Revealing the architecture of gene regulation: the promise of eQTL studies

Gene expression and genetic variation data implicate PCLO in bipolar disorder

BACKGROUND

Genetic variation may contribute to differential gene expression in the brain of individuals with psychiatric disorders. To test this hypothesis, we identified genes that were differentially expressed in individuals with bipolar disorder, along with nearby single nucleotide polymorphisms (SNPs) that were associated with expression of the same genes. We then tested these SNPs for association with bipolar disorder in large case-control samples.

METHODS

We used the Stanley Genomics Database to extract gene expression and SNP microarray data from individuals with bipolar disorder (n = 40) and unaffected controls (n = 43). We identified 367 genes that were differentially expressed in the prefrontal cortex of cases vs. controls (fold change > 1.3 and FDR q-value < .05) and 45 nearby SNPs that were associated with expression of those same genes (FDR q-value < .05). We tested these SNPs for association with bipolar disorder in a meta-analysis of genome-wide association studies (GWAS) including 4,936 cases and 6,654 healthy controls.

RESULTS

We identified 45 SNPs that were associated with expression of differentially expressed genes, including HBS1L (15 SNPs), HLA-DPB1 (15 SNPs), AMFR (8 SNPs), PCLO (2 SNPs) and WDR41 (2 SNPs). Of these, one SNP (rs13438494), in an intron of the piccolo (PCLO) gene, was significantly associated with bipolar disorder (FDR adjusted p < .05) in the meta-analysis of GWAS.

CONCLUSIONS

These results support the previous findings implicating PCLO in mood disorders and demonstrate the utility of combining gene expression and genetic variation data to improve our understanding of the genetic contribution to bipolar disorder.

Integrating expression profiling and whole-genome association for dissection of fat traits in a porcine model

Traits related to fatness, important as economic factors in pork production, are associated with serious diseases in humans. Genetical genomics is a useful approach for studying the effects of genetic variation at the molecular level in biological systems. Here we applied a whole-genome association analysis to hepatic gene expression traits, focusing on transcripts with expression levels that correlated with fatness traits in a porcine model. A total of 150 crossbred pigs [Pietrain × (German Large White × German Landrace)] were studied for transcript levels in the liver. The 24K Affymetrix expression microarrays and 60K Illumina single nucleotide polymorphism (SNP) chips were used for genotyping. A total of 663 genes, whose expression significantly correlated with the trait "fat area," were analyzed for enrichment of functional annotation groups as defined in the Ingenuity Pathways Knowledge Base (IPKB). Genes involved in metabolism of various macromolecules and nutrients as well as functions related to dynamic cellular processes correlated with fatness traits. Regions affecting the transcription levels of these genes were mapped and revealed 4,727 expression quantitative trait loci (eQTL) at P < 10⁻⁵, including 448 cis-eQTL. In this study, genome-wide association analysis of trait-correlated expression was successfully used in a porcine model to display molecular networks and list genes relevant to fatness traits.

Gene expression analysis for the identification of selection and local adaptation in fishes

In recent years, variation in gene expression has been recognized as an important component of environmental adaptation in multiple model species, including a few fish species. There is, however, still little known about the genetic basis of adaptation in gene expression resulting from variation in the aquatic environment (e.g. temperature, salinity and oxygen) and the physiological effect and costs of such differences in gene expression. This review presents and discusses progress and pitfalls of applying gene expression analyses to fishes and suggests simple frameworks to get started with gene expression analysis. It is emphasized that well-planned gene expression studies can serve as an important tool for the identification of selection in local populations of fishes, even for non-traditional model species where limited genomic information is available. Recent studies focusing on gene expression variation among natural fish populations are reviewed, highlighting the latest applications that combine genetic evidence from neutral markers and gene expression data.

What can whole genome expression data tell us about the ecology and evolution of personality?

Consistent individual differences in behaviour, aka personality, pose several evolutionary questions. For example, it is difficult to explain within-individual consistency in behaviour because behavioural plasticity is often advantageous. In addition, selection erodes heritable behavioural variation that is related to fitness, therefore we wish to know the mechanisms that can maintain between-individual variation in behaviour. In this paper, we argue that whole genome expression data can reveal new insights into the proximate mechanisms underlying personality, as well as its evolutionary consequences. After introducing the basics of whole genome expression analysis, we show how whole genome expression data can be used to understand whether behaviours in different contexts are affected by the same molecular mechanisms. We suggest strategies for using the power of genomics to understand what maintains behavioural variation, to study the evolution of behavioural correlations and to compare personality traits across diverse organisms.

RNA sequencing reveals the role of splicing polymorphisms in regulating human gene expression

Expression levels of many human genes are under the genetic control of expression quantitative trait loci (eQTLs). Despite technological advances, the precise molecular mechanisms underlying most eQTLs remain elusive. Here, we use deep mRNA sequencing of two CEU individuals to investigate those mechanisms, with particular focus on the role of splicing control loci (sQTLs). We identify a large number of genes that are differentially spliced between the two samples and associate many of those differences with nearby single nucleotide polymorphisms (SNPs). Subsequently, we investigate the potential effect of splicing SNPs on eQTL control in general. We find a significant enrichment of alternative splicing (AS) events within a set of highly confident eQTL targets discovered in previous studies, suggesting a role of AS in regulating overall gene expression levels. Next, we demonstrate high correlation between the levels of mature (exonic) and unprocessed (intronic) RNA, implying that ∼75% of eQTL target variance can be explained by control at the level of transcription, but that the remaining 25% may be regulated co- or post-transcriptionally. We focus on eQTL targets with discordant mRNA and pre-mRNA expression patterns and use four examples: USMG5, MMAB, MRPL43, and OAS1, to dissect the exact downstream effects of the associated genetic variants.

