Codes for Gene-Environment Pathways to Cognitive Intelligence and Psychotic-Like Experiences in Children, Park et al.(2023), eLife https://doi.org/10.7554/eLife.88117.1
Main analysis: R codes for linear mixed model analysis
Main analysis: Integrated generalized structured component analysis (Hwang et al., 2021) A novel Structural Equation Modelling method capable of both latent factor variables and component variables. The GSCA Pro software from https://www.gscapro.com for free. We share an image file of our IGSCA model. One can easily build the same model used in our study by taking a look into this image.
Sensitivity analysis: Adjustment for ethnic confounding R codes for analysis with European ancestry samples.
Sensitivity analysis: Adjustment for unobserved confounders R codes for null treatment approach (Miao et al., 2022).
Sensitivity analysis: Adjustment for schizophrenia polygenic scores
Sensitivity analysis: Linear mixed model analyses with Gene x Environment Interactions
Due to ABCD Study's policy on data sharing, we share a synthetic dataset instead of actual, real observations. The synthetic dataset is made from our final samples (N=6,602), using CTGAN (Xu et al., 2019). After hyperparameters optimization using Optuna (Akiba et al., 2019), the created synthetic dataset from the CTGAN model showed an overall quality score of 84.15%. That is, the synthetic dataset is approximately 84% similar to the original dataset that we used in our study.
Please note that the analyses results have slight differences from those published in the paper in terms of effect sizes & statistical significance, as the synthetic dataset is not completely identical to the original dataset.
Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019, July). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2623-2631).
Hwang, H., Cho, G., Jung, K., Falk, C. F., Flake, J. K., Jin, M. J., & Lee, S. H. (2021). An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis. Psychological Methods, 26(3), 273.
Miao, W., Hu, W., Ogburn, E. L., & Zhou, X. H. (2022). Identifying effects of multiple treatments in the presence of unmeasured confounding. Journal of the American Statistical Association, 1-15.
Xu, L., Skoularidou, M., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. Advances in Neural Information Processing Systems, 32.