Sophie van der Sluis

Assistant Professor
Team Leader

Research Focus

Dr. Sophie van der Sluis is Assistant professor at the Department of Clinical Genetics, section Complex Trait Genetics, at the VU Medical Center, which is associated to Neuroscience Campus Amsterdam (NCA) and the Center for Neurogenomics and Cognitive Research (CNCR). She studied psychology, focusing on psychological methods, at the University of Amsterdam and after graduating cum laude, she obtained her PhD in 2005 on research related to working memory capacity in children with learning deficiencies.

Between 2005 and 2009, van der Sluis worked as a post doc at the University of Amsterdam and the VU University Amsterdam. In 2009, she received a personal NWO-VENI grant, which allowed her to focus her research on psychometric issues in the context of behavioral genetics research. In 2013, she received an NWO-VIDI grant, which allowed her to continue this research together with two PhD students Mats Nagel and Cesar Vroom.

Van der Sluis’ research focusses on the question how heritability estimates, and genetic association or linkage signals are affected by choices concerning the operationalization (i.e., conceptualization, measurement and modelling) of the phenotypes involved. Topics that feature prominently in her research are: uni- versus multidimensionality, resolution of phenotypic instruments, the use of sum-scores versus item/symptom scores, and genetic heterogeneity of complex phenotypes.
Within this field, Van der Sluis closely collaborate with Prof Conor Dolan (VU University), and Prof Danielle Posthuma (VU Medical Center and VU University Amsterdam).

Besides these psychometric studies in the context of human behavior genetics studies , van der Sluis is also involved in mouse behavioral studies. Together with PhD student Emmeke Aarts and the group of Prof Matthijs Verhage, she studies behavioral differences between genetically different mouse strains, such as measured using an automated home-cage testing device. Their studies mainly focus on ways to model these longitudinally collected behavioral data within and across genetically distinct strains.

People

PhD
Mats Nagel
Cesar Vroom
Emmeke Aarts

Teaching

  • Coordinator of the course Cognitive Neuroscience in the minor Brain & Mind together with Christiaan de Kock.
  • Coordinator of the course Quantitative Methods for PhD students of the ONWAR research school.

Publications

See for all publications of Sophie van der Sluis in PubMed

Highlighted publications

Vroom, C-R., Posthuma, D., Li, M-X, Dolan, C.V., & van der Sluis, S. (2016). Multivariate gene-based association test in family data in MGAS. Behavior Genetics. [pdf]

Van der Sluis, S., Dolan, C.V., Li, J., Youqiang, S., Sham, P., Posthuma, D., & Li, M-X. (2015). MGAS: a powerful tool for multivariate gene-based genome-wide analysis. Bioinformatics, 31(7), 1007-1015. [pdf]

Aarts, E.A., Dolan, C.V., Verhage, M., & van der Sluis, S. (2015). Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives. BMC Neuroscience, 16(94). [pdf]

Aarts, E., Verhage, M., Heenvliet, J.V., Dolan, C.V., & van der Sluis, S. (2014). A solution to dependency: using multilevel analysis to accommodate nested data. Nature Neuroscience, 17(4), 491-496 [pdf]

Van der Sluis, S., Posthuma, D., Nivard, M.G., Verhage, M. & Dolan, C.V. (2013). Power in GWAS: Lifting the curse of the clinical phenotype. Molecular Psychiatry, 18(1), 2-3; doi:10.1038/mp.2012.65 [pdf]

Van der Sluis, S., Posthuma, D., & Dolan, C.V. (2013) TATES: Efficient multivariate genotype-phenotype analysis for genome-wide association studies. Plos Genetics, 9(1), e1003235, DOI: 10.1371/journal.pgen.1003235 [pdf]

Van der Sluis, S., Posthuma, D., & Dolan, C.V. (2012) A note on false positives in GxE modelling in twin data: necessary extensions of the univariate moderation model proposed by Purcell. Behavior Genetics, 42(1), 170-186. [pdf]

Van der Sluis, S., Verhage, M., Posthuma, D., Dolan, C.V. (2010). Phenotypic complexity, poor phenotypic resolution, and measurement bias contribute to the missing heritability problem in genetic association studies, Plos One, 5(11), 1-13. [pdf]

CV

Download CV

Software

TATES

The following files can be used to run TATES, see

Van der Sluis, S., Posthuma, D., & Dolan, C.V. (2013) TATES: Efficient multivariate genotype-phenotype analysis for genome-wide association studies. Plos Genetics, 9(1), e1003235, DOI: 10.1371/journal.pgen.1003235 [pdf]

We recommend running TATES in Fortran if the analysis concerns many phenotypes and/or many SNPs. TATES in Fortran can handle up to 50 phenotypes and 5 million SNPs.

TATES in R
TATES in Fortran

Simulation scripts

  • The following scripts were used for the analyses published in

Van der Sluis, S., Posthuma, D., Nivard, M.G., Verhage, M. & Dolan, C.V. (2013). Power in GWAS: Lifting the curse of the clinical phenotype. Molecular Psychiatry, 18(1), 2-3; doi:10.1038/mp.2012.65 [pdf] :

README
sim_skew.r
results_skewsim.r

Supplemental material

The following files were supplemental to

Aarts, E., Verhage, M., Heenvliet, J.V., Dolan, C.V., & van der Sluis, S. (2014). A solution to dependency: using multilevel analysis to accommodate nested data. Nature Neuroscience, 17(4), 491-496 [pdf]

Download data and syntax of worked example in Supplemental (3.49 KB)
Download example dichotomous or Poisson distributed nested data (127.12 KB)
Download example longitudinal nested data (386.98 KB)
Download example Type II design nested data (324.05 KB)

Contact info

Address:

VU/VUMC
Center for Neurogenomics and Cognitive Research
Dept Complex Trait Genetics
W&N building, room B-627
De Boelelaan 1085
1081 HV Amsterdam

Telephone:

+31 20 598 6833

E-mail:s.vander.sluis@vu.nl