An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.

INTRODUCTION: Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement. METHODS: The UK Biobank was utilized to build linear and deep learning models for the prediction of surgery relevant outcomes. An initial GWAS for the relevant outcomes was initially conducted to select the Single Nucleotide Polymorphisms for inclusion in the models. Model performance was assessed with Receiver Operator Characteristics of the Area Under the Curve and optimum precision and recall. Feature importance was assessed with SHapley Additive exPlanations. RESULTS: Models were generated for atrial fibrillation, venous thromboembolism and pneumonia as genetics only, clinical features only and a combined model. For venous thromboembolism, the ROC-AUCs were 60.1% [59.6%-60.4%], 63.4% [63.2%-63.4%] and 66.6% [66.2%-66.9%] for the linear models and 51.5% [49.4%-53.4%], 63.2% [61.2%-65.0%] and 62.6% [60.7%-64.5%] for the deep learning SNP, clinical and combined models, respectively. For atrial fibrillation, the ROC-AUCs were 60.3% [60.0%-60.4%], 78.7% [78.7%-78.7%] and 80.0% [79.9%-80.0%] for the linear models and 59.4% [58.2%-60.9%], 78.8% [77.8%-79.8%] and 79.8% [78.8%-80.9%] for the deep learning SNP, clinical and combined models, respectively. For pneumonia, the ROC-AUCs were 50.1% [49.6%-50.6%], 69.2% [69.1%-69.2%] and 68.4% [68.0%-68.5%] for the linear models and 51.0% [49.7%-52.4%], 69.7% [.5%-70.8%] and 69.7% [68.6%-70.8%] for the deep learning SNP, clinical and combined models, respectively. CONCLUSION: In this report we presented linear and deep learning predictive models for surgery relevant outcomes. Overall, predictability was similar between linear and deep learning models and inclusion of genetics seemed to improve accuracy.

Effectiveness of secukinumab in radiographic and non-radiographic axial spondyloarthritis: a European routine-care observational study.

OBJECTIVES: To compare the treatment effectiveness of secukinumab in radiographic (r) versus non-radiographic (nr) axial spondyloarthritis (axSpA) patients treated in routine care across Europe. METHODS: Prospectively collected data on secukinumab-treated axSpA patients with known radiographic status were pooled from nine countries.Remission rates based on patient-reported outcomes (PROs; Numeric Rating Scale (0-10), for example, pain ≤2/Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) ≤2 and Ankylosing Spondylitis Disease Activity Score (ASDAS) inactive disease (ID) <1.3 after 6/12/24 months of secukinumab treatment were calculated.Remission and drug retention rates in r-axSpA versus nr-axSpA patients were compared by logistic and Cox regression models (unadjusted/adjusted for age+sex/adjusted for multiple confounders). RESULTS: Overall, 1161 secukinumab-treated patients were included (r-axSpA/nr-axSpA: 922/239). At baseline, r-axSpA patients had longer disease duration and higher C reactive protein, were more often male and HLA-B27 positive and had received fewer prior biological or targeted synthetic disease-modifying antirheumatic drugs compared with nr-axSpA patients, whereas PROs were largely similar.During follow-up, crude PRO remission rates were significantly higher in r-axSpA compared with nr-axSpA patients (6 months: pain≤2: 40%/28%, OR=1.7; BASDAI≤2: 37%/25%, OR=1.8), as were drug retention rates (24 months: 66%/58%, HR 0.73 (ref: r-axSpA)). Proportions of patients achieving ASDAS ID were low for both groups, particularly nr-axSpA (6 months: 11%/8%).However, when adjusting for age+sex, these differences diminished, and after adjusting for multiple confounders, no significant between-group differences remained for either remission or drug retention rates. CONCLUSION: Crude remission/drug retention rates in European secukinumab-treated patients were higher in r-axSpA compared with nr-axSpA patients. In adjusted analyses, secukinumab effectiveness was similar in both groups, suggesting that observed differences were related to factors other than radiographic status.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning.

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Comparing DAPSA, DAPSA28, and DAS28-CRP in Patients With Psoriatic Arthritis Initiating a First Tumor Necrosis Factor Inhibitor Across Nine European Countries.

