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.

Obesity-related inflammatory protein signature in cardiovascular clinical outcomes: results from the Gutenberg Health Study.

OBJECTIVE: The objective of this study was to investigate whether an obesity-related inflammatory protein signature (OIPS) is associated with adverse cardiovascular events. METHODS: The Olink Target 96 Inflammation panel was performed in 6662 participants from the population-based Gutenberg Health Study (GHS). The OIPS was selected by a logistic regression model, and its association with cardiovascular outcomes was evaluated by Cox regression analysis. The GHS-derived OIPS was externally validated in the MyoVasc study. RESULTS: The identified OIPS entailed 21 proteins involved in chemokine activity, tumor necrosis factor (TNF) receptor binding, and growth factor receptor binding. The signature revealed a novel positive association of axis inhibition protein 1 with obesity. The OIPS was associated with increased risk of all-cause and cardiac deaths, major adverse cardiovascular events, and incident coronary artery disease, independent of clinical covariates and established risk instruments. A BMI-stratified analysis confirmed the association of OIPS with increased death in those with obesity and overweight and with increased risk for coronary artery disease in those with obesity. The association of OIPS with increased risk of all-cause and cardiac deaths was validated in the MyoVasc cohort. CONCLUSIONS: The OIPS showed a significant association with adverse clinical outcomes, particularly in those with overweight and obesity, and represents a promising tool for identifying patients at higher risk for worse cardiovascular outcomes.

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.

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.

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.

Heart rate variability: reference values and role for clinical profile and mortality in individuals with heart failure.

AIMS: To establish reference values and clinically relevant determinants for measures of heart rate variability (HRV) and to assess their relevance for clinical outcome prediction in individuals with heart failure. METHODS: Data from the MyoVasc study (NCT04064450; N = 3289), a prospective cohort on chronic heart failure with a highly standardized, 5 h examination, and Holter ECG recording were investigated. HRV markers were selected using a systematic literature screen and a data-driven approach. Reference values were determined from a healthy subsample. Clinical determinants of HRV were investigated via multivariable linear regression analyses, while their relationship with mortality was investigated by multivariable Cox regression analyses. RESULTS: Holter ECG recordings were available for analysis in 1001 study participants (mean age 64.5 ± 10.5 years; female sex 35.4%). While the most frequently reported HRV markers in literature were from time and frequency domains, the data-driven approach revealed predominantly non-linear HRV measures. Age, sex, dyslipidemia, family history of myocardial infarction or stroke, peripheral artery disease, and heart failure were strongly related to HRV in multivariable models. In a follow-up period of 6.5 years, acceleration capacity [HRperSD 1.53 (95% CI 1.21/1.93), p = 0.0004], deceleration capacity [HRperSD: 0.70 (95% CI 0.55/0.88), p = 0.002], and time lag [HRperSD 1.22 (95% CI 1.03/1.44), p = 0.018] were the strongest predictors of all-cause mortality in individuals with heart failure independently of cardiovascular risk factors, comorbidities, and medication. CONCLUSION: HRV markers are associated with the cardiovascular clinical profile and are strong and independent predictors of survival in heart failure. This underscores clinical relevance and interventional potential for individuals with heart failure. GOV IDENTIFIER: NCT04064450.

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.

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.

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.

Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning.

BACKGROUND: The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve the results' interpretability. METHODS: We applied model-based gradient boosting and adaptive lasso to deal with the small, unbalanced study design via simultaneous variable selection and model fitting. In addition, we used stability selection to identify a stable subset of selected biomarkers and, as a result, obtain even more interpretable results. In each step, we compared the performance of the different analytical pipelines and validated our approaches via simulation scenarios. RESULTS: In the simulation study, model-based gradient boosting showed a more accurate prediction performance in the small, unbalanced, and high-dimensional datasets than adaptive lasso and could identify more relevant variables. Furthermore, using model-based gradient boosting, we discovered a subset of promising serum biomarkers that may potentially improve the current screening procedure of FPC. CONCLUSION: Advanced statistical learning methods helped us overcome the shortcomings of an unbalanced study design in a valuable clinical dataset. The discovered serum biomarkers provide us with a clear direction for further investigations and more precise clinical hypotheses regarding the development of FPC and optimal strategies for its early detection.

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.

Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation.

Ewing sarcoma (EwS) is characterized by EWSR1-ETS fusion transcription factors converting polymorphic GGAA microsatellites (mSats) into potent neo-enhancers. Although the paucity of additional mutations makes EwS a genuine model to study principles of cooperation between dominant fusion oncogenes and neo-enhancers, this is impeded by the limited number of well-characterized models. Here we present the Ewing Sarcoma Cell Line Atlas (ESCLA), comprising whole-genome, DNA methylation, transcriptome, proteome, and chromatin immunoprecipitation sequencing (ChIP-seq) data of 18 cell lines with inducible EWSR1-ETS knockdown. The ESCLA shows hundreds of EWSR1-ETS-targets, the nature of EWSR1-ETS-preferred GGAA mSats, and putative indirect modes of EWSR1-ETS-mediated gene regulation, converging in the duality of a specific but plastic EwS signature. We identify heterogeneously regulated EWSR1-ETS-targets as potential prognostic EwS biomarkers. Our freely available ESCLA (http://r2platform.com/escla/) is a rich resource for EwS research and highlights the power of comprehensive datasets to unravel principles of heterogeneous gene regulation by chimeric transcription factors.

