Targeted long-read sequencing of the Ewing sarcoma 6p25.1 susceptibility locus identifies germline-somatic interactions with EWSR1-FLI1 binding.

Ewing sarcoma (EwS) is a rare bone and soft tissue malignancy driven by chromosomal translocations encoding chimeric transcription factors, such as EWSR1-FLI1, that bind GGAA motifs forming novel enhancers that alter nearby expression. We propose that germline microsatellite variation at the 6p25.1 EwS susceptibility locus could impact downstream gene expression and EwS biology. We performed targeted long-read sequencing of EwS blood DNA to characterize variation and genomic features important for EWSR1-FLI1 binding. We identified 50 microsatellite alleles at 6p25.1 and observed that EwS-affected individuals had longer alleles (>135 bp) with more GGAA repeats. The 6p25.1 GGAA microsatellite showed chromatin features of an EWSR1-FLI1 enhancer and regulated expression of RREB1, a transcription factor associated with RAS/MAPK signaling. RREB1 knockdown reduced proliferation and clonogenic potential and reduced expression of cell cycle and DNA replication genes. Our integrative analysis at 6p25.1 details increased binding of longer GGAA microsatellite alleles with acquired EWSR-FLI1 to promote Ewing sarcomagenesis by RREB1-mediated proliferation.

Implications of the Co-Dominance Model for Hardy-Weinberg Testing in Genetic Association Studies.

INTRODUCTION: The standard way of using tests for compatibility of genetic markers with the Hardy-Weinberg equilibrium (HWE) assumptionvas a means of quality control in genetic association studies (GAS) is to vcarry out this step of preliminary data analysis with the sample of non-diseased vindividuals only. We show that this strategy has no rational basis whenever the genotype--phenotype relation for avmarker under consideration satisfies the assumption of co-dominance. METHODS/RESULTS: The justification of this statement is the fact rigorously shown here that under co-dominance, the genotype distribution of a diallelic marker is in HWE among the controls if and only if the same holds true for the cases. CONCLUSION: The major practical consequence of that theoretical result is that under the co-dominance model, testing for HWE should be done both for cases and controls aiming to establish the combined (intersection) hypothesis of compatibility of both underlying genotype distributions with the HWE assumption. A particularly useful procedure serving this purpose is obtained through applying the confidence-interval inclusion rule derived by Wellek, Goddard and Ziegler (Biom J. 2010; 52:253-270) to both samples separately and combining these two tests by means of the intersection-union principle.

Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer.

INTRODUCTION: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. METHODS: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). RESULTS: Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. CONCLUSION: Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

First mitochondrial genome-wide association study with metabolomics.

In the era of personalized medicine with more and more patient-specific targeted therapies being used, we need reliable, dynamic, faster and sensitive biomarkers both to track the causes of disease and to develop and evolve therapies during the course of treatment. Metabolomics recently has shown substantial evidence to support its emerging role in disease diagnosis and prognosis. Aside from biomarkers and development of therapies, it is also an important goal to understand the involvement of mitochondrial DNA (mtDNA) in metabolic regulation, aging and disease development. Somatic mutations of the mitochondrial genome are also heavily implicated in age-related disease and aging. The general hypothesis is that an alteration in the concentration of metabolite profiles (possibly conveyed by lifestyle and environmental factors) influences the increase of mutation rate in the mtDNA and thereby contributes to a range of pathophysiological alterations observed in complex diseases. We performed an inverted mitochondrial genome-wide association analysis between mitochondrial nucleotide variants (mtSNVs) and concentration of metabolites. We used 151 metabolites and the whole sequenced mitochondrial genome from 2718 individuals to identify the genetic variants associated with metabolite profiles. Because of the high coverage, next-generation sequencing-based analysis of the mitochondrial genome allows for an accurate detection of mitochondrial heteroplasmy and for the identification of variants associated with the metabolome. The strongest association was found for mt715G > A located in the MT-12SrRNA with the metabolite ratio of C2/C10:1 (P-value = 6.82*10-09, ß = 0.909). The second most significant mtSNV was found for mt3714A > G located in the MT-ND1 with the metabolite ratio of phosphatidylcholine (PC) ae C42:5/PC ae C44:5 (P-value = 1.02*10-08, ß = 3.631). A large number of significant metabolite ratios were observed involving PC aa C36:6 and the variant mt10689G > A, located in the MT-ND4L gene. These results show an important interconnection between mitochondria and metabolite concentrations. Considering that some of the significant metabolites found in this study have been previously related to complex diseases, such as neurological disorders and metabolic conditions, these associations found here might play a crucial role for further investigations of such complex diseases. Understanding the mechanisms that control human health and disease, in particular, the role of genetic predispositions and their interaction with environmental factors is a prerequisite for the development of safe and efficient therapies for complex disorders.

Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study.

Progressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior-posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = -0.02, SE = 0.004, P-value = 2.10 × 10-8). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10-4). In exome-array single-variant analysis (P-value threshold = 9 × 10-7), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10-5). In the gene-based analysis (P-value threshold = 1.85 × 10-6), PCSK5 showed an association with AAD (P-value = 8.03 × 10-7). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = -0.003, P-value = 0.02), triglycerides (beta = -0.16, P-value = 0.008) and height (beta = 0.03, P-value < 0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases.

Genome-wide association study identifies novel susceptibility loci for KIT D816V positive mastocytosis.

Mastocytosis is a rare myeloid neoplasm characterized by uncontrolled expansion of mast cells, driven in >80% of affected individuals by acquisition of the KIT D816V mutation. To explore the hypothesis that inherited variation predisposes to mastocytosis, we performed a two-stage genome-wide association study, analyzing 1,035 individuals with KIT D816V positive disease and 17,960 healthy control individuals from five European populations. After quality control, we tested 592,007 SNPs at stage 1 and 75 SNPs at stage 2 for association by using logistic regression and performed a fixed effects meta-analysis to combine evidence across the two stages. From the meta-analysis, we identified three intergenic SNPs associated with mastocytosis that achieved genome-wide significance without heterogeneity between cohorts: rs4616402 (pmeta = 1.37 × 10-15, OR = 1.52), rs4662380 (pmeta = 2.11 × 10-12, OR = 1.46), and rs13077541 (pmeta = 2.10 × 10-9, OR = 1.33). Expression quantitative trait analyses demonstrated that rs4616402 is associated with the expression of CEBPA (peQTL = 2.3 × 10-14), a gene encoding a transcription factor known to play a critical role in myelopoiesis. The role of the other two SNPs is less clear: rs4662380 is associated with expression of the long non-coding RNA gene TEX41 (peQTL = 2.55 × 10-11), whereas rs13077541 is associated with the expression of TBL1XR1, which encodes transducin (ß)-like 1 X-linked receptor 1 (peQTL = 5.70 × 10-8). In individuals with available data and non-advanced disease, rs4616402 was associated with age at presentation (p = 0.009; beta = 4.41; n = 422). Additional focused analysis identified suggestive associations between mastocytosis and genetic variation at TERT, TPSAB1/TPSB2, and IL13. These findings demonstrate that multiple germline variants predispose to KIT D816V positive mastocytosis and provide novel avenues for functional investigation.

Circulating metabolites modulated by diet are associated with depression.

Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.

Autoantibodies against the chemokine receptor 3 predict cardiovascular risk.

BACKGROUND AND AIMS: Chronic inflammation and autoimmunity contribute to cardiovascular (CV) disease. Recently, autoantibodies (aAbs) against the CXC-motif-chemokine receptor 3 (CXCR3), a G protein-coupled receptor with a key role in atherosclerosis, have been identified. The role of anti-CXCR3 aAbs for CV risk and disease is unclear. METHODS: Anti-CXCR3 aAbs were quantified by a commercially available enzyme-linked immunosorbent assay in 5000 participants (availability: 97.1%) of the population-based Gutenberg Health Study with extensive clinical phenotyping. Regression analyses were carried out to identify determinants of anti-CXCR3 aAbs and relevance for clinical outcome (i.e. all-cause mortality, cardiac death, heart failure, and major adverse cardiac events comprising incident coronary artery disease, myocardial infarction, and cardiac death). Last, immunization with CXCR3 and passive transfer of aAbs were performed in ApoE(-/-) mice for preclinical validation. RESULTS: The analysis sample included 4195 individuals (48% female, mean age 55.5 ± 11 years) after exclusion of individuals with autoimmune disease, immunomodulatory medication, acute infection, and history of cancer. Independent of age, sex, renal function, and traditional CV risk factors, increasing concentrations of anti-CXCR3 aAbs translated into higher intima-media thickness, left ventricular mass, and N-terminal pro-B-type natriuretic peptide. Adjusted for age and sex, anti-CXCR3 aAbs above the 75th percentile predicted all-cause death [hazard ratio (HR) (95% confidence interval) 1.25 (1.02, 1.52), P = .029], driven by excess cardiac mortality [HR 2.51 (1.21, 5.22), P = .014]. A trend towards a higher risk for major adverse cardiac events [HR 1.42 (1.0, 2.0), P = .05] along with increased risk of incident heart failure [HR per standard deviation increase of anti-CXCR3 aAbs: 1.26 (1.02, 1.56), P = .03] may contribute to this observation. Targeted proteomics revealed a molecular signature of anti-CXCR3 aAbs reflecting immune cell activation and cytokine-cytokine receptor interactions associated with an ongoing T helper cell 1 response. Finally, ApoE(-/-) mice immunized against CXCR3 displayed increased anti-CXCR3 aAbs and exhibited a higher burden of atherosclerosis compared to non-immunized controls, correlating with concentrations of anti-CXCR3 aAbs in the passive transfer model. CONCLUSIONS: In individuals free of autoimmune disease, anti-CXCR3 aAbs were abundant, related to CV end-organ damage, and predicted all-cause death as well as cardiac morbidity and mortality in conjunction with the acceleration of experimental atherosclerosis.