Integrative analysis of low- and high-resolution eQTL

The study of expression quantitative trait loci (eQTL) is a powerful way of detecting transcriptional regulators at a genomic scale and for elucidating how natural genetic variation impacts gene expression. Power and genetic resolution are heavily affected by the study population: whereas recombinant inbred (RI) strains yield greater statistical power with low genetic resolution, using diverse inbred or outbred strains improves genetic resolution at the cost of lower power. In order to overcome the limitations of both individual approaches, we combine data from RI strains with genetically more diverse strains and analyze hippocampus eQTL data obtained from mouse RI strains (BXD) and from a panel of diverse inbred strains (Mouse Diversity Panel, MDP). We perform a systematic analysis of the consistency of eQTL independently obtained from these two populations and demonstrate that a significant fraction of eQTL can be replicated. Based on existing knowledge from pathway databases we assess different approaches for using the high-resolution MDP data for fine mapping BXD eQTL. Finally, we apply this framework to an eQTL hotspot on chromosome 1 (Qrr1), which has been implicated in a range of neurological traits. Here we present the first systematic examination of the consistency between eQTL obtained independently from the BXD and MDP populations. Our analysis of fine-mapping approaches is based on 'real life' data as opposed to simulated data and it allows us to propose a strategy for using MDP data to fine map BXD eQTL. Application of this framework to Qrr1 reveals that this eQTL hotspot is not caused by just one (or few) 'master regulators', but actually by a set of polymorphic genes specific to the central nervous system.

The genetic basis of evolutionary change in gene expression levels

The regulation of gene expression is an important determinant of organismal phenotype and evolution. However, the widespread recognition of this fact occurred long after the synthesis of evolution and genetics. Here, we give a brief sketch of thoughts regarding gene regulation in the history of evolution and genetics. We then review the development of genome-wide studies of gene regulatory variation in the context of the location and mode of action of the causative genetic changes. In particular, we review mapping of the genetic basis of expression variation through expression quantitative trait locus studies and measuring the cis/trans component of expression variation in allele-specific expression studies. We conclude by proposing a systematic integration of ideas that combines global mapping studies, cis/trans tests and modern population genetics methodologies, in order to directly estimate the forces acting on regulatory variation within and between species.

The role of gene expression in ecological speciation

Ecological speciation is the process by which barriers to gene flow between populations evolve due to adaptive divergence via natural selection. A relatively unexplored area in ecological speciation is the role of gene expression. Gene expression may be associated with ecologically important phenotypes not evident from morphology and play a role during colonization of new environments. Here we review two potential roles of gene expression in ecological speciation: (1) its indirect role in facilitating population persistence and (2) its direct role in contributing to genetically based reproductive isolation. We find indirect evidence that gene expression facilitates population persistence, but direct tests are lacking. We also find clear examples of gene expression having effects on phenotypic traits and adaptive genetic divergence, but links to the evolution of reproductive isolation itself remain indirect. Gene expression during adaptive divergence seems to often involve complex genetic architectures controlled by gene networks, regulatory regions, and “eQTL hotspots.” Nonetheless, we review how approaches for isolating the functional mutations contributing to adaptive divergence are proving to be successful. The study of gene expression has promise for increasing our understanding ecological speciation, particularly when integrative approaches are applied.

Quantitative trait loci for electrocardiographic parameters and arrhythmia in the mouse

Cardiac arrhythmias associated with sudden death are influenced by multiple biological pathways and are modulated by numerous genetic and environmental factors. Elevated heart rate and prolonged ECG indices of conduction and repolarization have been associated with risk of sudden death. Insight into the genetic underpinnings of these parameters thus provides an important means to the dissection of the genetic components modulating risk of sudden cardiac death. In this study we mapped quantitative trait loci (QTL) modulating heart rate, ECG indices of conduction and repolarization, and susceptibility to arrhythmia, in a conduction disease-sensitized F(2) mouse population. Heart rate, P-duration, PR-, QRS- and QT-interval were measured at baseline (n=502) and after flecainide administration (n=370) in mutant F(2) progeny (F(2)-MUT) resulting from the FVB/NJ-Scn5a1798(insD/+) X 129P2-Scn5a1798(insD/+) mouse cross. Episodes of sinus arrhythmia and ventricular tachyarrhythmia occurring post-flecainide were treated as binary traits. F(2)-MUT mice were genotyped using a genome-wide 768 single nucleotide polymorphism (SNP) panel. Interval mapping uncovered multiple QTL for ECG parameters and arrhythmia. A sex-interacting scan identified QTL displaying sex-dependency, and a two-dimensional QTL scan unmasked locus-locus (epistasis) interactions influencing ECG traits. A number of QTL coincided at specific chromosomal locations, suggesting pleiotropic effects at these loci. Through transcript profiling in myocardium from the parental mouse strains we identified genes co-localizing at the identified QTL that constitute highly relevant candidates for the observed effects. The detection of QTL influencing ECG indices and arrhythmia is an essential step towards identifying genetic networks for sudden, arrhythmic, cardiac death.

Polymorphic cis- and trans-regulation of human gene expression

Using genetic and molecular analyses, we identified over 1,000 polymorphic regulators that regulate expression levels of human genes.

Expression levels of human genes vary extensively among individuals. This variation facilitates analyses of expression levels as quantitative phenotypes in genetic studies where the entire genome can be scanned for regulators without prior knowledge of the regulatory mechanisms, thus enabling the identification of unknown regulatory relationships. Here, we carried out such genetic analyses with a large sample size and identified cis- and trans-acting polymorphic regulators for about 1,000 human genes. We validated the cis-acting regulators by demonstrating differential allelic expression with sequencing of transcriptomes (RNA-Seq) and the trans-regulators by gene knockdown, metabolic assays, and chromosome conformation capture analysis. The majority of the regulators act in trans to the target (regulated) genes. Most of these trans-regulators were not known to play a role in gene expression regulation. The identification of these regulators enabled the characterization of polymorphic regulation of human gene expression at a resolution that was unattainable in the past.

