TY - JOUR
T1 - A genetically informed prediction model for suicidal and aggressive behaviour in teens
AU - Tate, Ashley E.
AU - Akingbuwa, Wonuola A.
AU - Karlsson, Robert
AU - Hottenga, Jouke-Jan
AU - Pool, René
AU - Boman, Magnus
AU - Larsson, Henrik
AU - Lundström, Sebastian
AU - Lichtenstein, Paul
AU - Middeldorp, Christel M.
AU - Bartels, Meike
AU - Kuja-Halkola, Ralf
N1 - Funding Information: The computations and data handling were/was enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppmax partially funded by the Swedish Research Council through grant agreement no. 2018–05973. This project has received funding from the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska Curie Actions – MSCA-ITN-2016 – Innovative Training Networks under grant agreement No [721567]. WAA and AET received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 721567. MeB is funded by an ERC Consolidator Grant (WELL-BEING 771057). Publisher Copyright: © 2022, The Author(s).
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Suicidal and aggressive behaviours cause significant personal and societal burden. As risk factors associated with these behaviours frequently overlap, combined approaches in predicting the behaviours may be useful in identifying those at risk for either. The current study aimed to create a model that predicted if individuals will exhibit suicidal behaviour, aggressive behaviour, both, or neither in late adolescence. A sample of 5,974 twins from the Child and Adolescent Twin Study in Sweden (CATSS) was broken down into a training (80%), tune (10%) and test (10%) set. The Netherlands Twin Register (NTR; N = 2702) was used for external validation. Our longitudinal data featured genetic, environmental, and psychosocial predictors derived from parental and self-report data. A stacked ensemble model was created which contained a gradient boosted machine, random forest, elastic net, and neural network. Model performance was transferable between CATSS and NTR (macro area under the receiver operating characteristic curve (AUC) [95% CI] AUCCATSS(test set) = 0.709 (0.671–0.747); AUCNTR = 0.685 (0.656–0.715), suggesting model generalisability across Northern Europe. The notable exception is suicidal behaviours in the NTR, which was no better than chance. The 25 highest scoring variable importance scores for the gradient boosted machines and random forest models included self-reported psychiatric symptoms in mid-adolescence, sex, and polygenic scores for psychiatric traits. The model’s performance is comparable to current prediction models that use clinical interviews and is not yet suitable for clinical use. Moreover, genetic variables may have a role to play in predictive models of adolescent psychopathology.
AB - Suicidal and aggressive behaviours cause significant personal and societal burden. As risk factors associated with these behaviours frequently overlap, combined approaches in predicting the behaviours may be useful in identifying those at risk for either. The current study aimed to create a model that predicted if individuals will exhibit suicidal behaviour, aggressive behaviour, both, or neither in late adolescence. A sample of 5,974 twins from the Child and Adolescent Twin Study in Sweden (CATSS) was broken down into a training (80%), tune (10%) and test (10%) set. The Netherlands Twin Register (NTR; N = 2702) was used for external validation. Our longitudinal data featured genetic, environmental, and psychosocial predictors derived from parental and self-report data. A stacked ensemble model was created which contained a gradient boosted machine, random forest, elastic net, and neural network. Model performance was transferable between CATSS and NTR (macro area under the receiver operating characteristic curve (AUC) [95% CI] AUCCATSS(test set) = 0.709 (0.671–0.747); AUCNTR = 0.685 (0.656–0.715), suggesting model generalisability across Northern Europe. The notable exception is suicidal behaviours in the NTR, which was no better than chance. The 25 highest scoring variable importance scores for the gradient boosted machines and random forest models included self-reported psychiatric symptoms in mid-adolescence, sex, and polygenic scores for psychiatric traits. The model’s performance is comparable to current prediction models that use clinical interviews and is not yet suitable for clinical use. Moreover, genetic variables may have a role to play in predictive models of adolescent psychopathology.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142290343&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/36411277
UR - http://www.scopus.com/inward/record.url?scp=85142290343&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142290343&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41398-022-02245-w
DO - https://doi.org/10.1038/s41398-022-02245-w
M3 - Article
C2 - 36411277
SN - 2158-3188
VL - 12
JO - Translational Psychiatry
JF - Translational Psychiatry
IS - 1
M1 - 488
ER -