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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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          Most cited references32

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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
                (View ORCID Profile)
                Journal
                Scientific Reports
                Sci Rep
                Springer Science and Business Media LLC
                2045-2322
                December 2018
                May 31 2018
                December 2018
                : 8
                : 1
                Article
                10.1038/s41598-018-26930-3
                d1c9de7a-5e38-45c9-a68e-22e550d09271
                © 2018

                https://creativecommons.org/licenses/by/4.0

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