Difference between revisions of "Module 2: Metabolome-wide genome-wide association study"

 
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Course materials found in the shared folder : https://drive.switch.ch/index.php/s/aCCfsfd8NyjFipc
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*  Title: "Metabolome-wide genome-wide association study"
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* Paper to be examined: “Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links”, PLoS Genet10(2): e1004132. [http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004132l]
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* Key claim of the paper: "Genetic variants that correlate with any of the measured metabolome features in a large set of individuals generate a signature that facilitates the identification of the metabolite whose concentration is most likely affected by these variants."
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* Slides for introduction to GWAS and metabomatching: [[:File:GWASandMetabomatching.pptx]]
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* Data and Code: [https://drive.switch.ch/index.php/s/aCCfsfd8NyjFipc]
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* Schedule:
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** H1: General introduction to the paper/motivation
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** H2-3: Write code to import the data and inspect them
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** H4-6: Run regression analyses between SNP and metabolite features. Plot significance profiles.
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** H7: QQ-plots
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** H8: Matbomatching (sing the PhenoMenal portal: [https://public.phenomenal-h2020.eu]
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** H9: Wrap-up & evaluation
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* Key bioinformatics concept of this module: "Molecular traits as substrate for GWAS. Data in- and export. Regression. Multiple hypotheses testing."
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* back to [[UNIL MSc course: "Case studies in bioinformatics 2017"]]

Revision as of 14:59, 14 November 2017

  • Title: "Metabolome-wide genome-wide association study"
  • Paper to be examined: “Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links”, PLoS Genet10(2): e1004132. [1]
  • Key claim of the paper: "Genetic variants that correlate with any of the measured metabolome features in a large set of individuals generate a signature that facilitates the identification of the metabolite whose concentration is most likely affected by these variants."
  • Schedule:
    • H1: General introduction to the paper/motivation
    • H2-3: Write code to import the data and inspect them
    • H4-6: Run regression analyses between SNP and metabolite features. Plot significance profiles.
    • H7: QQ-plots
    • H8: Matbomatching (sing the PhenoMenal portal: [3]
    • H9: Wrap-up & evaluation
  • Key bioinformatics concept of this module: "Molecular traits as substrate for GWAS. Data in- and export. Regression. Multiple hypotheses testing."
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