Interpretability of bi-level variable selection methods.

Variable selection is usually performed to increase interpretability, as sparser models are easier to understand than full models. However, a focus on sparsity is not always suitable, for example, when features are related due to contextual similarities or high correlations. Here, it may be more appropriate to identify groups and their predictive members, a task that can be accomplished with bi-level selection procedures. To investigate whether such techniques lead to increased interpretability, group exponential LASSO (GEL), sparse group LASSO (SGL), composite minimax concave penalty (cMCP), and least absolute shrinkage, and selection operator (LASSO) as reference methods were used to select predictors in time-to-event, regression, and classification tasks in bootstrap samples from a cohort of 1001 patients. Different groupings based on prior knowledge, correlation structure, and random assignment were compared in terms of selection relevance, group consistency, and collinearity tolerance. The results show that bi-level selection methods are superior to LASSO in all criteria. The cMCP demonstrated superiority in selection relevance, while SGL was convincing in group consistency. An all-round capacity was achieved by GEL: the approach jointly selected correlated and content-related predictors while maintaining high selection relevance. This method seems recommendable when variables are grouped, and interpretation is of primary interest.

Sparse Group Penalties for bi-level variable selection.

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

Protein expression profiling suggests relevance of noncanonical pathways in isolated pulmonary embolism.

Patients with isolated pulmonary embolism (PE) have a distinct clinical profile from those with deep vein thrombosis (DVT)-associated PE, with more pulmonary conditions and atherosclerosis. These findings suggest a distinct molecular pathophysiology and the potential involvement of alternative pathways in isolated PE. To test this hypothesis, data from 532 individuals from the Genotyping and Molecular Phenotyping of Venous ThromboEmbolism Project, a multicenter prospective cohort study with extensive biobanking, were analyzed. Targeted, high-throughput proteomics, machine learning, and bioinformatic methods were applied to contrast the acute-phase plasma proteomes of isolated PE patients (n = 96) against those of patients with DVT-associated PE (n = 276) or isolated DVT (n = 160). This resulted in the identification of shared molecular processes between PE phenotypes, as well as an isolated PE-specific protein signature. Shared processes included upregulation of inflammation, response to oxidative stress, and the loss of pulmonary surfactant. The isolated PE-specific signature consisted of 5 proteins: interferon-γ, glial cell line-derived neurotrophic growth factor, polypeptide N-acetylgalactosaminyltransferase 3, peptidyl arginine deiminase type-2, and interleukin-15 receptor subunit α. These proteins were orthogonally validated using cis protein quantitative trait loci. External replication in an independent population-based cohort (n = 5778) further validated the proteomic results and showed that they were prognostic for incident primary isolated PE in individuals without history of VTE (median time to event: 2.9 years; interquartile range: 1.6-4.2 years), supporting their possible involvement in the early pathogenesis. This study has identified molecular overlaps and differences between VTE phenotypes. In particular, the results implicate noncanonical pathways more commonly associated with respiratory and atherosclerotic disease in the acute pathophysiology of isolated PE.

A systematic review and evaluation of statistical methods for group variable selection.

This review condenses the knowledge on variable selection methods implemented in R and appropriate for datasets with grouped features. The focus is on regularized regressions identified through a systematic review of the literature, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 14 methods are discussed, most of which use penalty terms to perform group variable selection. Depending on how the methods account for the group structure, they can be classified into knowledge and data-driven approaches. The first encompass group-level and bi-level selection methods, while two-step approaches and collinearity-tolerant methods constitute the second category. The identified methods are briefly explained and their performance compared in a simulation study. This comparison demonstrated that group-level selection methods, such as the group minimax concave penalty, are superior to other methods in selecting relevant variable groups but are inferior in identifying important individual variables in scenarios where not all variables in the groups are predictive. This can be better achieved by bi-level selection methods such as group bridge. Two-step and collinearity-tolerant approaches such as elastic net and ordered homogeneity pursuit least absolute shrinkage and selection operator are inferior to knowledge-driven methods but provide results without requiring prior knowledge. Possible applications in proteomics are considered, leading to suggestions on which method to use depending on existing prior knowledge and research question.

