Genetics of gene expression and its effect on disease. Nature. 2008;452:423-428. [PMID: 18344981 DOI: 10.1038/nature06758]

A network view of disease and compound screening

The large-scale generation and integration of genomic, proteomic, signalling and metabolomic data are increasingly allowing the construction of complex networks that provide a new framework for understanding the molecular basis of physiological or pathophysiological states. Network-based drug discovery aims to harness this knowledge to investigate and understand the impact of interventions, such as candidate drugs, on the molecular networks that define these states. In this article, we describe how such an approach offers a novel way to understand biology, characterize disease and ultimately develop improved therapies, and discuss the challenges to realizing these goals.

Genomics and cardiovascular drug development

In the last half century, phenomenal advances have been made in understanding the pathophysiology of cardiovascular disease and in developing therapies to reduce cardiovascular risk. Nevertheless, cardiovascular disease remains the leading cause of death and morbidity in the industrialized world, with rapidly rising prevalence in developing countries, accounting for approximately 30% of all deaths worldwide. Since the initial availability of statin drugs in 1987, few novel cardiovascular therapies have emerged. Whereas statins reduce the mortality and morbidity from atherosclerotic heart disease by approximately 30%, the staggering 70% residual cardiovascular risk underscores the persistent need for novel therapies. Substantial advances in genomic research offer promise to greatly facilitate cardiovascular drug development. Over the past decade, often termed "the genomics revolution," such advancements as the emergence of genome-wide genotyping in humans, the industrialization of messenger ribonucleic acid expression profiling, and the maturation of proteomic and metabolomic methodologies have been made. In addition, the advancement of informatics to allow the intersection of multiple complex datasets has led to the field of systems biology. Genomic approaches are already being utilized to drive novel compound pipelines by helping with the identification and validation of novel targets. In the future, the study of genomics is expected to support biomarker discovery and development and the identification of responder patient segments. The focus of the present review is the application of genomics to the development of novel atherosclerosis therapies.

Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments

With tens of billions of dollars spent each year on the development of drugs to treat human diseases, and with fewer and fewer applications for investigational new drugs filed each year despite this massive spending, questions now abound on what changes to the drug discovery paradigm can be made to achieve greater success. The high rate of failure of drug candidates in clinical development, where the great majority of these drugs fail due to lack of efficacy, speak directly to the need for more innovative approaches to study the mechanisms of disease and drug discovery. Here we review systems biology approaches that have been devised over the last several years to understand the biology of disease at a more holistic level. By integrating a diversity of data like DNA variation, gene expression, protein-protein interaction, DNA-protein binding, and other types of molecular phenotype data, more comprehensive networks of genes both within and between tissues can be constructed to paint a more complete picture of the molecular processes underlying physiological states associated with disease. These more integrative, systems-level methods lead to networks that are demonstrably predictive, which in turn provides a deeper context within which single genes operate such as those identified from genome-wide association studies or those targeted for therapeutic intervention. The more comprehensive views of disease that result from these methods have the potential to dramatically enhance the way in which novel drug targets are identified and developed, ultimately increasing the probability of success for taking new drugs through clinical development. We highlight a number of the integrative approaches via examples that have resulted not only in the identification of novel genes for diabetes and cardiovascular disease, but in more comprehensive networks as well that describe the context in which the disease genes operate.

Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13

Following the recent success of genome-wide association studies in uncovering disease-associated genetic variants, the next challenge is to understand how these variants affect downstream pathways. The most proximal trait to a disease-associated variant, most commonly a single nucleotide polymorphism (SNP), is differential gene expression due to the cis effect of SNP alleles on transcription, translation, and/or splicing gene expression quantitative trait loci (eQTL). Several genome-wide SNP-gene expression association studies have already provided convincing evidence of widespread association of eQTLs. As a consequence, some eQTL associations are found in the same genomic region as a disease variant, either as a coincidence or a causal relationship. Cis-regulation of RPS26 gene expression and a type 1 diabetes (T1D) susceptibility locus have been colocalized to the 12q13 genomic region. A recent study has also suggested RPS26 as the most likely susceptibility gene for T1D in this genomic region. However, it is still not clear whether this colocalization is the result of chance alone or if RPS26 expression is directly correlated with T1D susceptibility, and therefore, potentially causal. Here, we derive and apply a statistical test of this hypothesis. We conclude that RPS26 expression is unlikely to be the molecular trait responsible for T1D susceptibility at this locus, at least not in a direct, linear connection.

