Veröffentlicht: 24.04.2024

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.
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