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Polygenic risk score analysis for amyotrophic lateral sclerosis leveraging cognitive performance, educational attainment and schizophrenia

European Journal of Human Genetics volume 30, pages 532–539 (2022) Cite this article

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Abstract

Amyotrophic Lateral Sclerosis (ALS) is recognised to be a complex neurodegenerative disease involving both genetic and non-genetic risk factors. The underlying causes and risk factors for the majority of cases remain unknown; however, ever-larger genetic data studies and methodologies promise an enhanced understanding. Recent analyses using published summary statistics from the largest ALS genome-wide association study (GWAS) (20,806 ALS cases and 59,804 healthy controls) identified that schizophrenia (SCZ), cognitive performance (CP) and educational attainment (EA) related traits were genetically correlated with ALS. To provide additional evidence for these correlations, we built single and multi-trait genetic predictors using GWAS summary statistics for ALS and these traits, (SCZ, CP, EA) in an independent Australian cohort (846 ALS cases and 665 healthy controls). We compared methods for generating the risk predictors and found that the combination of traits improved the prediction (Nagelkerke-R2) of the case–control logistic regression. The combination of ALS, SCZ, CP, and EA, using the SBayesR predictor method gave the highest prediction (Nagelkerke-R2) of 0.027 (P value = 4.6 × 10−8), with the odds-ratio for estimated disease risk between the highest and lowest deciles of individuals being 3.15 (95% CI 1.96–5.05). These results support the genetic correlation between ALS, SCZ, CP and EA providing a better understanding of the complexity of ALS.

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Fig. 1: Prediction accuracy of single-trait predictors of ALS in the Australian cohort.
The alternative text for this image may have been generated using AI.
Fig. 2: Prediction accuracy of multi-trait predictors compared to the ALS only predictor.
The alternative text for this image may have been generated using AI.

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Acknowledgements

We kindly thank all those who contributed to this research including the participants for providing a blood sample and clinical data, the research nurses and support staff for participant recruitment across clinic sites and the laboratory researchers for their care in generating the DNA data. We acknowledge funding from the National Health and Medical Research Council (NHMRC) (1078901, 1083187, 1113400, 1121962, 1405325,1084417, and 1079583), a NHMRC/Australian Research Council Strategic Award (401162), the Motor Neurone Disease Research Institute Australia (MNDRIA) Ice Bucket Challenge Grant and the MNDRIA Bill Gole Postdoctoral Fellowship (FCG). The twin study (OATS) was facilitated through Twins Research Australia, a national resource in part supported by a NHMRC Centre for Research Excellence.

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Authors and Affiliations

  1. Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia

    Restuadi Restuadi, Fleur C. Garton, Beben Benyamin, Tian Lin, Anna Vinkhuyzen, Zhihong Zhu, Leanne Wallace, Anjali K. Henders, Peter M. Visscher, Naomi R. Wray & Allan F. McRae

  2. Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, SA, Australia

    Beben Benyamin

  3. Centre for MND Research, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia

    Kelly L. Williams, Garth Nicholson, Dominic B. Rowe & Ian P. Blair

  4. UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Netherlands

    Wouter van Rheenen

  5. Centre for Medical Research, University of Western Australia, Nedlands, WA, Australia

    Nigel G. Laing

  6. Harry Perkins Institute of Medical Research, Nedlands, WA, Australia

    Nigel G. Laing

  7. Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia

    Karen A. Mather & Perminder S. Sachdev

  8. Neuroscience Research Australia Institute, Randwick, NSW, Australia

    Karen A. Mather

  9. Neuropsychiatric Institute, The Prince of Wales Hospital, UNSW, Randwick, NSW, Australia

    Perminder S. Sachdev

  10. Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia

    Shyuan T. Ngo, Peter M. Visscher, Robert D. Henderson & Naomi R. Wray

  11. The Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia

    Shyuan T. Ngo & Robert D. Henderson

  12. Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia

    Shyuan T. Ngo, Frederik J. Steyn & Pamela A. McCombe

  13. School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia

    Frederik J. Steyn

  14. Fiona Stanley Hospital, Perth, WA, Australia

    Merrilee Needham

  15. Notre Dame University, Fremantle, WA, Australia

    Merrilee Needham

  16. Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, Australia

    Merrilee Needham

  17. Calvary Health Care Bethlehem, Parkdale, VIC, Australia

    Susan Mathers

  18. ANZAC Research Institute, Concord Repatriation General Hospital, Sydney, NSW, Australia

    Garth Nicholson

  19. Department of Neurology, Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia

    Robert D. Henderson & Pamela A. McCombe

  20. Discipline of Pathology and Department of Neuropathology, Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia

    Roger Pamphlett

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  1. Restuadi Restuadi
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Correspondence to Allan F. McRae.

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Restuadi, R., Garton, F.C., Benyamin, B. et al. Polygenic risk score analysis for amyotrophic lateral sclerosis leveraging cognitive performance, educational attainment and schizophrenia. Eur J Hum Genet 30, 532–539 (2022). https://doi.org/10.1038/s41431-021-00885-y

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