A genome-wide association study of human urine metabolome
Background: High-throughput data are now commonly used in molecular biology to identify genomic features associated with outcomes and make predictions. Genome-Wide Association Study (GWAS) is a routine approach to reveal such associations. Typically in GWAS, individuals are genotyped to identify their genotypes across common genetic variations and these genetic variants linked to certain diseases / disease risk factors. Nonetheless in principle GWAS can be used to detect associations between genetic variants and any trait. If this trait to be metabolome of the organism, the study is called metabolome-GWAS (mGWAS).
In the flow of genetic information, metabolome is the result of upstream biological informations like genetic code, transcriptome, proteome and it shows the current state of the cell. Meanwhile metabolome itself, is in the upstream of the resulting phenotype, e.g disease. Agreeably current knowledge points to prognostic properties of metabolites as biomarkers of approaching diseases. Overall its well position between genetic code and phenotype and its prognostic value makes metabolome an interesting omics data to use in GWAS.
Goal: Students will learn the key concepts of the design and analysis of GWAS for common diseases and complex traits. With the help of statistical tools they will observe the links between genotype - metabotype - phenotype and confirm their findings with the published research.
Mathematical tools: Students will be encouraged to use Matlab to run the association analysis with main emphasis on multiple linear regression. Various data visualizations like correlation plots, qq-plots and Manhattan plots will also be used. Metabomatching, a software tool to predict metabolites from mGWAS results will be used under PhenoMeNal H2020 Galaxy instance.
Biological or Medical aspects: The students will get insight of this new groundbreaking discipline to explore the relationship between sequence variations and disease susceptibility.
Supervisor: Reyhan Sonmez Flitman
Kick-off meeting presentation File:Reyhan teaching 2017.pdf