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Discriminating head trauma outcomes using machine learning and genomics

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Abstract

A percentage of the population suffers prolonged and persistent post-concussion symptoms (PCS) following average head injuries or develops severe neurological dysfunction following minor head trauma. Genetic variants that may contribute to individual response to head trauma have been investigated in some studies, but to date none have explored the use of machine learning (ML) methods with genomic data to specifically explore outcomes of head trauma. Whole exome sequencing (WES) was completed for three groups of individuals (N = 60): (a) 16 individuals with severe neurological responses to minor head trauma, (b) 26 individuals with persistent PCS and (c) 18 individuals with normal recovery from concussion or mTBI. Gradient boosted tree algorithms were applied to the data using XGBoost. By using variants with CADD scores above 15 in the training set (randomly sampled 70%), we identified signatures that accurately distinguish to accurately distinguish the test groups with an average area under the curve (AUC) of 0.8 (SE = 0.019). Metrics including positive and negative prediction values, as well as kappa were all within acceptable range to support the prediction accuracy. This study illustrates how ML methods in combination with WES data have the potential to predict severe or prolonged responses to head trauma from healthy recovery.

Key messages

  • Linear association analysis has been inconclusive in concussion genetics.

  • Non-linear methods as boosted trees can offer better insights in small samples.

  • Strong discrimination trends can be achieved from exome data of cases and controls.

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Availability of data and material

Summary-level data is available at the author’s discretion.

Code availability

All the codes used for this study are freely available upon request.

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Funding

This work was supported by The Assistant Secretary of Defence for Health Affairs endorsed by the Department of Defence, through FY 2018 Peer Reviewed Medical Research Program Discovery Award, under Award No. W81XWH1910098. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the Department of Defense. This work was supported by the National Health and Medical Research Council through grant GNT1122387—Identifying novel gene mutations for molecular diagnosis of familial hemiplegic migraine.

Author information

Authors and Affiliations

  1. Genomics Research Centre, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia

    Omar Ibrahim, Heidi G. Sutherland, Rodney A. Lea, Neven Maksemous, Robert A. Smith, Larisa M. Haupt & Lyn R. Griffiths

  2. The Queensland Brain Institute, The University of Queensland, Brisbane, Australia

    Fatima Nasrallah

Authors
  1. Omar Ibrahim
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  2. Heidi G. Sutherland
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  3. Rodney A. Lea
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  4. Fatima Nasrallah
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  5. Neven Maksemous
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  6. Robert A. Smith
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  7. Larisa M. Haupt
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  8. Lyn R. Griffiths
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Contributions

Conceptualization: Omar Ibrahim, Lyn R. Griffiths; methodology: Omar Ibrahim, Neven Maksemous, Rodney A. Lea, Heidi G. Sutherland; formal analysis and investigation: Omar Ibrahim, Rod A Lea; writing—original draft preparation: Omar Ibrahim; writing—review and editing: Omar Ibrahim, Heidi G. Sutherland, Rodney A. Lea, Fatima Nasrallah, Neven Maksemous, Robert A. Smith, Larisa M. Haupt, Lyn R. Griffiths; funding acquisition: Lyn R. Griffiths; resources: Larisa M. Haupt, Fatima Nasrallah; supervision: Lyn R. Griffiths, Larisa M. Haupt, Heidi G. Sutherland.

Corresponding author

Correspondence to Lyn R. Griffiths.

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Ethics approval

Ethical approval for this project was provided by the Queensland University of Technology Human Research Ethics Committee (project approvals 1700000811 and 1800000611).

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All the participants provided appropriate consent for participation in the research under the terms of the ethical approval for this study.

Conflict of interest

The authors declare no competing interests.

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Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the Department of Defence.

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Ibrahim, O., Sutherland, H.G., Lea, R.A. et al. Discriminating head trauma outcomes using machine learning and genomics. J Mol Med 100, 303–312 (2022). https://doi.org/10.1007/s00109-021-02158-z

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  • DOI: https://doi.org/10.1007/s00109-021-02158-z

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