OBJECTIVE: Because 66/68 joint counts are not always performed in routine care, we aimed to determine which of the modified 28-joint disease activity index for psoriatic arthritis (DAPSA28) or 28-joint disease activity score with C-reactive protein (DAS28-CRP) should be preferred for monitoring disease activity in psoriatic arthritis (PsA) when the original DAPSA (66/68 joints) is not available. METHODS: Prospectively collected real-world data of European bionaive patients with PsA initiating a first tumor necrosis factor inhibitor were pooled. Remission and response status were evaluated at 6 months by remission (DAPSA ≤ 4, DAPSA28 ≤ 4, and DAS28-CRP < 2.6), response (75% improvement for DAPSA and DAPSA28), and combined EULAR good/moderate responses for DAS28-CRP. Logistic regression analyses on multiple imputed data were used to identify baseline predictors. RESULTS: Remission and response cohorts included 3,159 and 1,866 patients, respectively. The 6-month proportions achieving remission/response were DAPSA (27%/44%), DAPSA28 (28%/44%), and DAS28-CRP (59%/80%). Of 14 possible baseline predictors, 11 predicted both DAPSA and DAPSA28 remission (8 of which also predicted their response, indicated by "*"): longer disease duration*, male sex*, and higher CRP* were positive, whereas older age*, higher body mass index*, patient fatigue*, and global, physician global, health assessment questionnaire score*, and tender and swollen* joint counts were negative predictors. Eight and five of these predicted DAS28-CRP remission and response, respectively. CONCLUSION: In patients with PsA, DAPSA28 should be preferred over DAS28-CRP as a substitute for DAPSA when 66/68 joint counts are not available because of the large overlap in remission and response status and in predictors between DAPSA and DAPSA28.

Patient-reported outcomes in axial spondyloarthritis and psoriatic arthritis patients treated with secukinumab for 24 months in daily clinical practice.

OBJECTIVES: In patients with axial spondyloarthritis (axSpA) or psoriatic arthritis (PsA) initiating secukinumab, we aimed to assess and compare the proportion of patients achieving 6-, 12- and 24-month patient-reported outcomes (PRO) remission and the 24-month retention rates. PATIENTS AND METHODS: Patients with axSpA or PsA from 16 European registries, who initiated secukinumab in routine care were included. PRO remission rates were defined as pain, fatigue, Patient Global Assessment (PGA) ≤2 (Numeric Rating Scale (NRS) 0-10) and Health Assessment Questionnaire (HAQ) ≤0.5, for both axSpA and PsA, and were calculated as crude values and adjusted for drug adherence (LUNDEX). Comparisons of axSpA and PsA remission rates were performed using logistic regression analyses (unadjusted and adjusted for multiple confounders). Kaplan-Meier plots with log-rank test and Cox regression analyses were conducted to assess and compare secukinumab retention rates. RESULTS: We included 3087 axSpA and 3246 PsA patients initiating secukinumab. Crude pain, fatigue, PGA and HAQ remission rates were higher in axSpA than in PsA patients, whereas LUNDEX-adjusted remission rates were similar. No differences were found between the patient groups after adjustment for confounders. The 24-month retention rates were similar in axSpA vs. PsA in fully adjusted analyses (HR [95 %CI] = 0.92 [0.84-1.02]). CONCLUSION: In this large European real-world study of axSpA and PsA patients treated with secukinumab, we demonstrate for the first time a comparable effectiveness in PRO remission and treatment retention rates between these two conditions when adjusted for confounders.

The evolutionary impact of childhood cancer on the human gene pool.

Germline pathogenic variants associated with increased childhood mortality must be subject to natural selection. Here, we analyze publicly available germline genetic metadata from 4,574 children with cancer [11 studies; 1,083 whole exome sequences (WES), 1,950 whole genome sequences (WGS), and 1,541 gene panel] and 141,456 adults [125,748 WES and 15,708 WGS]. We find that pediatric cancer predisposition syndrome (pCPS) genes [n = 85] are highly constrained, harboring only a quarter of the loss-of-function variants that would be expected. This strong indication of selective pressure on pCPS genes is found across multiple lines of germline genomics data from both pediatric and adult cohorts. For six genes [ELP1, GPR161, VHL and SDHA/B/C], a clear lack of mutational constraint calls the pediatric penetrance and/or severity of associated cancers into question. Conversely, out of 23 known pCPS genes associated with biallelic risk, two [9%, DIS3L2 and MSH2] show significant constraint, indicating that they may monoallelically increase childhood cancer risk. In summary, we show that population genetic data provide empirical evidence that heritable childhood cancer leads to natural selection powerful enough to have significantly impacted the present-day gene pool.

FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data.

MOTIVATION: Protein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex. RESULTS: To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.

Patient-Reported Outcomes (PROs) and PRO Remission Rates in 12,262 Biologic-Naïve Patients With Psoriatic Arthritis Treated With Tumor Necrosis Factor Inhibitors in Routine Care.

