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      GWAS identifies 14 loci for device-measured physical activity and sleep duration

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          Abstract

          Physical activity and sleep duration are established risk factors for many diseases, but their aetiology is poorly understood, partly due to relying on self-reported evidence. Here we report a genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 UK Biobank participants, finding 14 significant loci (7 novel). These loci account for 0.06% of activity and 0.39% of sleep duration variation. Genome-wide estimates of ~ 15% phenotypic variation indicate high polygenicity. Heritability is higher in women than men for overall activity (23 vs. 20%, p = 1.5 × 10 −4) and sedentary behaviours (18 vs. 15%, p = 9.7 × 10 −4). Heritability partitioning, enrichment and pathway analyses indicate the central nervous system plays a role in activity behaviours. Two-sample Mendelian randomisation suggests that increased activity might causally lower diastolic blood pressure (beta mmHg/SD: −0.91, SE = 0.18, p = 8.2 × 10 −7), and odds of hypertension (Odds ratio/SD: 0.84, SE = 0.03, p = 4.9 × 10 −8). Our results advocate the value of physical activity for reducing blood pressure.

          Abstract

          Studying the genetic underpinnings of physical activity and sleep duration can be confounded by self-reporting. Here, Doherty et al. use data from 91,105 UK Biobank participants, whose activity had been monitored for a week by a wearable device, for genome-wide association analysis and identify 14 loci.

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          Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies.

          Aims To assess the relationship between duration of sleep and morbidity and mortality from coronary heart disease (CHD), stroke, and total cardiovascular disease (CVD). Methods and results We performed a systematic search of publications using MEDLINE (1966-2009), EMBASE (from 1980), the Cochrane Library, and manual searches without language restrictions. Studies were included if they were prospective, follow-up >3 years, had duration of sleep at baseline, and incident cases of CHD, stroke, or CVD. Relative risks (RR) and 95% confidence interval (CI) were pooled using a random-effect model. Overall, 15 studies (24 cohort samples) included 474 684 male and female participants (follow-up 6.9-25 years), and 16 067 events (4169 for CHD, 3478 for stroke, and 8420 for total CVD). Sleep duration was assessed by questionnaire and incident cases through certification and event registers. Short duration of sleep was associated with a greater risk of developing or dying of CHD (RR 1.48, 95% CI 1.22-1.80, P < 0.0001), stroke (1.15, 1.00-1.31, P = 0.047), but not total CVD (1.03, 0.93-1.15, P = 0.52) with no evidence of publication bias (P = 0.95, P = 0.30, and P = 0.46, respectively). Long duration of sleep was also associated with a greater risk of CHD (1.38, 1.15-1.66, P = 0.0005), stroke (1.65, 1.45-1.87, P < 0.0001), and total CVD (1.41, 1.19-1.68, P < 0.0001) with no evidence of publication bias (P = 0.92, P = 0.96, and P = 0.79, respectively). Conclusion Both short and long duration of sleep are predictors, or markers, of cardiovascular outcomes.
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            Orienting the causal relationship between imprecisely measured traits using GWAS summary data

            Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.
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              An introduction to hidden Markov models

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                Author and article information

                Contributors
                aiden.doherty@bdi.ox.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                10 December 2018
                10 December 2018
                2018
                : 9
                : 5257
                Affiliations
                [1 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, , University of Oxford, ; Oxford, OX3 7LF UK
                [2 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Nuffield Department of Population Health, BHF Centre of Research Excellence, , University of Oxford, ; Oxford, OX3 7LF UK
                [3 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Institute of Biomedical Engineering, Department of Engineering Science, , University of Oxford, ; Oxford, OX3 7DQ UK
                [4 ]ISNI 0000 0001 2306 7492, GRID grid.8348.7, NIHR Oxford Biomedical Research Centre, , Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, ; Oxford, OX3 9DU UK
                [5 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Cancer Epidemiology Unit, Nuffield Department of Population Health, , University of Oxford, ; Oxford, OX3 7LF UK
                [6 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Wellcome Trust Centre for Human Genetics, , University of Oxford, ; Oxford, OX3 7BN UK
                [7 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, , University of Oxford, ; Oxford, OX3 7LF UK
                [8 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, , University of Oxford, ; Oxford, OX3 7LF UK
                [9 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Department of Statistics, , University of Oxford, ; Oxford, OX1 3LB UK
                [10 ]ISNI 0000000090126352, GRID grid.7692.a, Department of Genetics, Center for Molecular Medicine, , University Medical Center Utrecht, ; Utrecht, 3584 CX The Netherlands
                [11 ]GRID grid.66859.34, Program in Medical and Population Genetics, , Broad Institute, ; Cambridge, 02142 MA USA
                Author information
                http://orcid.org/0000-0003-1840-0451
                Article
                7743
                10.1038/s41467-018-07743-4
                6288145
                30531941
                9367e1e7-ff25-4409-99d3-1157067c4be6
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 February 2018
                : 22 November 2018
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