Cellular characteristics and functions are determined largely by gene expression and expression levels differ among individuals, however it is not clear how these levels are regulated. While many cis-acting DNA sequence variants in promoters and enhancers that influence gene expression have been identified, only a few polymorphic trans-regulators of human genes are known. Here, we used human B-cells from individuals belonging to large families and identified polymorphic trans-regulators for about 1,000 human genes. We validated these results by gene knockdown, metabolic perturbation studies and chromosome conformation capture assays. Although these regulatory relationships were identified in cultured B-cells, we show that some of the relationships were also found in primary fibroblasts. The large number of regulators allowed us to better understand gene expression regulation, to uncover new gene functions, and to identify their roles in disease processes. This study shows that genetic variation is a powerful tool not only for gene mapping but also to study gene interaction and regulation.

Epigenome mapping in normal and disease States

Epigenomes are comprised, in part, of all genome-wide chromatin modifications, including DNA methylation and histone modifications. Unlike the genome, epigenomes are dynamic during development and differentiation to establish and maintain cell type-specific gene expression states that underlie cellular identity and function. Chromatin modifications are particularly labile, providing a mechanism for organisms to respond and adapt to environmental cues. Results from studies in animal models clearly demonstrate that epigenomic variability leads to phenotypic variability, including susceptibility to disease that is not recognized at the DNA sequence level. Thus, capturing epigenomic information is invaluable for comprehensively understanding development, differentiation, and disease. Herein, we provide a brief overview of epigenetic processes, how they are relevant to human health, and review studies using technologies that enable epigenome mapping. We conclude by describing feasible applications of epigenome mapping, focusing on epigenome-wide association studies (eGWAS), which have the potential to revolutionize current studies of human diseases and will likely promote the discovery of novel diagnostic, preventative, and treatment strategies.

Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants

BACKGROUND

Mendelian Randomization (MR) studies assess the causality of an exposure-disease association using genetic determinants [i.e. instrumental variables (IVs)] of the exposure. Power and IV strength requirements for MR studies using multiple genetic variants have not been explored.

METHODS

We simulated cohort data sets consisting of a normally distributed disease trait, a normally distributed exposure, which affects this trait and a biallelic genetic variant that affects the exposure. We estimated power to detect an effect of exposure on disease for varying allele frequencies, effect sizes and samples sizes (using two-stage least squares regression on 10,000 data sets-Stage 1 is a regression of exposure on the variant. Stage 2 is a regression of disease on the fitted exposure). Similar analyses were conducted using multiple genetic variants (5, 10, 20) as independent or combined IVs. We assessed IV strength using the first-stage F statistic.

RESULTS

Simulations of realistic scenarios indicate that MR studies will require large (n > 1000), often very large (n > 10,000), sample sizes. In many cases, so-called 'weak IV' problems arise when using multiple variants as independent IVs (even with as few as five), resulting in biased effect estimates. Combining genetic factors into fewer IVs results in modest power decreases, but alleviates weak IV problems. Ideal methods for combining genetic factors depend upon knowledge of the genetic architecture underlying the exposure.

CONCLUSIONS

The feasibility of well-powered, unbiased MR studies will depend upon the amount of variance in the exposure that can be explained by known genetic factors and the 'strength' of the IV set derived from these genetic factors.

Correction for hidden confounders in the genetic analysis of gene expression

Understanding the genetic underpinnings of disease is important for screening, treatment, drug development, and basic biological insight. One way of getting at such an understanding is to find out which parts of our DNA, such as single-nucleotide polymorphisms, affect particular intermediary processes such as gene expression. Naively, such associations can be identified using a simple statistical test on all paired combinations of genetic variants and gene transcripts. However, a wide variety of confounders lie hidden in the data, leading to both spurious associations and missed associations if not properly addressed. We present a statistical model that jointly corrects for two particular kinds of hidden structure--population structure (e.g., race, family-relatedness), and microarray expression artifacts (e.g., batch effects), when these confounders are unknown. Applying our method to both real and synthetic, human and mouse data, we demonstrate the need for such a joint correction of confounders, and also the disadvantages of other possible approaches based on those in the current literature. In particular, we show that our class of models has maximum power to detect eQTL on synthetic data, and has the best performance on a bronze standard applied to real data. Lastly, our software and the associations we found with it are available at http://www.microsoft.com/science.

Gene expression variability within and between human populations and implications toward disease susceptibility

Variations in gene expression level might lead to phenotypic diversity across individuals or populations. Although many human genes are found to have differential mRNA levels between populations, the extent of gene expression that could vary within and between populations largely remains elusive. To investigate the dynamic range of gene expression, we analyzed the expression variability of ∼18, 000 human genes across individuals within HapMap populations. Although ∼20% of human genes show differentiated mRNA levels between populations, our results show that expression variability of most human genes in one population is not significantly deviant from another population, except for a small fraction that do show substantially higher expression variability in a particular population. By associating expression variability with sequence polymorphism, intriguingly, we found SNPs in the untranslated regions (5′ and 3′UTRs) of these variable genes show consistently elevated population heterozygosity. We performed differential expression analysis on a genome-wide scale, and found substantially reduced expression variability for a large number of genes, prohibiting them from being differentially expressed between populations. Functional analysis revealed that genes with the greatest within-population expression variability are significantly enriched for chemokine signaling in HIV-1 infection, and for HIV-interacting proteins that control viral entry, replication, and propagation. This observation combined with the finding that known human HIV host factors show substantially elevated expression variability, collectively suggest that gene expression variability might explain differential HIV susceptibility across individuals.

Many human genes have population-specific expression levels, which are linked to population-specific polymorphisms and copy-number variations. However, it is unclear whether human genes show similar dynamic range of expression between populations. In this work we analyzed HapMap gene expression compendium, and quantified the between-population and within-population expression variability for ∼18,000 human transcripts. We first concluded that the majority of the human genes have similar levels of within-population variability. However, a small fraction (∼4%) does show much higher expression variability in one population, and the deviation is consistently associated with increased SNP heterozygosity in their UTR regulatory regions. We further showed that genes with the greatest within-population expression variability are significantly enriched for chemokine signaling associated with HIV-1 infection. Combined with the finding that human HIV-1 host factors tend to have increased expression variability within populations, our analysis may explain, at least in part, different susceptibility to HIV infection within the human population. This work provides a fresh angle for analyzing gene expression variations in populations.

