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MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Identification of genetic variants influencing the transcription of miRNAs can provide an understanding of their genetic regulation and implication in human disease. Here we present genome-wide association studies of 2,083 plasma circulating miRNAs measured by next-generation sequencing in 2,178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We report 4,310 cis- and trans-miR-eQTLs for 64 miRNAs that have been replicated across independent studies. Many of these miR-eQTLs overlap with gene expression, protein, and metabolite-QTLs and with disease-associated variants. The consequences of perturbation in miRNA transcription on a wide range of clinical conditions are systematically investigated in phenome-wide association studies, with their causality tested using Mendelian randomization. Integration of genomics and miRNAs enables interrogation of the genetic architecture of miRNAs, revealing their clinical importance, and providing valuable resources for future studies of miRNAs in human disease.

Attention-deficit/hyperactivity disorder (ADHD) often co-occurs with obesity, however, the potential causality between the traits remains unclear. We examined both genetic and prenatal evidence for causality using Mendelian Randomisation (MR) and polygenic risk scores (PRS). We conducted bi-directional MR on ADHD liability and six obesity-related traits using summary statistics from the largest available meta-analyses of genome-wide association studies. We also examined the shared genetic aetiology between ADHD symptoms (inattention and hyperactivity) and body mass index (BMI) by PRS association analysis using longitudinal data from Northern Finland Birth Cohort 1986 (NFBC1986, n = 2984). Lastly, we examined the impact of the prenatal environment by association analysis of maternal pre-pregnancy BMI and offspring ADHD symptoms, adjusted for PRS of both traits, in NFBC1986 dataset. Through MR analyses, we found evidence for bidirectional causality between ADHD liability and obesity-related traits. PRS association analyses showed evidence for genetic overlap between ADHD symptoms and BMI. We found no evidence for a difference between inattention and hyperactivity symptoms, suggesting that neither symptom subtype is driving the association. We found evidence for association between maternal pre-pregnancy BMI and offspring ADHD symptoms after adjusting for both BMI and ADHD PRS (association p-value = 0.027 for inattention, p = 0.008 for hyperactivity). These results are consistent with the hypothesis that the co-occurrence between ADHD and obesity has both genetic and prenatal environmental origins.

Motivation Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p≫n) data, such as OMICS. The sparse variant of canonical correlation analysis (CCA) approach is a promising one that seeks to penalize the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sparse CCA (sCCA) have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets. Results Through a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al., penalized matrix decomposition CCA proposed by Witten and Tibshirani and its extension proposed by Suo et al. The aforementioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets. Availability and implementation https://github.com/theorod93/sCCA. Supplementary information Supplementary data are available at Bioinformatics online.

International audience; Background: Biomedical research increasingly relies on computational approaches to extract relevant information from large corpora of publications.Objective: To investigate the consequence of the ambiguity between the use of terms "Eczema" and "Atopic Dermatitis" (AD) from the Information Retrieval perspective, and its impact on meta-analyses, systematic reviews and text mining.Methods: Articles were retrieved by querying the PubMed using terms 'eczema' (D003876) and "dermatitis, atopic" (D004485). We used machine learning to investigate the differences between the contexts in which each term is used. We used a decision tree approach and trained model to predict if an article would be indexed with eczema or AD tags. We used text-mining tools to extract biological entities associated with eczema and AD, and investigated the discrepancy regarding the retrieval of key findings according to the terminology used.Results: Atopic dermatitis query yielded more articles related to veterinary science, biochemistry, cellular and molecular biology; the eczema query linked to public health, infectious disease and respiratory system. Medical Subject Headings terms associated with "AD" or "Eczema" differed, with an agreement between the top 40 lists of 52%. The presence of terms related to cellular mechanisms, especially allergies and inflammation, characterized AD literature. The metabolites mentioned more frequently than expected in articles with AD tag differed from those indexed with eczema. Fewer enriched genes were retrieved when using eczema compared to AD query.Conclusions and clinical relevance: There is a considerable discrepancy when using text mining to extract bio-entities related to eczema or AD. Our results suggest that any systematic approach (particularly when looking for metabolites or genes related to the condition) should be performed using both terms jointly. We propose to use decision tree learning as a tool to spot and characterize ambiguity, and provide the source code for disambiguation at https://github.com/cfrainay/ResearchCodeBase.

