Integrative genomics and systems genetics to study genotype-to-phenotype relationships
The goal of the research group is to devise computational and statistical approaches to unravel the interplay of genotype, cellular factors and external influences and their implications for phenotype. We combine statistics with mechanistic modelling concepts to tie together genetic variation data, molecular profiling information and organismal phenotypes. Current research directions include statistical method development for genome-wide association studies, methods to dissect the genetics of molecular traits and statistical tools for single cell omics data. Methodological research aims are embedded in close collaborations with experimental partners, providing ample opportunities to apply innovative methods to address pertinent biological questions.
We currently offer exciting projects in the area of understanding the effect of genetic and epigenetic factors on phenotype. Most projects have a strong computational focus with the goal of developing new methodology, however we are equally interested in application-driven research with the aim to understand basic biology. Current research aims are building on large-scale datasets that are being generated in the context of the Human Induced Pluripotent Stem Cell Initiative we are part of (http://www.hipsci.org). Ultimately, these data will allow for tying together genetic variation, epigenetic factors, transcriptional data and single-cell assays for hundreds of cell lines derived from human samples.
- Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Stegle, O., Parts L., Piipari M., Winn J., and Durbin R. Nature protocols. Volume 7, Number 3, (2012), p.500–507
- Genotype-Environment Interactions Reveal Causal Pathways That Mediate Genetic Effects on Phenotype. Gagneur, Julien, Stegle Oliver, Zhu Chenchen, Jakob Petra, Tekkedil Manu M., Aiyar Raeka S., Schuon Ann-Kathrin, Pe'er Dana, and Steinmetz Lars M. PLOS Genetics. Volume 9, Number 9, (2013), p.e1003803
- Transcriptome and genome sequencing uncovers functional variation in humans Lappalainen, Tuuli, Sammeth Michael, Friedländer Marc R., Hoen Peter A. C. ‘t, Monlong Jean, Rivas Manuel A., Gonzàlez-Porta Mar, Kurbatova Natalja, Griebel Thasso,Ferreira Pedro G., and others. Nature. (2013),
- Joint genetic analysis of gene expression data with inferred cellular phenotypes Parts, L., Stegle O., Winn J., and Durbin R. PLoS genetics. Volume 7, Number 1, (2011), p.e1001276