On the origin of species: insights from the ecological genomics of lake whitefish

In contrast to the large amount of ecological information supporting the role of natural selection as a main cause of population divergence and speciation, an understanding of the genomic basis underlying those processes is in its infancy. In this paper, we review the main findings of a long-term research programme that we have been conducting on the ecological genomics of sympatric forms of whitefish (Coregonus spp.) engaged in the process of speciation. We present this system as an example of how applying a combination of approaches under the conceptual framework of the theory of adaptive radiation has yielded substantial insight into evolutionary processes in a non-model species. We also discuss how the joint use of recent biotechnological developments will provide a powerful means to address issues raised by observations made to date. Namely, we present data illustrating the potential offered by combining next generation sequencing technologies with other genomic approaches to reveal the genomic bases of adaptive divergence and reproductive isolation. Given increasing access to these new genomic tools, we argue that non-model species studied in their ecological context such as whitefish will play an increasingly important role in generalizing knowledge of speciation.

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.

Involvement of the atrial natriuretic peptide transcription factor GATA4 in alcohol dependence, relapse risk and treatment response to acamprosate

In alcoholism, both relapse to alcohol drinking and treatment response are suggested to be genetically modulated. This study set out to determine whether the top 15 single nucleotide polymorphisms (SNPs) of a recent genome-wide association (GWA) and follow-up study of alcohol dependence are associated with relapse behavior and pharmacological treatment response in 374 alcohol-dependent subjects who underwent a randomized, double-blind, placebo-controlled trial with acamprosate, naltrexone or placebo. The single nucleotide polymorphism, rs13273672, an intronic SNP in the gene for GATA-binding protein 4 (GATA4), was associated with relapse within the 90-day medical treatment period (P<0.01). Subsequent pharmacogenetic analyses showed that this association was mainly based on patients treated with acamprosate (P<0.01). In line with the observation that natriuretic peptide promoters are modulated by GATA4, a significant gene dose effect on the variance of atrial natriuretic peptide (ANP) plasma concentration in the different GATA4 genotypes (P<0.01) was found. Hence, genetic variations in GATA4 might influence relapse and treatment response to acamprosate in alcohol-dependent patients via modulation of ANP plasma levels. These results could help to identify those alcohol-dependent patients who may be at an increased risk of relapse and who may better respond to treatment with acamprosate.

Expectations, validity, and reality in gene expression profiling

OBJECTIVE

To provide a critical overview of gene expression profiling methodology and discuss areas of future development.

RESULTS

Gene expression profiling has been used extensively in biological research and has resulted in significant advances in the understanding of the molecular mechanisms of complex disorders, including cancer, heart disease, and metabolic disorders. However, translating this technology into genomic medicine for use in diagnosis and prognosis faces many challenges. In addition, gene expression profile analysis is frequently controversial, because its conclusions often lack reproducibility and claims of effective dissemination into translational medicine have, in some cases, been remarkably unjustified. In the last decade, a large number of methodological and technical solutions have been offered to overcome the challenges.

STUDY DESIGN AND SETTING

We consider the strengths, limitations, and appropriate applications of gene expression profiling techniques, with particular reference to the clinical relevance.

CONCLUSION

Some studies have demonstrated the ability and clinical utility of gene expression profiling for use as diagnostic, prognostic, and predictive molecular markers. The challenges of gene expression profiling lie with the standardization of analytic approaches and the evaluation of the clinical merit in broader heterogeneous populations by prospective clinical trials.

An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility Loci for osteoporosis-related traits

Osteoporosis is a complex disorder and commonly leads to fractures in elderly persons. Genome-wide association studies (GWAS) have become an unbiased approach to identify variations in the genome that potentially affect health. However, the genetic variants identified so far only explain a small proportion of the heritability for complex traits. Due to the modest genetic effect size and inadequate power, true association signals may not be revealed based on a stringent genome-wide significance threshold. Here, we take advantage of SNP and transcript arrays and integrate GWAS and expression signature profiling relevant to the skeletal system in cellular and animal models to prioritize the discovery of novel candidate genes for osteoporosis-related traits, including bone mineral density (BMD) at the lumbar spine (LS) and femoral neck (FN), as well as geometric indices of the hip (femoral neck-shaft angle, NSA; femoral neck length, NL; and narrow-neck width, NW). A two-stage meta-analysis of GWAS from 7,633 Caucasian women and 3,657 men, revealed three novel loci associated with osteoporosis-related traits, including chromosome 1p13.2 (RAP1A, p = 3.6x10(-8)), 2q11.2 (TBC1D8), and 18q11.2 (OSBPL1A), and confirmed a previously reported region near TNFRSF11B/OPG gene. We also prioritized 16 suggestive genome-wide significant candidate genes based on their potential involvement in skeletal metabolism. Among them, 3 candidate genes were associated with BMD in women. Notably, 2 out of these 3 genes (GPR177, p = 2.6x10(-13); SOX6, p = 6.4x10(-10)) associated with BMD in women have been successfully replicated in a large-scale meta-analysis of BMD, but none of the non-prioritized candidates (associated with BMD) did. Our results support the concept of our prioritization strategy. In the absence of direct biological support for identified genes, we highlighted the efficiency of subsequent functional characterization using publicly available expression profiling relevant to the skeletal system in cellular or whole animal models to prioritize candidate genes for further functional validation.

Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes

To get beyond the “low-hanging fruits” so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24–28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4×10⁻⁴). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of ∼2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity.

