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

Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine

Gene products can affect the concentrations of small molecules (aka “metabolites”), and conversely, some metabolites can modulate the concentrations of gene transcripts. While many specific instances of this interplay have been revealed, a global approach to systematically uncover human gene-metabolite interactions is still lacking. We performed a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome using untargeted metabolome features, extracted from ¹H nuclear magnetic resonance spectroscopy (NMR) of urine samples, and gene expression levels, quantified from RNA-Seq of lymphoblastoid cell lines (LCL) from 555 healthy individuals. We identified 20 study-wide significant associations corresponding to 15 genes, of which 5 associations (with 2 genes) were confirmed with follow-up NMR data. Using metabomatching, we identified the metabolites corresponding to metabolome features associated with the genes, namely, N-acetylated compounds with ALMS1 and trimethylamine (TMA) with HPS1. Finally, Mendelian randomization analysis supported a potential causal link between the expression of genes in both the ALMS1- and HPS1-loci and their associated metabolite concentrations. In the case of HPS1, we additionally observed that TMA concentration likely exhibits a reverse causal effect on HPS1 expression levels, indicating a negative feedback loop. Our study highlights how the integration of metabolomics, gene expression, and genetic data can pinpoint causal genes modulating metabolite concentrations.

Gene expression changes in lymphoblastoid cell lines and primary B cells by dexamethasone

BACKGROUND

Human Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs) have been thought to be a useful model system for pharmacogenomics studies. The purpose of this study was to determine the effect of Epstein-Barr virus transformation on gene expression changes by dexamethasone (Dex) in LCLs and primary B cells (PBCs) derived from the same individuals.

PATIENTS AND METHODS

We prepared LCLs and purified PBCs from the same six male donors participating in the Childhood Asthma Management Program clinical trial, and compared mRNA profiles after 6 h incubation with Dex (10 mol/l) or sham buffer. We assessed differential expression and put the list of differentially expressed genes into the web interface of ConsensusPathDB to find the pathway-level interpretation of our genes specified. As a supplementary analysis, we looked at the expression of the Dex-regulated (inducing or repressing) genes in treatment-naive PBCs and LCLs (pre-Dex treatment) from the GSE30916 dataset.

RESULTS

By hierarchical clustering, we found clustering of probes by cell types but not by individuals irrespective of Dex treatment. We observed that the Dex-regulated genes significantly overlapped in PBCs and LCLs. In addition, the expression of these genes showed significant correlations between treatment-naive PBCs and LCLs. Common genes showing significantly decreased expressions by the Dex treatment in both cells were enriched in immune responses and proinflammatory signaling pathways.

CONCLUSION

Taken together, these results suggest the uses of LCLs are representative of the primary biologic effects of corticosteroids treatment.

Implications for the migraine SNP rs1835740 in a Swedish cluster headache population

BACKGROUND

Cluster headache is a severe headache disorder with unknown aetiology. The pathophysiology and symptoms present certain common features with migraine. Specifically, activation of the trigeminal vascular system seems to be involved in both disorders, which is hypothesized to result in neurogenic inflammation and vasodilation of the cerebral vessels. In addition, genetic factors have been implicated in both migraine and cluster headache.

OBJECTIVE

In order to determine whether or not migraine and cluster headache share genetic risk factors, we screened two genetic variants known to increase the risk of migraine in Sweden in a Swedish cluster headache case-control study population.

METHODS

In all, 541 patients and 581 control subjects were genotyped for rs1835740 in close proximity to MTDH (metadherin) and rs2651899 in the PRDM16 (PR/SET domain 16) gene, using TaqMan® real-time PCR and pyrosequencing. In addition, we analyzed MTDH gene expression in a subset of the material, using reverse transcription real-time PCR to determine relative mRNA levels in primary fibroblast cell lines from patients and controls.

RESULTS

We found a trend for association between rs1835740, which is reported to affect MTDH mRNA levels, and cluster headache in our Swedish case-control material (p = 0.043, Χ2 = 4.102). This association was stronger in a subgroup of patients suffering from both cluster headache and migraine (p = 0.031, Χ2 = 6.964). We could further confirm that rs1835740 has an effect on the transcriptional activity of MTDH. In this Swedish cluster headache cohort we did not find an association with the rs2651899 variant.

CONCLUSIONS

We conclude that rs1835740 is a potential risk factor for cluster headache in Sweden. Our data indicates that rs1835740 and MTDH might be involved in neurovascular headaches in general whilst rs2651899 is specifically related to migraine.

Comprehensive evaluation of disease- and trait-specific enrichment for eight functional elements among GWAS-identified variants

Genome-wide association study (GWAS)-identified variants are enriched for functional elements. However, we have limited knowledge of how functional enrichment may differ by disease/trait and tissue type. We tested a broad set of eight functional elements for enrichment among GWAS-identified SNPs (p < 5×10^-8) from the NHGRI-EBI Catalog across seven disease/trait categories: cancer, cardiovascular disease, diabetes, autoimmune disease, psychiatric disease, neurological disease, and anthropometric traits. SNPs were annotated using HaploReg for the eight functional elements across any tissue: DNase sites, expression quantitative trait loci (eQTL), sequence conservation, enhancers, promoters, missense variants, sequence motifs, and protein binding sites. In addition, tissue-specific annotations were considered for brain vs. blood. Disease/trait SNPs were compared to a control set of 4809 SNPs matched to the GWAS SNPs (N = 1639) on allele frequency, gene density, distance to nearest gene, and linkage disequilibrium at ~3:1 ratio. Enrichment analyses were conducted using logistic regression, with Bonferroni correction. Overall, a significant enrichment was observed for all functional elements, except sequence motifs. Missense SNPs showed the strongest magnitude of enrichment. eQTLs were the only functional element significantly enriched across all diseases/traits. Magnitudes of enrichment were generally similar across diseases/traits, where enrichment was statistically significant. Blood vs. brain tissue effects on enrichment were dependent on disease/trait and functional element (e.g., cardiovascular disease: eQTLs P _{TissueDifference} = 1.28 × 10^-6 vs. enhancers P _{TissueDifference} = 0.94). Identifying disease/trait-relevant functional elements and tissue types could provide new insight into the underlying biology, by guiding a priori GWAS analyses (e.g., brain enhancer elements for psychiatric disease) or facilitating post hoc interpretation.

