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Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum.

ABSTRACT: AIMS:Psychosis spectrum disorder has a complex pathoetiology characterised by interacting environmental and genetic vulnerabilities. The present study aims to investigate the role of gene-environment interaction using aggregate scores of genetic (polygenic risk score for schizophrenia (PRS-SCZ)) and environment liability for schizophrenia (exposome score for schizophrenia (ES-SCZ)) across the psychosis continuum. METHODS:The sample consisted of 1699 patients, 1753 unaffected siblings, and 1542 healthy comparison participants. The Structured Interview for Schizotypy-Revised (SIS-R) was administered to analyse scores of total, positive, and negative schizotypy in siblings and healthy comparison participants. The PRS-SCZ was trained using the Psychiatric Genomics Consortiums results and the ES-SCZ was calculated guided by the approach validated in a previous report in the current data set. Regression models were applied to test the independent and joint effects of PRS-SCZ and ES-SCZ (adjusted for age, sex, and ancestry using 10 principal components). RESULTS:Both genetic and environmental vulnerability were associated with case-control status. Furthermore, there was evidence for additive interaction between binary modes of PRS-SCZ and ES-SCZ (above 75% of the control distribution) increasing the odds for schizophrenia spectrum diagnosis (relative excess risk due to interaction = 6.79, [95% confidential interval (CI) 3.32, 10.26], p < 0.001). Sensitivity analyses using continuous PRS-SCZ and ES-SCZ confirmed gene-environment interaction (relative excess risk due to interaction = 1.80 [95% CI 1.01, 3.32], p = 0.004). In siblings and healthy comparison participants, PRS-SCZ and ES-SCZ were associated with all SIS-R dimensions and evidence was found for an interaction between PRS-SCZ and ES-SCZ on the total (B = 0.006 [95% CI 0.003, 0.009], p < 0.001), positive (B = 0.006 [95% CI, 0.002, 0.009], p = 0.002), and negative (B = 0.006, [95% CI 0.004, 0.009], p < 0.001) schizotypy dimensions. CONCLUSIONS:The interplay between exposome load and schizophrenia genetic liability contributing to psychosis across the spectrum of expression provide further empirical support to the notion of aetiological continuity underlying an extended psychosis phenotype.

SUBMITTER: Pries LK

PROVIDER: S-EPMC7681168 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum.

Pries L-K LK Dal Ferro G A GA van Os J J Delespaul P P Kenis G G Lin B D BD Luykx J J JJ Richards A L AL Akdede B B Binbay T T Altınyazar V V Yalınçetin B B Gümüş-Akay G G Cihan B B Soygür H H Ulaş H H Şahin Cankurtaran E E Ulusoy Kaymak S S Mihaljevic M M MM Andric Petrovic S S Mirjanic T T Bernardo M M Mezquida G G Amoretti S S Bobes J J Saiz P A PA García-Portilla M Paz MP Sanjuan J J Aguilar E J EJ Santos J L JL Jiménez-López E E Arrojo M M Carracedo A A López G G González-Peñas J J Parellada M M Maric N P NP Atbaşoğlu C C Ucok A A Alptekin K K Can Saka M M Arango C C O'Donovan M M Tosato S S Rutten B P F BPF Guloksuz S S

Epidemiology and psychiatric sciences 20201117

<h4>Aims</h4>Psychosis spectrum disorder has a complex pathoetiology characterised by interacting environmental and genetic vulnerabilities. The present study aims to investigate the role of gene-environment interaction using aggregate scores of genetic (polygenic risk score for schizophrenia (PRS-SCZ)) and environment liability for schizophrenia (exposome score for schizophrenia (ES-SCZ)) across the psychosis continuum.<h4>Methods</h4>The sample consisted of 1699 patients, 1753 unaffected sibli ...[more]

