Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population

BACKGROUND

Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource.

RESULTS

We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue.

CONCLUSIONS

By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis.

Rare variant enrichment analysis supports GREB1L as a contributory driver gene in the etiology of Mayer-Rokitansky-Küster-Hauser syndrome

Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is characterized by aplasia of the female reproductive tract; the syndrome can include renal anomalies, absence or dysgenesis, and skeletal anomalies. While functional models have elucidated several candidate genes, only WNT4 (MIM: 603490) variants have been definitively associated with a subtype of MRKH with hyperandrogenism (MIM: 158330). DNA from 148 clinically diagnosed MRKH probands across 144 unrelated families and available family members from North America, Europe, and South America were exome sequenced (ES) and by family-based genomics analyzed for rare likely deleterious variants. A replication cohort consisting of 442 Han Chinese individuals with MRKH was used to further reproduce GREB1L findings in diverse genetic backgrounds. Proband and OMIM phenotypes annotated using the Human Phenotype Ontology were analyzed to quantitatively delineate the phenotypic spectrum associated with GREB1L variant alleles found in our MRKH cohort and those previously published. This study reports 18 novel GREB1L variant alleles, 16 within a multiethnic MRKH cohort and two within a congenital scoliosis cohort. Cohort-wide analyses for a burden of rare variants within a single gene identified likely damaging variants in GREB1L (MIM: 617782), a known disease gene for renal hypoplasia and uterine abnormalities (MIM: 617805), in 16 of 590 MRKH probands. GREB1L variant alleles, including a CNV null allele, were found in 8 MRKH type 1 probands and 8 MRKH type II probands. This study used quantitative phenotypic analyses in a worldwide multiethnic cohort to identify and strengthen the association of GREB1L to isolated uterine agenesis (MRKH type I) and syndromic MRKH type II.

Obesity Reshapes the Microbial Population Structure along the Gut-Liver-Lung Axis in Mice

The microbiome is emerging as a major player in tissue homeostasis in health and disease. Gut microbiome dysbiosis correlates with several autoimmune and metabolic diseases, while high-fat diets and ensuing obesity are known to affect the complexity and diversity of the microbiome, thus modulating pathophysiology. Moreover, the existence of a gut-liver microbial axis has been proposed, which may extend to the lung. In this context, we systematically compared the microbiomes of the gut, liver, and lung of mice fed a high-fat diet to those of littermates fed a matched control diet. We carried out deep sequencing of seven hypervariable regions of the 16S rRNA microbial gene to examine microbial diversity in the tissues of interest. Comparison of the local microbiomes indicated that lung tissue has the least diverse microbiome under healthy conditions, while microbial diversity in the healthy liver clustered closer to the gut. Obesity increased microbial complexity in all three tissues, with lung microbial diversity being the most modified. Obesity promoted the expansion of Firmicutes along the gut-liver-lung axis, highlighting staphylococcus as a possible pathologic link between obesity and systemic pathophysiology, especially in the lungs.

Perturbations of genes essential for Müllerian duct and Wölffian duct development in Mayer-Rokitansky-Küster-Hauser syndrome

Mayer-Rokitansky-Küster-Hauser syndrome (MRKHS) is associated with congenital absence of the uterus, cervix, and the upper part of the vagina; it is a sex-limited trait. Disrupted development of the Müllerian ducts (MD)/Wölffian ducts (WD) through multifactorial mechanisms has been proposed to underlie MRKHS. In this study, exome sequencing (ES) was performed on a Chinese discovery cohort (442 affected subjects and 941 female control subjects) and a replication MRKHS cohort (150 affected subjects of mixed ethnicity from North America, South America, and Europe). Phenotypic follow-up of the female reproductive system was performed on an additional cohort of PAX8-associated congenital hypothyroidism (CH) (n = 5, Chinese). By analyzing 19 candidate genes essential for MD/WD development, we identified 12 likely gene-disrupting (LGD) variants in 7 genes: PAX8 (n = 4), BMP4 (n = 2), BMP7 (n = 2), TBX6 (n = 1), HOXA10 (n = 1), EMX2 (n = 1), and WNT9B (n = 1), while LGD variants in these genes were not detected in control samples (p = 1.27E-06). Interestingly, a sex-limited penetrance with paternal inheritance was observed in multiple families. One additional PAX8 LGD variant from the replication cohort and two missense variants from both cohorts were revealed to cause loss-of-function of the protein. From the PAX8-associated CH cohort, we identified one individual presenting a syndromic condition characterized by CH and MRKHS (CH-MRKHS). Our study demonstrates the comprehensive utilization of knowledge from developmental biology toward elucidating genetic perturbations, i.e., rare pathogenic alleles involving the same loci, contributing to human birth defects.