Genetic control of human brain transcript expression in Alzheimer disease

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

Systems biology for identifying liver toxicity pathways

Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States, exceeding all other causes combined. The objective of this paper is to describe systems biology methods for identifying pathways involved in liver toxicity induced by free fatty acids (FFA) and tumor necrosis factor (TNF)-α in human hepatoblastoma cells (HepG2/C3A). Systems biology approaches were developed to integrate multi-level data, i.e., gene expression, metabolite profile, toxicity measurements and a priori knowledge to identify gene targets for modulating liver toxicity. Targets that modulate liver toxicity, in vitro, were computationally predicted and some targets were experimentally validated.

Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks

A major task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription, and phenotypic information. Here we validated our method through the characterization of transgenic and knockout mouse models of candidate genes that were predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being novel, resulted in significant changes in obesity related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F2 intercross studies allows high confidence prediction of causal genes and identification of involved pathways and networks.

Altered beta-2 adrenergic receptor gene expression in human clinical hypertension

OBJECTIVES

The beta-2 adrenergic receptor is involved in mediating vasodilatation via neurohumoral and sympathetic nervous system pathways. Alterations in beta-2 adrenergic receptor gene expression (mRNA transcription) may contribute to the hypertensive phenotype. Human gene expression in clinical phenotypes remains largely unexplored due to ethical constraints involved in obtaining human tissue. We devised a method to obtain normally discarded internal mammary artery tissue from coronary artery bypass graft patients. We then investigated differences in hypertensive and normotensive participants' beta-2 adrenergic receptor gene expression in this tissue.

METHODS

We collected arterial tissue samples from 46 coronary artery bypass patients in a surgical setting. Using 41 of the samples, we performed TaqMan real-time polymerase chain reaction (RT-PCR) and used the delta delta cycle threshold (DeltaDeltaCt) relative quantitation method for determination of fold-differences in gene expression between normotensive and hypertensive participants. The beta-2 adrenergic receptor target was normalized to glyceraldehyde-phosphate dehydrogenase.

RESULTS

Participants with hypertension had significantly less-expressed beta-2 adrenoceptor gene (2.76-fold, p<.05) compared to normotensive participants. After Bonferroni correction, gene expression did not differ by race, gender, type/dose of beta-blocker prescribed, positive family history of hypertension, or diagnosis of diabetes mellitus type 2.

CONCLUSIONS

These data support the possibility of a molecular basis for impaired adrenoceptor-mediated vascular tone in hypertension. Modification and extension of this research is required.

Mapping complex disease traits with global gene expression

Variation in gene expression is an important mechanism underlying susceptibility to complex disease. The simultaneous genome-wide assay of gene expression and genetic variation allows the mapping of the genetic factors that underpin individual differences in quantitative levels of expression (expression QTLs; eQTLs). The availability of systematically generated eQTL information could provide immediate insight into a biological basis for disease associations identified through genome-wide association (GWA) studies, and can help to identify networks of genes involved in disease pathogenesis. Although there are limitations to current eQTL maps, understanding of disease will be enhanced with novel technologies and international efforts that extend to a wide range of new samples and tissues.

Systems genetics of complex traits in Drosophila melanogaster

Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically inter-correlated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression, and transcription factor binding sites. The high transcriptional connectivity allows us to infer genetic networks and the function of predicted genes based on annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provides insight into the molecular basis of pleiotropy between complex traits.

Senescence-accelerated mouse (SAM) with special references to neurodegeneration models, SAMP8 and SAMP10 mice

The SAM strains, a group of related inbred strains consisting of senescence-prone inbred strains (SAMP) and senescence-resistant inbred strains (SAMR), have been successfully developed by selective inbreeding of the AKR/J strain of mice donated by the Jackson laboratory in 1968. The characteristic feature of aging common to the SAMP and SAMR is accelerated senescence and normal aging, respectively. Furthermore, SAMP and SAMR strains of mice manifest various pathobiological phenotypes spontaneously. Among SAMP strains, SAMP8 and SAMP10 mice show age-related behavioral deterioration such as deficits in learning and memory, emotional disorders (reduced anxiety-like behavior and depressive behavior) and altered circadian rhythm associated with certain pathological, biochemical and pharmacological changes. Here, the previous and recent literature on SAM mice are reviewed with an emphasis on SAMP8 and SAMP10 mice. A spontaneous model like SAM with distinct advantages over the gene-modified model is hoped by investigators to be used more widely as a biogerontological resource to explore the etiopathogenesis of accelerated senescence and neurodegenerative disorders.

