Phenotypic modelling in genetics

Van der Sluis, S., Posthuma, D., Nivard, M.G., Verhage, M., & Dolan, C.V. (2013). Power in GWAS: lifting the curse of the clinical cut-off. Molecular Psychiatry, 18(1), 2-3.

Van der Sluis, S., Posthuma, D., & Dolan, C.V. (2013). TATES: Efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoSGenetics, 9(1), e1003235.

Molenaar, D., van der Sluis, S., Boomsma, D.I., Haworth, C.M.A., Hewitt, J.K., Martin, N.G., Plomin, R., Wright, M.J., & Dolan, C.V. (2013). Genotype by Environment Interactions in Cognitive Ability: A Survey of 14 Studies from 4 Countries covering 4 Age Groups. Behavior Genetics. 43(3), 208-219.

Cramer, A.O.J., van der Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S.H., Kendler, K.S., & Borsboom, D. (2012) Dimensions of normal personality as networks in search of equilibrium: You can’t like parties if you don’t like people. European Journal of Personality. 26 (4), Special Issue, 414–431.

Cramer, A.O.J., van der Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S.H., Kendler, K.S., & Borsboom, D. (2012) Measurable Like Temperature or Mereological Like Flocking? On the Nature of Personality Traits. European Journal of Personality. 26 (4), Special Issue, 451–459.

Research focus
While twin- and family studies have shown that many traits are considerably heritable, the role of specific genes in heritable traits remains poorly understood and genes that are identified, only explain a small portion of the trait variance. The focus of this group is on modeling complex and dynamic traits in such a way that the probability to uncover the genetic basis is maximized.

Complexity
Techniques used to search the entire genome for genes associated with specific traits are generally univariate in nature. This forces researchers to summarize their often originally multivariate phenotypic information into one composite score (e.g., a simple sum score calculated across all symptoms, or items in a questionnaire). We previously showed that this often comes with a considerable drop in the statistical power to detect underlying genetic variants. The extent of this power loss depends heavily on the actual behavior-generating model (e.g., is it a one- or multidimensional factor model, or a network model). As this behavior-generating model is in practice unknown, exploratory (i.e., model-free) statistical techniques need to be developed that allow researchers to search for genes without having to adhere to one specific behavior-generating model. We recently developed such a technique (TATES), and are currently extending this exploratory method in several ways, and applying it to various genotype-phenotype data sets.

Dynamics
Current gene-finding techniques usually assume behavior to be stable over time. Certain traits are, however, clearly unstable in nature (e.g., bipolar disorder). We currently develop new, or extend existing, statistical techniques that allow the modeling of changeable behavior in a genetic setting.