Network reconstruction for trans acting genetic loci using multi-omics data and prior information.

BACKGROUND: Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information. METHODS: We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. RESULTS: Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. CONCLUSIONS: We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.

Interactive and Reproducible Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer, Ideal, and GeneTonic.

The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer Basic Protocol 2: Differential Expression Analysis with ideal Basic Protocol 3: Interpretation of RNA-seq results with GeneTonic Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses.

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.

Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function.

We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP-CpG associations (methylation quantitative trait loci (meQTL), P < 10-14), including 467,915 meQTL that operate in trans. The meQTL are enriched for functionally relevant characteristics, including shared chromatin state, High-throuhgput chromosome conformation interaction, and association with gene expression, metabolic variation and clinical traits. We use molecular interaction and colocalization analyses to identify multiple nuclear regulatory pathways linking meQTL loci to phenotypic variation, including UBASH3B (body mass index), NFKBIE (rheumatoid arthritis), MGA (blood pressure) and COMMD7 (white cell counts). For rs6511961 , chromatin immunoprecipitation followed by sequencing (ChIP-seq) validates zinc finger protein (ZNF)333 as the likely trans acting effector protein. Finally, we used interaction analyses to identify population- and lineage-specific meQTL, including rs174548 in FADS1, with the strongest effect in CD8+ T cells, thus linking fatty acid metabolism with immune dysregulation and asthma. Our study advances understanding of the potential pathways linking genetic variation to human phenotype.

Multi-household social gatherings contribute to the second SARS-CoV-2 wave in Rhineland-Palatinate, Germany, August to November 2020.

BACKGROUND: Although the private household setting is considered a major driver of viral spread, only little is known about the contextual details of SARS-CoV-2 household transmission, thus hampering political decision-making. MATERIALS AND METHODS: We analyzed individual case and cluster data from statutory notifications from August to November 2020 in Rhineland-Palatinate - the period preceding the second SARS-CoV-2 wave. We also conducted an into-depth survey on contextual details of household transmission in a representative sample of 149 private household clusters that had occurred during this period. RESULTS: During the study period, 18,695 PCR-confirmed SARS-CoV-2 cases were notified, 3,642 of which occurred in 911 clusters (private households (67.3%), the workplace (7.8%), elderly homes (1.8%), others (23.2%). Demographically, clustered cases were representative of all notified cases. Two-thirds (77/113, 68%) of sample response clusters involved more than one private household. These caused on average more close contact persons (mean 13.5, ±SD 15.8) and secondary cases (3.9, ±SD 0.4) than clusters involving one household only (5.1 ± 13.8 and 2.9 ± 0.2). About one in six multi-household clusters in the private setting (13/77) followed a social gathering (e.g. birthday party). Breaches of one or more of the three major barrier concepts (mask, ventilation, and distance) were identified in most (10/13) of these social gatherings. SARS-CoV-2 clusters following social gatherings were overrepresented during the second half of the study period. CONCLUSION: In times of increasing infectious pressure in a given population, multi-household social gatherings appear to be an important target for reducing SARS-CoV-2 transmission.

FEV1 Predicts Cardiac Status and Outcome in Chronic Heart Failure.

BACKGROUND: COPD is an established predictor of clinical outcome in patients with chronic heart failure (HF). However, little evidence is available about the predictive value of FEV1 in chronic HF. RESEARCH QUESTION: Is pulmonary function related to the progression of chronic HF? STUDY DESIGN AND METHODS: The MyoVasc study (ClinicalTrials.gov Identifier: NCT04064450) is a prospective cohort study of HF. Information on pulmonary and cardiac functional and structural status was obtained by body plethysmography and echocardiography. The primary study end point was worsening of HF. RESULTS: Overall 2,998 participants (age range, 35-84 years) with available FEV1 data were eligible for analysis. Linear multivariate regression analysis revealed an independent relationship of FEV1 (per -1 SD) with deteriorated systolic and diastolic left ventricle (LV) function as well as LV hypertrophy under adjustment of age, sex, height, cardiovascular risk factors (CVRFs), and clinical profile (LV ejection fraction: ß-estimate, -1.63% [95% CI, -2.00% to -1.26%]; E/E' ratio: ß-estimate, 0.82 [95% CI, 0.64-0.99]; and LV mass/height2.7: ß-estimate, 1.58 [95% CI, 1.07-2.10]; P < .001 for all). During a median time to follow-up of 2.6 years (interquartile range, 1.1-4.1 years), worsening of HF occurred in 235 individuals. In Cox regression model adjusted for age, sex, height, CVRF, and clinical profile, pulmonary function (FEV1 per -1 SD) was an independent predictor of worsening of HF (hazard ratio [HR], 1.44 [95% CI, 1.27-1.63]; P < .001). Additional adjustment for obstructive airway pattern and C-reactive protein mitigated, but did not substantially alter, the results underlining the robustness of the observed effect (HRFEV1, 1.39 [95% CI, 1.20-1.61]; P < .001). The predictive value of FEV1 was consistent across subgroups, including individuals without obstruction (HR, 1.55 [95% CI, 1.34-1.77]; P < .001) and nonsmokers (HR, 1.72 [95% CI, 1.39-1.96]; P < .001). INTERPRETATION: FEV1 represents a strong candidate to improve future risk stratification and prevention strategies in individuals with chronic, stable HF. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT04064450; URL: www.clinicaltrials.gov.