Understanding quantitative genetics in the systems biology era

Biology is now entering the new era of systems biology and exerting a growing influence on the future development of various disciplines within life sciences. In early classical and molecular periods of Biology, the theoretical frames of classical and molecular quantitative genetics have been systematically established, respectively. With the new advent of systems biology, there is occurring a paradigm shift in the field of quantitative genetics. Where and how the quantitative genetics would develop after having undergone its classical and molecular periods? This is a difficult question to answer exactly. In this perspective article, the major effort was made to discuss the possible development of quantitative genetics in the systems biology era, and for which there is a high potentiality to develop towards "systems quantitative genetics". In our opinion, the systems quantitative genetics can be defined as a new discipline to address the generalized genetic laws of bioalleles controlling the heritable phenotypes of complex traits following a new dynamic network model. Other issues from quantitative genetic perspective relating to the genetical genomics, the updates of network model, and the future research prospects were also discussed.

System-wide molecular evidence for phenotypic buffering in Arabidopsis

We profiled 162 lines of Arabidopsis for variation in transcript, protein and metabolite abundance using mRNA microarrays, two-dimensional polyacrylamide gel electrophoresis, gas chromatography time-of-flight mass spectrometry, liquid chromatography quadrupole time-of-flight mass spectrometry, and proton nuclear magnetic resonance. We added all publicly available phenotypic data from the same lines and mapped quantitative trait loci (QTL) for 40,580 molecular and 139 phenotypic traits. We found six QTL hot spots with major, system-wide effects, suggesting there are six breakpoints in a system otherwise buffered against many of the 500,000 SNPs.

Altered energy homeostasis and resistance to diet-induced obesity in KRAP-deficient mice

Obesity and related metabolic disorders have become leading causes of adult morbidity and mortality. KRAP (Ki-ras-induced actin-interacting protein) is a cytoskeleton-associated protein and a ubiquitous protein among tissues, originally identified as a cancer-related molecule, however, its physiological roles remain unknown. Here we demonstrate that KRAP-deficient (KRAP(-/-)) mice show enhanced metabolic rate, decreased adiposity, improved glucose tolerance, hypoinsulinemia and hypoleptinemia. KRAP(-/-) mice are also protected against high-fat diet-induced obesity and insulin resistance despite of hyperphagia. Notably, glucose uptake in the brown adipose tissue (BAT) in KRAP(-/-) mice is enhanced in an insulin-independent manner, suggesting that BAT is involved in altered energy homeostasis in KRAP(-/-) mice, although UCP (Uncoupling protein) expressions are not altered. Of interest is the down-regulation of fatty acid metabolism-related molecules, including acetyl-CoA carboxylase (ACC)-1, ACC-2 and fatty acid synthase in the liver of KRAP(-/-) mice, which could in part account for the metabolic phenotype in KRAP(-/-) mice. Thus, KRAP is a novel regulator in whole-body energy homeostasis and may be a therapeutic target in obesity and related diseases.

Identifying genetic components of drug response in mice

Individual variations in drug response are crucial factors in both the development and deployment of therapy, yet we are still woefully ignorant of the majority of this genetic basis. Here we discuss the convergence of genetics and genomics to dissect such pharmacological variation, with emphasis on satisfying the requirements of both genetics and pharmacology itself, the appropriate use of model organisms and the often overlooked power of genetic dissection to inform understanding of physiological process.

Genetic architecture of mouse skin inflammation and tumour susceptibility

Germline polymorphisms in model organisms and humans influence susceptibility to complex trait diseases such as inflammation and cancer. Mice of the Mus spretus species are resistant to tumour development, and crosses between M. spretus and susceptible Mus musculus strains have been used to map locations of genetic variants that contribute to skin cancer susceptibility. We have integrated germline polymorphisms with gene expression in normal skin from a M. musculus x M. spretus backcross to generate a network view of the gene expression architecture of mouse skin. Here we demonstrate how this approach identifies expression motifs that contribute to tissue organization and biological functions related to inflammation, haematopoiesis, cell cycle control and tumour susceptibility. Motifs associated with inflammation, epidermal barrier function and proliferation are differentially regulated in backcross mice susceptible or resistant to tumour development. The intestinal stem cell marker Lgr5 is identified as a candidate master regulator of the hair follicle, and the vitamin D receptor (Vdr) is linked to coordinated control of epidermal barrier function, inflammation and tumour susceptibility.

Complex nature of SNP genotype effects on gene expression in primary human leucocytes

BACKGROUND

Genome wide association studies have been hugely successful in identifying disease risk variants, yet most variants do not lead to coding changes and how variants influence biological function is usually unknown.

