Difference between revisions of "Module 2: Metabolome-wide genome-wide association study"
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− | + | * 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. [http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004132l] | ||
+ | |||
+ | * 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." | ||
+ | |||
+ | * Slides for introduction to GWAS and metabomatching: [[:File:GWASandMetabomatching.pptx]] | ||
+ | * Data and Code: [https://drive.switch.ch/index.php/s/aCCfsfd8NyjFipc] | ||
+ | |||
+ | * 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: [https://public.phenomenal-h2020.eu] | ||
+ | ** H9: Wrap-up & evaluation | ||
+ | |||
+ | * Key bioinformatics concept of this module: "Molecular traits as substrate for GWAS. Data in- and export. Regression. Multiple hypotheses testing." | ||
+ | |||
+ | * 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."
- Slides for introduction to GWAS and metabomatching: File:GWASandMetabomatching.pptx
- Data and Code: [2]
- 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."