Obesity has a strong genetic component and an estimated 45%–85% of the variation in adult relative weight is genetically determined. Many genes have recently been identified in genome-wide association studies. The individual effects of the identified genes, however, have been very modest, and their identification required very large sample sizes. New approaches are therefore needed to uncover further genetic variants that contribute to the development of obesity and related conditions. Much can be learned from studying the expression of genes in adipose tissue of obese and non-obese subjects, but it is very difficult to distinguish which genes' expression differences represent reactions to obesity from those related to causal processes. We studied monozygotic twin pairs discordant for obesity and contrasted the gene expression profiles of obese and lean co-twins (controlling for genetic variation) to those from unrelated individuals to try to discern the cause-and-effect relationships of the identified changes in gene expression in fat. Testing the identified genes in 21,000 individuals identified numerous new genes with possible roles in the development of obesity. Among the top findings was a gene involved in blood coagulation (Factor XIIIA1), possibly linking obesity with known complications including deep vein thrombosis, heart attack, and stroke.

An information-flow-based model with dissipation, saturation and direction for active pathway inference

Background

Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow.

Results

In this study, we propose new concepts of dissipation, saturation and direction to decipher the information flow behaviours in the pathways and thereby infer the biological pathways from a given source to its target. This model takes into account explicitly the common features of the information transmission and provides a general framework to model the biological pathways. It can incorporate different types of bio-molecular interactions to infer the signal transduction pathways and interpret the expression quantitative trait loci (eQTL) associations. The model is formulated as a linear programming problem and thus is solved efficiently. Experiments on the real data of yeast indicate that the reproduced pathways are highly consistent with the current knowledge.

Conclusions

Our model explicitly treats the biological pathways as information flows with dissipation, saturation and direction. The effective applications suggest that the three new concepts may be valid to describe the organization rules of biological pathways. The deduced linear programming should be a promising tool to infer the various biological pathways from the high-throughput data.

Genetics and beyond--the transcriptome of human monocytes and disease susceptibility

Background

Variability of gene expression in human may link gene sequence variability and phenotypes; however, non-genetic variations, alone or in combination with genetics, may also influence expression traits and have a critical role in physiological and disease processes.

Methodology/Principal Findings

To get better insight into the overall variability of gene expression, we assessed the transcriptome of circulating monocytes, a key cell involved in immunity-related diseases and atherosclerosis, in 1,490 unrelated individuals and investigated its association with >675,000 SNPs and 10 common cardiovascular risk factors. Out of 12,808 expressed genes, 2,745 expression quantitative trait loci were detected (P<5.78×10⁻¹²), most of them (90%) being cis-modulated. Extensive analyses showed that associations identified by genome-wide association studies of lipids, body mass index or blood pressure were rarely compatible with a mediation by monocyte expression level at the locus. At a study-wide level (P<3.9×10⁻⁷), 1,662 expression traits (13.0%) were significantly associated with at least one risk factor. Genome-wide interaction analyses suggested that genetic variability and risk factors mostly acted additively on gene expression. Because of the structure of correlation among expression traits, the variability of risk factors could be characterized by a limited set of independent gene expressions which may have biological and clinical relevance. For example expression traits associated with cigarette smoking were more strongly associated with carotid atherosclerosis than smoking itself.

Conclusions/Significance

This study demonstrates that the monocyte transcriptome is a potent integrator of genetic and non-genetic influences of relevance for disease pathophysiology and risk assessment.

Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain

A fundamental challenge in the post-genome era is to understand and annotate the consequences of genetic variation, particularly within the context of human tissues. We present a set of integrated experiments that investigate the effects of common genetic variability on DNA methylation and mRNA expression in four human brain regions each from 150 individuals (600 samples total). We find an abundance of genetic cis regulation of mRNA expression and show for the first time abundant quantitative trait loci for DNA CpG methylation across the genome. We show peak enrichment for cis expression QTLs to be approximately 68,000 bp away from individual transcription start sites; however, the peak enrichment for cis CpG methylation QTLs is located much closer, only 45 bp from the CpG site in question. We observe that the largest magnitude quantitative trait loci occur across distinct brain tissues. Our analyses reveal that CpG methylation quantitative trait loci are more likely to occur for CpG sites outside of islands. Lastly, we show that while we can observe individual QTLs that appear to affect both the level of a transcript and a physically close CpG methylation site, these are quite rare. We believe these data, which we have made publicly available, will provide a critical step toward understanding the biological effects of genetic variation.

In this paper, we describe a comprehensive assessment of the correlation between common genetic variability across the human genome, gene expression, and DNA methylation, within human brain. We studied the cerebellum, frontal cortex, temporal cortex, and pons regions of 150 individuals (600 tissue samples). In each tissue, we assessed 27,578 DNA methylation sites and the expression level of 22,184 genes. Our research shows that DNA methylation and RNA expression patterns differ between brain regions. Further, we show that DNA genotype is correlated with gene expression and DNA methylation, particularly when the genetic variation is close to the DNA methylation site or gene.

Key considerations for measuring allelic expression on a genomic scale using high-throughput sequencing

Differences in gene expression are thought to be an important source of phenotypic diversity, so dissecting the genetic components of natural variation in gene expression is important for understanding the evolutionary mechanisms that lead to adaptation. Gene expression is a complex trait that, in diploid organisms, results from transcription of both maternal and paternal alleles. Directly measuring allelic expression rather than total gene expression offers greater insight into regulatory variation. The recent emergence of high-throughput sequencing offers an unprecedented opportunity to study allelic transcription at a genomic scale for virtually any species. By sequencing transcript pools derived from heterozygous individuals, estimates of allelic expression can be directly obtained. The statistical power of this approach is influenced by the number of transcripts sequenced and the ability to unambiguously assign individual sequence fragments to specific alleles on the basis of transcribed nucleotide polymorphisms. Here, using mathematical modelling and computer simulations, we determine the minimum sequencing depth required to accurately measure relative allelic expression and detect allelic imbalance via high-throughput sequencing under a variety of conditions. We conclude that, within a species, a minimum of 500-1000 sequencing reads per gene are needed to test for allelic imbalance, and consequently, at least five to 10 millions reads are required for studying a genome expressing 10 000 genes. Finally, using 454 sequencing, we illustrate an application of allelic expression by testing for cis-regulatory divergence between closely related Drosophila species.

