Correction: Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy.

[This corrects the article DOI: 10.1371/journal.pone.0172995.].

Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy.

AIMS: Dilated cardiomyopathy (DCM) is an important cause of heart failure with a strong familial component. We performed an exome-wide array-based association study (EWAS) to assess the contribution of missense variants to sporadic DCM. METHODS AND RESULTS: 116,855 single nucleotide variants (SNVs) were analyzed in 2796 DCM patients and 6877 control subjects from 6 populations of European ancestry. We confirmed two previously identified associations with SNVs in BAG3 and ZBTB17 and discovered six novel DCM-associated loci (Q-value<0.01). The lead-SNVs at novel loci are common and located in TTN, SLC39A8, MLIP, FLNC, ALPK3 and FHOD3. In silico fine mapping identified HSPB7 as the most likely candidate at the ZBTB17 locus. Rare variant analysis (MAF<0.01) demonstrated significant association for TTN variants only (P = 0.0085). All candidate genes but one (SLC39A8) exhibit preferential expression in striated muscle tissues and mutations in TTN, BAG3, FLNC and FHOD3 are known to cause familial cardiomyopathy. We also investigated a panel of 48 known cardiomyopathy genes. Collectively, rare (n = 228, P = 0.0033) or common (n = 36, P = 0.019) variants with elevated in silico severity scores were associated with DCM, indicating that the spectrum of genes contributing to sporadic DCM extends beyond those identified here. CONCLUSION: We identified eight loci independently associated with sporadic DCM. The functions of the best candidate genes at these loci suggest that proteostasis regulation might play a role in DCM pathophysiology.

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data.

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data.

Molecular Characterization of the NLRC4 Expression in Relation to Interleukin-18 Levels.

BACKGROUND: Interleukin-18 (IL-18) is a pleiotropic cytokine centrally involved in the cytokine cascade with complex immunomodulatory functions in innate and acquired immunity. Circulating IL-18 concentrations are associated with type 2 diabetes mellitus, cardiovascular events, and diverse inflammatory and autoimmune disorders. METHODS AND RESULTS: To identify causal variants affecting circulating IL-18 concentrations, we applied various omics and molecular biology approaches. By genome-wide association study, we confirmed association of IL-18 levels with a single nucleotide polymorphism in the untranslated exon 2 of the inflammasome component NLRC4 (NLR family, caspase recruitment domain-containing 4) gene on chromosome 2 (rs385076; P=2.4 × 10(-45)). Subsequent molecular analyses by gene expression analysis and reporter gene assays indicated an effect of rs385076 on NLRC4 expression and differential isoform usage by modulating binding of the transcription factor PU.1. CONCLUSIONS: Our study provides evidence for the functional causality of single nucleotide polymorphism rs385076 within the NLRC4 gene in relation to IL-18 activation.

SASH1, a new potential link between smoking and atherosclerosis.

OBJECTIVE: We have previously reported that SASH1 expression is increased in circulating human monocytes from smokers and was positively correlated with the number of carotid atherosclerotic plaques. The aim of this study was to further validate the link between smoking, SASH1 and atherosclerosis within the vascular wall and to assess the impact of SASH1 expression on endothelial cell functions. METHOD: Human carotids with atherosclerotic plaques were obtained from 58 patients (45 of them with known smoking status: smoker, non-smoker, ex-smokers), and were processed for gene expression analyses and immunostaining. To investigate its function, SASH1 was silenced in human aortic endothelial cells (HAECs) using two different siRNA and subcellular localization of SASH1 was determined by immunostaining and subcellular fractionation. Subsequently the transcriptomic analyses and functional experiments (wound healing, WST-1 proliferation or Matrigel assays) were performed to characterize SASH1 function. RESULTS: SASH1 was expressed in human vascular cells (HAECs, smooth muscle cells) and in monocytes/macrophages. Its tissue expression was significantly higher in the atherosclerotic carotids of smokers compared to non-smokers (p < 0.01). In HAECs, SASH1 was expressed mostly in the cytoplasm and SASH1 knockdown resulted in an increased cell migration, proliferation and angiogenesis. Transcriptomic and pathway analyses showed that SASH1 silencing results in a decreased CYP1A1 expression possibly through the inhibition of TP53 activity. CONCLUSION: We showed that SASH1 expression is increased in atherosclerotic carotids in smokers and its silencing affects endothelial angiogenic functions; therefore we provide a potential link between smoking and atherosclerosis through SASH1 expression.

