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      Variation in faecal microbiota in a group of horses managed at pasture over a 12-month period

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          Abstract

          Colic (abdominal pain) is a common cause of mortality in horses. Change in management of horses is associated with increased colic risk and seasonal patterns of increased risk have been identified. Shifts in gut microbiota composition in response to management change have been proposed as one potential underlying mechanism for colic. However, the intestinal microbiota in normal horses and how this varies over different seasons has not previously been investigated. In this study the faecal microbiota composition was studied over 12 months in a population of horses managed at pasture with minimal changes in management. We hypothesised that gut microbiota would be stable in this population over time. Faecal samples were collected every 14 days from 7 horses for 52 weeks and the faecal microbiota was characterised by next-generation sequencing of 16S rRNA genes. The faecal microbiota was dominated by members of the phylum Firmicutes and Bacteroidetes throughout. Season, supplementary forage and ambient weather conditions were significantly associated with change in the faecal microbiota composition. These results provide important baseline information demonstrating physiologic variation in the faecal microbiota of normal horses over a 12-month period without development of colic.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Multiple Comparisons among Means

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              Independent filtering increases detection power for high-throughput experiments.

              With high-dimensional data, variable-by-variable statistical testing is often used to select variables whose behavior differs across conditions. Such an approach requires adjustment for multiple testing, which can result in low statistical power. A two-stage approach that first filters variables by a criterion independent of the test statistic, and then only tests variables which pass the filter, can provide higher power. We show that use of some filter/test statistics pairs presented in the literature may, however, lead to loss of type I error control. We describe other pairs which avoid this problem. In an application to microarray data, we found that gene-by-gene filtering by overall variance followed by a t-test increased the number of discoveries by 50%. We also show that this particular statistic pair induces a lower bound on fold-change among the set of discoveries. Independent filtering-using filter/test pairs that are independent under the null hypothesis but correlated under the alternative-is a general approach that can substantially increase the efficiency of experiments.
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                Author and article information

                Contributors
                s.e.shebl@gmail.com
                darcher@liverpool.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 May 2018
                31 May 2018
                2018
                : 8
                : 8510
                Affiliations
                [1 ]ISNI 0000 0004 1936 8470, GRID grid.10025.36, Department of Cellular and Molecular Physiology, Institute of Translational Medicine, , University of Liverpool, ; Liverpool, L69 3BX UK
                [2 ]ISNI 0000 0001 2158 2757, GRID grid.31451.32, Department of Surgery, Faculty of Veterinary Medicine, , Zagazig University, ; Zagazig, 44519 Egypt
                [3 ]ISNI 0000 0004 1936 8470, GRID grid.10025.36, Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, , University of Liverpool, Leahurst Campus, ; Wirral, CH64 7TE UK
                [4 ]ISNI 0000 0004 1936 8411, GRID grid.9918.9, Department of Genetics, College of Medicine, Biological Sciences and Psychology, , University of Leicester, ; Leicester, LE1 7RH UK
                [5 ]ISNI 0000 0004 1936 8470, GRID grid.10025.36, Centre of Computational Biology and Modelling, Institute of Integrative Biology, , University of Liverpool, ; Liverpool, L69 7ZB UK
                [6 ]ISNI 0000 0004 1936 8470, GRID grid.10025.36, Department of Epidemiology and Population Health, Institute of Infection and Global Health, , University of Liverpool, Leahurst Campus, ; Wirral, CH64 7TE UK
                [7 ]ISNI 0000 0004 1936 8470, GRID grid.10025.36, Philip Leverhulme Equine Hospital, Institute of Veterinary Science, , University of Liverpool, ; Wirral, CH64 7TE UK
                Author information
                http://orcid.org/0000-0001-5315-8934
                Article
                26930
                10.1038/s41598-018-26930-3
                5981443
                29855517
                d1c9de7a-5e38-45c9-a68e-22e550d09271
                © 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
                : 27 September 2017
                : 18 May 2018
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