Functional human variation
Group leader: Ramneek Gupta
Members: Henrik Nielsen, Lea Benedicte Skov Hansen, Natasja Spring Ehlers, Rachita Yadav
Guest members: Bent Petersen, Caroline Anna Brorsson, Christian Simon, Helle Krogh Pedersen, Kalliopi Popi Tsafou, Kasper Nielsen, Maria Maddalena Sperotto, Mette Kristina Beck, Peter Wad Sackett, Thomas Sicheritz Pontén, Valborg Gudmundsdottir
Catalog of previous student projects.
Advances in genomic sequencing, both in speed and lower cost, have finally created an opportunity to understand the human genome at the individual level and not just a composite reference. Variation between individuals can be at the single nucleotide level, stretches of nucleotides or in copy number of sections of the genome. Current estimates indicate that an individual differs by three million single nucleotide polymorphisms (SNPs) from the commonly accepted human reference. Additional sources of variation are alternative splicings of exons within a gene, post-translational modifications (PTMs) on proteins and last but certainly not least, the microbiota within the human being that contributes an important diet-related role.
The mission of the FHV group is to understand how this variation translates to function. What individual single nucleotide polymorphisms (SNPs) influence susceptibility to disease and sensitivity to drug response? Why do some children with leukemia respond well to chemotherapy but others develop toxic side-effects or relapse a few years later? What mutations in a cancer are drivers versus passengers? Which nucleotide changes influence protein structural changes or changes in PTMs?
The group works closely with CBS's metagenomics group and collaborates with external clinical groups on diseases such as leukemia, breast cancer, male infertility and childhood asthma. Genotype-phenotype database infrastructure development has been used for these clinical phenotypes as well as non-clinical phenotypes on collaborations with ancient DNA studies. In partnership with DTU's Multi-Assay Core Facility (DMAC), the group has developed multiplexed targeted sequencing strategies for low-cost and high-throughput custom genotyping. The group also provides several online machine learning based prediction methods for signal peptides, protein structural features and post-translational modifications.
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