Obesity, and ensuing type 2 diabetes mellitus (T2D) represent a major health challenge for modern healthcare systems. Childhood obesity can appear as early as 3-5 years of age, and once developed has a >90% likelihood to persist. The aim of this project is to couple genetic with multi-omics data to describe disease trajectories in childhood obesity and discover mechanistic pathways involved in disease onset and progression. The successful applicant will work on >400 samples from the Leipzig Adipose tissue childhood biobank, where chromatin conformation data, RNA-sequencing, quantitative proteomics and methylation typing data are being generated in adipose tissue along with genotyping data.

Your responsibilities:

• Perform eQTL, pQTL and meQTL discovery, as well as meta-analysis with public datasets;
• Examine correlations between omics profiles and ametabolic dysfunction;
• Fine-map established diabetes and obesity-associated variants (e.g. using eQTL colocalization);
• Perform pathway and variant class enrichment analysis.

Your profile:

• A degree in Human or Statistical Genetics, Bioinformatics, or a related field;
• Experience with GWAS data, RNA-Seq or other omics is desirable;
• Practical experience of data analysis and visualisation with R or Python;
• Familiarity with *NIX environments and the bash scripting language;
• Knowledge of basic bioinformatics resources (Ensembl, the GWAS Catalog, genome annotation and expression databases…);
• Familiarity with common statistical genetics tools (e.g. plink, bcftools);
• Spoken and written fluency in English.