Genetic Regulation Of Gene Expression Research Articles

On Using Local Ancestry to Characterize the Genetic Architecture of Human Traits: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations

Understanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of the use of local ancestry on high-dimensional omics analyses, including, most prominently, expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored. Here, we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches. Applying our method to National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that the use of local ancestry can improve eQTL mapping in admixed and multiethnic populations, respectively. We estimate the trait variance explained by ancestry by using local admixture relatedness between individuals. By using simulations of diverse genetic architectures and degrees of confounding, we show improved accuracy in estimating heritability when accounting for local ancestry similarity. Furthermore, we characterize the sparse versus polygenic components of gene expression in admixed individuals. Our study has important methodological implications for genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

41 - EXPLORING THE ROLE OF GENETIC REGULATION OF GENE EXPRESSION IN SUBSTANCE USE AND DEPENDENCE

Background Recent large-scale Genome-Wide Association Studies (GWAS) have identified genetic loci associated with various substance use traits. However, little is known about the functional mechanisms through which these genetic loci ascertain their effect. This study aims to identify and functionally annotate genetic loci associated with substance use traits based on their role in genetic regulation of gene expression. Methods We evaluated expression quantitative trait loci (eQTLs) in human post mortem tissue from 10 brain regions and whole blood of the Genotype-Tissue Expression (GTEx) database. The role of eQTLs was examined for eight substance use traits, using summary statistics from GWAS of: alcohol consumption (N=70,460), cigarettes per day (CPD; N=38,181), former vs. current smoker (N=41,278), age of smoking initiation (N=24,114), ever smoker (N= 74,035), cocaine dependence (N=4,769), cannabis dependence (N=12,168), and lifetime cannabis use (N=32,330). Results eQTLs with significant trait association (FDR Discussion Annotating genes based on transcriptomic regulation in brain and non-brain tissues improves the identification of novel loci and the functional characterization of genetic risk loci for substance use traits.

Fasting and time of day independently modulate circadian rhythm relevant gene expression in adipose and skin tissue

BackgroundIntermittent fasting and time-restricted diets are associated with lower risk biomarkers for cardio-metabolic disease. The shared mechanisms underpinning the similar physiological response to these events is not established, but circadian rhythm could be involved. Here we investigated the transcriptional response to fasting in a large cross-sectional study of adipose and skin tissue from healthy volunteers (N = 625) controlling for confounders of circadian rhythm: time of day and season.ResultsWe identified 367 genes in adipose and 79 in skin whose expression levels were associated (FDR < 5%) with hours of fasting conditionally independent of time of day and season, with 19 genes common to both tissues. Among these genes, we replicated 38 in human, 157 in non-human studies, and 178 are novel associations. Fasting-responsive genes were enriched for regulation of and response to circadian rhythm. We identified 99 genes in adipose and 54 genes in skin whose expression was associated to time of day; these genes were also enriched for circadian rhythm processes. In genes associated to both exposures the effect of time of day was stronger and in an opposite direction to that of hours fasted. We also investigated the relationship between fasting and genetic regulation of gene expression, including GxE eQTL analysis to identify personal responses to fasting.ConclusionThis study robustly implicates circadian rhythm genes in the response to hours fasting independently of time of day, seasonality, age and BMI. We identified tissue-shared and tissue-specific differences in the transcriptional response to fasting in a large sample of healthy volunteers.

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (rb). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples (hat r_b = 0.70 for cis-eQTLs and hat r_ b = 0.78 for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.

Epigenetic regulation of T-helper cell differentiation, memory, and plasticity in allergic asthma.

An estimated 300million people currently suffer from asthma, which causes approximately 250000 deaths a year. Allergen-specific T-helper (Th) cells produce cytokines that induce many of the hallmark features of asthma including airways hyperreactivity, eosinophilic and neutrophilic inflammation, mucus hypersecretion, and airway remodeling. Cytokine-producing Th subsets including Th1 (IFN-γ), Th2 (IL-4, IL-5, IL-13), Th9 (IL-9), Th17 (IL-17), Th22 (IL-22), and T regulatory (IL-10) cells have all been suggested to play a role in the development of asthma. Th differentiation involves genetic regulation of gene expression through the concerted action of cytokines, transcription factors, and epigenetic regulators. We describe how Th differentiation and plasticity is regulated by epigenetic histone and DNA modifications, with a focus on the regulation of histone methylation by members of the polycomb and trithorax complexes. In addition, we outline environmental influences that could influence epigenetic regulation of Th cells and discuss the potential to regulate Th plasticity and function through drugs targeting the epigenetic machinery. It is also becoming apparent that epigenetic regulation of allergen-specific memory Th cells may be important in the development and persistence of chronic allergies. Finally, we describe how epigenetic modifiers regulate cytokine memory in Th cells and describe recently identified hybrid, plastic, and pathogenic memory Th subsets the context of allergic asthma.