Tryptophan catabolites and depression in the general population: results from the Gutenberg Health Study.

Previous studies reported significantly altered tryptophan catabolite concentrations in major depression. Thus, tryptophan catabolites were considered as potential biomarkers of depression and their modulators as potential targets for psychopharmacotherapy. However, the results were based mainly on studies with small sample sizes limiting their generalizability. Against this background, we investigated the relationship of peripheral tryptophan catabolites with depression in a population-based sample with n = 3,389 participants (with fasting status ≥ 8 h and C-reactive protein < 10 mg/L). N = 248 had clinically significant depression according to a PHQ-9 score of ≥ 10, n = 1,101 subjects had mild depressive symptoms with PHQ-9 scores between 5 and 9, and n = 2,040 had no depression. After multivariable adjustment, clinically significant depression was associated with lower kynurenine and kynurenic acid. Spearman correlation coefficients of the tryptophan catabolites with the severity of depression were very small (rho ≤ 0.080, p ≤ 0.015). None of the tryptophan catabolites could diagnostically separate depressed from not depressed persons. Concerning linear associations, kynurenine and kynurenic acid were associated only with the severity and the cognitive dimension of depression but not its somatic dimension. Tryptophan catabolites were not associated with persistence or recurrence of depression at the 5 year follow-up. The results replicated the association between kynurenine and kynurenic acid with depression. However, the associations were small raising doubts about their clinical utility. Findings underline the complexity of the relationships between depression and tryptophan catabolites. The search for subgroups of depression with a potentially higher impact of depression might be warranted.

Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study.

It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo ); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PLMetabo ); validation of two comprehensive polygenic risk scores: GRSGola based on Metabochip data, and GRSKhera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDIrel ) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRSMetabo and PLMetabo , GRSGola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDIrel : 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRSKhera yielded slightly worse prediction results than GRSGola .

Genome-wide association analysis in dilated cardiomyopathy reveals two new players in systolic heart failure on chromosomes 3p25.1 and 22q11.23.

AIMS: Our objective was to better understand the genetic bases of dilated cardiomyopathy (DCM), a leading cause of systolic heart failure. METHODS AND RESULTS: We conducted the largest genome-wide association study performed so far in DCM, with 2719 cases and 4440 controls in the discovery population. We identified and replicated two new DCM-associated loci on chromosome 3p25.1 [lead single-nucleotide polymorphism (SNP) rs62232870, P = 8.7 × 10-11 and 7.7 × 10-4 in the discovery and replication steps, respectively] and chromosome 22q11.23 (lead SNP rs7284877, P = 3.3 × 10-8 and 1.4 × 10-3 in the discovery and replication steps, respectively), while confirming two previously identified DCM loci on chromosomes 10 and 1, BAG3 and HSPB7. A genetic risk score constructed from the number of risk alleles at these four DCM loci revealed a 3-fold increased risk of DCM for individuals with 8 risk alleles compared to individuals with 5 risk alleles (median of the referral population). In silico annotation and functional 4C-sequencing analyses on iPSC-derived cardiomyocytes identify SLC6A6 as the most likely DCM gene at the 3p25.1 locus. This gene encodes a taurine transporter whose involvement in myocardial dysfunction and DCM is supported by numerous observations in humans and animals. At the 22q11.23 locus, in silico and data mining annotations, and to a lesser extent functional analysis, strongly suggest SMARCB1 as the candidate culprit gene. CONCLUSION: This study provides a better understanding of the genetic architecture of DCM and sheds light on novel biological pathways underlying heart failure.

GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data.

BACKGROUND: The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats-normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. RESULTS: We developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. CONCLUSION: GeneTonic is distributed as an R package in the Bioconductor project ( https://bioconductor.org/packages/GeneTonic/ ) under the MIT license. Offering both bird's-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.

Deoxyribonuclease 1 Q222R single nucleotide polymorphism and long-term mortality after acute myocardial infarction.

