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 or how to link genotypes to metabolites"
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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: https://drive.switch.ch/index.php/s/ujkIB2cSeH4OvMb
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* Code and answers: https://tinyurl.com/y8bymagn
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* Contents:
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** General introduction to the paper/motivation
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** Write code to import the data and inspect them
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** Run regression analyses between SNP and metabolite features. Plot significance profiles.
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** Matbomatching (sing the PhenoMenal portal: [https://public.phenomenal-h2020.eu]
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** 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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* You will be asked to perform an analysis using the software Metabomatching which is available here: https://public.phenomenal-h2020.eu/?tool_id=metabomatching&version=1.0.0&__identifer=4sdrz9hhfus
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* back to [[UNIL MSc course: "Case studies in bioinformatics"]]

Latest revision as of 14:44, 4 November 2019

  • Title: "Metabolome-wide genome-wide association study or how to link genotypes to metabolites"
  • 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."
  • Contents:
    • General introduction to the paper/motivation
    • Write code to import the data and inspect them
    • Run regression analyses between SNP and metabolite features. Plot significance profiles.
    • Matbomatching (sing the PhenoMenal portal: [2]
    • 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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