Large-Scale Profiling Reveals the Influence of Genetic Variation on Gene Expression in Human Induced Pluripotent Stem Cells

In this study, we used whole-genome sequencing and gene expression profiling of 215 human induced pluripotent stem cell (iPSC) lines from different donors to identify genetic variants associated with RNA expression for 5,746 genes. We were able to predict causal variants for these expression quantitative trait loci (eQTLs) that disrupt transcription factor binding and validated a subset of them experimentally. We also identified copy-number variant (CNV) eQTLs, including some that appear to affect gene expression by altering the copy number of intergenic regulatory regions. In addition, we were able to identify effects on gene expression of rare genic CNVs and regulatory single-nucleotide variants and found that reactivation of gene expression on the X chromosome depends on gene chromosomal position. Our work highlights the value of iPSCs for genetic association analyses and provides a unique resource for investigating the genetic regulation of gene expression in pluripotent cells.

Genetic regulation of gene expression in the epileptic human hippocampus.

Epilepsy is a serious and common neurological disorder. Expression quantitative loci (eQTL) analysis is a vital aid for the identification and interpretation of disease-risk loci. Many eQTLs operate in a tissue- and condition-specific manner. We have performed the first genome-wide cis-eQTL analysis of human hippocampal tissue to include not only normal (n = 22) but also epileptic (n = 22) samples. We demonstrate that disease-associated variants from an epilepsy GWAS meta-analysis and a febrile seizures (FS) GWAS are significantly more enriched with epilepsy-eQTLs than with normal hippocampal eQTLs from two larger independent published studies. In contrast, GWAS meta-analyses of two other brain diseases associated with hippocampal pathology (Alzheimer’s disease and schizophrenia) are more enriched with normal hippocampal eQTLs than with epilepsy-eQTLs. These observations suggest that an eQTL analysis that includes disease-affected brain tissue is advantageous for detecting additional risk SNPs for the afflicting and closely related disorders, but not for distinct diseases affecting the same brain regions. We also show that epilepsy eQTLs are enriched within epilepsy-causing genes: an epilepsy cis-gene is significantly more likely to be a causal gene for a Mendelian epilepsy syndrome than to be a causal gene for another Mendelian disorder. Epilepsy cis-genes, compared to normal hippocampal cis-genes, are more enriched within epilepsy-causing genes. Hence, we utilize the epilepsy eQTL data for the functional interpretation of epilepsy disease-risk variants and, thereby, highlight novel potential causal genes for sporadic epilepsy. In conclusion, an epilepsy-eQTL analysis is superior to normal hippocampal tissue eQTL analyses for identifying the variants and genes underlying epilepsy.

The genetic regulation of transcription in human endometrial tissue.

Do genetic effects regulate gene expression in human endometrium? This study demonstrated strong genetic effects on endometrial gene expression and some evidence for genetic regulation of gene expression in a menstrual cycle stage-specific manner. Genetic effects on expression levels for many genes are tissue specific. Endometrial gene expression varies across menstrual cycle stages and between individuals, but there are limited data on genetic control of expression in endometrium. We analysed genome-wide genotype and gene expression data to map cis expression quantitative trait loci (eQTL) in endometrium. We recruited 123 women of European ancestry. DNA samples from blood were genotyped on Illumina HumanCoreExome chips. Total RNA was extracted from endometrial tissues. Whole-transcriptome profiles were characterized using Illumina Human HT-12 v4.0 Expression Beadchips. We performed eQTL mapping with ~8 000 000 genotyped and imputed single nucleotide polymorphisms (SNPs) and 12 329 genes. We identified a total of 18 595 cis SNP-probe associations at a study-wide level of significance (P < 1 × 10-7), which correspond to independent eQTLs for 198 unique genes. The eQTLs with the largest effect in endometrial tissue were rs4902335 for CHURC1 (P = 1.05 × 10-32) and rs147253019 for ZP3 (P = 8.22 × 10-30). We further performed a context-specific eQTL analysis to investigate if genetic effects on gene expression regulation act in a menstrual cycle-specific manner. Interestingly, five cis-eQTLs were identified with a significant stage-by-genotype interaction. The strongest stage interaction was the eQTL for C10ORF33 (PYROXD2) with SNP rs2296438 (P = 2.0 × 10-4), where we observe a 2-fold difference in the average expression levels of heterozygous samples depending on the stage of the menstrual cycle. The summary eQTL results are publicly available to browse or download. A limitation of the present study was the relatively modest sample size. It was not powered to identify trans-eQTLs and larger sample sizes will also be needed to provide better power to detect cis-eQTLs and cycle stage-specific effects, given the substantial changes in expression across the menstrual cycle for many genes. Identification of endometrial eQTLs provides a platform for better understanding genetic effects on endometriosis risk and other endometrial-related pathologies. Funding for this work was provided by NHMRC Project Grants GNT1026033, GNT1049472, GNT1046880, GNT1050208, GNT1105321 and APP1083405. There are no competing interests.