METHODS

We correlated gene expression and genetic variation in untouched primary leucocytes (n = 110) from individuals with celiac disease - a common condition with multiple risk variants identified. We compared our observations with an EBV-transformed HapMap B cell line dataset (n = 90), and performed a meta-analysis to increase power to detect non-tissue specific effects.

RESULTS

In celiac peripheral blood, 2,315 SNP variants influenced gene expression at 765 different transcripts (< 250 kb from SNP, at FDR = 0.05, cis expression quantitative trait loci, eQTLs). 135 of the detected SNP-probe effects (reflecting 51 unique probes) were also detected in a HapMap B cell line published dataset, all with effects in the same allelic direction. Overall gene expression differences within the two datasets predominantly explain the limited overlap in observed cis-eQTLs. Celiac associated risk variants from two regions, containing genes IL18RAP and CCR3, showed significant cis genotype-expression correlations in the peripheral blood but not in the B cell line datasets. We identified 14 genes where a SNP affected the expression of different probes within the same gene, but in opposite allelic directions. By incorporating genetic variation in co-expression analyses, functional relationships between genes can be more significantly detected.

CONCLUSION

In conclusion, the complex nature of genotypic effects in human populations makes the use of a relevant tissue, large datasets, and analysis of different exons essential to enable the identification of the function for many genetic risk variants in common diseases.

Genome-wide association studies: potential next steps on a genetic journey

Genome-wide association studies have successfully identified numerous loci at which common variants influence disease risk or quantitative traits. Despite these successes, the variants identified by these studies have generally explained only a small fraction of the heritable component of disease risk, and have not pinpointed with certainty the causal variant(s) at the associated loci. Furthermore, the mechanisms of action by which associated loci influence disease or quantitative phenotypes are often unclear, because we do not know through which gene(s) the associated variants exert their effects or because these gene(s) are of unknown function or have no clear connection to known disease biology. Thus, the initial set of genome-wide association studies serve as a starting point for future genetic and functional studies. We outline possible next steps that may help accelerate progress from genetic studies to the biological knowledge that can guide the development of predictive, preventive, or therapeutic measures.

MEIS1 intronic risk haplotype associated with restless legs syndrome affects its mRNA and protein expression levels

Restless legs syndrome (RLS) is a common neurological disorder characterized by an irresistible urge to move the legs at night, which is often accompanied by unpleasant sensations. A recent genomewide association study identified an association between RLS and intronic markers from the MEIS1 gene. Comparative genomic analysis indicates that MEIS1 is the only gene encompassed in this evolutionarily conserved chromosomal segment, i.e. a conservation synteny block, from mammals to fish. We carried out a series of experiments to delineate the role of MEIS1 in RLS pathogenesis and the underlying genetic mechanism. We sequenced all 13 MEIS1 exons and their splice junctions in 285 RLS probands with confirmed clinical diagnosis and did not identify any causative coding or exon-intron junction mutations. We found no evidence of structural variation or disease-associated haplotype differential splicing. However, sequencing of conserved regions of MEIS1 introns 8 and 9 identified a novel single nucleotide polymorphism (C13B_2) significantly associated with RLS (allelic association, P = 1.81E-07). We detected a significant decrease in MEIS1 mRNA expression by quantitative real-time polymerase chain reaction in lymphoblastoid cell lines (LCLs) and brain tissues from RLS patients homozygous for the intronic RLS risk haplotype, compared with those homozygous for the non-risk haplotype. Finally, we found significantly decreased MEIS1 protein levels in the same batch of LCLs and brain tissues from the homozygous carriers of the risk haplotype, compared with the homozygous non-carriers. Therefore, these data suggest that reduced expression of the MEIS1 gene, possibly through intronic cis-regulatory element(s), predisposes to RLS.

Using gene expression to investigate the genetic basis of complex disorders

The identification of complex disease susceptibility loci through genome-wide association studies (GWAS) has recently become possible and is now a method of choice for investigating the genetic basis of complex traits. The number of results from such studies is constantly increasing but the challenge lying forward is to identify the biological context in which these statistically significant candidate variants act. Regulatory variation plays an important role in shaping phenotypic differences among individuals and thus is very likely to also influence disease susceptibility. As such, integrating gene expression data and other disease relevant intermediate phenotypes with GWAS results could potentially help prioritize fine-mapping efforts and provide a shortcut to disease biology. Combining these different levels of information in a meaningful way is however not trivial. In the present review, we outline the several approaches that have been explored so far in this sense and their achievements. We also discuss the limitations of the methods and how upcoming technological developments could help circumvent these limitations. Overall, such efforts will be very helpful in understanding initially regulatory effects on disease and disease etiology in general.