New insights into the genetic control of gene expression using a Bayesian multi-tissue approach

The majority of expression quantitative trait locus (eQTL) studies have been carried out in single tissues or cell types, using methods that ignore information shared across tissues. Although global analysis of RNA expression in multiple tissues is now feasible, few integrated statistical frameworks for joint analysis of gene expression across tissues combined with simultaneous analysis of multiple genetic variants have been developed to date. Here, we propose Sparse Bayesian Regression models for mapping eQTLs within individual tissues and simultaneously across tissues. Testing these on a set of 2,000 genes in four tissues, we demonstrate that our methods are more powerful than traditional approaches in revealing the true complexity of the eQTL landscape at the systems-level. Highlighting the power of our method, we identified a two-eQTL model (cis/trans) for the Hopx gene that was experimentally validated and was not detected by conventional approaches. We showed common genetic regulation of gene expression across four tissues for ∼27% of transcripts, providing >5 fold increase in eQTLs detection when compared with single tissue analyses at 5% FDR level. These findings provide a new opportunity to uncover complex genetic regulatory mechanisms controlling global gene expression while the generality of our modelling approach makes it adaptable to other model systems and humans, with broad application to analysis of multiple intermediate and whole-body phenotypes.

Integrated analysis of genome-wide genetic polymorphisms and gene expression profiles from different tissues or cell types has been highly successful in identifying genes modulating complex phenotypes in animal models and humans. However, an important limitation of the current approaches consists in their sole application to individual tissues, thus ignoring information shared across different tissues. To uncover complex genetic regulatory mechanisms controlling gene expression at the whole organism's level, it is essential to develop appropriate analytical methods for the analysis of genome-wide genetic polymorphisms and gene expression profiles simultaneously in multiple tissues. This paper presents a novel, fully integrated Bayesian approach for mapping the genetic components of gene expression within and across multiple tissues. In addition to increased power and enhanced mapping resolution when compared with traditional approaches, our model directly provides information on potential systemic effects on transcriptional profiles and co-existing local (cis) and distant (trans) genetic control of gene expression. We also discuss the possibility to extend our approach for the analysis of different phenotypes, and other study designs, thus providing an integrated computational tool to explore the genetic control underlying transcriptional regulation at the systems-level, beyond the single tissue resolution.

Genetic variants and risk of lung cancer in never smokers: a genome-wide association study

BACKGROUND

Lung cancer in individuals who have never smoked tobacco products is an increasing medical and public-health issue. We aimed to unravel the genetic basis of lung cancer in never smokers.

METHODS

We did a four-stage investigation. First, a genome-wide association study of single nucleotide polymorphisms (SNPs) was done with 754 never smokers (377 matched case-control pairs at Mayo Clinic, Rochester, MN, USA). Second, the top candidate SNPs from the first study were validated in two independent studies among 735 (MD Anderson Cancer Center, Houston, TX, USA) and 253 (Harvard University, Boston, MA, USA) never smokers. Third, further replication of the top SNP was done in 530 never smokers (UCLA, Los Angeles, CA, USA). Fourth, expression quantitative trait loci (eQTL) and gene-expression differences were analysed to further elucidate the causal relation between the validated SNPs and the risk of lung cancer in never smokers.

FINDINGS

44 top candidate SNPs were identified that might alter the risk of lung cancer in never smokers. rs2352028 at chromosome 13q31.3 was subsequently replicated with an additive genetic model in the four independent studies, with a combined odds ratio of 1.46 (95% CI 1.26-1.70, p=5.94x10(-6)). A cis eQTL analysis showed there was a strong correlation between genotypes of the replicated SNPs and the transcription level of the gene GPC5 in normal lung tissues (p=1.96x10(-4)), with the high-risk allele linked with lower expression. Additionally, the transcription level of GPC5 in normal lung tissue was twice that detected in matched lung adenocarcinoma tissue (p=6.75x10(-11)).

INTERPRETATION

Genetic variants at 13q31.3 alter the expression of GPC5, and are associated with susceptibility to lung cancer in never smokers. Downregulation of GPC5 might contribute to the development of lung cancer in never smokers.

FUNDING

US National Institutes of Health; Mayo Foundation.

Modeling expression quantitative trait loci in data combining ethnic populations

Background

Combining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to analyzing each population independently. In such studies, however, the genetic diversity of minor allele frequencies among populations has rarely been taken into account. Due to the fact that allele frequency diversity and population-level expression differences are present in populations, a consensus regarding the optimal statistical approach for analysis of eQTL in data combining different populations remains inconclusive.

Results

In this report, we explored the applicability of a constrained two-way model to identify eQTL for combined ethnic data that might contain genetic diversity among ethnic populations. In addition, gene expression differences resulted from ethnic allele frequency diversity between populations were directly estimated and analyzed by the constrained two-way model. Through simulation, we investigated effects of genetic diversity on eQTL identification by examining gene expression data pooled from normal quantile transformation of each population. Using the constrained two-way model to reanalyze data from Caucasians and Asian individuals available from HapMap, a large number of eQTL were identified with similar genetic effects on the gene expression levels in these two populations. Furthermore, 19 single nucleotide polymorphisms with inter-population differences with respect to both genotype frequency and gene expression levels directed by genotypes were identified and reflected a clear distinction between Caucasians and Asian individuals.

Conclusions

This study illustrates the influence of minor allele frequencies on common eQTL identification using either separate or combined population data. Our findings are important for future eQTL studies in which different datasets are combined to increase the power of eQTL identification.

Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells

Population-based variability in protein expression patterns, especially in humans, is often observed but poorly understood. Moreover, very little is known about how interindividual genetic variation contributes to protein expression patterns. To begin to address this, we describe elements of technical and biological variations contributing to expression of 544 proteins in a population of 24 individual human lymphoblastoid cell lines that have been extensively genotyped as part of the International HapMap Project. We determined that expression levels of 10% of the proteins were tightly correlated to cell doubling rates. Using the publicly available genotypes for these lymphoblastoid cell lines, we applied a genetic association approach to identify quantitative trait loci associated with protein expression variation. Results identified 24 protein forms corresponding to 15 proteins for which genetic elements were responsible for >50% of the expression variation. The genetic variation associated with protein expression levels were located in cis with the gene coding for the transcript of the protein for 19 of these protein forms. Four of the genetic elements identified were coding non-synonymous single nucleotide polymorphisms that resulted in migration pattern changes in the two-dimensional gel. This is the first description of large scale proteomics analysis demonstrating the direct relationship between genome and proteome variations in human cells.

Explaining inter-individual variability in phenotype: is epigenetics up to the challenge?

The hypothesis that environmental factors alter somatically heritable epigenetic marks and change long-term patterns of gene expression is an exciting possibility in human disease research. Because most common diseases, and many quantitative traits, are influenced by both genetic and environmental factors, environmentally induced changes in epigenetic structures can provide a mechanistic link between genes and environment. We believe that inter-individual differences in the epigenetic modification of genes will explain a much greater fraction of inter-individual phenotypic variation than differences in genotype, alone.

Overview of linkage analysis in complex traits

Linkage analysis is a well-established and powerful method for mapping disease genes. While linkage analysis has been most successful when applied to disorders with clear patterns of Mendelian inheritance, it can also be a useful technique for mapping susceptibility genes for common complex diseases. In this unit, we outline the key concepts of complex disease, and how linkage analysis for complex traits differs from simple Mendelian traits. Optimal genetic studies require careful study design, ascertainment strategy, and analysis methods. We describe how disease parameters such as prevalence, heritability estimates, and mode of inheritance should be considered before data is collected. Furthermore, we outline a general strategic approach for conducting linkage analysis of a complex disease, along with several design considerations that can optimize statistical power to detect disease loci and generally improve the quality of a study. Finally, we discuss the benefits and weaknesses of linkage analysis in contrast to genome-wide association studies.

Genotypic analysis of gene expression in the dissection of the aetiology of complex neurological and psychiatric diseases

Over the last year, many reliable genetic associations with complex diseases have been reported including some associations with complex neurological and psychiatric diseases. Many of these disease associations do not map to coding changes in protein open reading frames, but rather are believed to lead to genetic variability in gene expression and splicing. Such effects can be dissected by studies, which use genetic variability in mRNA expression as quantitative traits and regress these traits against genotype.

From evolutionary genetics to human immunology: how selection shapes host defence genes

Pathogens have always been a major cause of human mortality, so they impose strong selective pressure on the human genome. Data from population genetic studies, including genome-wide scans for selection, are providing important insights into how natural selection has shaped immunity and host defence genes in specific human populations and in the human species as a whole. These findings are helping to delineate genes that are important for host defence and to increase our understanding of how past selection has had an impact on disease susceptibility in modern populations. A tighter integration between population genetic studies and immunological phenotype studies is now necessary to reveal the mechanisms that have been crucial for our past and present survival against infection.

Construction of a high-resolution genetic linkage map and comparative genome analysis for the reef-building coral Acropora millepora

A high-resolution genetic linkage map for the coral Acropora millepora is constructed and compared with other metazoan genomes, revealing syntenic blocks.

Background

Worldwide, coral reefs are in decline due to a range of anthropogenic disturbances, and are now also under threat from global climate change. Virtually nothing is currently known about the genetic factors that might determine whether corals adapt to the changing climate or continue to decline. Quantitative genetics studies aiming to identify the adaptively important genomic loci will require a high-resolution genetic linkage map. The phylogenetic position of corals also suggests important applications for a coral genetic map in studies of ancestral metazoan genome architecture.

Results

We constructed a high-resolution genetic linkage map for the reef-building coral Acropora millepora, the first genetic map reported for any coral, or any non-Bilaterian animal. More than 500 single nucleotide polymorphism (SNP) markers were developed, most of which are transferable in populations from Orpheus Island and Great Keppel Island. The map contains 429 markers (393 gene-based SNPs and 36 microsatellites) distributed in 14 linkage groups, and spans 1,493 cM with an average marker interval of 3.4 cM. Sex differences in recombination were observed in a few linkage groups, which may be caused by haploid selection. Comparison of the coral map with other metazoan genomes (human, nematode, fly, anemone and placozoan) revealed synteny regions.

Conclusions

Our study develops a framework that will be essential for future studies of adaptation in coral and it also provides an important resource for future genome sequence assembly and for comparative genomics studies on the evolution of metazoan genome structure.

Inherited variation in gene expression

Variation in gene expression constitutes an important source of biological variability within and between populations that is likely to contribute significantly to phenotypic diversity. Recent conceptual, technical, and methodological advances have enabled the genome-scale dissection of transcriptional variation. Here, we outline common approaches for detecting gene expression quantitative trait loci, and summarize the insights gleaned from these studies regarding the genetic architecture of transcriptional variation and the nature of regulatory alleles. Particular emphasis is placed on human studies, and we discuss experimental designs that ensure that increasingly large and complex studies continue to advance our understanding of gene expression variation. We conclude by discussing the evolution of gene expression levels, and we explore prospects for leveraging new technological developments to investigate inherited variation in gene expression in even greater depth.

Expression quantitative trait loci detected in cell lines are often present in primary tissues

Expression quantitative trait loci (eQTL) mapping is a powerful tool for identifying genetic regulatory variation. However, at present, most eQTLs in humans were identified using gene expression data from cell lines, and it remains unknown whether these eQTLs also have a regulatory function in other expression contexts, such as human primary tissues. Here we investigate this question using a targeted strategy. Specifically, we selected a subset of large-effect eQTLs identified in the HapMap lymphoblastoid cell lines, and examined the association of these eQTLs with gene expression levels across individuals in five human primary tissues (heart, kidney, liver, lung and testes). We show that genotypes at the eQTLs we selected are often predictive of variation in gene expression levels in one or more of the five primary tissues. The genotype effects in the primary tissues are consistently in the same direction as the effects inferred in the cell lines. Additionally, a number of the eQTLs we tested are found in more than one of the tissues. Our results indicate that functional studies in cell lines may uncover a substantial amount of genetic variation that affects gene expression levels in human primary tissues.