Several cyber-security data sources are collected in enterprise networks providing relational information between different types of nodes in the network, namely computers, users and ports. This relational data can be expressed as adjacency matrices detailing inter-type relationships corresponding to relations between nodes of different types and intra-type relationships showing relationships between nodes of the same type. In this paper, we propose an extension of Non-Negative Matrix Tri-Factorisation (NMTF) to simultaneously cluster nodes based on their intra and inter-type relationships. Existing NMTF based clustering methods suffer from long computational times due to large matrix multiplications. In our approach, we enforce stricter cluster indicator constraints on the factor matrices to circumvent these issues. Additionally, to make our proposed approach less susceptible to variation in results due to random initialisation, we propose a novel initialisation procedure based on Non-Negative Double Singular Value Decomposition for multi-type relational clustering. Finally, a new performance measure suitable for assessing clustering performance on unlabelled multi-type relational data sets is presented. Our algorithm is assessed on both a simulated and real computer network against standard approaches showing its strong performance.

MicroRNAs (miRNAs) have emerged as key regulators of gene expression. Differential expression of miRNAs has been linked to diabetes, but underlying pathways remain poorly understood. We performed genome-wide miRNAs profiling and tested the causal associations between miRNAs and type 2 diabetes in the general population. Subsequently, we investigated target genes and metabolites of miRNAs to provide insight into the metabolic disturbances that emerge with type 2 diabetes. Between 2002 and 2005, plasma levels of 2083 circulatory miRNAs were profiled in 1900 participants (mean age 71.4 years) of the population-based Rotterdam Study cohort. The associations of 591 well-expressed miRNAs with prevalent and incident type 2 diabetes were examined until 2015. Two-sample Mendelian Randomization (MR) was conducted to investigate the causal associations and miRNA-target genes and metabolites were studied in relation to type 2 diabetes. At baseline, higher plasma levels of miR-139-5p and miR-193a-5p were associated (FDR9.0 years, 209 participants developed type 2 diabetes. Plasma levels of miR-99a-5p, miR-4664-3p, miR-29a-3p, miR-122-5p, and miR-125b-5p were significantly associated with incident type 2 diabetes (n=209). Two-sample MR confirmed a causal effect for miR-139-5p (MR-IWV-beta=0.10, p=3.51×10−4) on type 2 diabetes. We found several target genes and metabolites that could link miR-139-5p to pathways underlying type 2 diabetes. Our study indicates a causal relationship between miR-139-5p and type 2 diabetes and suggests this miRNA as a plasma biomarker of type 2 diabetes.

Background Observational studies suggest interconnections between thyroid status, metabolism, and risk of coronary artery disease (CAD), but causality remains to be proven. The present study aimed to investigate the potential causal relationship between thyroid status and cardiovascular disease and to characterize the metabolomic profile associated with thyroid status. Methods Multi-cohort two-sample Mendelian randomization (MR) was performed utilizing genome-wide significant variants as instruments for standardized thyrotropin (TSH) and free thyroxine (fT4) within the reference range. Associations between TSH and fT4 and metabolic profile were investigated in a two-stage manner: associations between TSH and fT4 and the full panel of 161 metabolomic markers were first assessed hypothesis-free, then directional consistency was assessed through Mendelian randomization, another metabolic profile platform, and in individuals with biochemically defined thyroid dysfunction. Results Circulating TSH was associated with 52/161 metabolomic markers, and fT4 levels were associated with 21/161 metabolomic markers among 9432 euthyroid individuals (median age varied from 23.0 to 75.4 years, 54.5% women). Positive associations between circulating TSH levels and concentrations of very low-density lipoprotein subclasses and components, triglycerides, and triglyceride content of lipoproteins were directionally consistent across the multivariable regression, MR, metabolomic platforms, and for individuals with hypo- and hyperthyroidism. Associations with fT4 levels inversely reflected those observed with TSH. Among 91,810 CAD cases and 656,091 controls of European ancestry, per 1-SD increase of genetically determined TSH concentration risk of CAD increased slightly, but not significantly, with an OR of 1.03 (95% CI 0.99–1.07; p value 0.16), whereas higher genetically determined fT4 levels were not associated with CAD risk (OR 1.00 per SD increase of fT4; 95% CI 0.96–1.04; p value 0.59). Conclusions Lower thyroid status leads to an unfavorable lipid profile and a somewhat increased cardiovascular disease risk.