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

Exome Sequencing in BRCA1- and BRCA2-Negative Greek Families Identifies MDM1 and NBEAL1 as Candidate Risk Genes for Hereditary Breast Cancer

Approximately 10% of breast cancer (BC) cases are hereditary BC (HBC), with HBC most commonly encountered in the context of hereditary breast and ovarian cancer (HBOC) syndrome. Although thousands of loss-of-function (LoF) alleles in over 20 genes have been associated with HBC susceptibility, the genetic etiology of approximately 50% of cases remains unexplained, even when polygenic risk models are considered. We focused on one of the least-studied European populations and applied whole-exome sequencing (WES) to 52 individuals from 17 Greek HBOC families, in which at least one patient was negative for known HBC risk variants. Initial screening revealed pathogenic variants in known cancer genes, including BARD1:p.Trp91* detected in a cancer-free individual, and MEN1:p.Glu260Lys detected in a BC patient. Gene- and variant-based approaches were applied to exome data to identify candidate risk variants outside of known risk genes. Findings were verified in a collection of Canadian HBOC patients of European ancestry (FBRCAX), in an independent group of Canadian BC patients (CHUM-BC) and controls (CARTaGENE), as well as in individuals from The Cancer Genome Atlas (TCGA) and the UK Biobank (UKB). Rare LoF variants were uncovered in MDM1 and NBEAL1 in Greek and Canadian HBOC patients. We also report prioritized missense variants SETBP1:c.4129G > C and C7orf34:c.248C > T. These variants comprise promising candidates whose role in cancer pathogenicity needs to be explored further.

Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS) including 26,488 cases and 83,964 controls of European, East Asian, South Asian, and Mexican and Mexican American ancestry. We observed significant excess in directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven novel T2D susceptibility loci. Furthermore, we observed considerable improvements in fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterisation of complex trait loci, and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.

Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits

Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10(-8)), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.

Mapping cis- and trans-regulatory effects across multiple tissues in twins

Sequence-based variation in gene expression is a key driver of disease risk. Common variants regulating expression in cis have been mapped in many expression quantitative trait locus (eQTL) studies, typically in single tissues from unrelated individuals. Here, we present a comprehensive analysis of gene expression across multiple tissues conducted in a large set of mono- and dizygotic twins that allows systematic dissection of genetic (cis and trans) and non-genetic effects on gene expression. Using identity-by-descent estimates, we show that at least 40% of the total heritable cis effect on expression cannot be accounted for by common cis variants, a finding that reveals the contribution of low-frequency and rare regulatory variants with respect to both transcriptional regulation and complex trait susceptibility. We show that a substantial proportion of gene expression heritability is trans to the structural gene, and we identify several replicating trans variants that act predominantly in a tissue-restricted manner and may regulate the transcription of many genes.

Sex-biased genetic effects on gene regulation in humans

Human regulatory variation, reported as expression quantitative trait loci (eQTLs), contributes to differences between populations and tissues. The contribution of eQTLs to differences between sexes, however, has not been investigated to date. Here we explore regulatory variation in females and males and demonstrate that 12%–15% of autosomal eQTLs function in a sex-biased manner. We show that genes possessing sex-biased eQTLs are expressed at similar levels across the sexes and highlight cases of genes controlling sexually dimorphic and shared traits that are under the control of distinct regulatory elements in females and males. This study illustrates that sex provides important context that can modify the effects of functional genetic variants.

Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis.

Patterns of cis regulatory variation in diverse human populations

The genetic basis of gene expression variation has long been studied with the aim to understand the landscape of regulatory variants, but also more recently to assist in the interpretation and elucidation of disease signals. To date, many studies have looked in specific tissues and population-based samples, but there has been limited assessment of the degree of inter-population variability in regulatory variation. We analyzed genome-wide gene expression in lymphoblastoid cell lines from a total of 726 individuals from 8 global populations from the HapMap3 project and correlated gene expression levels with HapMap3 SNPs located in cis to the genes. We describe the influence of ancestry on gene expression levels within and between these diverse human populations and uncover a non-negligible impact on global patterns of gene expression. We further dissect the specific functional pathways differentiated between populations. We also identify 5,691 expression quantitative trait loci (eQTLs) after controlling for both non-genetic factors and population admixture and observe that half of the cis-eQTLs are replicated in one or more of the populations. We highlight patterns of eQTL-sharing between populations, which are partially determined by population genetic relatedness, and discover significant sharing of eQTL effects between Asians, European-admixed, and African subpopulations. Specifically, we observe that both the effect size and the direction of effect for eQTLs are highly conserved across populations. We observe an increasing proximity of eQTLs toward the transcription start site as sharing of eQTLs among populations increases, highlighting that variants close to TSS have stronger effects and therefore are more likely to be detected across a wider panel of populations. Together these results offer a unique picture and resource of the degree of differentiation among human populations in functional regulatory variation and provide an estimate for the transferability of complex trait variants across populations.

