SummaryBackgroundA sexual dimorphism exists in the incidence and prevalence of coronary artery disease—men are more commonly affected than are age-matched women. We explored the role of the Y chromosome in coronary artery disease in the context of this sexual inequity.MethodsWe genotyped 11 markers of the male-specific region of the Y chromosome in 3233 biologically unrelated British men from three cohorts: the British Heart Foundation Family Heart Study (BHF-FHS), West of Scotland Coronary Prevention Study (WOSCOPS), and Cardiogenics Study. On the basis of this information, each Y chromosome was tracked back into one of 13 ancient lineages defined as haplogroups. We then examined associations between common Y chromosome haplogroups and the risk of coronary artery disease in cross-sectional BHF-FHS and prospective WOSCOPS. Finally, we undertook functional analysis of Y chromosome effects on monocyte and macrophage transcriptome in British men from the Cardiogenics Study.FindingsOf nine haplogroups identified, two (R1b1b2 and I) accounted for roughly 90% of the Y chromosome variants among British men. Carriers of haplogroup I had about a 50% higher age-adjusted risk of coronary artery disease than did men with other Y chromosome lineages in BHF-FHS (odds ratio 1·75, 95% CI 1·20–2·54, p=0·004), WOSCOPS (1·45, 1·08–1·95, p=0·012), and joint analysis of both populations (1·56, 1·24–1·97, p=0·0002). The association between haplogroup I and increased risk of coronary artery disease was independent of traditional cardiovascular and socioeconomic risk factors. Analysis of macrophage transcriptome in the Cardiogenics Study revealed that 19 molecular pathways showing strong differential expression between men with haplogroup I and other lineages of the Y chromosome were interconnected by common genes related to inflammation and immunity, and that some of them have a strong relevance to atherosclerosis.InterpretationThe human Y chromosome is associated with risk of coronary artery disease in men of European ancestry, possibly through interactions of immunity and inflammation.FundingBritish Heart Foundation; UK National Institute for Health Research; LEW Carty Charitable Fund; National Health and Medical Research Council of Australia; European Union 6th Framework Programme; Wellcome Trust.

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This work presents a scientific data mining process model for metabolomics that provides a systematic and formalised framework for guiding and performing metabolomics data analysis in a justifiable and traceable manner. The process model is designed to promote the achievement of the analytical objectives of metabolomics investigations and to ensure the validity, interpretability and reproducibility of their results. It satisfies the requirements of metabolomics data mining, focuses on the contextual meaning of metabolomics knowledge, and addresses the shortcomings of existing data mining process models, while paying attention to the practical aspects of metabolomics investigations and other desirable features. The process model development involved investigating the ontologies and standards of science, data mining and metabolomics and its design was based on the principles, best practices and inspirations from Process Engineering, Software Engineering, Scientific Methodology and Machine Learning. A software environment was built to realise and automate the process model execution and was then applied to a number of metabolomics datasets to demonstrate and evaluate its applicability to different metabolomics investigations, approaches and data acquisition instruments on one hand, and to different data mining approaches, goals, tasks and techniques on the other. The process model was successful in satisfying the requirements of metabolomics data mining and can be generalised to perform data mining in other scientific disciplines.

This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its strengths and unique features. The demonstrated applications provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting. The data analysed in these applications were captured by chromatographic separation and mass spectrometry technique (LC-MS), Fourier transform infrared spectroscopy (FT-IR), and nuclear magnetic resonance spectroscopy (NMR) and involve the analysis of plant, animal, and human samples. The process was executed using both data-driven and hypothesis-driven data mining approaches in order to perform various data mining goals and tasks by applying a number of data mining techniques. The applications were selected to achieve a range of analytical goals and research questions and to provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting using datasets that were captured by NMR, LC-MS, and FT-IR using samples of a plant, animal, and human origin. The process was applied using an implementation environment which was created in order to provide a computer-aided realisation of the process model execution.data mining only a stage in the knowledge discovery process [5,4]. Knowledge discovery and data mining have several applications in metabolomics which covers fields including drugs design, disease diagenesis, plant biology, environmental studies, nutrition, animal breeding, genetic studies and many other. Examples for these applications are reported in [8,9,10,11,12,13,6,7].The applications presented in this paper aim to demonstrate MeKDDaM process model's fulfilment of the requirements of metabolomics data mining and its ability to achieve various types of analytical goals and research questions and provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting. The data analysed in these applications were captured by chromatographic separation and mass spectrometry technique (LC-MS), Fourier transform infrared spectroscopy (FT-IR), and nuclear magnetic resonance spectroscopy (NMR) which involve the analysis of plant, animal, and human samples.The applications were selected to provide coverage of both data-driven and hypothesis-driven data mining approaches and demonstrate the ability of the process to fulfil a number of data mining goals including prediction, description and verification. The selected applications are used to demonstrate MeKDDaM's ability to perform a range of data mining tasks including classification, segmentation, hypothesis testing, correlation analysis, dimensionality reduction, and feature extraction and analysis using different data mining techniques.Each of the applications demonstrated in this research starts with a general description of the application domain. It covers the origin of the sample, the design of the assay, and goals of the o...

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.