The genetics of quantitative traits: challenges and prospects

A major challenge in current biology is to understand the genetic basis of variation for quantitative traits. We review the principles of quantitative trait locus mapping and summarize insights about the genetic architecture of quantitative traits that have been obtained over the past decades. We are currently in the midst of a genomic revolution, which enables us to incorporate genetic variation in transcript abundance and other intermediate molecular phenotypes into a quantitative trait locus mapping framework. This systems genetics approach enables us to understand the biology inside the 'black box' that lies between genotype and phenotype in terms of causal networks of interacting genes.

Genetics of human gene expression: mapping DNA variants that influence gene expression

There is extensive natural variation in human gene expression. As quantitative phenotypes, expression levels of genes are heritable. Genetic linkage and association mapping have identified cis- and trans-acting DNA variants that influence expression levels of human genes. New insights into human gene regulation are emerging from genetic analyses of gene expression in cells at rest and following exposure to stimuli. The integration of these genetic mapping results with data from co-expression networks is leading to a better understanding of how expression levels of individual genes are regulated and how genes interact with each other. These findings are important for basic understanding of gene regulation and of diseases that result from disruption of normal gene regulation.

Replication and narrowing of gene expression quantitative trait loci using inbred mice

Gene expression quantitative trait locus (eQTL) mapping has become a powerful tool in systems biology. While many authors have made important discoveries using this approach, one persistent challenge in eQTL studies is the selection of loci and genes that should receive further biological investigation. In this study we compared eQTL generated from gene expression profiling in the livers of two panels of mouse strains: 41 BXD recombinant inbred and 36 Mouse Diversity Panel (MDP) strains. Cis-eQTL, loci in which the transcript and its maximum QTL are colocated, have been shown to be more reproducible than trans-eQTL, which are not colocated with the transcript. We observed that between 9.9 and 12.1% of cis-eQTL and between 2.0 and 12.6% of trans-eQTL replicated between the two panels depending on the degree of statistical stringency. Notably, a significant eQTL hotspot on distal chromosome 12 observed in the BXD panel was reproduced in the MDP. Furthermore, the shorter linkage disequilibrium in the MDP strains allowed us to considerably narrow the locus and limit the number of candidate genes to a cluster of Serpin genes, which code for extracellular proteases. We conclude that this strategy has some utility in increasing confidence and resolution in eQTL mapping studies; however, due to the high false-positive rate in the MDP, eQTL mapping in inbred strains is best carried out in combination with an eQTL linkage study.

Cis-ruption mechanisms: disruption of cis-regulatory control as a cause of human genetic disease

The spatiotemporally and quantitatively correct activity of a gene requires the presence of intact coding sequence as well as properly functioning regulatory control. One of the great challenges of the post-genome era is to gain a better understanding of the mechanisms of gene control. Proper gene regulation depends not only on the required transcription factors and associated complexes being present (in the correct dosage), but also on the integrity, chromatin conformation and nuclear positioning of the gene's chromosomal segment. Thus, when either the cis-trans regulatory system of a gene or the normal context of its chromatin structure are disrupted, gene expression may be adversely affected, potentially leading to disease. As transcriptional regulation is a highly complex process depending on many factors, there are many different mechanisms that can cause aberrant gene expression. Traditionally, the term 'position effect' was used to refer to situations where the level of expression of a gene is deleteriously affected by an alteration in its chromosomal environment, while maintaining an intact transcription unit. Over the past years, an ever increasing number of such disease-related position effect cases have come to light, and detailed studies have revealed insight into the variety of causes, which can be categorized into a number of different mechanistic groups. We suggest replacing the outdated term of 'position effect disease' with the new generic name of 'cis-ruption disorder' to describe genetic disease cases that are caused by disruption of the normal cis-regulatory architecture of the disease gene locus. Here, we review these various cis-ruption mechanisms and discuss how their studies have contributed to our understanding of long- range gene regulation.

Adaptive radiations: from field to genomic studies

Adaptive radiations were central to Darwin's formation of his theory of natural selection, and today they are still the centerpiece for many studies of adaptation and speciation. Here, we review the advantages of adaptive radiations, especially recent ones, for detecting evolutionary trends and the genetic dissection of adaptive traits. We focus on Aquilegia as a primary example of these advantages and highlight progress in understanding the genetic basis of flower color. Phylogenetic analysis of Aquilegia indicates that flower color transitions proceed by changes in the types of anthocyanin pigments produced or their complete loss. Biochemical, crossing, and gene expression studies have provided a wealth of information about the genetic basis of these transitions in Aquilegia. To obtain both enzymatic and regulatory candidate genes for the entire flavonoid pathway, which produces anthocyanins, we used a combination of sequence searches of the Aquilegia Gene Index, phylogenetic analyses, and the isolation of novel sequences by using degenerate PCR and RACE. In total we identified 34 genes that are likely involved in the flavonoid pathway. A number of these genes appear to be single copy in Aquilegia and thus variation in their expression may have been key for floral color evolution. Future studies will be able to use these sequences along with next-generation sequencing technologies to follow expression and sequence variation at the population level. The genetic dissection of other adaptive traits in Aquilegia should also be possible soon as genomic resources such as whole-genome sequencing become available.

The value of animal models in predicting genetic susceptibility to complex diseases such as rheumatoid arthritis

For a long time, genetic studies of complex diseases were most successfully conducted in animal models. However, the field of genetics is now rapidly evolving, and human genetics has also started to produce strong candidate genes for complex diseases. This raises the question of how to continue gene-finding attempts in animals and how to use animal models to enhance our understanding of gene function. In this review we summarize the uses and advantages of animal studies in identification of disease susceptibility genes, focusing on rheumatoid arthritis. We are convinced that animal genetics will remain a valuable tool for the identification and investigation of pathways that lead to disease, well into the future.