OBJECTIVE: To evaluate patient-reported outcomes (PROs) after initiation of tumor necrosis factor inhibitor (TNFi) treatment in European real-world patients with psoriatic arthritis (PsA). Further, to investigate PRO remission rates across treatment courses, registries, disease duration, sex, and age at disease onset. METHODS: Visual analog scale or numerical rating scale scores for pain, fatigue, patient global assessment (PtGA), and the Health Assessment Questionnaire-Disability Index (HAQ-DI) from 12,262 patients with PsA initiating a TNFi in 13 registries were pooled. PRO remission rates (pain ≤ 1, fatigue ≤ 2, PtGA ≤ 2, and HAQ-DI ≤ 0.5) were calculated for patients still on the treatment. RESULTS: For the first TNFi, median pain score was reduced by approximately 50%, from 6 to 3, 3, and 2; as were fatigue scores, from 6 to 4, 4, and 3; PtGA scores, from 6 to 3, 3, and 2; and HAQ-DI scores, from 0.9 to 0.5, 0.5, and 0.4 at baseline, 6, 12, and 24 months, respectively. Six-month Lund Efficacy Index (LUNDEX)-adjusted remission rates for pain, fatigue, PtGA, and HAQ-DI scores were 24%, 31%, 36%, and 43% (first TNFi); 14%, 19%, 23%, and 29% (second TNFi); and 9%, 14%, 17%, and 20% (third TNFi), respectively. For biologic-naïve patients with disease duration < 5 years, 6-month LUNDEX-adjusted remission rates for pain, fatigue, PtGA, and HAQ-DI scores were 22%, 28%, 33%, and 42%, respectively. Corresponding rates for patients with disease duration > 10 years were 27%, 32%, 41%, and 43%, respectively. Remission rates were 33%, 40%, 45%, and 56% for men and 17%, 23%, 24%, and 32% for women, respectively. For patients aged < 45 years at diagnosis, 6-month LUNDEX-adjusted remission rate for pain was 29% vs 18% for patients ≥ 45 years. CONCLUSION: In 12,262 biologic-naïve patients with PsA, 6 months of treatment with a TNFi reduced pain by approximately 50%. Marked differences in PRO remission rates across treatment courses, registries, disease duration, sex, and age at onset of disease were observed, emphasizing the potential influence of factors other than disease activity on PROs.

Mass spectrometry-based proteomics data from thousands of HeLa control samples.

Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

Predictors of DAPSA28 remission in patients with psoriatic arthritis initiating a first TNF inhibitor: results from 13 European registries.

OBJECTIVES: In bio-naïve patients with PsA initiating a TNF inhibitor (TNFi), we aimed to identify baseline predictors of Disease Activity index for PsA in 28 joints (DAPSA28) remission (primary objective) and DAPSA28 moderate response at 6 months, as well as drug retention at 12 months across 13 European registries. METHODS: Baseline demographic and clinical characteristics were retrieved and the three outcomes investigated per registry and in pooled data, using logistic regression analyses on multiply imputed data. In the pooled cohort, selected predictors that were either consistently positive or negative across all three outcomes were defined as common predictors. RESULTS: In the pooled cohort (n = 13 369), 6-month proportions of remission, moderate response and 12-month drug retention were 25%, 34% and 63% in patients with available data (n = 6954, n = 5275 and n = 13 369, respectively). Five common baseline predictors of remission, moderate response and 12-month drug retention were identified across all three outcomes. The odds ratios (95% CIs) for DAPSA28 remission were: age, per year: 0.97 (0.96-0.98); disease duration, years (<2 years as reference): 2-3 years: 1.20 (0.89-1.60), 4-9 years: 1.42 (1.09-1.84), ≥10 years: 1.66 (1.26-2.20); men vs women: 1.85 (1.54-2.23); CRP of >10 vs ≤10 mg/l: 1.52 (1.22-1.89) and 1 mm increase in patient fatigue score: 0.99 (0.98-0.99). CONCLUSION: Baseline predictors of remission, response and adherence to TNFi therapy were identified, of which five were common for all three outcomes, indicating that the predictors emerging from our pooled cohort may be considered generalizable from country level to disease level.

Markers of inflammation predict survival in newly diagnosed cirrhosis: a prospective registry study.

