Stefan McKinnon Høj-Edwards: Improving biological understanding by integrating information in genomic feature models
PhD defence, Friday 24 October 2014. Cand.scient. Stefan McKinnon Høj-Edwards
In his PhD-study, Stefan McKinnon Høj-Edwards has investigated an approach to integrate external data into the analysis of genetic variants. The approach relies on the robust statistical framework of linear mixed models and has allowed us to integrate virtually any externally founded information, such as KEGG pathways, Gene Ontology gene sets, or genomic features, and estimate the joint contribution of the genetic variants within these sets to complex trait phenotypes. The analysis of complex trait phenotypes is hampered by the myriad of genes that control the trait, as these genes have small to moderate effect that can be difficult to detect. However, by looking at sets of genes it may become easier to assess an association between the set of genes and the complex trait.
The study was conducted at Center of Quantitative Genetics and Genomics (QGG), Department of Molecular Biology and Genetics, Aarhus University
Time: Friday 24 October 2014 at 10.00
Place: Building A10, room 3 & 4, AU Foulum, Blichers Allé 20, 8830 Tjele
Title of dissertation: Improving Biological Understanding and Complex Trait Prediction by Integrating Prior Information in Genomic Feature Models
Contact information: Stefan McKinnon Høj-Edwards, e-mail: Stefan.Hoj-Edwards@agrsci.dk, tel.: +45 8715 7969
Members of the assessment committee:
Professor John Woolliams, Roslin Institute, University of Edinburgh
Professor Anders Krogh, Bioinformatics Centre, Section for Computational and RNA Biology, Department of Biology, Copenhagen University
Professor Just Jensen, Department of Molecular Biology and Genetics, Aarhus University (chair)
Main supervisor: Senior researcher Peter Sørensen, QGG, Aarhus University
Co supervisors:
Senior researcher Per Madsen, QGG, Aarhus University
Senior researcher David Edwards, QGG, Aarhus University
Language: The dissertation will be defended in English
The defence is public.
The dissertation is available for reading at the Graduate School of Science and Technology/GSST, Ny Munkegade 120, building 1521, room 112, 8000 Aarhus C