Needle and surgical biopsy techniques differentially affect adipose tissue gene expression profiles

BACKGROUND

Adipose tissue gene expression analysis in humans now provides a tremendous means to discover the physiopathologic gene targets critical for our understanding and treatment of obesity. Clinical studies are emerging in which adipose gene expression has been examined in hundreds of subjects, and it will be fundamentally important that these studies can be compared so that a common consensus can be reached and new therapeutic targets for obesity proposed.

OBJECTIVE

We studied the effect of the biopsy sampling methods (needle-aspirated and surgical) used in clinical investigation programs on the functional interpretation of adipose tissue gene expression profiles.

DESIGN

A comparative microarray analysis of the different subcutaneous adipose tissue sampling methods was performed in age-matched lean (n = 19) and obese (n = 18) female subjects. Appropriate statistical (principal components analysis) and bioinformatic (FunNet) functional enrichment software were used to evaluate data. The morphology of adipose tissue samples obtained by needle-aspiration and surgical methods was examined by immunohistochemistry.

RESULTS

Biopsy techniques influence the gene expression underlying the biological themes currently discussed in obesity (eg, inflammation, extracellular matrix, and metabolism). Immunohistochemistry experiments showed that the easier to obtain needle-aspirated biopsies poorly aspirate the fibrotic fraction of subcutaneous adipose tissue, resulting in an underrepresentation of the stroma-vascular fraction.

CONCLUSIONS

The adipose tissue biopsy technique is an important caveat to consider when designing, interpreting, and, most important, comparing microarray experiments. These results will have crucial implications for the clinical and physiopathologic understanding of human obesity and therapeutic approaches.

Robust signaling networks of the adipose secretome

Type 2 diabetes is a prototypical complex systems disease that has a strong hereditary component and etiologic links with a sedentary lifestyle, overeating and obesity. Adipose tissue has been shown to be a central driver of type 2 diabetes progression, establishing and maintaining a chronic state of low-level inflammation. The number and diversity of identified endocrine factors from adipose tissue (adipokines) is growing rapidly. Here, I argue that a systems biology approach to understanding the robust multi-level signaling networks established by the adipose secretome will be crucial for developing efficient type 2 diabetes treatment. Recent advances in whole-genome association studies, global molecular profiling and quantitative modeling are currently fueling the emergence of this novel research strategy.

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.

Reverse engineering the genotype-phenotype map with natural genetic variation

The genetic variation that occurs naturally in a population is a powerful resource for studying how genotype affects phenotype. Each allele is a perturbation of the biological system, and genetic crosses, through the processes of recombination and segregation, randomize the distribution of these alleles among the progeny of a cross. The randomized genetic perturbations affect traits directly and indirectly, and the similarities and differences between traits in their responses to common perturbations allow inferences about whether variation in a trait is a cause of a phenotype (such as disease) or whether the trait variation is, instead, an effect of that phenotype. It is then possible to use this information about causes and effects to build models of probabilistic 'causal networks'. These networks are beginning to define the outlines of the 'genotype-phenotype map'.

WGCNA: an R package for weighted correlation network analysis

Background

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.

Results

The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.

Conclusion

The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

WGCNA: an R package for weighted correlation network analysis

BACKGROUND

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.

RESULTS

The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.

CONCLUSION

The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

Is replication the gold standard for validating genome-wide association findings?

With the advent of genome-wide association (GWA) studies, researchers are hoping that reliable genetic association of common human complex diseases/traits can be detected. Currently, there is an increasing enthusiasm about GWA and a number of GWA studies have been published. In the field a common practice is that replication should be used as the gold standard to validate an association finding. In this article, based on empirical and theoretical data, we emphasize that replication of GWA findings can be quite difficult, and should not always be expected, even when true variants are identified. The probability of replication becomes smaller with the increasing number of independent GWA studies if the power of individual replication studies is less than 100% (which is usually the case), and even a finding that is replicated may not necessarily be true. We argue that the field may have unreasonably high expectations on success of replication. We also wish to raise the question whether it is sufficient or necessary to treat replication as the ultimate and gold standard for defining true variants. We finally discuss the usefulness of integrating evidence from multiple levels/sources such as genetic epidemiological studies (at the DNA level), gene expression studies (at the RNA level), proteomics (at the protein level), and follow-up molecular and cellular studies for eventual validation and illumination of the functional relevance of the genes uncovered.

Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity

Obesity results from the interaction of genetic and environmental factors. To search for sequence variants that affect variation in two common measures of obesity, weight and body mass index (BMI), both of which are highly heritable, we performed a genome-wide association (GWA) study with 305,846 SNPs typed in 25,344 Icelandic, 2,998 Dutch, 1,890 European Americans and 1,160 African American subjects and combined the results with previously published results from the Diabetes Genetics Initiative (DGI) on 3,024 Scandinavians. We selected 43 variants in 19 regions for follow-up in 5,586 Danish individuals and compared the results to a genome-wide study on obesity-related traits from the GIANT consortium. In total, 29 variants, some correlated, in 11 chromosomal regions reached a genome-wide significance threshold of P < 1.6 x 10(-7). This includes previously identified variants close to or in the FTO, MC4R, BDNF and SH2B1 genes, in addition to variants at seven loci not previously connected with obesity.

FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease

Differential expressed genes are more likely to have variants associated with disease. A new tool, fitSNP, prioritizes candidate SNPs from association studies.

Background

Candidate single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWASs) were often selected for validation based on their functional annotation, which was inadequate and biased. We propose to use the more than 200,000 microarray studies in the Gene Expression Omnibus to systematically prioritize candidate SNPs from GWASs.

Results

We analyzed all human microarray studies from the Gene Expression Omnibus, and calculated the observed frequency of differential expression, which we called differential expression ratio, for every human gene. Analysis conducted in a comprehensive list of curated disease genes revealed a positive association between differential expression ratio values and the likelihood of harboring disease-associated variants. By considering highly differentially expressed genes, we were able to rediscover disease genes with 79% specificity and 37% sensitivity. We successfully distinguished true disease genes from false positives in multiple GWASs for multiple diseases. We then derived a list of functionally interpolating SNPs (fitSNPs) to analyze the top seven loci of Wellcome Trust Case Control Consortium type 1 diabetes mellitus GWASs, rediscovered all type 1 diabetes mellitus genes, and predicted a novel gene (KIAA1109) for an unexplained locus 4q27. We suggest that fitSNPs would work equally well for both Mendelian and complex diseases (being more effective for cancer) and proposed candidate genes to sequence for their association with 597 syndromes with unknown molecular basis.

Conclusions

Our study demonstrates that highly differentially expressed genes are more likely to harbor disease-associated DNA variants. FitSNPs can serve as an effective tool to systematically prioritize candidate SNPs from GWASs.

Effects of cis and trans genetic ancestry on gene expression in African Americans

Variation in gene expression is a fundamental aspect of human phenotypic variation. Several recent studies have analyzed gene expression levels in populations of different continental ancestry and reported population differences at a large number of genes. However, these differences could largely be due to non-genetic (e.g., environmental) effects. Here, we analyze gene expression levels in African American cell lines, which differ from previously analyzed cell lines in that individuals from this population inherit variable proportions of two continental ancestries. We first relate gene expression levels in individual African Americans to their genome-wide proportion of European ancestry. The results provide strong evidence of a genetic contribution to expression differences between European and African populations, validating previous findings. Second, we infer local ancestry (0, 1, or 2 European chromosomes) at each location in the genome and investigate the effects of ancestry proximal to the expressed gene (cis) versus ancestry elsewhere in the genome (trans). Both effects are highly significant, and we estimate that 12+/-3% of all heritable variation in human gene expression is due to cis variants.

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.

Genes in asthma: new genes and new ways

PURPOSE OF REVIEW

Asthma is a disease of unknown aetiology characterized by intermittent inflammation of the small airways of the lung. Asthma is familial due to an interaction between strong genetic and environmental factors. This article aims to review the current understanding of the genetic factors underlying asthma, looking historically as well as highlighting the latest developments in the field.

RECENT FINDINGS

Findings from recent candidate gene studies and microsatellite genome screens have continued to highlight the importance of the epithelial barrier and its defence mechanisms in asthma. Completion of the human genome sequence and the advent of genome-wide association studies have resulted in the identification of two novel asthma susceptibility genes, ORMDL3 and CHI3L1, in the past year.

SUMMARY

With the advances in genetics and genomics substantial steps have been taken in the last decade in understanding the genetic factors underlying asthma. Studies have highlighted the importance of the role of the epithelium with many of the genes so far identified being expressed in this key barrier. With the application of genome-wide expression, microRNA studies, metagenomics, proteomics and metabolomics the next decade will undoubtedly result in a further substantial increment in our understanding of the mechanisms underlying asthma.

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.

