Abstract
Schizophrenia is a complex disease with severely disabling symptoms. A consistent leading causal gene for the disease onset has not been found. There is also a lack of consensus on the disease etiology and diagnosis. Sweden poses a paradigmatic case, where relatively high misdiagnosis rates (19%) have been reported.
A large-scale case-control dataset based on the Swedish population was reduced to its most representative variants and the distinction between cases and controls was further scrutinized through gene-annotation based Machine Learning (ML) models.
The intra-group differences on cases and controls were accentuated by training the model on the entire dataset. The cases and controls with a higher likelihood to be misclassified, and hence more likely to be misdiagnosed were excluded from subsequent analysis. The model was then conventionally trained on the reduced dataset and the performances were compared.
The results indicate that the reported prevalence and misdiagnosis rates for Schizophrenia may be transposed to case-control cohorts, hence, reducing the performance of eventual association studies based on such datasets. After the sample filtering procedure, a simple Machine Learning model reached a performance more concurrent with the Schizophrenia heritability estimates on the literature.
Sample selection on large-scale datasets sequenced for Association Studies may enable the adaptation of ML approaches and strategies to complex studies research.
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Acknowledgements
The datasets used for the analysis described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000473.v2.p2. Samples used for data analysis were provided by the Swedish Cohort Collection supported by the NIMH grant R01MH077139, the Sylvan C. Herman Foundation, the Stanley Medical Research Institute and The Swedish Research Council (grants 2009-4959 and 2011-4659). Support for the exome sequencing was provided by the NIMH Grand Opportunity grant RCMH089905, the Sylvan C. Herman Foundation, a grant from the Stanley Medical Research Institute and multiple gifts to the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard. This work is funded by the FCT - Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R&D Unit - UIDB/00326/2020 and the PhD Scholarship SFRH/BD/146094/2019.
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Martins, D., Egas, C., Arrais, J.P. (2023). The Impact of Schizophrenia Misdiagnosis Rates on Machine Learning Models Performance. In: Rocha, M., Fdez-Riverola, F., Mohamad, M.S., Gil-González, A.B. (eds) Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023). PACBB 2023. Lecture Notes in Networks and Systems, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-031-38079-2_1
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