MotivationRecent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that by integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. As OMICS datasets are heterogeneous and high-dimensional (p >> n) integrating them can be done through Sparse Canonical Correlation Analysis (sCCA) that penalises the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sCCA have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets.ResultsThrough a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al. [2009], penalised matrix decomposition CCA proposed by Witten and Tibshirani [2009] and its extension proposed by Suo et al. [2017]. The aferomentioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets.Contacttr1915@ic.ac.uk

MicroRNAs (miRNAs) regulate the expression of the majority of genes. However, it is not known whether they regulate genes in random or are organized according to their function. To this end, we chose cardiometabolic disorders as an example and investigated whether genes associated with cardiometabolic disorders are regulated by a random set of miRNAs or a limited number of them. Single-nucleotide polymorphisms (SNPs) reaching genome-wide level significance were retrieved from most recent genome-wide association studies on cardiometabolic traits, which were cross-referenced with Ensembl to identify related genes and combined with miRNA target prediction databases (TargetScan, miRTarBase, or miRecords) to identify miRNAs that regulate them. We retrieved 520 SNPs, of which 355 were intragenic, corresponding to 304 genes. While we found a higher proportion of genes reported from all GWAS that were predicted targets for miRNAs in comparison to all protein-coding genes (75.1%), the proportion was even higher for cardiometabolic genes (80.6%). Enrichment analysis was performed within each database. We found that cardiometabolic genes were over-represented in target genes for 29 miRNAs (based on TargetScan) and 3 miRNAs (miR-181a, miR-302d and miR-372) (based on miRecords) after Benjamini-Hochberg correction for multiple testing. Our work provides evidence for non-random assignment of genes to miRNAs and supports the idea that miRNAs regulate sets of genes that are functionally related.

As the number of cyber-attacks increases, there has been increasing emphasis on developing complementary methods of detection to the existing signature-based approaches. This work builds upon a previously discovered persistent structure within the Los Alamos National Laboratory network data sources, to develop a regression based streaming anomaly detection mechanism that can adapt to the network behaviour over time. The methodology has also been applied to a new data set of the same network to assess the extent of its pertinence in time.

Data-driven anomaly detection systems unrivalled potential as complementary defence systems to existing signature-based tools as the number of cyber attacks increases. In this manuscript an anomaly detection system is presented that detects any abnormal deviations from the normal behaviour of an individual device. Device behaviour is defined as the number of network traffic events involving the device of interest observed within a pre-specified time period. The behaviour of each device at normal state is modelled to depend on its observed historic behaviour. A number of statistical and machine learning approaches are explored for modelling this relationship and through a comparative study, the Quantile Regression Forests approach is found to have the best predictive power. Based on the prediction intervals of the Quantile Regression Forests an anomaly detection system is proposed that characterises as abnormal, any observed behaviour outside of these intervals. A series of experiments for contaminating normal device behaviour are presented for examining the performance of the anomaly detection system. Through the conducted analysis the proposed anomaly detection system is found to outperform two other detection systems. The presented work has been conducted on two enterprise networks.