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes

OBJECTIVE

Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.

RESEARCH DESIGN AND METHODS

We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.

RESULTS

Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.

CONCLUSIONS

We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.

The architecture of gene regulatory variation across multiple human tissues: the MuTHER study

While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits.

Regulation of gene expression is a fundamental cellular process determining a large proportion of the phenotypic variance. Previous studies have identified genetic loci influencing gene expression levels (eQTLs), but the complexity of their tissue-specific properties has not yet been well-characterized. In this study, we perform cis-eQTL analysis in a unique matched co-twin design for three human tissues derived simultaneously from the same set of individuals. The study design allows validation of the substantial discoveries we make in each tissue. We explore in depth the tissue-dependent features of regulatory variants and estimate the proportions of shared and specific effects. We use continuous measures of eQTL sharing to circumvent the statistical power limitations of comparing direct overlap of eQTLs in multiple tissues. In this framework, we demonstrate that 30% of eQTLs are shared among tissues, while 29% are exclusively tissue-specific. Furthermore, we show that the fold change in expression between eQTL genotypic classes differs between tissues. Even among shared eQTLs, we report a substantial proportion (10%–20%) of significant tissue differences in magnitude of these effects. The complexities we highlight here are essential for understanding the impact of regulatory variants on complex traits.

Identification of cis- and trans-regulatory variation modulating microRNA expression levels in human fibroblasts

MicroRNAs (miRNAs) are regulatory noncoding RNAs that affect the production of a significant fraction of human mRNAs via post-transcriptional regulation. Interindividual variation of the miRNA expression levels is likely to influence the expression of miRNA target genes and may therefore contribute to phenotypic differences in humans, including susceptibility to common disorders. The extent to which miRNA levels are genetically controlled is largely unknown. In this report, we assayed the expression levels of miRNAs in primary fibroblasts from 180 European newborns of the GenCord project and performed association analysis to identify eQTLs (expression quantitative traits loci). We detected robust expression for 121 miRNAs out of 365 interrogated. We have identified significant cis- (10%) and trans- (11%) eQTLs. Furthermore, we detected one genomic locus (rs1522653) that influences the expression levels of five miRNAs, thus unraveling a novel mechanism for coregulation of miRNA expression.

Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1

Migraine is a common episodic neurological disorder, typically presenting with recurrent attacks of severe headache and autonomic dysfunction. Apart from rare monogenic subtypes, no genetic or molecular markers for migraine have been convincingly established. We identified the minor allele of rs1835740 on chromosome 8q22.1 to be associated with migraine (p=5.12 × 10⁻⁹, OR 1.23 [1.150-1.324]) in a genome-wide association study of 2,748 migraineurs from three European headache clinics and 10,747 population-matched controls. The association was replicated in 3,202 cases and 40,062 controls for an overall meta-analysis p-value of 1.60 × 10⁻¹¹ (OR 1.18 [1.127 – 1.244]). rs1835740 is located between the astrocyte elevated gene 1 (MTDH/AEG-1) and plasma glutamate carboxypeptidase (PGCP). In an expression quantitative trait study in lymphoblastoid cell lines transcript levels of the MTDH/AEG-1 were found to have a significant correlation to rs1835740. Our data establish rs1835740 as the first genetic risk factor for migraine.

Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies

Summary: Genevar (GENe Expression VARiation) is a database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression. Genevar allows researchers to investigate expression quantitative trait loci (eQTL) associations within a gene locus of interest in real time. The database and application can be installed on a standard computer in database mode and, in addition, on a server to share discoveries among affiliations or the broader community over the Internet via web services protocols.

Availability:http://www.sanger.ac.uk/resources/software/genevar

Contact:emmanouil.dermitzakis@unige.ch

Data analysis issues for allele-specific expression using Illumina's GoldenGate assay

Background

High-throughput measurement of allele-specific expression (ASE) is a relatively new and exciting application area for array-based technologies. In this paper, we explore several data sets which make use of Illumina's GoldenGate BeadArray technology to measure ASE. This platform exploits coding SNPs to obtain relative expression measurements for alleles at approximately 1500 positions in the genome.