Bariatric Surgery Induces Disruption in Inflammatory Signaling Pathways Mediated by Immune Cells in Adipose Tissue: A RNA-Seq Study.

BACKGROUND: Bariatric surgery is associated to improvements in obesity-associated comorbidities thought to be mediated by a decrease of adipose inflammation. However, the molecular mechanisms behind these beneficial effects are poorly understood. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed RNA-seq expression profiles in adipose tissue from 22 obese women before and 3 months after surgery. Of 15,972 detected genes, 1214 were differentially expressed after surgery at a 5% false discovery rate. Upregulated genes were mostly involved in the basal cellular machinery. Downregulated genes were enriched in metabolic functions of adipose tissue. At baseline, 26 modules of coexpressed genes were identified. The four most stable modules reflected the innate and adaptive immune responses of adipose tissue. A first module reflecting a non-specific signature of innate immune cells, mainly macrophages, was highly conserved after surgery with the exception of DUSP2 and CD300C. A second module reflected the adaptive immune response elicited by T lymphocytes; after surgery, a disconnection was observed between genes involved in T-cell signaling and mediators of the signal transduction such as CXCR1, CXCR2, GPR97, CCR7 and IL7R. A third module reflected neutrophil-mediated inflammation; after surgery, several genes were dissociated from the module, including S100A8, S100A12, CD300E, VNN2, TUBB1 and FAM65B. We also identified a dense network of 19 genes involved in the interferon-signaling pathway which was strongly preserved after surgery, with the exception of DDX60, an antiviral factor involved in RIG-I-mediated interferon signaling. A similar loss of connection was observed in lean mice compared to their obese counterparts. CONCLUSIONS/SIGNIFICANCE: These results suggest that improvements of the inflammatory state following surgery might be explained by a disruption of immuno-inflammatory cascades involving a few crucial molecules which could serve as potential therapeutic targets.

Adrenomedullin and arterial stiffness: integrative approach combining monocyte ADM expression, plasma MR-Pro-ADM, and genome-wide association study.

BACKGROUND: Adrenomedullin (ADM) is a circulating vasoactive peptide involved in vascular homeostasis and endothelial function. Single nucleotide polymorphisms of the ADM gene are associated with blood pressure variability, and elevated levels of plasma midregional proadrenomedullin (MR-pro-ADM) are associated with cardiovascular diseases. METHODS AND RESULTS: We investigated the sources of variability of ADM gene expression and plasma MR-pro-ADM concentrations in the general population, and their relationship with markers of atherosclerosis. MR-pro-ADM levels were assessed in 4155 individuals who underwent evaluation of carotid intima-media thickness and arterial rigidity (reflection index and stiffness index). In a subsample of 1372 individuals, ADM gene expression was assessed as part of a transcriptomic study of circulating monocytes. Nongenetic factors explained 45.8% and 7.5% of MR-pro-ADM and ADM expression variability, respectively. ADM expression correlated with plasma C-reactive protein, interleukin-receptor 1A, and myeloperoxidase, whereas MR-pro-ADM levels correlated with C-terminal proendothelin-1, creatinine, and N-terminal pro-B-type natriuretic peptide. Genome-wide association study of ADM expression and MR-pro-ADM levels both identified a single locus encompassing the ADM gene. ADM expression was associated with 1 single nucleotide polymorphism rs11042717 (P=2.36×10(-12)), whereas MR-pro-ADM was associated with 2 single nucleotide polymorphisms with additive effects, rs2957692 (P=1.54×10(-13)) and rs2957717 (P=4.24×10(-8)). Reflection index was independently associated with rs11042717 (P<10(-4)) and ADM expression (P=0.0002) but not with MR-pro-ADM. Weaker associations were observed for stiffness index. Intima-media thickness was not related to ADM single nucleotide polymorphisms or expression. CONCLUSIONS: These results support an involvement of the ADM gene in the modulation of peripheral vascular tone.

GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm.

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n  =  3,175), when compared with the largest published meta-GWAS (n > 100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.

Graphical modeling of gene expression in monocytes suggests molecular mechanisms explaining increased atherosclerosis in smokers.

Smoking is a risk factor for atherosclerosis with reported widespread effects on gene expression in circulating blood cells. We hypothesized that a molecular signature mediating the relation between smoking and atherosclerosis may be found in the transcriptome of circulating monocytes. Genome-wide expression profiles and counts of atherosclerotic plaques in carotid arteries were collected in 248 smokers and 688 non-smokers from the general population. Patterns of co-expressed genes were identified by Independent Component Analysis (ICA) and network structure of the pattern-specific gene modules was inferred by the PC-algorithm. A likelihood-based causality test was implemented to select patterns that fit models containing a path "smoking→gene expression→plaques". Robustness of the causal inference was assessed by bootstrapping. At a FDR ≤0.10, 3,368 genes were associated to smoking or plaques, of which 93% were associated to smoking only. SASH1 showed the strongest association to smoking and PPARG the strongest association to plaques. Twenty-nine gene patterns were identified by ICA. Modules containing SASH1 and PPARG did not show evidence for the "smoking→gene expression→plaques" causality model. Conversely, three modules had good support for causal effects and exhibited a network topology consistent with gene expression mediating the relation between smoking and plaques. The network with the strongest support for causal effects was connected to plaques through SLC39A8, a gene with known association to HDL-cholesterol and cellular uptake of cadmium from tobacco, while smoking was directly connected to GAS6, a gene reported to have anti-inflammatory effects in atherosclerosis and to be up-regulated in the placenta of women smoking during pregnancy. Our analysis of the transcriptome of monocytes recovered genes relevant for association to smoking and atherosclerosis, and connected genes that before, were only studied in separate contexts. Inspection of correlation structure revealed candidates that would be missed by expression-phenotype association analysis alone.

Genome-wide haplotype analysis of cis expression quantitative trait loci in monocytes.

In order to assess whether gene expression variability could be influenced by several SNPs acting in cis, either through additive or more complex haplotype effects, a systematic genome-wide search for cis haplotype expression quantitative trait loci (eQTL) was conducted in a sample of 758 individuals, part of the Cardiogenics Transcriptomic Study, for which genome-wide monocyte expression and GWAS data were available. 19,805 RNA probes were assessed for cis haplotypic regulation through investigation of ~2,1 × 10(9) haplotypic combinations. 2,650 probes demonstrated haplotypic p-values >10(4)-fold smaller than the best single SNP p-value. Replication of significant haplotype effects were tested for 412 probes for which SNPs (or proxies) that defined the detected haplotypes were available in the Gutenberg Health Study composed of 1,374 individuals. At the Bonferroni correction level of 1.2 × 10(-4) (~0.05/412), 193 haplotypic signals replicated. 1000 G imputation was then conducted, and 105 haplotypic signals still remained more informative than imputed SNPs. In-depth analysis of these 105 cis eQTL revealed that at 76 loci genetic associations were compatible with additive effects of several SNPs, while for the 29 remaining regions data could be compatible with a more complex haplotypic pattern. As 24 of the 105 cis eQTL have previously been reported to be disease-associated loci, this work highlights the need for conducting haplotype-based and 1000 G imputed cis eQTL analysis before commencing functional studies at disease-associated loci.

Comprehensive exploration of the effects of miRNA SNPs on monocyte gene expression.