Genetically regulated hepatic transcripts and pathways orchestrate haematological, biochemical and body composition traits.

The liver is the central metabolic organ and exhibits fundamental functions in haematological traits. Hepatic expression, haematological, plasma biochemical, and body composition traits were assessed in a porcine model (n = 297) to establish tissue-specific genetic variations that influence the function of immune-metabolism-correlated expression networks. At FDR (false discovery rate) <1%, more than 3,600 transcripts were jointly correlated (r = |0.22–0.48|) with the traits. Functional enrichment analysis demonstrated common links of metabolic and immune traits. To understand how immune and metabolic traits are affected via genetic regulation of gene expression, eQTLs were assessed. 20517 significant (FDR < 5%) eQTLs for 1401 transcripts were identified, among which 443 transcripts were associated with at least one of the examined traits and had cis-eQTL (such as ACO1 (6.52 × 10−7) and SOD1 (6.41 × 10−30). The present study establishes a comprehensive view of hepatic gene activity which links together metabolic and immune traits in a porcine model for medical research.

Natural genetic variation profoundly regulates gene expression in immune cells and dictates susceptibility to CNS autoimmunity.

Regulation of gene expression in immune cells is known to be under genetic control, and likely contributes to susceptibility to autoimmune diseases, such as multiple sclerosis (MS). How this occurs in concert across multiple immune cell types is poorly understood. Using a mouse model that harnesses the genetic diversity of wild-derived mice, more accurately reflecting genetically diverse human populations, we provide an extensive characterization of the genetic regulation of gene expression in five different naïve immune cell types relevant to MS. The immune cell transcriptome is shown to be under profound genetic control, exhibiting diverse patterns: global, cell-specific, and sex-specific. Bioinformatic analysis of the genetically-controlled transcript networks reveals reduced cell type-specificity and inflammatory activity in wild-derived PWD/PhJ mice, compared with the conventional laboratory strain C57BL/6J. Additionally, candidate MS-GWAS genes were significantly enriched among transcripts overrepresented in C57BL/6J cells compared to PWD. These expression level differences correlate with robust differences in susceptibility to experimental autoimmune encephalomyelitis, the principal model of MS, and skewing of the encephalitogenic T cell responses. Taken together, our results provide functional insights into the genetic regulation of the immune transcriptome, and shed light on how this in turn contributes to susceptibility to autoimmune disease.

Abstract 4436: Synergistic effects of promoter associated DNA methylation and genetic alterations to better understand oncogenic gene expression profiles

Abstract The emergence of Next Generation Sequencing (NGS) along with computational biology has broadened the scope in which diverse cellular processes can be interrogated. While there has been considerable progress in understanding the impact of genetic and epigenetic mechanisms in tumorigenesis using whole genomic, epigenomic and transcriptional analysis by NGS, there has been little consideration of the importance of interplay between these processes. We performed a comparative analysis of array and NGS technologies to identify differentially methylated CpG sites in colorectal cancer cell lines. NGS had more specificity in addition to profiling more CpG sites relative to Illumina 450K arrays. Base-level resolution of sequencing data can identify any strand specific methylation bias. Our analysis shows that methylation frequency between the sense and antisense strand are highly correlated (average R2 ∼ 0.81), and coefficient of variance (CV) between the strands is generally low (about half of observed sites have &amp;lt;10% CV). However, a small percentage of bases had strand specific biases. Using a minimum of 100% CV and difference in methylation frequency greater than 50% as filtering criteria, we found 1210, 569, 638, and 1484 CpG sites have strand specific biases with 7 overlapping bases among the samples tested. Further investigation will identify whether these bases are random or reside within a particular region, where biases occur, and what genes are potentially affected. We also used NGS and publically available gene expression datasets in colorectal cancer cell lines-HCT116 and HCT116 DKO (cell line with genetic knockouts of both DNA methyltransferases DNMT1 and DMNT3b) to identify roles of differential methylation in regulating gene expression. A majority of genes were down-regulated between HCT116 and HCT116 DKO cell lines including those involved in chromatin, nucleic acid, and nucleotide binding and cell cycle regulation. Interestingly, many differentially expressed genes are also involved in immune response. We then used bisulfite treated genomic data to evaluate genetic regulation of gene expression. For this, we converted bisulfite treated data into genomic space using custom in-house developed bio-informatics tools that were first tested using DNA isolated from NA12878 cell line. Our analysis showed that 65% of the known variants detected in NA12878 cell line by the genome in a bottle consortium can be identified by bisulfite sequencing of promoter associated CpG islands. One limitation of this analysis is the inability to identify C&amp;gt;T genomic variants. This data is being analyzed to evaluate effects of genetic mutations in promoter binding sites on gene expression in colorectal cancers. Comparative analysis of genetic and epigenetic regulation of gene expression will allow better understanding of gene regulatory networks in colorectal cancer. Citation Format: Claire Olson, Fang Yin Lo, Kerry Deutsch, Sharon Austin, Kellie Howard, Amanda Leonti, Lindsey Maassel, Christopher Subia, Tuuli Saloranta, Nicole Christopherson, Kathryn Shiji, Shradha Patil, Steven Anderson, Anup Madan. Synergistic effects of promoter associated DNA methylation and genetic alterations to better understand oncogenic gene expression profiles. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4436.

Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication.

BackgroundInter-tissue molecular interactions are critical to the function and behavior of biological systems in multicellular organisms, but systematic studies of interactions between tissues are lacking. Also, existing studies of inter-tissue interactions are based on direct gene expression correlations, which can’t distinguish correlations due to common genetic architectures versus biochemical or molecular signal exchange between tissues.MethodsWe developed a novel strategy to study inter-tissue interaction by removing effects of genetic regulation of gene expression (genetic decorrelation). We applied our method to the comprehensive atlas of gene expression across nine human tissues in the Genotype-Tissue Expression (GTEx) project to generate novel genetically decorrelated inter-tissue networks. From this we derived modules of genes important in inter-tissue interactions that are likely driven by biological signal exchange instead of their common genetic basis. Importantly we highlighted communication between tissues and elucidated gene activities in one tissue inducing gene expression changes in others.ResultsWe reveal global unidirectional inter-tissue coordination of specific biological pathways such as protein synthesis. Using our data, we highlighted a clinically relevant example whereby heart expression of DPP4 was coordinated with a gene expression signature characteristic for whole blood proliferation, potentially impacting peripheral stem cell mobilization. We also showed that expression of the poorly characterized FOCAD in heart correlated with protein biosynthetic processes in the lung.ConclusionsIn summary, this is the first resource of human multi-tissue networks enabling the investigation of molecular inter-tissue interactions. With the networks in hand, we may systematically design combination therapies that simultaneously target multiple tissues or pinpoint potential side effects of a drug in other tissues.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0268-1) contains supplementary material, which is available to authorized users.

Genome-wide analysis of the genetic regulation of gene expression in human neutrophils.

Neutrophils are an abundant immune cell type involved in both antimicrobial defence and autoimmunity. The regulation of their gene expression, however, is still largely unknown. Here we report an eQTL study on isolated neutrophils from 114 healthy individuals of Chinese ethnicity, identifying 21,210 eQTLs on 832 unique genes. Unsupervised clustering analysis of these eQTLs confirms their role in inflammatory responses and immunological diseases but also indicates strong involvement in dermatological pathologies. One of the strongest eQTL identified (rs2058660) is also the tagSNP of a linkage block reported to affect leprosy and Crohn's disease in opposite directions. In a functional study, we can link the C allele with low expression of the β-chain of IL18-receptor (IL18RAP). In neutrophils, this results in a reduced responsiveness to IL-18, detected both on the RNA and protein level. Thus, the polymorphic regulation of human neutrophils can impact beneficial as well as pathological inflammatory responses.

Genomic modulators of gene expression in human neutrophils.

Neutrophils form the most abundant leukocyte subset and are central to many disease processes. Technical challenges in transcriptomic profiling have prohibited genomic approaches to date. Here we map expression quantitative trait loci (eQTL) in peripheral blood CD16+ neutrophils from 101 healthy European adults. We identify cis-eQTL for 3281 neutrophil-expressed genes including many implicated in neutrophil function, with 450 of these not previously observed in myeloid or lymphoid cells. Paired comparison with monocyte eQTL demonstrates nuanced conditioning of genetic regulation of gene expression by cellular context, which relates to cell-type-specific DNA methylation and histone modifications. Neutrophil eQTL are markedly enriched for trait-associated variants particularly autoimmune, allergy and infectious disease. We further demonstrate how eQTL in PADI4 and NOD2 delineate risk variant function in rheumatoid arthritis, leprosy and Crohn's disease. Taken together, these data help advance understanding of the genetics of gene expression, neutrophil biology and immune-related diseases.

