Investigating the effects of genetic risk of schizophrenia on behavioural traits

Adam Socrates,Jessye Maxwell,Kylie P Glanville,Evangelos Vassos,Robin M Murray,Paul F O’Reilly,Marta Di Forti

doi:10.1038/s41537-020-00131-2

Adam Socrates, Jessye Maxwell + Show 5 more

Open Access

https://doi.org/10.1038/s41537-020-00131-2

Copy DOI

Abstract

To characterise the trait-effects of increased genetic risk for schizophrenia, and highlight potential risk mediators, we test the association between schizophrenia polygenic risk scores (PRSs) and 529 behavioural traits (personality, psychological, lifestyle, nutritional) in the UK Biobank. Our primary analysis is performed on individuals aged 38–71 with no history of schizophrenia or related disorders, allowing us to report the effects of schizophrenia genetic risk in the sub-clinical general population. Higher schizophrenia PRSs were associated with a range of traits, including lower verbal-numerical reasoning (P = 6 × 10–61), higher nervous feelings (P = 1 × 10−46) and higher self-reported risk-taking (P = 3 × 10−38). We follow-up the risk-taking association, hypothesising that the association may be due to a genetic propensity for risk-taking leading to greater migration, urbanicity or drug-taking — reported environmental risk factors for schizophrenia, and all positively associated with risk-taking in these data. Next, to identify potential disorder or medication effects, we compare the PRS–trait associations in the general population to the trait values in 599 medicated and non-medicated individuals diagnosed with schizophrenia in the biobank. This analysis highlights, for example, levels of BMI, physical activity and risk-taking in cases in the opposite directions than expected from the PRS–trait associations in the general population. Our analyses offer simple yet potentially revealing insights into the possible causes of observed trait–disorder associations, which can complement approaches such as Mendelian Randomisation. While we urge caution in causal interpretations in PRS cross-trait studies that are highly powered to detect weak horizontal pleiotropy or population structure, we propose that well-designed polygenic score analyses have the potential to highlight modifiable risk factors that lie on the path between genetic risk and disorder.

Highlights

While much is known about behavioural traits associated with schizophrenia diagnosis and medication[1], and prospective studies have provided insights into behavioural traits common during the years leading to diagnosis[2], less is known about behavioural traits associated with elevated genetic risk for schizophrenia in the sub-clinical general population
Quantile plots Quantile plots were generated by creating 20 bins of the screened subjects based on their Polygenic risk scores (PRSs), with the lowest quantile containing the 5% of the subjects with the lowest genetic liability up to the 5% with the highest
All of the analyses were adjusted for age, sex, Townsend deprivation index and educational attainment, as described in the main text and corresponding table captions

Summary

Introduction

While much is known about behavioural traits associated with schizophrenia diagnosis and medication[1], and prospective studies have provided insights into behavioural traits common during the years leading to diagnosis[2], less is known about behavioural traits associated with elevated genetic risk for schizophrenia in the sub-clinical general population The latter could be useful for highlighting potential mediators of disorder risk because their study should be less affected by the confounding effects of medication or disorder pathophysiology, which may have pre-clinical onset. Modification of lifestyle or behavioural risk factors has the potential of reducing disorder risk before the initiation of pathology Identifying such risk factors using clinical trials is typically infeasible and from standard observational data is highly challenging due to the complex network of traits that generates a myriad of non-causal associations[3]. Once genetic overlap has been established, the step is to infer which of the following is most likely responsible: horizontal pleiotropy (i.e. the trait is non-causal of the disorder), vertical pleiotropy (the trait affects the disorder or vice versa) or that the observed overlap is an artefact of population structure or ascertainment[5,10]

Results

Discussion

Conclusion