The inflammatory activity in cirrhosis is often pronounced and related to episodes of decompensation. Systemic markers of inflammation may contain prognostic information, and we investigated their possible correlation with admissions and mortality among patients with newly diagnosed liver cirrhosis. We collected plasma samples from 149 patients with newly diagnosed (within the past 6 months) cirrhosis, and registered deaths and hospital admissions within 180 days. Ninety-two inflammatory markers were quantified and correlated with clinical variables, mortality, and admissions. Prediction models were calculated by logistic regression. We compared the disease courses of our cohort with a validation cohort of 86 patients with cirrhosis. Twenty of 92 markers of inflammation correlated significantly with mortality within 180 days (q-values of 0.00-0.044), whereas we found no significant correlations with liver-related admissions. The logistic regression models yielded AUROCs of 0.73 to 0.79 for mortality and 0.61 to 0.73 for liver-related admissions, based on a variety of modalities (clinical variables, inflammatory markers, clinical scores, or combinations thereof). The models performed moderately well in the validation cohort and were better able to predict mortality than liver-related admissions. In conclusion, markers of inflammation can be used to predict 180-day mortality in patients with newly diagnosed cirrhosis. Prediction models for newly diagnosed cirrhotic patients need further validation before implementation in clinical practice.Trial registration: NCT04422223 (and NCT03443934 for the validation cohort), and Scientific Ethics Committee No.: H-19024348.

Commonalities and differences in set-up and data collection across European spondyloarthritis registries - results from the EuroSpA collaboration.

BACKGROUND: In European axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA) clinical registries, we aimed to investigate commonalities and differences in (1) set-up, clinical data collection; (2) data availability and completeness; and (3) wording, recall period, and scale used for selected patient-reported outcome measures (PROMs). METHODS: Data was obtained as part of the EuroSpA Research Collaboration Network and consisted of (1) an online survey and follow-up interview, (2) upload of real-world data, and (3) selected PROMs included in the online survey. RESULTS: Fifteen registries participated, contributing 33,948 patients (axSpA: 21,330 (63%), PsA: 12,618 (37%)). The reported coverage of eligible patients ranged from 0.5 to 100%. Information on age, sex, biological/targeted synthetic disease-modifying anti-rheumatic drug treatment, disease duration, and C-reactive protein was available in all registries with data completeness between 85% and 100%. All PROMs (Bath Ankylosing Spondylitis Disease Activity and Functional Indices, Health Assessment Questionnaire, and patient global, pain and fatigue assessments) were more complete after 2015 (68-86%) compared to prior (50-79%). Patient global, pain and fatigue assessments showed heterogeneity between registries in terms of wording, recall periods, and scale. CONCLUSION: Important heterogeneity in registry design and data collection across fifteen European axSpA and PsA registries was observed. Several core measures were widely available, and an increase in data completeness of PROMs in recent years was identified. This study might serve as a basis for examining how differences in data collection across registries may impact the results of collaborative research in the future.

Adversarial and variational autoencoders improve metagenomic binning.

Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline Integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB, with moderate additional runtime.

Global within-species phylogenetics of sewage microbes suggest that local adaptation shapes geographical bacterial clustering.

Most investigations of geographical within-species differences are limited to focusing on a single species. Here, we investigate global differences for multiple bacterial species using a dataset of 757 metagenomics sewage samples from 101 countries worldwide. The within-species variations were determined by performing genome reconstructions, and the analyses were expanded by gene focused approaches. Applying these methods, we recovered 3353 near complete (NC) metagenome assembled genomes (MAGs) encompassing 1439 different MAG species and found that within-species genomic variation was in 36% of the investigated species (12/33) coherent with regional separation. Additionally, we found that variation of organelle genes correlated less with geography compared to metabolic and membrane genes, suggesting that the global differences of these species are caused by regional environmental selection rather than dissemination limitations. From the combination of the large and globally distributed dataset and in-depth analysis, we present a wide investigation of global within-species phylogeny of sewage bacteria. The global differences found here emphasize the need for worldwide data sets when making global conclusions.

Deep integrative models for large-scale human genomics.

Polygenic risk scores (PRSs) are expected to play a critical role in precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. We developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large-scale genomics data. The framework supports multi-task learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual-level data from the UK Biobank, the GLN model demonstrated a competitive performance compared to established neural network architectures, particularly for certain traits, showcasing its potential in modeling complex genetic relationships. Furthermore, the GLN model outperformed linear PRS methods for Type 1 Diabetes, likely due to modeling non-additive genetic effects and epistasis. This was supported by our identification of widespread non-additive genetic effects and epistasis in the context of T1D. Finally, we constructed PRS models that integrated genotype, blood, urine, and anthropometric data and found that this improved performance for 93% of the 290 diseases and disorders considered. EIR is available at https://github.com/arnor-sigurdsson/EIR.

Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan.