Allele-specific gene expression patterns in primary leukemic cells reveal regulation of gene expression by CpG site methylation

To identify genes that are regulated by cis-acting functional elements in acute lymphoblastic leukemia (ALL) we determined the allele-specific expression (ASE) levels of 2, 529 genes by genotyping a genome-wide panel of single nucleotide polymorphisms in RNA and DNA from bone marrow and blood samples of 197 children with ALL. Using a reproducible, quantitative genotyping method and stringent criteria for scoring ASE, we found that 16% of the analyzed genes display ASE in multiple ALL cell samples. For most of the genes, the level of ASE varied largely between the samples, from 1.4-fold overexpression of one allele to apparent monoallelic expression. For genes exhibiting ASE, 55% displayed bidirectional ASE in which overexpression of either of the two SNP alleles occurred. For bidirectional ASE we also observed overall higher levels of ASE and correlation with the methylation level of these sites. Our results demonstrate that CpG site methylation is one of the factors that regulates gene expression in ALL cells.

Genetic mapping in human disease

Genetic mapping provides a powerful approach to identify genes and biological processes underlying any trait influenced by inheritance, including human diseases. We discuss the intellectual foundations of genetic mapping of Mendelian and complex traits in humans, examine lessons emerging from linkage analysis of Mendelian diseases and genome-wide association studies of common diseases, and discuss questions and challenges that lie ahead.

Molecular pathology of age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness in the world. Although the etiology and pathogenesis of AMD remain largely unclear, a complex interaction of genetic and environmental factors is thought to exist. AMD pathology is characterized by degeneration involving the retinal photoreceptors, retinal pigment epithelium, and Bruch's membrane, as well as, in some cases, alterations in choroidal capillaries. Recent research on the genetic and molecular underpinnings of AMD brings to light several basic molecular pathways and pathophysiological processes that might mediate AMD risk, progression, and/or response to therapy. This review summarizes, in detail, the molecular pathological findings in both humans and animal models, including genetic variations in CFH, CX3CR1, and ARMS2/HtrA1, as well as the role of numerous molecules implicated in inflammation, apoptosis, cholesterol trafficking, angiogenesis, and oxidative stress.

Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome

Background

Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.

Results

We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways.

Conclusion

We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.

Systems Epidemiology in Cancer

Prospective studies in cancer epidemiology have conserved their study design over the last decades. In this context, current epidemiologic studies investigating gene-environment interactions are based on biobank for the analysis of genetic variation and biomarkers, using notified cancer as outcome. These studies result from the use of high-throughput technologies rather than from the development of novel design strategies. In this article, we propose the globolomic design to run integrated analyses of cancer risk covering the major -omics in blood and tumor tissue. We defined this design as an extension of the existing prospective design by collecting tissue and blood samples at time of diagnosis, including biological material suitable for transcriptome analysis. The globolomic design opens up for several new analytic strategies and, where gene expression profiles could be used to verify mechanistic information from experimental biology, adds a new dimension to causality in epidemiology. This could improve, for example, the interpretation of risk estimates related to single nucleotide polymorphisms in gene-environment studies by changing the criterion of biological plausibility from a subjective discussion of in vitro information to observational data of human in vivo gene expression. This ambitious design should consider the complexity of the multistage carcinogenic process, the latency time, and the changing lifestyle of the cohort members. This design could open the new research discipline of systems epidemiology, defined in this article as a counterpart to systems biology. Systems epidemiology with a focus on gene functions challenges the current concept of biobanking, which focuses mainly on DNA analyses. (Cancer Epidemiol Biomarkers Prev 2008;17(11):2954–7)

Endoplasmic reticulum stress markers are associated with obesity in nondiabetic subjects

OBJECTIVE

Adipocyte and hepatocyte endoplasmic reticulum (ER) stress response is activated in dietary and genetic models of obesity in mice. We hypothesized that ER stress was also activated and associated with reduced insulin sensitivity (SI) in human obesity.

RESEARCH DESIGN AND METHODS

We recruited 78 healthy, nondiabetic individuals over a spectrum of body mass index (BMI) who underwent oral and iv glucose tolerance tests, and fasting sc adipose and muscle biopsies. We tested expression of 18 genes and levels of total and phosphorylated eukaryotic initiation factor 2alpha, c-jun, and c-Jun N-terminal kinase 1 in adipose tissue. We compared gene expression in stromal vascular and adipocyte fractions in paired samples from 22 individuals, and tested clustering on gene and protein markers.