Upon activation, neutrophils release neutrophil extracellular traps (NETs), which contribute to circulating DNA burden and thrombosis, including ST-segment elevation myocardial infarction (STEMI). Deoxyribonuclease (DNase) 1 degrades circulating DNA and NETs. Lower DNase activity correlates with NET burden and infarct size. The DNase 1 Q222R single nucleotide polymorphism (SNP), impairing DNase 1 function, is linked with myocardial infarction. We assessed whether the Q222R SNP is connected to increased NET burden in STEMI and influences long-term outcomes. We enrolled 711 STEMI patients undergoing primary percutaneous coronary intervention (pPCI), and 1422 controls. Genotyping was performed for DNase 1 Q222R SNP. DNase activity, double-stranded (ds)DNA and citrullinated histone H3 were determined in culprit site and peripheral plasma during pPCI. The association of the Q222R variant on cardiovascular and all-cause mortality was assessed by multivariable Cox regression adjusted for cardiovascular risk factors. Homozygous Q222R DNase 1 variant was present in 64 (9.0%) STEMI patients, at the same frequency as in controls. Patients homozygous for Q222R displayed less DNase activity and increased circulating DNA burden. In overall patients, median survival was 60 months. Homozygous Q222R variant was independently associated with cardiovascular and all-cause mortality after STEMI. dsDNA/DNase ratio independently predicted cardiovascular and all-cause mortality. These findings highlight that the Q222R DNase 1 SNP is associated with increased NET burden and decreased compensatory DNase activity, and may serve as an independent risk factor for poor outcome after STEMI.

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.

Distribution of HOMA-IR in a population-based cohort and proposal for reference intervals.

OBJECTIVES: Insulin resistance (IR) is a hallmark of type 2 diabetes mellitus (DM). The homeostatic model assessment of insulin resistance (HOMA-IR) provides an estimate for IR from fasting glucose and insulin serum concentrations. The aim of this study was to obtain a reference interval for HOMA-IR for a specific insulin immunoassay. METHODS: The Gutenberg Health Study (GHS) is a population-based, prospective, single-center cohort study in Germany with 15,030 participants aged 35-74 years. Fasting glucose, insulin, and C-peptide were available in 10,340 participants. HOMA-IR was calculated in this group and three reference subgroups with increasingly more stringent inclusion criteria. Age- and sex-dependent distributions of HOMA-IR and reference intervals were obtained. In a substudy three insulin assays were compared and HOMA-IR estimated for each assay. RESULTS: Among the 10,340 participants analyzed there were 6,590 non-diabetic, 2,901 prediabetic, and 849 diabetic individuals. Median (interquartile range [IQR]) HOMA-IR was 1.54 (1.13/2.19), 2.00 (1.39/2.99), and 4.00 (2.52/6.51), respectively. The most stringently selected reference group consisted of 1,065 persons. Median (IQR) HOMA-IR was 1.09 (0.85/1.42) with no significant difference between men and women. The 97.5th percentile was 2.35. There was a non-significant trend towards higher values with older age. Comparison of three immunoassays for insulin showed an unsatisfactory correlation among the assays and systematic differences in calculated HOMA-IR. CONCLUSIONS: We present HOMA-IR reference intervals for adults derived by more or less stringent selection criteria for the reference cohort. In addition we show that assay specific reference intervals for HOMA-IR are required.

The haemochromatosis gene Hfe and Kupffer cells control LDL cholesterol homeostasis and impact on atherosclerosis development.

AIMS: Imbalances of iron metabolism have been linked to the development of atherosclerosis. However, subjects with hereditary haemochromatosis have a lower prevalence of cardiovascular disease. The aim of our study was to understand the underlying mechanisms by combining data from genome-wide association study analyses in humans, CRISPR/Cas9 genome editing, and loss-of-function studies in mice. METHODS AND RESULTS: Our analysis of the Global Lipids Genetics Consortium (GLGC) dataset revealed that single nucleotide polymorphisms (SNPs) in the haemochromatosis gene HFE associate with reduced low-density lipoprotein cholesterol (LDL-C) in human plasma. The LDL-C lowering effect could be phenocopied in dyslipidaemic ApoE-/- mice lacking Hfe, which translated into reduced atherosclerosis burden. Mechanistically, we identified HFE as a negative regulator of LDL receptor expression in hepatocytes. Moreover, we uncovered liver-resident Kupffer cells (KCs) as central players in cholesterol homeostasis as they were found to acquire and transfer LDL-derived cholesterol to hepatocytes in an Abca1-dependent fashion, which is controlled by iron availability. CONCLUSION: Our results disentangle novel regulatory interactions between iron metabolism, KC biology and cholesterol homeostasis which are promising targets for treating dyslipidaemia but also provide a mechanistic explanation for reduced cardiovascular morbidity in subjects with haemochromatosis.