apply an in vitro study to confirm the validity of this integrative method, and thus provide a novel approach to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.

Sex specific gene regulation and expression QTLs in mouse macrophages from a strain intercross

BACKGROUND

A powerful way to identify genes for complex traits it to combine genetic and genomic methods. Many trait quantitative trait loci (QTLs) for complex traits are sex specific, but the reason for this is not well understood.

METHODOLOGY/PRINCIPAL FINDINGS

RNA was prepared from bone marrow derived macrophages of 93 female and 114 male F(2) mice derived from a strain intercross between apoE-deficient mice on the AKR and DBA/2 genetic backgrounds, and was subjected to transcriptome profiling using microarrays. A high density genome scan was performed using a mouse SNP chip, and expression QTLs (eQTLs) were located for expressed transcripts. Using suggestive and significant LOD score cutoffs of 3.0 and 4.3, respectively, thousands of eQTLs in the female and male cohorts were identified. At the suggestive LOD threshold the majority of the eQTLs were trans eQTLs, mapping unlinked to the position of the gene. Cis eQTLs, which mapped to the location of the gene, had much higher LOD scores than trans eQTLs, indicating their more direct effect on gene expression. The majority of cis eQTLs were common to both males and females, but only approximately 1% of the trans eQTLs were shared by both sexes. At the significant LOD threshold, the majority of eQTLs were cis eQTLs, which were mostly sex-shared, while the trans eQTLs were overwhelmingly sex-specific. Pooling the male and female data, 31% of expressed transcripts were expressed at different levels in males vs. females after correction for multiple testing.

CONCLUSIONS/SIGNIFICANCE

These studies demonstrate a large sex effect on gene expression and trans regulation, under conditions where male and female derived cells were cultured ex vivo and thus without the influence of endogenous sex steroids. These data suggest that eQTL data from male and female mice should be analyzed separately, as many effects, such as trans regulation are sex specific.

An overview of cardiac systems biology

The cardiac system has been a major target for intensive studies in the multi-scale modeling field for many years. Reproduction of the action potential and the ionic currents of single cardiomyocytes, as well as the construction of a whole organ model is well established. Still, there are major hurdles to overcome in creating a realistic and predictive functional cardiac model due to the lack of a profound understanding of the complex molecular interactions and their outcomes controlling both normal and pathological cardiophysiology. The recent advent of systems biology offers the conceptual and practical frameworks to tackle such biological complexities. This review provides an overview of major themes in the developing field of cardiac systems biology, summarizing some of the high-throughput experiments and strategies used to integrate the datasets, and various types of computational approaches used for developing useful quantitative models capable of predicting complex biological behavior.

Board-invited review: Applications of genomic information in livestock

The availability of whole genome sequences for individual species will change the landscape for livestock genomic research. Animal scientists will have access to whole-genome sequence-based technologies such as high-throughput SNP genotyping assays, gene expression profiling, methylation profiling, RNA interference, and genome resequencing that will revolutionize the scale upon which research will be conducted. These technologies will also alter the ways we think about addressing industry and scientific problems. In this review, we discuss the scientific bases for these emerging technologies and present recent highlights of their application in human, model species, and livestock as well as their potential for future applications in livestock. Additionally, we discuss strategies for their use in the genetic improvement and management of livestock. In particular, we present a strategy for the simultaneous identification of causal mutations underlying phenotypic traits in livestock and discuss issues that will arise in the application of whole genome selection for the prediction of genetic merit in livestock. We also point out that the statistical analysis that underlies the whole genome selection methodology is a sophisticated enhancement of single marker association mapping analysis to allow the entire genome to be simultaneously analyzed.