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Master Thesis

Étude de données de microbiote intestinal issues de la cohorte de jumeaux "TwinsUK" à l'aide de méthodes de régression incorporant la notion de distance

Abstract : Objective : to show an association between gut microbiota composition and phenotypes as age, sex and BMI. Methods : data were count tables from NGS sequencing of 16S bacterial RNA. We also analyzed data from metagenomics analysis of E. coli population. Statistical analysis is performed in two steps. First, a MDS transform is apply on the count tables to embed the notion of genetic distance between Operational Taxonomic Unit (OTU) or reference genome. Then standard multivariate regression model are used on transformed data. Results : phenotypic data were available for 512 individuals who were mainly women (n=445, 87%). The mean age was 60y (sd 11y) and mean BMI was 26kg/m² (sd 5,2kg/m²). After filtering and quality control, we analyzed 842 OTU and 411 samples for the 16S analysis and 56 reference genome and 439 sample for the E.coli metapopulation analysis. The dimensionality for the MDS reconstruction was 29 for the 16S analysis and 7 for the E.coli metapopulation analysis. We showed a significant association between the gut bacterial composition measured by the 16S pipeline and age, sex and BMI (p=0.0011, p=0.026 et p=0.0017 respectively). But we failed to show the same association from E.coli metapopulation data (p=0.86, p=0.44 et p=0.22 respectively). Conclusion : taking into account a priori information on the genetic structure underlying the count table seems to allow showing statistical associations between gut microbiota and phenotypes while avoiding the pitfalls of multiple testing.
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Master Thesis
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https://dumas.ccsd.cnrs.fr/dumas-02890105
Contributor : Université Paris Descartes - Scd <>
Submitted on : Monday, July 6, 2020 - 10:06:51 AM
Last modification on : Sunday, January 10, 2021 - 3:24:29 AM

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  • HAL Id : dumas-02890105, version 1

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Jimmy Mullaert. Étude de données de microbiote intestinal issues de la cohorte de jumeaux "TwinsUK" à l'aide de méthodes de régression incorporant la notion de distance. Médecine humaine et pathologie. 2019. ⟨dumas-02890105⟩

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