Polygenic risk scores and the prediction of onset of mood and psychotic disorders in adolescents and young adults

Author:

Scott Jan12ORCID,Crouse Jacob J.1,Medland Sarah34,Byrne Enda3,Iorfino Frank1,Mitchell Brittany3,Gillespie Nathan A.5,Martin Nicholas3,Wray Naomi46,Hickie Ian B.1

Affiliation:

1. Brain and Mind Centre The University of Sydney Sydney Australia

2. Institute of Neuroscience Newcastle University Newcastle UK

3. QIMR Berghofer Institute of Medical Research Brisbane Australia

4. Institute of Molecular Bioscience The University of Queensland Brisbane Australia

5. Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University Richmond Virginia USA

6. Queensland Brain Institute The University of Queensland Brisbane Queensland Australia

Abstract

AbstractAimTo examine whether polygenic risk scores (PRS) for neuroticism, depression, bipolar disorder and schizophrenia are higher in individuals manifesting trans‐diagnostic risk factors for the development of major mental disorders and whether PRS enhance prediction of early onset full‐threshold disorders.MethodsUsing data from the Brisbane Longitudinal Twin Study, we examined individual PRS for neuroticism, depression, bipolar disorder and schizophrenia, recorded evidence of subthreshold syndromes and family history of mood and/or psychotic disorders and noted progression to trans‐diagnostic clinical caseness (onset of major mental disorders) at follow‐up. We undertook multivariate, receiver operating curve and logistic regression analyses that were adjusted for known variables of influence (age, twin status, and so on).ResultsOf 1473 eligible participants (female = 866, 59%; mean age 26.3 years), 28% (n = 409) met caseness criteria for a mood and/or psychotic disorder. All PRS were higher in cases versus non‐cases but associations with different levels of risk were inconsistent. The prediction of caseness (reported as area under the curve with 95% confidence intervals [CI]) improved from 0.68 (95% CI: 0.65, 0.71) when estimated using clinical risk factors alone up to 0.71 (95% CI: 0.69, 0.73) when PRS were added to the model. Logistic regression identified five variables that optimally classified individuals according to caseness: age, sex, individual risk characteristics, PRS for depression and mental health case status of cotwins or siblings.ConclusionsThe findings need replication. However, this exploratory study suggests that combining PRS with other risk factors has the potential to improve outcome prediction in youth.

Funder

National Health and Medical Research Council

Publisher

Wiley

Subject

Biological Psychiatry,Psychiatry and Mental health,Pshychiatric Mental Health

Reference25 articles.

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3. Global burden of disease in young people aged 10–24 years: A systematic analysis;Gore F. M.;Lancet,2011

4. Clinical stage transitions in persons aged 12 to 25 years presenting to early intervention mental health services with anxiety, mood, and psychotic disorders;Iorfino F.;Journal of the American Medical Association Psychiatry,2019

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