Lymphoblastoid Cell Lines as a Tool to Study Inter-Individual Differences in the Response to Glucose

Background

White blood cells have been shown in animal studies to play a central role in the pathogenesis of diabetic retinopathy. Lymphoblastoid cells are immortalized EBV-transformed primary B-cell leukocytes that have been extensively used as a model for conditions in which white blood cells play a primary role. The purpose of this study was to investigate whether lymphoblastoid cell lines, by retaining many of the key features of primary leukocytes, can be induced with glucose to demonstrate relevant biological responses to those found in diabetic retinopathy.

Methods

Lymphoblastoid cell lines were obtained from twenty-three human subjects. Differences between high and standard glucose conditions were assessed for expression, endothelial adhesion, and reactive oxygen species.

Results

Collectively, stimulation of the lymphoblastoid cell lines with high glucose demonstrated corresponding changes on molecular, cellular and functional levels. Lymphoblastoid cell lines up-regulated expression of a panel of genes associated with the leukocyte-mediated inflammation found in diabetic retinopathy that include: a cytokine (IL-1B fold change = 2.11, p-value = 0.02), an enzyme (PKCB fold change = 2.30, p-value = 0.01), transcription factors (NFKB-p50 fold change = 2.05, p-value = 0.01), (NFKB-p65 fold change = 2.82, p-value = 0.003), and an adhesion molecule (CD18 fold change = 2.59, 0.02). Protein expression of CD18 was also increased (p-value = 2.14x10^-5). The lymphoblastoid cell lines demonstrated increased adhesiveness to endothelial cells (p = 1.28x10^-5). Reactive oxygen species were increased (p = 2.56x10^-6). Significant inter-individual variation among the lymphoblastoid cell lines in these responses was evident (F = 18.70, p < 0.0001).

Conclusions

Exposure of lymphoblastoid cell lines derived from different human subjects to high glucose demonstrated differential and heterogeneous gene expression, adhesion, and cellular effects that recapitulated features found in the diabetic state. Lymphoblastoid cells may represent a useful tool to guide an individualized understanding of the development and potential treatment of diabetic complications like retinopathy.

Functional Analysis of a Novel Genome-Wide Association Study Signal in SMAD3 That Confers Protection From Coronary Artery Disease

OBJECTIVE

A recent genome-wide association study meta-analysis identified an intronic single nucleotide polymorphism in SMAD3, rs56062135C>T, the minor allele (T) which associates with protection from coronary artery disease. Relevant to atherosclerosis, SMAD3 is a key contributor to transforming growth factor-β pathway signaling. Here, we seek to identify ≥1 causal coronary artery disease-associated single nucleotide polymorphisms at the SMAD3 locus and characterize mechanisms whereby the risk allele(s) contribute to coronary artery disease risk.

APPROACH AND RESULTS

By genetic and epigenetic fine mapping, we identified a candidate causal single nucleotide polymorphism rs17293632C>T (D', 0.97; r(2), 0.94 with rs56062135) in intron 1 of SMAD3 with predicted functional effects. We show that the sequence encompassing rs17293632 acts as a strong enhancer in human arterial smooth muscle cells. The common allele (C) preserves an activator protein (AP)-1 site and enhancer function, whereas the protective (T) allele disrupts the AP-1 site and significantly reduces enhancer activity (P<0.001). Pharmacological inhibition of AP-1 activity upstream demonstrates that this allele-specific enhancer effect is AP-1 dependent (P<0.001). Chromatin immunoprecipitation experiments reveal binding of several AP-1 component proteins with preferential binding to the (C) allele. We show that rs17293632 is an expression quantitative trait locus for SMAD3 in blood and atherosclerotic plaque with reduced expression of SMAD3 in carriers of the protective allele. Finally, siRNA knockdown of SMAD3 in human arterial smooth muscle cells increases cell viability, consistent with an antiproliferative role.

CONCLUSIONS

The coronary artery disease-associated rs17293632C>T single nucleotide polymorphism represents a novel functional cis-acting element at the SMAD3 locus. The protective (T) allele of rs17293632 disrupts a consensus AP-1 binding site in a SMAD3 intron 1 enhancer, reduces enhancer activity and SMAD3 expression, altering human arterial smooth muscle cell proliferation.

eQTL analysis links inflammatory bowel disease associated 1q21 locus to ECM1 gene

Genome-wide association studies (GWAS) have been highly successful in inflammatory bowel disease (IBD) with 163 confirmed associations so far. We used expression quantitative trait loci (eQTL) mapping to analyze IBD associated regions for which causative gene from the region is still unknown. First, we performed an extensive literature search and in silico analysis of published GWAS in IBD and eQTL studies and extracted 402 IBD associated SNPs assigned to 208 candidate loci, and 9562 eQTL correlations. When crossing GWA and eQTL data we found that for 50 % of loci there is no eQTL gene, while for 31.2 % we can determine one gene, for 11.1 % two genes and for the remaining 7.7 % three or more genes. Based on that we selected loci with one, two, and three or more eQTL genes and analyzed them in peripheral blood lymphocytes and intestine tissue samples of 606 Slovene patients with IBD and in 449 controls. Association analysis of selected SNPs showed statistical significance for three (rs2631372 and rs1050152 on 5q locus and rs13294 on 1q locus) out of six selected SNPs with at least one phenotype. Furthermore, with eQTL analysis of selected chromosomal regions, we confirmed a link between SNP and gene for four (SLC22A5 on 5q, ECM1 on 1q, ORMDL3 on 17q, and PUS10 on 2p locus) out of five selected regions. For 1q21 loci, we confirmed gene ECM1 as the most plausible gene from this region to be involved in pathogenesis of IBD and thereby contributed new eQTL correlation from this genomic region.

Epigenetic modifications are associated with inter-species gene expression variation in primates

BACKGROUND

Changes in gene regulation have long been thought to play an important role in evolution and speciation, especially in primates. Over the past decade, comparative genomic studies have revealed extensive inter-species differences in gene expression levels, yet we know much less about the extent to which regulatory mechanisms differ between species.

RESULTS

To begin addressing this gap, we perform a comparative epigenetic study in primate lymphoblastoid cell lines, to query the contribution of RNA polymerase II and four histone modifications, H3K4me1, H3K4me3, H3K27ac, and H3K27me3, to inter-species variation in gene expression levels. We find that inter-species differences in mark enrichment near transcription start sites are significantly more often associated with inter-species differences in the corresponding gene expression level than expected by chance alone. Interestingly, we also find that first-order interactions among the five marks, as well as chromatin states, do not markedly contribute to the degree of association between the marks and inter-species variation in gene expression levels, suggesting that the marginal effects of the five marks dominate this contribution.

