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

Restuadi Restuadi¹, Fleur C Garton¹, Beben Benyamin^{1

2}, Tian Lin¹, Kelly L Williams³, Anna Vinkhuyzen¹, Wouter van Rheenen⁴, Zhihong Zhu¹, Nigel G Laing^{5

6}, Karen A Mather^{7

8}, Perminder S Sachdev^{7

9}, Shyuan T Ngo^{10

11

12}, Frederik J Steyn^{12

13}, Leanne Wallace¹, Anjali K Henders¹, Peter M Visscher^{1

10}, Merrilee Needham^{14

15

16}, Susan Mathers¹⁷, Garth Nicholson^{3

18}, Dominic B Rowe³, Robert D Henderson^{10

11

19}, Pamela A McCombe^{12

19}, Roger Pamphlett²⁰, Ian P Blair³, Naomi R Wray^{1

10}, Allan F McRae²¹

Affiliations

¹ Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
² Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, SA, Australia.
³ Centre for MND Research, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia.
⁴ UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Netherlands.
⁵ Centre for Medical Research, University of Western Australia, Nedlands, WA, Australia.
⁶ Harry Perkins Institute of Medical Research, Nedlands, WA, Australia.
⁷ Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
⁸ Neuroscience Research Australia Institute, Randwick, NSW, Australia.
⁹ Neuropsychiatric Institute, The Prince of Wales Hospital, UNSW, Randwick, NSW, Australia.
¹⁰ Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
¹¹ The Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia.
¹² Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia.
¹³ School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia.
¹⁴ Fiona Stanley Hospital, Perth, WA, Australia.
¹⁵ Notre Dame University, Fremantle, WA, Australia.
¹⁶ Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, Australia.
¹⁷ Calvary Health Care Bethlehem, Parkdale, VIC, Australia.
¹⁸ ANZAC Research Institute, Concord Repatriation General Hospital, Sydney, NSW, Australia.
¹⁹ Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
²⁰ Discipline of Pathology and Department of Neuropathology, Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
²¹ Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. a.mcrae@uq.edu.au.

PMID: 33907316
PMCID: PMC9090723
DOI: 10.1038/s41431-021-00885-y

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

Restuadi Restuadi et al. Eur J Hum Genet. 2022 May.

. 2022 May;30(5):532-539.

doi: 10.1038/s41431-021-00885-y. Epub 2021 Apr 27.

Authors

Affiliations

¹ Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
² Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, SA, Australia.
³ Centre for MND Research, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia.
⁴ UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Netherlands.
⁵ Centre for Medical Research, University of Western Australia, Nedlands, WA, Australia.
⁶ Harry Perkins Institute of Medical Research, Nedlands, WA, Australia.
⁷ Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
⁸ Neuroscience Research Australia Institute, Randwick, NSW, Australia.
⁹ Neuropsychiatric Institute, The Prince of Wales Hospital, UNSW, Randwick, NSW, Australia.
¹⁰ Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
¹¹ The Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia.
¹² Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia.
¹³ School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia.
¹⁴ Fiona Stanley Hospital, Perth, WA, Australia.
¹⁵ Notre Dame University, Fremantle, WA, Australia.
¹⁶ Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, Australia.
¹⁷ Calvary Health Care Bethlehem, Parkdale, VIC, Australia.
¹⁸ ANZAC Research Institute, Concord Repatriation General Hospital, Sydney, NSW, Australia.
¹⁹ Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
²⁰ Discipline of Pathology and Department of Neuropathology, Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
²¹ Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. a.mcrae@uq.edu.au.

PMID: 33907316
PMCID: PMC9090723
DOI: 10.1038/s41431-021-00885-y

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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Conflict of interest statement

The authors declare no competing interests.

Figures

**Fig. 1. Prediction accuracy of single-trait predictors of ALS in the Australian cohort.**
Predictors constructed using GWAS summary statistics of CP, EA and SCZ had small but significant predictive ability for ALS case–control status.

**Fig. 2. Prediction accuracy of multi-trait predictors compared to the ALS only predictor.**
Predictors constructed using combined SNP effects (multi-trait) of ALS and correlated traits, improved predictive ability for ALS case–control status.

See this image and copyright information in PMC

References

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Medical

Miscellaneous