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Semicontinuous data, characterized by an excess of zeros followed by a non-negative and right-skewed distribution, are frequently observed in biomedical research. Different statistical models have been proposed to investigate the association of covariates with such outcome. Motivated by the search of genetic factors associated with Neutrophil Extracellular Traps (NETs), a semicontinuous biomarker involved in thrombosis, we here investigated the impact of the selected model for semicontinuous traits in the context of a Genome Wide Association Study (GWAS). We compared three models that jointly model zero and positive values while allowing the estimation of a single association parameter of covariates with the global mean: Tobit, Negative Binomial and Compound Poisson-Gamma. We assessed the fit of these models to a sample of 657 participants of the FARIVE study measured for NETs plasma levels. For each of these three models, we performed a GWAS on NETs in FARIVE participants and results were compared. A simulation study was also conducted to evaluate the control of the type I error. Compound Poisson-Gamma and Negative Binomial models fitted NETs data observed in FARIVE better than the Tobit model. However, the Negative Binomial model suffered from an inflation of its type I error, attributable to extreme positive values of the NETs and low frequency variants. Conversely, the Compound Poisson-Gamma model was robust to both phenomena. Using the latter model, a GWAS identified a genome wide significant locus on chr21q21.3. The lead variant was rs57502213, a deletion of two nucleotides located ∼40kb upstream the non-coding RNA (miR155HG) hosting the miR-155 that was recently highlighted to have a role in NETs formation. This work indicates that the modeling strategy for a semicontinuous outcome in the framework of GWAS studies is crucial. The choice of the model should take into account the nature of the process generating zero values and the presence of extreme values. Our work also suggests that the Compound Poisson-Gamma model, while still marginally employed, can be a robust modeling strategy for GWAS analysis on a semicontinuous trait.

Venous thromboembolism (VTE), comprising both deep vein thrombosis (DVT) and pulmonary embolism (PE) is a common, multi-causal disease with potentially serious short- and long-term complications. In clinical practice, there is a need for improved plasma biomarker-based tools for VTE diagnosis and risk prediction. We used multiplex proteomics profiling to screen plasma from patients with suspected acute VTE, and a case-control study of patients followed up after ending anticoagulant treatment for a first VTE. With replication in 5 independent studies, together totalling 1137 patients and 1272 controls, we identify Complement Factor H Related Protein (CFHR5), a regulator of the alternative pathway of complement activation, as a novel VTE associated plasma biomarker. Using GWAS analysis of 2967 individuals we identified a genome-wide significant pQTL signal on chr1q31.3 associated with CFHR5 levels. We showed that higher CFHR5 levels are associated with increased thrombin generation in patient plasma and that recombinant CFHR5 enhances platelet activationin vitro. Thrombotic complications are a frequent feature of COVID-19; in hospitalised patients we found CFHR5 levels at baseline were associated with short-time prognosis of disease severity, defined as maximum level of respiratory support needed during hospital stay. Our results indicate a clinically important role for regulation of the alternative pathway of complement activation in the pathogenesis of VTE and pulmonary complications in acute COVID-19. Thus, CFHR5 is a potential diagnostic and/or risk predictive plasma biomarker reflecting underlying pathology in VTE and acute COVID-19.

In studies of time-to-events, it is common to collect information about events that occurred before the inclusion in a prospective cohort. In an ambispective design, when the risk factors studied are independent of time, including both pre- and post-inclusion events in the analyses increases the statistical power but may lead to a selection bias. To avoid such a bias, we propose a survival analysis weighted by the inverse of the survival probability at the time of data collection about the events.This method is applied to the study of the association of ABO blood groups with the risk of venous thromboembolism (VT) recurrence in the MARTHA and MEGA cohorts. The former relying on an ambispective design and the latter on a standard prospective one. In the combined sample totalling 2,752 patients including 993 recurrences, compared with the O1 group, A1 has an increased risk (Hazard Ratio (HR) of 1.18, p=4.2×10−3), homogeneously in MARTHA and in MEGA. The same trend (HR=1.19, p=0.06) was observed for the less frequent A2 group.In conclusion, this work clarified the association ofABOblood groups with the risk of VT recurrence. Besides, the methodology proposed here to analyse time-independent risk factors of events in an ambispective design has an immediate field of application in the context of genome wide association studies.