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future. NMRC (Natl Medical Research Council, S’pore) MOH (Min. of Health, S’pore)

High blood pressure is the foremost heritable global risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits to date (systolic, diastolic, pulse pressure) in over one million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also reveal shared loci influencing lifestyle exposures. Our findings offer the potential for a precision medicine strategy for future cardiovascular disease prevention.

This paper takes an unsupervised learning approach for monitoring edge activity within an enterprise computer network. Using NetFlow records, features are gathered across the active connections (edges) in 15-minute time windows. Then, edges are grouped into clusters using the k-means algorithm. This process is repeated over contiguous windows. A series of informative indicators are derived by examining the relationship of edges with the observed cluster structure. This leads to an intuitive method for monitoring network behaviour and a temporal description of edge behaviour at global and local levels.

Motivation: Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date. Methods: We present two novel methods for identifying associated pathways. In the proposed hierarchical model, the SNP effects are analytically integrated out of the analysis, allowing computationally tractable model fitting to genome-wide data. The first method uses Bayes factors for calculating the effect of the pathways, whereas the second method uses a machine learning algorithm and adaptive lasso for finding a sparse solution of associated pathways. Results: The performance of the proposed methods was explored on both simulated and real data. The results of the simulation study showed that the methods outperformed some well-established association methods: the commonly used Fisher’s method for combining P-values and also the recently published BGSA. The methods were applied to two genome-wide association study datasets that aimed to find the genetic structure of platelet function and body mass index, respectively. The results of the analyses replicated the results of previously published pathway analysis of these phenotypes but also identified novel pathways that are potentially involved. Availability: An R package is under preparation. In the meantime, the scripts of the methods are available on request from the authors. Contact: marina.evangelou@cimr.cam.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online.

In the version of this article originally published, the name of author Martin H. de Borst was coded incorrectly in the XML. The error has now been corrected in the HTML version of the paper.

We investigate where and how key dependency structure between measures of network activity change throughout the course of daily activity. Our approach to data-mining is probabilistic in nature, we formulate the identification of dependency patterns as a regularised statistical estimation problem. The resulting model can be interpreted as a set of time-varying graphs and provides a useful visual interpretation of network activity. We believe this is the first application of dynamic graphical modelling to network traffic of this kind. Investigations are performed on 9 days of real-world network traffic across a subset of IP's. We demonstrate that dependency between features may change across time and discuss how these change at an intra and inter-day level. Such variation in feature dependency may have important consequences for the design and implementation of probabilistic intrusion detection systems.

The behaviour of individual devices connected to an enterprise network can vary dramatically, as a device’s activity depends on the user operating the device as well as on all behind the scenes operations between the device and the network. Being able to understand and predict a device’s behaviour in a network can work as the foundation of an anomaly detection framework, as devices may show abnormal activity as part of a cyber attack. The aim of this work is the construction of a predictive regression model for a device’s behaviour at normal state. The behaviour of a device is presented by a quantitative response and modelled to depend on historic data recorded by NetFlow.

Objectives Computed tomography (CT) is an essential part of preoperative planning prior to cytoreductive surgery for primary and relapsed epithelial ovarian cancer (EOC). Our aim is to correlate pre-operative CT results with intraoperative surgical and histopathological findings at debulking surgery. Methods We performed a systematic comparison of intraoperative tumor dissemination patterns and surgical resections with preoperative CT assessments of infiltrative disease at key resection sites, in women who underwent multivisceral debulking surgery due to EOC between January 2013 and December 2014 at a tertiary referral center. The key sites were defined as follows: diaphragmatic involvement(DI), splenic disease (SI), large (LBI) and small (SBI) bowel involvement, rectal involvement (RI), porta hepatis involvement (PHI), mesenteric disease (MI) and lymph node involvement (LNI). Results A total of 155 patients, mostly with FIGO stage IIIC disease (65%) were evaluated (primary=105, relapsed=50). Total macroscopic cytoreduction rates were: 89%. Pre-operative CT findings displayed high specificity across all tumor sites apart from the retroperitoneal lymph node status, with a specificity of 65%. The ability however of the CT to accurately identify sites affected by invasive disease was relatively low with the following sensitivities as relating to final histology: 32% (DI), 26% (SI), 46% (LBI), 44% (SBI), 39% (RI), 57% (PHI), 31% (MI), 63% (LNI). Conclusion Pre-operative CT imaging shows high specificity but low sensitivity in detecting tumor involvement at key sites in ovarian cancer surgery. CT findings alone should not be used for surgical decision making.