CONCLUSIONS

Our observations suggest that epigenetic modifications are substantially associated with changes in gene expression levels among primates and may represent important molecular mechanisms in primate evolution.

Measuring circadian clock function in human cells

Circadian clocks are present in most cells and are essential for maintenance of daily rhythms in physiology, mood, and cognition. Thus, not only neurons of the central circadian pacemaker but also many other peripheral tissues possess the same functional and self-sustained circadian clocks. Surprisingly, however, their properties vary widely within the human population. In recent years, this clock variance has been studied extensively both in health and in disease using robust lentivirus-based reporter technologies to probe circadian function in human peripheral cells as proxies for those in neurologically and physiologically relevant but inaccessible tissues. The same procedures can be used to investigate other conserved signal transduction cascades affecting multiple aspects of human physiology, behavior, and disease. Accessing gene expression variation within human populations via these powerful in vitro cell-based technologies could provide important insights into basic phenotypic diversity or to better interpret patterns of gene expression variation in disease.

Genetic variation in human DNA replication timing

Genomic DNA replicates in a choreographed temporal order that impacts the distribution of mutations along the genome. We show here that DNA replication timing is shaped by genetic polymorphisms that act in cis upon megabase-scale DNA segments. In genome sequences from proliferating cells, read depth along chromosomes reflected DNA replication activity in those cells. We used this relationship to analyze variation in replication timing among 161 individuals sequenced by the 1000 Genomes Project. Genome-wide association of replication timing with genetic variation identified 16 loci at which inherited alleles associate with replication timing. We call these "replication timing quantitative trait loci" (rtQTLs). rtQTLs involved the differential use of replication origins, exhibited allele-specific effects on replication timing, and associated with gene expression variation at megabase scales. Our results show replication timing to be shaped by genetic polymorphism and identify a means by which inherited polymorphism regulates the mutability of nearby sequences.

Functional analysis and molecular characterization of spontaneously outgrown human lymphoblastoid cell lines

In vitro, the infection of human B-cells with the lymphotropic gammaherpesvirus Epstein-Barr virus (EBV) induces formation of permanently growing lymphoblastoid cell lines (LCL). In a spontaneously outgrown LCL (cell line CSIII), we detected nucleotide sequence variations of the EBV nuclear antigen 1 (EBNA1) RNA that was different from the reference sequence of EBNA1 in the prototypic EBV strain B95-8. In the present study, we molecularly and functionally characterized this virus isolate in comparison to LCL with the prototypic nucleotide sequence. Although we detected high functional similarity between CSIII and the other LCL, our data suggest that the lytic cycle might be ineffective in the CSIII LCL. DNA microarray analysis indicated that RNA binding motif, single stranded interacting protein 1 (RBMS1), which is typically expressed in latency III of EBV to prevent the lytic cycle, was the most overexpressed gene in CSIII LCL.

Identification of candidate genes involved in coronary artery calcification by transcriptome sequencing of cell lines

Background

Massively-parallel cDNA sequencing (RNA-Seq) is a new technique that holds great promise for cardiovascular genomics. Here, we used RNA-Seq to study the transcriptomes of matched coronary artery disease cases and controls in the ClinSeq® study, using cell lines as tissue surrogates.

Results

Lymphoblastoid cell lines (LCLs) from 16 cases and controls representing phenotypic extremes for coronary calcification were cultured and analyzed using RNA-Seq. All cell lines were then independently re-cultured and along with another set of 16 independent cases and controls, were profiled with Affymetrix microarrays to perform a technical validation of the RNA-Seq results. Statistically significant changes (p < 0.05) were detected in 186 transcripts, many of which are expressed at extremely low levels (5–10 copies/cell), which we confirmed through a separate spike-in control RNA-Seq experiment. Next, by fitting a linear model to exon-level RNA-Seq read counts, we detected signals of alternative splicing in 18 transcripts. Finally, we used the RNA-Seq data to identify differential expression (p < 0.0001) in eight previously unannotated regions that may represent novel transcripts. Overall, differentially expressed genes showed strong enrichment (p = 0.0002) for prior association with cardiovascular disease. At the network level, we found evidence for perturbation in pathways involving both cardiovascular system development and function as well as lipid metabolism.

Conclusions

We present a pilot study for transcriptome involvement in coronary artery calcification and demonstrate how RNA-Seq analyses using LCLs as a tissue surrogate may yield fruitful results in a clinical sequencing project. In addition to canonical gene expression, we present candidate variants from alternative splicing and novel transcript detection, which have been unexplored in the context of this disease.

Genetic variation is the major determinant of individual differences in leukocyte endothelial adhesion

Objective

To determine the genetic contribution to leukocyte endothelial adhesion.

Methods

Leukocyte endothelial adhesion was assessed through a novel cell-based assay using human lymphoblastoid cell lines. A high-throughput screening method was developed to evaluate the inter-individual variability in leukocyte endothelial adhesion using lymphoblastoid cell lines derived from different donors. To assess heritability, ninety-two lymphoblastoid cell lines derived from twenty-three monozygotic twin pairs and twenty-three sibling pairs were compared. These lymphoblastoid cell lines were plated with the endothelial cell line EA.hy926 and labeled with Calcein AM dye. Fluorescence was assessed to determine endothelial cell adhesion to each lymphoblastoid cell line. Intra-pair similarity was determined for monozygotic twins and siblings using Pearson pairwise correlation coefficients.

Results

A leukocyte endothelial adhesion assay for lymphoblastoid cell lines was developed and optimized (CV = 8.68, Z′-factor = 0.67, SNR = 18.41). A higher adhesion correlation was found between the twins than that between the siblings. Intra-pair similarity for leukocyte endothelial adhesion in monozygotic twins was 0.60 compared to 0.25 in the siblings. The extent to which these differences are attributable to underlying genetic factors was quantified and the heritability of leukocyte endothelial adhesion was calculated to be 69.66% (p-value<0.0001).

Conclusions

There is a heritable component to leukocyte endothelial adhesion. Underlying genetic predisposition plays a significant role in inter-individual variability of leukocyte endothelial adhesion.