Results

We analyze data from a mixture experiment where genomic DNA samples from pairs of individuals of known genotypes are pooled to create allelic imbalances at varying levels for the majority of SNPs on the array. We observe that GoldenGate has less sensitivity at detecting subtle allelic imbalances (around 1.3 fold) compared to extreme imbalances, and note the benefit of applying local background correction to the data. Analysis of data from a dye-swap control experiment allowed us to quantify dye-bias, which can be reduced considerably by careful normalization. The need to filter the data before carrying out further downstream analysis to remove non-responding probes, which show either weak, or non-specific signal for each allele, was also demonstrated. Throughout this paper, we find that a linear model analysis of the data from each SNP is a flexible modelling strategy that allows for testing of allelic imbalances in each sample when replicate hybridizations are available.

Conclusions

Our analysis shows that local background correction carried out by Illumina's software, together with quantile normalization of the red and green channels within each array, provides optimal performance in terms of false positive rates. In addition, we strongly encourage intensity-based filtering to remove SNPs which only measure non-specific signal. We anticipate that a similar analysis strategy will prove useful when quantifying ASE on Illumina's higher density Infinium BeadChips.

Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations

The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r²) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits.

Genome-wide association studies have led to the identification of susceptibility loci for a variety of human complex traits. What is still largely missing, however, is the understanding of the biological context in which these candidate variants act and of how they determine each trait. Given the localization of many GWAS loci outside coding regions and the important role of regulatory variation in shaping phenotypic variance, gene expression has been proposed as a plausible informative intermediate phenotype. Here we show that for a subset of the currently published GWAS this is indeed the case, by observing a significant excess of regulatory variants among disease loci. We propose an empirical methodology (regulatory trait concordance—RTC) able to integrate expression and disease data in order to detect causal regulatory effects. We show that the RTC outperforms simple correlation metrics under various simulated linkage disequilibrium (LD) scenarios. Our method is able to recover previously suspected causal regulatory effects from the literature and, as expected given the nature of the tested tissue, an overrepresentation of immunity-related candidates is observed. As the number of available tissues will increase, this prioritization approach will become even more useful in understanding the implication of regulatory variants in disease etiology.

Genetic variation of regulatory systems

The regulation of gene expression is one of the most important cellular functions that determines developmental and somatic cell fates. Studies that attempt to describe the molecular interactions that govern gene regulation highlight the complexity and multidimensionality of this process. The recent explosion of information on genetic variation has added yet another dimension to this complexity. Genetic variation in elements that control gene expression directly or indirectly through interactions with other elements or molecules, contributes to observed expression level differences between individuals of the same species. In this short review we will address some of the key principles of the past and recent studies describing the degree and nature of regulatory variation with a focus on human populations.

Common regulatory variation impacts gene expression in a cell type-dependent manner

Studies correlating genetic variation to gene expression facilitate the interpretation of common human phenotypes and disease. As functional variants may be operating in a tissue-dependent manner, we performed gene expression profiling and association with genetic variants (single-nucleotide polymorphisms) on three cell types of 75 individuals. We detected cell type-specific genetic effects, with 69 to 80% of regulatory variants operating in a cell type-specific manner, and identified multiple expressive quantitative trait loci (eQTLs) per gene, unique or shared among cell types and positively correlated with the number of transcripts per gene. Cell type-specific eQTLs were found at larger distances from genes and at lower effect size, similar to known enhancers. These data suggest that the complete regulatory variant repertoire can only be uncovered in the context of cell-type specificity.

Modifier effects between regulatory and protein-coding variation

Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.

The ultimate goal of genome-wide association studies (GWAS) is to explain the proportion of variation in a phenotypic trait that can be attributed to genetic factors. The past two years have seen a plethora of successes in this field, yet, for most traits, a large fraction of variation remains unexplained. Epistasis, or interaction between genetic variants, is a largely under-explored factor, which may shed some light in this area. We use the HapMap populations to investigate interactions between regulatory and protein-coding variants and their impact on gene expression. We show that if a specific protein-coding variant has a functional impact, this can be modified by a co-segregating regulatory variant (cis interaction). Furthermore, the authors demonstrate that such modification effects between variants at one locus may affect the expression of other genes in the cell in a trans manner. The aim of this article is to present a framework though which variation can be considered in the context of GWAS. Viewing variation from this underappreciated angle may, in some cases, provide an explanation for differential penetrance of complex disease traits, but also for non-replication of GWAS results that may arise as a consequence of such interactions.