Statistical anomaly detection is emerging as an important complement to signature-based methods for enterprise network defence. In this paper, we isolate a persistent structure in two different enterprise network data sources. This structure provides the basis of a regression-based anomaly detection method. The procedure is demonstrated on a large public domain data set.

Objective:Hypertension is the leading cause of cardiovascular morbidity. High blood pressure is highly heritable (∼30–50%), however all genetic variants identified so far only explain ∼3–4% of the trait variance. Hence much of the genetic architecture of blood pressure remains unexplained.Design and

Background A barrier to the successful development of new disease treatments is the timely recruitment of participants to experimental medicine studies that are primarily designed to investigate biological mechanisms rather than evaluate clinical efficacy. The aim of this study was to analyse the performance of three recruitment sources and the effect of publicity events during the Adaptive study of IL-2 dose on regulatory T cells in type 1 diabetes (DILT1D). Methods The final study outcome, demography, disease duration, residence and the effect of publicity events on the performance of three recruitment sources (clinics, type 1 diabetes (T1D) disease register and the internet) were analysed from a bespoke DILT1D recruitment database. For the internet source, the origin of website hits in relation to publicity events was also evaluated. Results A total of 735 potentially eligible participants were approached to identify the final 45 DILT1D participants. A total of 477 (64%) were identified via the disease register, but only 59 (12%) responded to contact. A total of 317 individuals registered with the DILT1D study team. Self-referral via the study website generated 170 (54%) registered individuals and was the most popular and successful source, with 88 (28%) sourced from diabetes clinics and 59 (19%) from the disease register. Of those with known T1D duration (N = 272), the internet and clinics sources identified a larger number (57, 21%) of newly diagnosed T1D (

It has been suggested that pathway analysis can complement single-SNP analysis in exploring genomewide association data. Pathway analysis incorporates the available biological knowledge of genes and SNPs and is expected to improve the chances of revealing the underlying genetic architecture of complex traits. Methods for pathway analysis can be classified as competitive (enrichment) or self-contained (association) according to the hypothesis tested. Although association tests are statistically more powerful than enrichment tests they can be difficult to calibrate because biases in analysis accumulate across multiple SNPs or genes. Furthermore, enrichment tests can be more scientifically relevant than association tests, as they detect pathways with relatively more evidence for association than the remaining genes. Here we show how some well known association tests can be simply adapted to test for enrichment, and compare their performance to some established enrichment tests. We propose versions of the Adaptive Rank Truncated Product (ARTP), Tail Strength Measure and Fisher's combination of p-values for testing the enrichment null hypothesis. We compare the behaviour of these proposed methods with the established Hypergeometric Test and Gene-Set Enrichment Analysis (GSEA). The results of the simulation study show that the modified version of the ARTP method has generally the best performance across the situations considered. The methods were also applied for finding enriched pathways for body mass index (BMI) and platelet function phenotypes. The pathway analysis of BMI identified the Vasoactive Intestinal Peptide pathway as significantly associated with BMI. This pathway has been previously reported as associated with BMI and the risk of obesity. The ARTP method was the method that identified the largest number of enriched pathways across all tested pathway databases and phenotypes. The simulation and data application results are in agreement with previous work on association tests and suggests that the ARTP should be preferred for both enrichment and association testing.