Mapping eQTLs in the Norfolk Island genetic isolate identifies candidate genes for CVD risk traits

Cardiovascular disease (CVD) affects millions of people worldwide and is influenced by numerous factors, including lifestyle and genetics. Expression quantitative trait loci (eQTLs) influence gene expression and are good candidates for CVD risk. Founder-effect pedigrees can provide additional power to map genes associated with disease risk. Therefore, we identified eQTLs in the genetic isolate of Norfolk Island (NI) and tested for associations between these and CVD risk factors. We measured genome-wide transcript levels of blood lymphocytes in 330 individuals and used pedigree-based heritability analysis to identify heritable transcripts. eQTLs were identified by genome-wide association testing of these transcripts. Testing for association between CVD risk factors (i.e., blood lipids, blood pressure, and body fat indices) and eQTLs revealed 1,712 heritable transcripts (p < 0.05) with heritability values ranging from 0.18 to 0.84. From these, we identified 200 cis-acting and 70 trans-acting eQTLs (p < 1.84 × 10(-7)) An eQTL-centric analysis of CVD risk traits revealed multiple associations, including 12 previously associated with CVD-related traits. Trait versus eQTL regression modeling identified four CVD risk candidates (NAAA, PAPSS1, NME1, and PRDX1), all of which have known biological roles in disease. In addition, we implicated several genes previously associated with CVD risk traits, including MTHFR and FN3KRP. We have successfully identified a panel of eQTLs in the NI pedigree and used this to implicate several genes in CVD risk. Future studies are required for further assessing the functional importance of these eQTLs and whether the findings here also relate to outbred populations.

Impact of interleukin-6 -174 G>C gene promoter polymorphism on neuroblastoma

BACKGROUND

Common variants in DNA may predispose to onset and progression of neuroblastoma (NB). The genotype GG of single nucleotide polymorphism (SNP) rs1800795 (-174 G>C) in interleukin (IL)-6 promoter has been associated with lower survival of high-risk NB.

RESULT

To evaluate the impact of IL-6 SNP rs1800795 on disease risk and phenotype, we analyzed 326 Italian NB patients and 511 controls. Moreover, we performed in silico and quantitative Real Time (qRT)-PCR analyses to evaluate the influence of the SNP on gene expression in 198 lymphoblastoid cell lines (LCLs) and in 31 NB tumors, respectively. Kaplan-Meier analysis was used to verify the association between IL-6 gene expression and patient survival. We found that IL-6 SNP is not involved in susceptibility to NB development. However, our results show that a low frequency of genotype CC is significantly associated with a low overall survival, advanced stage, and high-risk phenotype. The in silico (p = 2.61 × 10(-5)) and qRT-PCR (p = 0.03) analyses showed similar trend indicating that the CC genotype is correlated with increased level of IL-6 expression. In report gene assay, we showed that the -174 C variant had a significantly increased transcriptional activity compared with G allele (p = 0.0006). Moreover, Kaplan-Meier analysis demonstrated that high levels of IL-6 are associated with poor outcome in children with NB in two independent gene expression array datasets.

CONCLUSIONS

The biological effect of SNP IL-6-174 G>C in relation to promotion of cancer progression is consistent with the observed decreased survival time. The present study suggests that SNP IL-6-174 G>C may be a useful marker for NB prognosis.

ARLTS1 and prostate cancer risk--analysis of expression and regulation

Prostate cancer (PCa) is a heterogeneous trait for which several susceptibility loci have been implicated by genome-wide linkage and association studies. The genomic region 13q14 is frequently deleted in tumour tissues of both sporadic and familial PCa patients and is consequently recognised as a possible locus of tumour suppressor gene(s). Deletions of this region have been found in many other cancers. Recently, we showed that homozygous carriers for the T442C variant of the ARLTS1 gene (ADP-ribosylation factor-like tumour suppressor protein 1 or ARL11, located at 13q14) are associated with an increased risk for both unselected and familial PCa. Furthermore, the variant T442C was observed in greater frequency among malignant tissue samples, PCa cell lines and xenografts, supporting its role in PCa tumourigenesis. In this study, 84 PCa cases and 15 controls were analysed for ARLTS1 expression status in blood-derived RNA. A statistically significant (p = 0.0037) decrease of ARLTS1 expression in PCa cases was detected. Regulation of ARLTS1 expression was analysed with eQTL (expression quantitative trait loci) methods. Altogether fourteen significant cis-eQTLs affecting the ARLTS1 expression level were found. In addition, epistatic interactions of ARLTS1 genomic variants with genes involved in immune system processes were predicted with the MDR program. In conclusion, this study further supports the role of ARLTS1 as a tumour suppressor gene and reveals that the expression is regulated through variants localised in regulatory regions.

Integrative genomic analysis of the human immune response to influenza vaccination

Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy. We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine. We identified 20 genes exhibiting a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. The results show that variation at the level of genes involved in membrane trafficking and antigen processing significantly influences the human response to influenza vaccination. More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits. DOI:http://dx.doi.org/10.7554/eLife.00299.001.

Impact of natural genetic variation on gene expression dynamics

DNA sequence variation causes changes in gene expression, which in turn has profound effects on cellular states. These variations affect tissue development and may ultimately lead to pathological phenotypes. A genetic locus containing a sequence variation that affects gene expression is called an “expression quantitative trait locus” (eQTL). Whereas the impact of cellular context on expression levels in general is well established, a lot less is known about the cell-state specificity of eQTL. Previous studies differed with respect to how “dynamic eQTL” were defined. Here, we propose a unified framework distinguishing static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. Further, we introduce a new approach to simultaneously infer eQTL from different cell types. By using murine mRNA expression data from four stages of hematopoiesis and 14 related cellular traits, we demonstrate that static, conditional and dynamic eQTL, although derived from the same expression data, represent functionally distinct types of eQTL. While static eQTL affect generic cellular processes, non-static eQTL are more often involved in hematopoiesis and immune response. Our analysis revealed substantial effects of individual genetic variation on cell type-specific expression regulation. Among a total number of 3,941 eQTL we detected 2,729 static eQTL, 1,187 eQTL were conditionally active in one or several cell types, and 70 eQTL affected expression changes during cell type transitions. We also found evidence for feedback control mechanisms reverting the effect of an eQTL specifically in certain cell types. Loci correlated with hematological traits were enriched for conditional eQTL, thus, demonstrating the importance of conditional eQTL for understanding molecular mechanisms underlying physiological trait variation. The classification proposed here has the potential to streamline and unify future analysis of conditional and dynamic eQTL as well as many other kinds of QTL data.