Integration of disease association and eQTL data using a Bayesian colocalisation approach highlights six candidate causal genes in immune-mediated diseases.

The genes and cells that mediate genetic associations identified through genome-wide association studies (GWAS) are only partially understood. Several studies that have investigated the genetic regulation of gene expression have shown that disease-associated variants are over-represented amongst expression quantitative trait loci (eQTL) variants. Evidence for colocalisation of eQTL and disease causal variants can suggest causal genes and cells for these genetic associations. Here, we used colocalisation analysis to investigate whether 595 genetic associations to ten immune-mediated diseases are consistent with a causal variant that regulates, in cis, gene expression in resting B cells, and in resting and stimulated monocytes. Previously published candidate causal genes were over-represented amongst genes exhibiting colocalisation (odds ratio > 1.5), and we identified evidence for colocalisation (posterior odds > 5) between cis eQTLs in at least one cell type and at least one disease for six genes: ADAM15, RGS1, CARD9, LTBR, CTSH and SYNGR1. We identified cell-specific effects, such as for CTSH, the expression of which in monocytes, but not in B cells, may mediate type 1 diabetes and narcolepsy associations in the chromosome 15q25.1 region. Our results demonstrate the utility of integrating genetic studies of disease and gene expression for highlighting causal genes and cell types.

Genetic regulation of gene expression in the lung identifies CST3 and CD22 as potential causal genes for airflow obstruction

BackgroundCOPD is a complex chronic disease with poorly understood pathogenesis. Integrative genomic approaches have the potential to elucidate the biological networks underlying COPD and lung function. We recently combined genome-wide...

Variance Heterogeneity in Saccharomyces cerevisiae Expression Data: Trans-Regulation and Epistasis

Here, we describe the results from the first variance heterogeneity Genome Wide Association Study (VGWAS) on yeast expression data. Using this forward genetics approach, we show that the genetic regulation of gene-expression in the budding yeast, Saccharomyces cerevisiae, includes mechanisms that can lead to variance heterogeneity in the expression between genotypes. Additionally, we performed a mean effect association study (GWAS). Comparing the mean and variance heterogeneity analyses, we find that the mean expression level is under genetic regulation from a larger absolute number of loci but that a higher proportion of the variance controlling loci were trans-regulated. Both mean and variance regulating loci cluster in regulatory hotspots that affect a large number of phenotypes; a single variance-controlling locus, mapping close to DIA2, was found to be involved in more than 10% of the significant associations. It has been suggested in the literature that variance-heterogeneity between the genotypes might be due to genetic interactions. We therefore screened the multi-locus genotype-phenotype maps for several traits where multiple associations were found, for indications of epistasis. Several examples of two and three locus genetic interactions were found to involve variance-controlling loci, with reports from the literature corroborating the functional connections between the loci. By using a new analytical approach to re-analyze a powerful existing dataset, we are thus able to both provide novel insights to the genetic mechanisms involved in the regulation of gene-expression in budding yeast and experimentally validate epistasis as an important mechanism underlying genetic variance-heterogeneity between genotypes.

Tissue specificity of genetic regulation of gene expression

Gene expression is under partial genetic regulation, which may vary between different cell types and tissues. A new study finds that there is substantial but incomplete overlap among regulatory variants located near the regulated genes in three human tissues.

Liver expression quantitative trait loci: a foundation for pharmacogenomic research.

Expression quantitative trait loci (eQTL) analysis can provide insights into the genetic regulation of gene expression at a genomic level and this information is proving extremely useful in many different areas of research. As a consequence of the role of the liver in drug metabolism and disposition, the study of eQTLs in primary human liver tissue could provide a foundation for pharmacogenomics. Thus far, four genome-wide eQTL studies have been performed using human livers. Many liver eQTLs have been found to be reproducible and a proportion of these may be specific to the liver. Already these data have been used to interpret and inform clinic genome-wide association studies, providing potential mechanistic evidence for clinical associations and identifying genes which may impact clinical phenotypes. However, the utility of liver eQTL data has not yet been fully explored or realized in pharmacogenomics. As further liver eQTL research is undertaken, the genetic regulation of gene expression will become much better characterized and this knowledge will create a rational basis for the prospective pharmacogenomic study of many drugs.

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies

Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.