Parkinson’s disease determinants, prediction and gene–environment interactions in the UK Biobank

ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion o...

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Vydáno v:	Journal of neurology, neurosurgery and psychiatry Ročník 91; číslo 10; s. 1046 - 1054
Hlavní autoři:	Jacobs, Benjamin Meir, Belete, Daniel, Bestwick, Jonathan, Blauwendraat, Cornelis, Bandres-Ciga, Sara, Heilbron, Karl, Dobson, Ruth, Nalls, Mike A, Singleton, Andrew, Hardy, John, Giovannoni, Gavin, Lees, Andrew John, Schrag, Anette-Eleonore, Noyce, Alastair J
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England BMJ Publishing Group Ltd 01.10.2020 BMJ Publishing Group LTD BMJ Publishing Group
Edice:	Original research
Témata:	Adult Age Aged Alcohol Drinking - epidemiology Algorithms Biobanks Biological Specimen Banks Depression - epidemiology Diabetes Mellitus - epidemiology Disorders of Excessive Somnolence - epidemiology Epilepsy - epidemiology Erectile dysfunction Ethnicity Female Gene-Environment Interaction Genealogy Genetic Predisposition to Disease Genomes Health risk assessment Humans Liability Logistic Models Male Menarche Middle Aged Movement Disorders Parkinson Disease - epidemiology Parkinson Disease - genetics Parkinson's disease Prevention Protective Factors Quality control Risk Factors Smoking - epidemiology United Kingdom - epidemiology United Kingdom United Kingdom > UK
ISSN:	0022-3050, 1468-330X, 1468-330X
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Shrnutí:	ObjectiveTo systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson’s disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores.MethodsWe identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene–environment interactions and compare predictive models for PD.ResultsStrong evidence of association (false discovery rate <0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, daytime somnolence, epilepsy and earlier menarche. Individuals with the highest 10% of PRSs had increased risk of PD (OR 3.37, 95% CI 2.41 to 4.70) compared with the lowest risk decile. A higher PRS was associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm led to a modest improvement in model performance. We found evidence of an interaction between the PRS and diabetes.InterpretationHere, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene–environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD.
Bibliografie:	Original research ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0022-3050 1468-330X 1468-330X
DOI:	10.1136/jnnp-2020-323646