Complex physiological traits are affected through subtle changes of molecular traits like gene expression in the relevant tissues, which in turn are caused by genetic variation. A genetic locus containing a sequence variation affecting gene expression is called an expression quantitative trait locus (eQTL). Understanding the tissue and cell type specificity of eQTL effects is essential for revealing the molecular mechanisms underlying disease phenotypes. However, so far the cell-state dependence of eQTL is poorly understood. In order to systematically assess the importance of cell state-specific eQTL, we propose to distinguish static, conditional and dynamic eQTL and suggest strategies for mapping these eQTL classes. We applied our framework to mouse gene expression data from four hematopoietic stages and related cellular traits. The different eQTL classes, although derived from the same expression data, represent functionally distinct types of eQTL. Importantly, conditional eQTL are well correlated with relevant hematological traits. These findings emphasize the condition specificity of many regulatory relationships, even if the conditions under study are related. This calls for due caution when transferring conclusions about regulatory mechanisms across cell types or tissues. The proposed classification will also help to unravel dynamic behaviors in many other kinds of QTL data.

Global properties and functional complexity of human gene regulatory variation

Identification and functional interpretation of gene regulatory variants is a major focus of modern genomics. The application of genetic mapping to molecular and cellular traits has enabled the detection of regulatory variation on genome-wide scales and revealed an enormous diversity of regulatory architecture in humans and other species. In this review I summarise the insights gained and questions raised by a decade of genetic mapping of gene expression variation. I discuss recent extensions of this approach using alternative molecular phenotypes that have revealed some of the biological mechanisms that drive gene expression variation between individuals. Finally, I highlight outstanding problems and future directions for development.

Development of strategies for SNP detection in RNA-seq data: application to lymphoblastoid cell lines and evaluation using 1000 Genomes data

Next-generation RNA sequencing (RNA-seq) maps and analyzes transcriptomes and generates data on sequence variation in expressed genes. There are few reported studies on analysis strategies to maximize the yield of quality RNA-seq SNP data. We evaluated the performance of different SNP-calling methods following alignment to both genome and transcriptome by applying them to RNA-seq data from a HapMap lymphoblastoid cell line sample and comparing results with sequence variation data from 1000 Genomes. We determined that the best method to achieve high specificity and sensitivity, and greatest number of SNP calls, is to remove duplicate sequence reads after alignment to the genome and to call SNPs using SAMtools. The accuracy of SNP calls is dependent on sequence coverage available. In terms of specificity, 89% of RNA-seq SNPs calls were true variants where coverage is >10X. In terms of sensitivity, at >10X coverage 92% of all expected SNPs in expressed exons could be detected. Overall, the results indicate that RNA-seq SNP data are a very useful by-product of sequence-based transcriptome analysis. If RNA-seq is applied to disease tissue samples and assuming that genes carrying mutations relevant to disease biology are being expressed, a very high proportion of these mutations can be detected.

FGFR2 mutation confers a less drastic gain of function in mesenchymal stem cells than in fibroblasts

Gain-of-function mutations in FGFR2 cause Apert syndrome (AS), a disease characterized by craniosynostosis and limb bone defects both due to abnormalities in bone differentiation and remodeling. Although the periosteum is an important cell source for bone remodeling, its role in craniosynostosis remains poorly characterized. We hypothesized that periosteal mesenchymal stem cells (MSCs) and fibroblasts from AS patients have abnormal cell phenotypes that contribute to the recurrent fusion of the coronal sutures. MSCs and fibroblasts were obtained from the periostea of 3 AS patients (S252W) and 3 control individuals (WT). We evaluated the proliferation, migration, and osteogenic differentiation of these cells. Interestingly, S252W mutation had opposite effects on different cell types: S252W MSCs proliferated less than WT MSCs, while S252W fibroblasts proliferated more than WT fibroblasts. Under restrictive media conditions, only S252W fibroblasts showed enhanced migration. The presence of S252W mutation increased in vitro and in vivo osteogenic differentiation in both studied cell types, though the difference compared to WT cells was more pronounced in S252W fibroblasts. This osteogenic differentiation was reversed through inhibition of JNK. We demonstrated that S252W fibroblasts can induce osteogenic differentiation in periosteal MSCs but not in MSCs from another tissue. MSCs and fibroblasts responded differently to the pathogenic effects of the FGFR2^S252W mutation. We propose that cells from the periosteum have a more important role in the premature fusion of cranial sutures than previously thought and that molecules in JNK pathway are strong candidates for the treatment of AS patients.

Coordinating GWAS results with gene expression in a systems immunologic paradigm in autoimmunity

There has been considerable progress in our understanding of the genetic architecture of susceptibility to inflammatory diseases in recent years: several hundred susceptibility loci have been discovered in genome-wide association studies (GWAS) of human populations. This success has created an important challenge in identifying the functional consequences of these risk-associated variants and in elucidating how the repercussions of individual susceptibility loci integrate to yield dysregulation of immune pathways and, ultimately, syndromic clinical phenotypes. The integration of GWAS association signals with high-resolution transcriptome and other genomic data that capture the dynamics of cellular state and function in the context of individual's collection of susceptibility alleles has proven to be a successful avenue of investigation. The rapid pace of methodological development in this area has been coupled with an accumulation of experimental data that makes the elucidation of complex biological networks underlying susceptibility to these common inflammatory diseases a reasonable goal in the near future.

In depth comparison of an individual's DNA and its lymphoblastoid cell line using whole genome sequencing

BACKGROUND

A detailed analysis of whole genomes can be now achieved with next generation sequencing. Epstein Barr Virus (EBV) transformation is a widely used strategy in clinical research to obtain an unlimited source of a subject's DNA. Although the mechanism of transformation and immortalization by EBV is relatively well known at the transcriptional and proteomic level, the genetic consequences of EBV transformation are less well understood. A detailed analysis of the genetic alterations introduced by EBV transformation is highly relevant, as it will inform on the usefulness and limitations of this approach.

RESULTS

We used whole genome sequencing to assess the genomic signature of a low-passage lymphoblastoid cell line (LCL). Specifically, we sequenced the full genome (40X) of an individual using DNA purified from fresh whole blood as well as DNA from his LCL. A total of 217.33 Gb of sequence were generated from the cell line and 238.95 Gb from the normal genomic DNA. We determined with high confidence that 99.2% of the genomes were identical, with no reproducible changes in structural variation (chromosomal rearrangements and copy number variations) or insertion/deletion polymorphisms (indels).

CONCLUSIONS

Our results suggest that, at this level of resolution, the LCL is genetically indistinguishable from its genomic counterpart and therefore their use in clinical research is not likely to introduce a significant bias.

Genome-wide association study of prognosis in advanced non-small cell lung cancer patients receiving platinum-based chemotherapy

PURPOSE

Genetic variation may influence chemotherapy response and overall survival in cancer patients.