We aimed to assess whether pri-miRNA SNPs (miSNPs) could influence monocyte gene expression, either through marginal association or by interacting with polymorphisms located in 3'UTR regions (3utrSNPs). We then conducted a genome-wide search for marginal miSNPs effects and pairwise miSNPs × 3utrSNPs interactions in a sample of 1,467 individuals for which genome-wide monocyte expression and genotype data were available. Statistical associations that survived multiple testing correction were tested for replication in an independent sample of 758 individuals with both monocyte gene expression and genotype data. In both studies, the hsa-mir-1279 rs1463335 was found to modulate in cis the expression of LYZ and in trans the expression of CNTN6, CTRC, COPZ2, KRT9, LRRFIP1, NOD1, PCDHA6, ST5 and TRAF3IP2 genes, supporting the role of hsa-mir-1279 as a regulator of several genes in monocytes. In addition, we identified two robust miSNPs × 3utrSNPs interactions, one involving HLA-DPB1 rs1042448 and hsa-mir-219-1 rs107822, the second the H1F0 rs1894644 and hsa-mir-659 rs5750504, modulating the expression of the associated genes.As some of the aforementioned genes have previously been reported to reside at disease-associated loci, our findings provide novel arguments supporting the hypothesis that the genetic variability of miRNAs could also contribute to the susceptibility to human diseases.

Caution in interpreting results from imputation analysis when linkage disequilibrium extends over a large distance: a case study on venous thrombosis.

By applying an imputation strategy based on the 1000 Genomes project to two genome-wide association studies (GWAS), we detected a susceptibility locus for venous thrombosis on chromosome 11p11.2 that was missed by previous GWAS analyses that had been conducted on the same datasets. A comprehensive linkage disequilibrium and haplotype analysis of the whole locus where twelve SNPs exhibited association p-values lower than 2.23 10(-11) and the use of independent case-control samples demonstrated that the culprit variant was a rare variant located ~1 Mb away from the original hits, not tagged by current genome-wide genotyping arrays and even not well imputed in the original GWAS samples. This variant was in fact the rs1799963, also known as the FII G20210A prothrombin mutation. This work may be of major interest not only for its scientific impact but also for its methodological findings.

Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes.

One mechanism by which disease-associated DNA variation can alter disease risk is altering gene expression. However, linkage disequilibrium (LD) between variants, mostly single-nucleotide polymorphisms (SNPs), means it is not sufficient to show that a particular variant associates with both disease and expression, as there could be two distinct causal variants in LD. Here, we describe a formal statistical test of colocalization and apply it to type 1 diabetes (T1D)-associated regions identified mostly through genome-wide association studies and expression quantitative trait loci (eQTLs) discovered in a recently determined large monocyte expression data set from the Gutenberg Health Study (1370 individuals), with confirmation sought in an additional data set from the Cardiogenics Transcriptome Study (558 individuals). We excluded 39 out of 60 overlapping eQTLs in 49 T1D regions from possible colocalization and identified 21 coincident eQTLs, representing 21 genes in 14 distinct T1D regions. Our results reflect the importance of monocyte (and their derivatives, macrophage and dendritic cell) gene expression in human T1D and support the candidacy of several genes as causal factors in autoimmune pancreatic beta-cell destruction, including AFF3, CD226, CLECL1, DEXI, FKRP, PRKD2, RNLS, SMARCE1 and SUOX, in addition to the recently described GPR183 (EBI2) gene.

Single polymorphism nucleotide rs1333049 on chromosome 9p21 is associated with carotid plaques but not with common carotid intima-media thickness in older adults. A combined analysis of the Three-City and the EVA studies.