PMID: 33200977

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Project description:ImportanceBoth adulthood stressful life events (SLEs) and liability for schizophrenia have been associated with poor mental and physical health in the general population, but their interaction remains to be elucidated to improve population-based health outcomes.ObjectiveTo test whether recent SLEs interact with genetic and environmental liability for schizophrenia in models of mental and physical health.Design, setting, and participantsThe Netherlands Mental Health Survey and Incidence Study-2 is a population-based prospective cohort study designed to investigate the prevalence, incidence, course, and consequences of mental disorders in the Dutch general population. Participants were enrolled from November 5, 2007, to July 31, 2009, and followed up with 3 assessments during 9 years. Follow-up was completed on June 19, 2018, and data were analyzed from September 1 to November 1, 2019.ExposuresRecent SLEs assessed at each wave and aggregate scores of genetic and environmental liability for schizophrenia: polygenic risk score for schizophrenia (PRS-SCZ) trained using the Psychiatric Genomics Consortium analysis results and exposome score for schizophrenia (ES-SCZ) trained using an independent data set.Main outcomes and measuresIndependent and interacting associations of SLEs with ES-SCZ and PRS-SCZ on mental and physical health assessed at each wave using regression coefficients.ResultsOf the 6646 participants included at baseline, the mean (SD) age was 44.26 (12.54) years, and 3672 (55.25%) were female. The SLEs were associated with poorer physical health (B = -3.22 [95% CI, -3.66 to -2.79]) and mental health (B = -3.68 [95% CI, -4.05 to -3.32]). Genetic and environmental liability for schizophrenia was associated with poorer mental health (ES-SCZ: B = -3.07 [95% CI, -3.35 to -2.79]; PRS-SCZ: B = -0.93 [95% CI, -1.31 to -0.54]). Environmental liability was also associated with poorer physical health (B = -3.19 [95% CI, -3.56 to -2.82]). The interaction model showed that ES-SCZ moderated the association of SLEs with mental (B = -1.08 [95% CI, -1.47 to -0.69]) and physical health (B = -0.64 [95% CI, -1.11 to -0.17]), whereas PRS-SCZ did not. Several sensitivity analyses confirmed these results.Conclusions and relevanceIn this study, schizophrenia liability was associated with broad mental health outcomes at the population level. Consistent with the diathesis-stress model, exposure to SLEs, particularly in individuals with high environmental liability for schizophrenia, was associated with poorer health. These findings underline the importance of modifiable environmental factors during the life span for population-based mental health outcomes.

Project description:Emotion dysfunction has long been considered a cardinal feature across psychotic disorders, including schizophrenia and affective psychosis. However, few studies have used objective markers of emotional function to compare psychotic disorders to one another, and fewer studies have examined such markers within a longitudinal framework. Here, we examine one objective marker of emotional responsivity, the late positive potential (LPP), which is a centro-parietal event-related potential (ERP) that tracks the dynamic allocation of attention to emotional vs. neutral stimuli. We used the LPP to characterize abnormal emotional responsivity by relating it to negative, depressive, and psychotic symptoms among two clinical groups: individuals diagnosed with affective psychosis and individuals with schizophrenia. We also used a long-term longitudinal framework, examining concurrent associations between LPP amplitude and symptom severity, as well as prospective associations with symptoms 4 years later. Participants were 74 individuals with psychotic illness: 37 with schizophrenia spectrum disorders and 37 with a primary affective disorder (psychotic bipolar disorder, psychotic depression). There were no mean-level differences in LPP amplitude between the schizophrenia spectrum and primary affective psychosis group. In the primary affective psychosis group, reduced LPP amplitude was associated with greater depressive, negative, and psychotic symptom severity, both concurrently and at follow-up; associations between LPP and symptoms were not observed within the schizophrenia spectrum group. This pattern of results suggests that the neural correlates of emotion dysfunction may differ across psychotic disorders. One possibility is that schizophrenia is characterized by a decoupling of symptom severity and emotional processing. Such findings underscore the importance of analyzing transdiagnostic samples to determine common or specific symptom relationships across various patient populations.

Project description:Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

Dataset Information

Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum.

Publications

Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum.

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