EXPERIMENTAL DESIGN

We conducted a genome-wide scan in 535 advanced-stage non-small cell lung cancer (NSCLC) patients from two independent cohorts (307 from Nanjing and 228 from Beijing). A replication was carried out on an independent cohort of 340 patients from Southeastern China followed by a second validation on 409 patients from the Massachusetts General Hospital (Boston, MA).

RESULTS

Consistent associations with NSCLC survival were identified for five single-nucleotide polymorphisms (SNP) in Chinese populations with P values ranging from 3.63 × 10(-5) to 4.19 × 10(-7) in the additive genetic model. The minor allele of three SNPs (rs7629386 at 3p22.1, rs969088 at 5p14.1, and rs3850370 at 14q24.3) were associated with worse NSCLC survival while 2 (rs41997 at 7q31.31 and rs12000445 at 9p21.3) were associated with better NSCLC survival. In addition, rs7629386 at 3p22.1 (CTNNB1) and rs3850370 at 14q24.3 (SNW1-ALKBH1-NRXN3) were further replicated in the Caucasian population.

CONCLUSION

In this three-stage genome-wide association studies, we identified five SNPs as markers for survival of advanced-stage NSCLC patients treated with first-line platinum-based chemotherapy in Chinese Han populations. Two of these SNPs, rs7629386 and rs3850370, could also be markers for survival among Caucasian patients.

Comprehensive microRNA profiling in B-cells of human centenarians by massively parallel sequencing

BACKGROUND

MicroRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression and play a critical role in development, homeostasis, and disease. Despite their demonstrated roles in age-associated pathologies, little is known about the role of miRNAs in human aging and longevity.

RESULTS

We employed massively parallel sequencing technology to identify miRNAs expressed in B-cells from Ashkenazi Jewish centenarians, i.e., those living to a hundred and a human model of exceptional longevity, and younger controls without a family history of longevity. With data from 26.7 million reads comprising 9.4 × 108 bp from 3 centenarian and 3 control individuals, we discovered a total of 276 known miRNAs and 8 unknown miRNAs ranging several orders of magnitude in expression levels, a typical characteristics of saturated miRNA-sequencing. A total of 22 miRNAs were found to be significantly upregulated, with only 2 miRNAs downregulated, in centenarians as compared to controls. Gene Ontology analysis of the predicted and validated targets of the 24 differentially expressed miRNAs indicated enrichment of functional pathways involved in cell metabolism, cell cycle, cell signaling, and cell differentiation. A cross sectional expression analysis of the differentially expressed miRNAs in B-cells from Ashkenazi Jewish individuals between the 50th and 100th years of age indicated that expression levels of miR-363* declined significantly with age. Centenarians, however, maintained the youthful expression level. This result suggests that miR-363* may be a candidate longevity-associated miRNA.

CONCLUSION

Our comprehensive miRNA data provide a resource for further studies to identify genetic pathways associated with aging and longevity in humans.

Lymphoblastoid cell lines in pharmacogenomic discovery and clinical translation

The ability to predict how an individual patient will respond to a particular treatment is the ambitious goal of personalized medicine. The genetic make up of an individual has been shown to play a role in drug response. For pharmacogenomic studies, human lymphoblastoid cell lines (LCLs) comprise a useful model system for identifying genetic variants associated with pharmacologic phenotypes. The availability of extensive genotype data for many panels of LCLs derived from individuals of diverse ancestry allows for the study of genetic variants contributing to interethnic and interindividual variation in susceptibility to drugs. Many genome-wide association studies for drug-induced phenotypes have been performed in LCLs, often incorporating gene-expression data. LCLs are also being used in follow-up studies to clinical findings to determine how an associated variant functions to affect phenotype. This review describes the most recent pharmacogenomic findings made in LCLs, including the translation of some findings to clinical cohorts.

Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain

Genome-wide association studies have nominated many genetic variants for common human traits, including diseases, but in many cases the underlying biological reason for a trait association is unknown. Subsets of genetic polymorphisms show a statistical association with transcript expression levels, and have therefore been nominated as expression quantitative trait loci (eQTL). However, many tissue and cell types have specific gene expression patterns and so it is not clear how frequently eQTLs found in one tissue type will be replicated in others. In the present study we used two appropriately powered sample series to examine the genetic control of gene expression in blood and brain. We find that while many eQTLs associated with human traits are shared between these two tissues, there are also examples where blood and brain differ, either by restricted gene expression patterns in one tissue or because of differences in how genetic variants are associated with transcript levels. These observations suggest that design of eQTL mapping experiments should consider tissue of interest for the disease or other traits studied.

The combination of a genome-wide association study of lymphocyte count and analysis of gene expression data reveals novel asthma candidate genes

Recent genome-wide association studies (GWAS) have identified a number of novel genetic associations with complex human diseases. In spite of these successes, results from GWAS generally explain only a small proportion of disease heritability, an observation termed the ‘missing heritability problem’. Several sources for the missing heritability have been proposed, including the contribution of many common variants with small individual effect sizes, which cannot be reliably found using the standard GWAS approach. The goal of our study was to explore a complimentary approach, which combines GWAS results with functional data in order to identify novel genetic associations with small effect sizes. To do so, we conducted a GWAS for lymphocyte count, a physiologic quantitative trait associated with asthma, in 462 Hutterites. In parallel, we performed a genome-wide gene expression study in lymphoblastoid cell lines from 96 Hutterites. We found significant support for genetic associations using the GWAS data when we considered variants near the 193 genes whose expression levels across individuals were most correlated with lymphocyte counts. Interestingly, these variants are also enriched with signatures of an association with asthma susceptibility, an observation we were able to replicate. The associated loci include genes previously implicated in asthma susceptibility as well as novel candidate genes enriched for functions related to T cell receptor signaling and adenosine triphosphate synthesis. Our results, therefore, establish a new set of asthma susceptibility candidate genes. More generally, our observations support the notion that many loci of small effects influence variation in lymphocyte count and asthma susceptibility.

Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression

It is known that genetic variants can affect gene expression, but it is not yet completely clear through what mechanisms genetic variation mediate this expression. We therefore compared the cis-effect of single nucleotide polymorphisms (SNPs) on gene expression between blood samples from 1,240 human subjects and four primary non-blood tissues (liver, subcutaneous, and visceral adipose tissue and skeletal muscle) from 85 subjects. We characterized four different mechanisms for 2,072 probes that show tissue-dependent genetic regulation between blood and non-blood tissues: on average 33.2% only showed cis-regulation in non-blood tissues; 14.5% of the eQTL probes were regulated by different, independent SNPs depending on the tissue of investigation. 47.9% showed a different effect size although they were regulated by the same SNPs. Surprisingly, we observed that 4.4% were regulated by the same SNP but with opposite allelic direction. We show here that SNPs that are located in transcriptional regulatory elements are enriched for tissue-dependent regulation, including SNPs at 3' and 5' untranslated regions (P = 1.84×10(-5) and 4.7×10(-4), respectively) and SNPs that are synonymous-coding (P = 9.9×10(-4)). SNPs that are associated with complex traits more often exert a tissue-dependent effect on gene expression (P = 2.6×10(-10)). Our study yields new insights into the genetic basis of tissue-dependent expression and suggests that complex trait associated genetic variants have even more complex regulatory effects than previously anticipated.

Single nucleotide polymorphisms in JAZF1 and BCL11A gene are nominally associated with type 2 diabetes in African-American families from the GENNID study

Prior type 2 diabetes (T2D) genome-wide association studies (GWASs) have generated a list of well-replicated susceptibility loci in populations of European and Asian ancestry. To validate the trans-ethnic contribution of the single-nucleotide polymorphisms (SNPs) involved in these GWASs, we performed a family-based association analysis of 32 selected GWAS SNPs in a cohort of 1496 African-American (AA) subjects from the Genetics of NIDDM (GENNID) study. Functional roles of these SNPs were evaluated by screening cis-eQTLs in transformed lymphoblast cell lines available for a sub-group of Genetics of NIDDM (GENNID) families from Arkansas. Only three of the 32 GWAS-derived SNPs showed nominally significant association with T2D in our AA cohort. Among the replicated SNPs rs864745 in JAZF1 and rs10490072 in BCL11A gene (P=0.006 and 0.03, respectively, after adjustment for body mass index) were within the 1-lod drop support interval of T2D linkage peaks reported in these families. Genotyping of 19 tag SNPs in these two loci revealed no further common SNPs or haplotypes that may be a stronger predictor of T2D susceptibility than the index SNPs. Six T2D GWAS SNPs (rs6698181, rs9472138, rs730497, rs10811661, rs11037909 and rs1153188) were associated with nearby transcript expression in transformed lymphoblast cell lines of GENNID AA subjects. Thus, our study indicates a nominal role for JAZF1 and BCL11A variants in T2D susceptibility in AAs and suggested little overlap in known susceptibility to T2D between European- and African-derived populations when considering GWAS SNPs alone.

Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent

The degree to which the level of genetic variation for gene expression is shared across multiple tissues has important implications for research investigating the role of expression on the etiology of complex human traits and diseases. In the last few years, several studies have been published reporting the extent of overlap in expression quantitative trait loci (eQTL) identified in multiple tissues or cell types. Although these studies provide important information on the regulatory control of genes across tissues, their limited power means that they can typically only explain a small proportion of genetic variation for gene expression. Here, using expression data from monozygotic twins (MZ), we investigate the genetic control of gene expression in lymphoblastoid cell lines (LCL) and whole blood (WB). We estimate the genetic correlation that represents the combined effects of all causal loci across the whole genome and is a measure of the level of common genetic control of gene expression between the two RNA sources. Our results show that, when averaged across the genome, mean levels of genetic correlation for gene expression in LCL and WB samples are close to zero. We support our results with evidence from gene expression in an independent sample of LCL, T-cells, and fibroblasts. In addition, we provide evidence that housekeeping genes, which maintain basic cellular functions, are more likely to have high genetic correlations between the RNA sources than non-housekeeping genes, implying a relationship between the transcript function and the degree to which a gene has tissue-specific genetic regulatory control.

Genetic variability of transcript abundance in pig peri-mortem skeletal muscle: eQTL localized genes involved in stress response, cell death, muscle disorders and metabolism

Background

The genetics of transcript-level variation is an exciting field that has recently given rise to many studies. Genetical genomics studies have mainly focused on cell lines, blood cells or adipose tissues, from human clinical samples or mice inbred lines. Few eQTL studies have focused on animal tissues sampled from outbred populations to reflect natural genetic variation of gene expression levels in animals. In this work, we analyzed gene expression in a whole tissue, pig skeletal muscle sampled from individuals from a half sib F2 family shortly after slaughtering.

Results

QTL detection on transcriptome measurements was performed on a family structured population. The analysis identified 335 eQTLs affecting the expression of 272 transcripts. The ontologic annotation of these eQTLs revealed an over-representation of genes encoding proteins involved in processes that are expected to be induced during muscle development and metabolism, cell morphology, assembly and organization and also in stress response and apoptosis. A gene functional network approach was used to evidence existing biological relationships between all the genes whose expression levels are influenced by eQTLs. eQTLs localization revealed a significant clustered organization of about half the genes located on segments of chromosome 1, 2, 10, 13, 16, and 18. Finally, the combined expression and genetic approaches pointed to putative cis-drivers of gene expression programs in skeletal muscle as COQ4 (SSC1), LOC100513192 (SSC18) where both the gene transcription unit and the eQTL affecting its expression level were shown to be localized in the same genomic region. This suggests cis-causing genetic polymorphims affecting gene expression levels, with (e.g. COQ4) or without (e.g. LOC100513192) potential pleiotropic effects that affect the expression of other genes (cluster of trans-eQTLs).

Conclusion

Genetic analysis of transcription levels revealed dependence among molecular phenotypes as being affected by variation at the same loci. We observed the genetic variation of molecular phenotypes in a specific situation of cellular stress thus contributing to a better description of muscle physiologic response. In turn, this suggests that large amounts of genetic variation, mediated through transcriptional networks, can drive transient cell response phenotypes and contribute to organismal adaptative potential.

The use of genome-wide eQTL associations in lymphoblastoid cell lines to identify novel genetic pathways involved in complex traits

The integrated analysis of genotypic and expression data for association with complex traits could identify novel genetic pathways involved in complex traits. We profiled 19,573 expression probes in Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs) from 299 twins and correlated these with 44 quantitative traits (QTs). For 939 expressed probes correlating with more than one QT, we investigated the presence of eQTL associations in three datasets of 57 CEU HapMap founders and 86 unrelated twins. Genome-wide association analysis of these probes with 2.2 m SNPs revealed 131 potential eQTLs (1,989 eQTL SNPs) overlapping between the HapMap datasets, five of which were in cis (58 eQTL SNPs). We then tested 535 SNPs tagging the eQTL SNPs, for association with the relevant QT in 2,905 twins. We identified nine potential SNP-QT associations (P<0.01) but none significantly replicated in five large consortia of 1,097–16,129 subjects. We also failed to replicate previous reported eQTL associations with body mass index, plasma low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides levels derived from lymphocytes, adipose and liver tissue. Our results and additional power calculations suggest that proponents may have been overoptimistic in the power of LCLs in eQTL approaches to elucidate regulatory genetic effects on complex traits using the small datasets generated to date. Nevertheless, larger tissue-specific expression data sets relevant to specific traits are becoming available, and should enable the adoption of similar integrated analyses in the near future.

