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We focus on the development and application of new computational and statistical approaches aimed at utilizing high-dimensional datasets in biology and medicine to their full potential (eg by integration across different layers of information). In particular, we are keen to develop methodological advancements to accelerate the discovery and interpretation of multidimensional phenotypic consequences of common and rare genetic variation, as well as to use genetic information to infer direction of causality between different layers of phenotypic information.

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Over the last decade genetic and epidemiological research has generated a vast amount of data and expertise for evaluating the genetic contribution to human complex traits. These efforts have identified thousands of (mostly common) genetic variants predisposing to disease risk factors, however, they have been limited in understanding the impact of rare variants, identifying the causal biological mechanisms underlying the observed genotype-phenotype associations and prioritizing new biological targets in drug discovery. Despite the enormous opportunities coming from the availability of large-scale data, novel computational approaches are necessary for efficient data integration and analysis of different layers of information and will play an instrumental role in utilizing data to its full potential.

The Statistical Genetics group is currently focused on the development and application of such new approaches by exploring multivariate modelling, integration of functional enrichment information and appropriate handling of missing phenotype data. In particular, we are keen to develop methodological advancements to (i) accelerate the discovery and interpretation of rare genetic variation underlying complex disease risk, (ii) increase the power for discovery and interpretation of multidimensional phenotypic consequences of common genetic variation and (iii) increase the identification of causal variants for complex and disease traits, the inference on directionality of causal links between different molecular layers and the interpretability of the underlying biological mechanisms.

Our team