OBJECTIVES: To address the relationship of rs1333049, the 9p21 variant showing the strongest association with coronary heart disease (CHD), with carotid plaques and plaque-free common carotid artery intima-media thickness (CCA-IMT) in older adults from 2 French population-based cohorts. METHODS: We genotyped for rs1333049, 4097 CHD-free participants including 3191 aged 65-86 years from the Three-City (3C) Study and 906 aged 59-71 years from the Vascular Aging Study (EVA). Plaque-free mean CCA-IMT and the presence of carotid plaques were assessed. RESULTS: In multivariate analysis, each C allele copy of rs1333049 was associated with baseline carotid plaques (odds ratio (OR)=1.24; 95% confidence interval (CI)=1.13-1.36; p<0.001) but not with baseline CCA-IMT (p=0.19). Among the EVA participants, the C allele was associated with 4-year plaques progression (p=0.04) but not with CCA-IMT progression. CONCLUSION: The chromosome 9p21 locus might influence CHD risk through carotid plaques development.

Midregional proadrenomedullin for prediction of cardiovascular events in coronary artery disease: results from the AtheroGene study.

BACKGROUND: Midregional proadrenomedullin (MR-proADM) is a newly identified prognostic marker in heart failure. We evaluated the prognostic impact of MR-proADM in a cohort of patients with symptomatic coronary artery disease according to their clinical presentation. METHODS: We measured baseline MR-proADM concentrations in 2240 individuals from the prospective AtheroGene study and evaluated the prognostic impact on future fatal and nonfatal cardiovascular events during a follow-up period of 3.6 (1.6) years. RESULTS: The sample comprised 1355 individuals with stable angina pectoris (SAP) and 885 with acute coronary syndrome (ACS). A cardiovascular event occurred in 192 people. Individuals presenting with SAP had only slightly lower plasma MR-proADM concentrations than those with ACS (0.53 vs 0.55 nmol/L, P=0.006). MR-proADM showed a moderate association with age, serum N-terminal pro-B-type natriuretic peptide (NT-proBNP), glomerular filtration rate, serum C-reactive protein, hypertension, diabetes, and prevalent multivessel disease (all P<0.0005). Individuals suffering from a cardiovascular event had higher MR-proADM concentrations at baseline in both groups (SAP 0.63 vs 0.53 nmol/L and ACS 0.65 nmol/L vs 0.55 nmol/L, both P<0.0005). Cox regression analysis incorporating various variables of cardiovascular risk and NT-proBNP revealed a hazard ratio of 1.4 (95% CI 1.2-1.6; P<0.0005) per increment of MR-proADM by 1SD. In risk models for secondary prevention, MR-proADM provided information comparable to that of NT-proBNP. CONCLUSIONS: MR-proADM is an independent predictor for future cardiovascular events in patients with symptomatic coronary artery disease, providing information comparable to NT-proBNP for secondary risk stratification.

A multiple biomarker risk score for guiding clinical decisions using a decision curve approach.

AIMS: We assessed whether a cardiovascular risk model based on classic risk factors (e.g. cholesterol, blood pressure) could refine disease prediction if it included novel biomarkers (C-reactive protein, N-terminal pro-B-type natriuretic peptide, troponin I) using a decision curve approach which can incorporate clinical consequences. METHODS AND RESULTS: We evaluated whether a model including biomarkers and classic risk factors could improve prediction of 10 year risk of cardiovascular disease (CVD; chronic heart disease and ischaemic stroke) against a classic risk factor model using a decision curve approach in two prospective MORGAM cohorts. This included 7739 men and women with 457 CVD cases from the FINRISK97 cohort; and 2524 men with 259 CVD cases from PRIME Belfast. The biomarker model improved disease prediction in FINRISK across the high-risk group (20-40%) but not in the intermediate risk group, at the 23% risk threshold net benefit was 0.0033 (95% CI 0.0013-0.0052). However, in PRIME Belfast the net benefit of decisions guided by the decision curve was improved across intermediate risk thresholds (10-20%). At p(t) = 10% in PRIME, the net benefit was 0.0059 (95% CI 0.0007-0.0112) with a net increase in 6 true positive cases per 1000 people screened and net decrease of 53 false positive cases per 1000 potentially leading to 5% fewer treatments in patients not destined for an event. CONCLUSION: The biomarker model improves 10-year CVD prediction at intermediate and high-risk thresholds and in particular, could be clinically useful at advising middle-aged European males of their CVD risk.

Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans.