Distinct gut microbiome ecology may be implicated in the prevention of aging-related diseases as it influences systemic immune function and resistance to infections. Yet, the viral component of the microbiome throughout different stages in life remains unexplored. Here we present a characterization of the centenarian gut virome using previously published metagenomes from 195 individuals from Japan and Sardinia. Compared with gut viromes of younger adults (>18 yr) and older individuals (>60 yr), centenarians had a more diverse virome including previously undescribed viral genera, such as viruses associated with Clostridia. A population shift towards higher lytic activity was also observed. Finally, we investigated phage-encoded auxiliary functions that influence bacterial physiology, which revealed an enrichment of genes supporting key steps in sulfate metabolic pathways. Phage and bacterial members of the centenarian microbiome displayed an increased potential for converting methionine to homocysteine, sulfate to sulfide and taurine to sulfide. A greater metabolic output of microbial hydrogen sulfide in centenarians may in turn support mucosal integrity and resistance to pathobionts.

Combinatorial batching of DNA for ultralow-cost detection of pathogenic variants.

BACKGROUND: Next-generation sequencing (NGS) based population screening holds great promise for disease prevention and earlier diagnosis, but the costs associated with screening millions of humans remain prohibitive. New methods for population genetic testing that lower the costs of NGS without compromising diagnostic power are needed. METHODS: We developed double batched sequencing where DNA samples are batch-sequenced twice - directly pinpointing individuals with rare variants. We sequenced batches of at-birth blood spot DNA using a commercial 113-gene panel in an explorative (n = 100) and a validation (n = 100) cohort of children who went on to develop pediatric cancers. All results were benchmarked against individual whole genome sequencing data. RESULTS: We demonstrated fully replicable detection of cancer-causing germline variants, with positive and negative predictive values of 100% (95% CI, 0.91-1.00 and 95% CI, 0.98-1.00, respectively). Pathogenic and clinically actionable variants were detected in RB1, TP53, BRCA2, APC, and 19 other genes. Analyses of larger batches indicated that our approach is highly scalable, yielding more than 95% cost reduction or less than 3 cents per gene screened for rare disease-causing mutations. We also show that double batched sequencing could cost-effectively prevent childhood cancer deaths through broad genomic testing. CONCLUSIONS: Our ultracheap genetic diagnostic method, which uses existing sequencing hardware and standard newborn blood spots, should readily open up opportunities for population-wide risk stratification using genetic screening across many fields of clinical genetics and genomics.

Mapache: a flexible pipeline to map ancient DNA.

SUMMARY: We introduce mapache, a flexible, robust and scalable pipeline to map, quantify and impute ancient and present-day DNA in a reproducible way. Mapache is implemented in the workflow manager Snakemake and is optimized for low-space consumption, allowing to efficiently (re)map large datasets-such as reference panels and multiple extracts and libraries per sample - to one or several genomes. Mapache can easily be customized or combined with other Snakemake tools. AVAILABILITY AND IMPLEMENTATION: Mapache is freely available on GitHub (https://github.com/sneuensc/mapache). An extensive manual is provided at https://github.com/sneuensc/mapache/wiki. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The effect of antithrombotic treatment on the fecal immunochemical test for colorectal cancer screening: a nationwide cross-sectional study.

BACKGROUND : Screening for colorectal cancer (CRC) using the fecal immunochemical test (FIT) has been widely adopted. The use of antithrombotic treatment is increasing in the Western world. This study aimed to assess the effects of antithrombotic treatment on the FIT-based Danish national screening program for CRC. METHODS : This was a cross-sectional study of all individuals returning a FIT from 2014 until 2016. The effect of antithrombotic treatment on FIT positivity and the positive predictive value (PPV) were assessed using proportions and multivariable Poisson regression. RESULTS : Of 884 036 invited individuals, we identified 551 570 participants. A positive FIT was observed in 9052 of 77 007 individuals (11.8 %) receiving antithrombotic treatment compared with 28 387 of 474 587 individuals (6.0 %) receiving no treatment. The adjusted relative risk (RR) for a positive FIT was 1.59 (95 %CI 1.56-1.63) for any treatment. Nonvitamin K oral anticoagulants (NOACs) were associated with the largest increase in FIT positivity (adjusted RR 2.40, 95 %CI 2.48-2.54). The proportion of CRC detected at colonoscopy was slightly lower among patients on antithrombotic treatment (6.0 %, 95 %CI 5.5 %-6.6 %) than among treatment-naïve patients (6.4 %, 95 %CI 6.1 %-6.7 %). The PPV for CRC or high risk adenomas was decreased nearly twofold in patients treated with NOAC (adjusted RR 0.58, 95 %CI 0.51-0.66]). CONCLUSION : Antithrombotic treatment was associated with a decreased PPV in FIT-based CRC screening.