Interactions between glucocorticoid treatment and cis-regulatory polymorphisms contribute to cellular response phenotypes

Glucocorticoids (GCs) mediate physiological responses to environmental stress and are commonly used as pharmaceuticals. GCs act primarily through the GC receptor (GR, a transcription factor). Despite their clear biomedical importance, little is known about the genetic architecture of variation in GC response. Here we provide an initial assessment of variability in the cellular response to GC treatment by profiling gene expression and protein secretion in 114 EBV-transformed B lymphocytes of African and European ancestry. We found that genetic variation affects the response of nearby genes and exhibits distinctive patterns of genotype-treatment interactions, with genotypic effects evident in either only GC-treated or only control-treated conditions. Using a novel statistical framework, we identified interactions that influence the expression of 26 genes known to play central roles in GC-related pathways (e.g. NQO1, AIRE, and SGK1) and that influence the secretion of IL6.

MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects

MOTIVATION

Sample mix-ups can arise during sample collection, handling, genotyping or data management. It is unclear how often sample mix-ups occur in genome-wide studies, as there currently are no post hoc methods that can identify these mix-ups in unrelated samples. We have therefore developed an algorithm (MixupMapper) that can both detect and correct sample mix-ups in genome-wide studies that study gene expression levels.

RESULTS

We applied MixupMapper to five publicly available human genetical genomics datasets. On average, 3% of all analyzed samples had been assigned incorrect expression phenotypes: in one of the datasets 23% of the samples had incorrect expression phenotypes. The consequences of sample mix-ups are substantial: when we corrected these sample mix-ups, we identified on average 15% more significant cis-expression quantitative trait loci (cis-eQTLs). In one dataset, we identified three times as many significant cis-eQTLs after correction. Furthermore, we show through simulations that sample mix-ups can lead to an underestimation of the explained heritability of complex traits in genome-wide association datasets.

AVAILABILITY AND IMPLEMENTATION

MixupMapper is freely available at http://www.genenetwork.nl/mixupmapper/

Gene expression differences among primates are associated with changes in a histone epigenetic modification

Changes in gene regulation are thought to play an important role in speciation and adaptation, especially in primates. However, we still know relatively little about the mechanisms underlying regulatory evolution. In particular, the extent to which epigenetic modifications underlie gene expression differences between primates is not yet known. Our study focuses on an epigenetic histone modification, H3K4me3, which is thought to promote transcription. To investigate the contribution of H3K4me3 to regulatory differences between species, we collected gene expression data and identified H3K4me3-associated genomic regions in lymphoblastoid cell lines (LCLs) from humans, chimpanzees, and rhesus macaques, using three cell lines from each species. We found strong evidence for conservation of H3K4me3 localization in primates. Moreover, regardless of species, H3K4me3 is consistently enriched near annotated transcription start sites (TSS), and highly expressed genes are more likely than lowly expressed genes to have the histone modification near their TSS. Interestingly, we observed an enrichment of interspecies differences in H3K4me3 at the TSS of genes that are differentially expressed between species. We estimate that as much as 7% of gene expression differences between the LCLs of humans, chimpanzees, and rhesus macaques may be explained, at least in part, by changes in the status of H3K4me3 histone modifications. Our results suggest a modest, yet important role for epigenetic changes in gene expression differences between primates.

The effects of EBV transformation on gene expression levels and methylation profiles

Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines (LCLs) provide a conveniently accessible and renewable resource for functional genomic studies in humans. The ability to accumulate multidimensional data pertaining to the same individual cell lines, from complete genomic sequences to detailed gene regulatory profiles, further enhances the utility of LCLs as a model system. A lingering concern, however, is that the changes associated with EBV transformation of B cells reduce the usefulness of LCLs as a surrogate model for primary tissues. To evaluate the validity of this concern, we compared global gene expression and methylation profiles between CD20+ primary B cells sampled from six individuals and six independent replicates of transformed LCLs derived from each sample. These data allowed us to obtain a detailed catalog of the genes and pathways whose regulation is affected by EBV transformation. We found that the expression levels and promoter methylation profiles of more than half of the studied genes were affected by the EBV transformation, including enrichments of genes involved in transcription regulation, cell cycle and immune response. However, we show that most of the differences in gene expression levels between LCLs and B cells are of small magnitude, and that LCLs can often recapitulate the naturally occurring gene expression variation in primary B cells. Thus, our observations suggest that inference of the genetic architecture that underlies regulatory variation in LCLs can typically be generalized to primary B cells. In contrast, inference based on functional studies in LCLs may be more limited to the cell lines.

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.

Mechanisms of genetic regulation in gene expression: examples from drug metabolizing enzymes and transporters

Interindividual variability in the response to drug therapy is due, in part, to genetic mechanisms which influence the expression of genes involved with drug metabolism and transport. Genetic elements and processes such as DNA methylation, histone deacetylation, transcription factors, DNA sequence variants, and microRNAs (miRNAs) can impact at either the transcriptional or translational levels to modulate gene expression. Identification of such genetic regulators has greatly advanced in the last decade. Genome-wide analyses, using different types of approaches and methodologies, have uncovered many potential regulators of the expression of drug metabolizing enzymes and transporters. However, confirming the function of these putative regulators is necessary and requires further work in the laboratory, using techniques which are still evolving. It also still remains to be seen whether these findings have clinical implications for drug therapy but the realization of personalized medicine is a possible consequence of this research.

Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency–matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10⁻⁴–10⁻⁸), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits.

We show here that single nucleotide polymorphisms (SNPs) associated with complex traits (as identified in the catalog of results from genome-wide association studies http://www.genome.gov/gwastudies/) are more likely than other SNPs chosen from high-throughput genotyping platforms to predict expression levels of genes. These observations confirm that genetic risk factors for complex traits will often affect phenotype by altering the amount or timing of protein production, rather than by changing the type of protein produced. This knowledge can be used to improve our ability to discover genetic risk factors for complex traits and to improve our understanding of their underlying biology.