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Polygenic risk score analysis for amyotrophic lateral sclerosis leveraging cognitive performance, educational attainment and schizophrenia.
Restuadi, Restuadi; Garton, Fleur C; Benyamin, Beben; Lin, Tian; Williams, Kelly L; Vinkhuyzen, Anna; van Rheenen, Wouter; Zhu, Zhihong; Laing, Nigel G; Mather, Karen A; Sachdev, Perminder S; Ngo, Shyuan T; Steyn, Frederik J; Wallace, Leanne; Henders, Anjali K; Visscher, Peter M; Needham, Merrilee; Mathers, Susan; Nicholson, Garth; Rowe, Dominic B; Henderson, Robert D; McCombe, Pamela A; Pamphlett, Roger; Blair, Ian P; Wray, Naomi R; McRae, Allan F.
Afiliação
  • Restuadi R; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • Garton FC; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • Benyamin B; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • Lin T; Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, SA, Australia.
  • Williams KL; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • Vinkhuyzen A; Centre for MND Research, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW 2109, Australia.
  • van Rheenen W; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • Zhu Z; UMC Utrecht Brain Center Rudolf Magnus, Utrecht, Netherlands.
  • Laing NG; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • Mather KA; Centre for Medical Research, University of Western Australia, Nedlands, WA, Australia.
  • Sachdev PS; Harry Perkins Institute of Medical Research, Nedlands, WA, Australia.
  • Ngo ST; Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
  • Steyn FJ; Neuroscience Research Australia Institute, Randwick, NSW, Australia.
  • Wallace L; Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.
  • Henders AK; Neuropsychiatric Institute, The Prince of Wales Hospital, UNSW, Randwick, NSW, Australia.
  • Visscher PM; Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
  • Needham M; The Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia.
  • Mathers S; Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia.
  • Nicholson G; Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia.
  • Rowe DB; School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia.
  • Henderson RD; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • McCombe PA; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • Pamphlett R; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia.
  • Blair IP; Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
  • Wray NR; Fiona Stanley Hospital, Perth, WA, Australia.
  • McRae AF; Notre Dame University, Fremantle, WA, Australia.
Eur J Hum Genet ; 30(5): 532-539, 2022 05.
Article em En | MEDLINE | ID: mdl-33907316
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Esquizofrenia / Doenças Neurodegenerativas / Esclerose Lateral Amiotrófica Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Oceania Idioma: En Revista: Eur J Hum Genet Ano de publicação: 2022 Tipo de documento: Article
Texto completo
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Esquizofrenia / Doenças Neurodegenerativas / Esclerose Lateral Amiotrófica Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Oceania Idioma: En Revista: Eur J Hum Genet Ano de publicação: 2022 Tipo de documento: Article