Venous thromboembolism (VTE) is the third most common cardiovascular disease, affecting ∼ 1,000,000 individuals each year in Europe. VTE is characterized by an annual recurrent rate of ∼ 6%, and ∼ 30% of patients with unprovoked VTE will face a recurrent event after a six-month course of anticoagulant treatment. Even if guidelines recommend life-long treatment for these patients, about ∼ 70% of them will never experience a recurrence and will receive unnecessary lifelong anti-coagulation that is associated with increased risk of bleeding and is highly costly for the society. There is then urgent need to identify biomarkers that could distinguish VTE patients with high risk of recurrence from low-risk patients. Capitalizing on a sample of 913 patients followed up for the risk of VTE recurrence during a median of ∼ 10 years and profiled for 376 plasma proteomic antibodies, we here develop an artificial neural network (ANN) based strategy to identify a proteomic signature that helps discriminating patients at low and high risk of recurrence. In a first stage, we implemented a Repeated Editing Nearest Neighbors algorithm to select a homogeneous sub-sample of VTE patients. This sub-sample was then split in a training and a testing sets. The former was used for training our ANN, the latter for testing its discriminatory properties. In the testing dataset, our ANN led to an accuracy of 0.86 that compared to an accuracy of 0.79 as provided by a random forest classifier. We then applied a Deep Learning Important FeaTures (DeepLIFT) – based approach to identify the variables that contribute the most to the ANN predictions. In addition to sex, the proposed DeepLIFT strategy identified 6 important proteins (DDX1, HTRA3, LRG1, MAST2, NFATC4 and STXBP5) whose exact roles in the etiology of VTE recurrence now deserve further experimental validations. © 2021, Springer Nature Switzerland AG.

MicroRNAs (miRNAs) are small regulatory RNAs participating to several biological processes and known to be involved in various pathologies. Measurable in body fluids, miRNAs have been proposed to serve as efficient biomarkers for diseases and/or associated traits. Here, we performed a next-generation-sequencing based profiling of plasma miRNAs in 344 patients with venous thrombosis (VT) and assessed the association of plasma miRNA levels with several haemostatic traits and the risk of VT recurrence. Among the most significant findings, we detected an association between hsa-miR-199b-3p and haematocrit levels (P = 0.0016), these two markers having both been independently reported to associate with VT risk. We also observed suggestive evidence for association of hsa-miR-370-3p (P = 0.019), hsa-miR-27b-3p (P = 0.016) and hsa-miR-222-3p (P = 0.049) with VT recurrence, the observations at the latter two miRNAs confirming the recent findings of Wang et al. Besides, by conducting Genome-Wide Association Studies on miRNA levels and meta-analyzing our results with some publicly available, we identified 21 new associations of single nucleotide polymorphisms with plasma miRNA levels at the statistical significance threshold of P, Los micro-ARN (miARN) son pequeñas moléculas de ARN reguladoras que participan en varios procesos biológicos y están implicados en diversas patologías. Mensurables en los líquidos corporales, se ha planteado que los miARN pueden ser biomarcadores eficaces para el diagnóstico de enfermedades y/o características asociadas. Aquí hemos llevado a cabo un análisis de miARN plasmático con tecnología de secuenciación de última generación en 344 pacientes con trombosis venosa (TV) y hemos evaluado la asociación de los niveles de miARN con distintas características hemostáticas y el riesgo de recidiva de TV. Entre los hallazgos más significativos, hemos detectado una asociación entre hsa-miR-199b-3p y los niveles de hematocritos (p = 0,0016); dos marcadores que se habían asociado de forma independiente con el riesgo de sufrir TV. Asimismo, hemos observado una evidencia indicativa de asociación entre hsa-miR-370-3p (p = 0,019), hsa-miR-27b-3p (p = 0,016) y hsa-miR-222-3p (p = 0,049) y la recidiva de TV; los resultados los dos últimos miARN confirman los hallazgos recientes de Wang et al. (Clin Epigenetics, 2019). Además, al efectuar estudios de asociación del genoma completo sobre los niveles de miARN y al metaanalizar nuestros resultados con otros disponibles públicamente, hemos identificado 21 asociaciones nuevas de polimorfismos de un solo nucleótido (PSN) con niveles de miARN plasmático con un umbral de significación estadística de p