Research projects in CTGlab have a strong focus on developing novel statistical tools to benefit from available genomics datasets and aid in detecting genetic pathways underlying disease.
The recent rapid advances in genotyping technology need to be parallelled by advances in statistical methodology. CTGlab identifies current gaps in statistical methodology and develops novel models and software/scripts that aid in identifying genetic and neurobiological pathways underlying complex traits.
We focus on analysing genome-wide association data using datamining techniques, developing methods to detect the genetic control of environmental or phenotypic variability and using factor analytic methods to determine the underlying structure of multiple traits. These techniques can be applied to available human data or mouse data.
Our software and scripts are made available for free on this site. Recent packages focus on multivariate GWAS analyses (TATES and JAMP) and genetic pathway analyses (JAG and MAGMA).
We also host the Mx Scripts library, which was set-up in 2003 by Danielle Posthuma as part of the EU-funded GenomeEUTwin project, and includes statistical scripts for ‘classic’ Mx and the Bayesian tool BUGS that can be used to model twin and family data. Since Mx software is now incorporated in the R package, this site is no longer updated, but is kept in the air due to frequent pleas to do so from the Behavior Genetics/Twin Research community.