RESULTS

Adipocyte expression of most markers of ER stress, including chaperones downstream of activating transcription factor 6, were significantly correlated with BMI and percent fat (r>0.5; P<0.00001). Phosphorylation of eukaryotic initiation factor 2alpha but not of c-Jun N-terminal kinase 1 or c-jun was increased with obesity. ER stress response (as elsewhere) was also increased with obesity in a second set of 86 individuals, and in the combined sample (n=161). The increase was only partially attributable to the stromal vascular fraction and macrophage infiltration. ER stress markers were only modestly correlated with S(I). Clustering algorithms supported ER stress activation with high BMI but not low SI.

CONCLUSIONS

Multiple markers of ER stress are activated in human adipose with obesity, particularly for protective chaperones downstream of activating transcription factor 6alpha.

The genetics of cardiovascular disease

Recent advances in genotyping technology and insights into disease mechanisms have increased interest in the genetics of cardiovascular disease. Several candidate genes involved in cardiovascular diseases were identified from studies using animal models, and the translation of these findings to human disease is an exciting challenge. There is a trend towards large-scale genome-wide association studies that are subject to strict quality criteria with regard to both genotyping and phenotyping. Here, we review some of the strategies that have been developed to translate findings from experimental models to human disease and outline the need for optimizing global approaches to analyze such results. Findings from ongoing studies are interpreted in the context of disease pathways instead of the more traditional focus on single genetic variants.

Targeted screening of cis-regulatory variation in human haplotypes

Regulatory cis-acting variants account for a large proportion of gene expression variability in populations. Cis-acting differences can be specifically measured by comparing relative levels of allelic transcripts within a sample. Allelic expression (AE) mapping for cis-regulatory variant discovery has been hindered by the requirements of having informative or heterozygous single nucleotide polymorphisms (SNPs) within genes in order to assign the allelic origin of each transcript. In this study we have developed an approach to systematically screen for heritable cis-variants in common human haplotypes across >1,000 genes. In order to achieve the highest level of information per haplotype studied, we carried out allelic expression measurements by using both intronic and exonic SNPs in primary transcripts. We used a novel RNA pooling strategy in immortalized lymphoblastoid cell lines (LCLs) and primary human osteoblast cell lines (HObs) to allow for high-throughput AE. Screening hits from RNA pools were further validated by performing allelic expression mapping in individual samples. Our results indicate that >10% of expressed genes in human LCLs show genotype-linked AE. In addition, we have validated cis-acting variants in over 20 genes linked with common disease susceptibility in recent genome-wide studies. More generally, our results indicate that RNA pooling coupled with AE read-out by second generation sequencing or by other methods provides a high-throughput tool for cataloging the impact of common noncoding variants in the human genome.

Common variants on 1p36 and 1q42 are associated with cutaneous basal cell carcinoma but not with melanoma or pigmentation traits

To search for new sequence variants that confer risk of cutaneous basal cell carcinoma (BCC), we conducted a genome-wide SNP association study of 930 Icelanders with BCC and 33,117 controls. After analyzing 304,083 SNPs, we observed signals from loci at 1p36 and 1q42, and replicated these associations in additional sample sets from Iceland and Eastern Europe. Overall, the most significant signals were from rs7538876 on 1p36 (OR = 1.28, P = 4.4 x 10(-12)) and rs801114 on 1q42 (OR = 1.28, P = 5.9 x 10(-12)). The 1p36 locus contains the candidate genes PADI4, PADI6, RCC2 and ARHGEF10L, and the gene nearest to the 1q42 locus is the ras-homolog RHOU. Neither locus was associated with fair pigmentation traits that are known risk factors for BCC, and no risk was observed for melanoma. Approximately 1.6% of individuals of European ancestry are homozygous for both variants, and their estimated risk of BCC is 2.68 times that of noncarriers.

High-resolution mapping of expression-QTLs yields insight into human gene regulation

Recent studies of the HapMap lymphoblastoid cell lines have identified large numbers of quantitative trait loci for gene expression (eQTLs). Reanalyzing these data using a novel Bayesian hierarchical model, we were able to create a surprisingly high-resolution map of the typical locations of sites that affect mRNA levels in cis. Strikingly, we found a strong enrichment of eQTLs in the 250 bp just upstream of the transcription end site (TES), in addition to an enrichment around the transcription start site (TSS). Most eQTLs lie either within genes or close to genes; for example, we estimate that only 5% of eQTLs lie more than 20 kb upstream of the TSS. After controlling for position effects, SNPs in exons are approximately 2-fold more likely than SNPs in introns to be eQTLs. Our results suggest an important role for mRNA stability in determining steady-state mRNA levels, and highlight the potential of eQTL mapping as a high-resolution tool for studying the determinants of gene regulation.