One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies. So far, few cis expression quantitative trait loci (eQTLs) have been reliably related to disease susceptibility. Trans-regulating mechanisms may play a more prominent role in disease susceptibility. We analyzed 12,808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1,490 European unrelated subjects. We applied a method of extraction of expression patterns-independent component analysis-to identify sets of co-regulated genes. These patterns were then related to 675,350 SNPs to identify major trans-acting regulators. We detected three genomic regions significantly associated with co-regulated gene modules. Association of these loci with multiple expression traits was replicated in Cardiogenics, an independent study in which expression profiles of monocytes were available in 758 subjects. The locus 12q13 (lead SNP rs11171739), previously identified as a type 1 diabetes locus, was associated with a pattern including two cis eQTLs, RPS26 and SUOX, and 5 trans eQTLs, one of which (MADCAM1) is a potential candidate for mediating T1D susceptibility. The locus 12q24 (lead SNP rs653178), which has demonstrated extensive disease pleiotropy, including type 1 diabetes, hypertension, and celiac disease, was associated to a pattern strongly correlating to blood pressure level. The strongest trans eQTL in this pattern was CRIP1, a known marker of cellular proliferation in cancer. The locus 12q15 (lead SNP rs11177644) was associated with a pattern driven by two cis eQTLs, LYZ and YEATS4, and including 34 trans eQTLs, several of them tumor-related genes. This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease.

Genetics of venous thrombosis: insights from a new genome wide association study.

BACKGROUND: Venous Thrombosis (VT) is a common multifactorial disease associated with a major public health burden. Genetics factors are known to contribute to the susceptibility of the disease but how many genes are involved and their contribution to VT risk still remain obscure. We aimed to identify genetic variants associated with VT risk. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a genome-wide association study (GWAS) based on 551,141 SNPs genotyped in 1,542 cases and 1,110 controls. Twelve SNPs reached the genome-wide significance level of 2.0×10(-8) and encompassed four known VT-associated loci, ABO, F5, F11 and FGG. By means of haplotype analyses, we also provided novel arguments in favor of a role of HIVEP1, PROCR and STAB2, three loci recently hypothesized to participate in the susceptibility to VT. However, no novel VT-associated loci came out of our GWAS. Using a recently proposed statistical methodology, we also showed that common variants could explain about 35% of the genetic variance underlying VT susceptibility among which 3% could be attributable to the main identified VT loci. This analysis additionally suggested that the common variants left to be identified are not uniformly distributed across the genome and that chromosome 20, itself, could contribute to ∼7% of the total genetic variance. CONCLUSIONS/SIGNIFICANCE: This study might also provide a valuable source of information to expand our understanding of biological mechanisms regulating quantitative biomarkers for VT.

Bayesian detection of expression quantitative trait loci hot spots.

High-throughput genomics allows genome-wide quantification of gene expression levels in tissues and cell types and, when combined with sequence variation data, permits the identification of genetic control points of expression (expression QTL or eQTL). Clusters of eQTL influenced by single genetic polymorphisms can inform on hotspots of regulation of pathways and networks, although very few hotspots have been robustly detected, replicated, or experimentally verified. Here we present a novel modeling strategy to estimate the propensity of a genetic marker to influence several expression traits at the same time, based on a hierarchical formulation of related regressions. We implement this hierarchical regression model in a Bayesian framework using a stochastic search algorithm, HESS, that efficiently probes sparse subsets of genetic markers in a high-dimensional data matrix to identify hotspots and to pinpoint the individual genetic effects (eQTL). Simulating complex regulatory scenarios, we demonstrate that our method outperforms current state-of-the-art approaches, in particular when the number of transcripts is large. We also illustrate the applicability of HESS to diverse real-case data sets, in mouse and human genetic settings, and show that it provides new insights into regulatory hotspots that were not detected by conventional methods. The results suggest that the combination of our modeling strategy and algorithmic implementation provides significant advantages for the identification of functional eQTL hotspots, revealing key regulators underlying pathways.