S81INFLUENCES OF POLYGENIC RISK SCORES AND SCHIZOPHRENIA ASSOCIATED CNVS ON BROADLY AND NARROWLY DEFINED PSYCHOSES: PROSPECTIVE STUDY AND META-ANALYSIS

BackgroundResearch on gene×environment interaction in major depressive disorder (MDD) has thus far primarily focused on candidate genes, although genetic effects are known to be polygenic.AimsTo test whether the effect of polygenic risk scores on MDD is moderated by childhood trauma.MethodThe study sample consisted of 1645 participants with a DSM-IV diagnosis of MDD and 340 screened controls from The Netherlands. Chronic or remitted episodes (severe MDD) were present in 956 participants. The occurrence of childhood trauma was assessed with the Childhood Trauma Interview and the polygenic risk scores were based on genome-wide meta-analysis results from the Psychiatric Genomics Consortium.ResultsThe polygenic risk scores and childhood trauma independently affected MDD risk, and evidence was found for interaction as departure from both multiplicativity and additivity, indicating that the effect of polygenic risk scores on depression is increased in the presence of childhood trauma. The interaction effects were similar in predicting all MDD risk and severe MDD risk, and explained a proportion of variation in MDD risk comparable to the polygenic risk scores themselves.ConclusionsThe interaction effect found between polygenic risk scores and childhood trauma implies that (1) studies on direct genetic effect on MDD gain power by focusing on individuals exposed to childhood trauma, and that (2) individuals with both high polygenic risk scores and exposure to childhood trauma are particularly at risk for developing MDD.

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S175. CLINICAL OUTCOMES IN PEOPLE AT HIGH RISK FOR PSYCHOSIS RELATED TO INTERACTIONS BETWEEN POLYGENIC RISK SCORES AND CHILDHOOD ADVERSITY

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa031.241 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S104-S104

Author(s):

Anja Richter ◽

Evangelos Vassos ◽

Matthew J Kempton ◽

Mark van der Gaag ◽

Lieuwe de Haan ◽

...

Keyword(s):

Risk Factors ◽

Sexual Abuse ◽

High Risk ◽

Prospective Study ◽

Physical Abuse ◽

Clinical Outcomes ◽

Childhood Trauma ◽

Childhood Adversity ◽

Risk Scores ◽

Polygenic Risk

Abstract Background Genetic vulnerability to psychosis is polygenic, involving multiple genes with small individual effects (Psychiatric Genomics Consortium (PGC), 2014). The risk of psychosis is also related to environmental factors, such as childhood trauma (Lardinois et al, 2011). Although the onset of psychosis is thought to result from the interaction of genetic and environmental risk factors (Walker & Diforio, 1997), the extent to which the influence of childhood trauma depends on genetic susceptibility remains unclear. We sought to address this issue in a large prospective study of people at clinical high risk (CHR) for psychosis. These individuals present with psychotic and affective symptoms, and are at increased risk of developing both schizophreniform and affective psychoses. Methods We studied subjects of European ancestry, drawn from EU-GEI, a large multi-centre prospective study of people at CHR for psychosis. At baseline, DNA was obtained from subjects who met the CAARMS criteria for the CHR state (n=266) and healthy controls (HC; n=42). Childhood trauma was assessed using the childhood trauma questionnaire (CTQ), which comprises 5 subdomains: emotional abuse, physical abuse, sexual abuse, physical neglect, and emotional neglect. Polygenic risk scores (PRSs) for schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) were constructed separately, using results from meta-analyses by the corresponding Disorder Working Groups of the PGC. The CHR subjects were clinically monitored for up to 5 years and clinical outcomes were assessed in terms of transition to psychosis (as defined by the CAARMS), remission from the CHR state (subject no longer meets CAARMS inclusion criteria) and level of functioning (GAF Disability Scale). Logistic regression models were used to investigate the association between each PRSs and childhood trauma as predictors of transition and remission, adjusted by population stratification using the first 10 principal components, age, sex and site. All findings are reported at p<0.017, Bonferroni-corrected for the 3 PRSs. Results Within the CHR sample, the onset of psychosis during follow up was related to interactions between the BD PRS and the total childhood trauma score (OR=0.959, 95% CI 0.930–0.988, p=0.006), and between the BD PRS and physical abuse (OR=0.787, 95% CI 0.689–0.900, p<0.001). Remission from the CHR state was related to an interaction between the SCZ PRS and childhood sexual abuse (OR: 1.110, 95% CI 1.004–1.226, p=0.041). Discussion These data indicate that clinical outcomes in CHR subjects are related to interactions between the polygenic risk for psychotic disorders and childhood adversity. The measurement of interactions between genomic and environmental risk factors may help to predict individual outcomes in people at high risk in a clinical setting.

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Global biobank analyses provide lessons for computing polygenic risk scores across diverse cohorts

10.1101/2021.11.18.21266545 ◽

2021 ◽

Author(s):

Ying Wang ◽

Shinichi Namba ◽

Esteban Lopera ◽

Sini Kerminen ◽

Kristin Tsuo ◽

...

Keyword(s):

Best Practices ◽

Prediction Accuracy ◽

Disease Risk ◽

Association Studies ◽

Meta Analysis ◽

European Ancestry ◽

Risk Scores ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Polygenic Risk

SummaryWith the increasing availability of biobank-scale datasets that incorporate both genomic data and electronic health records, many associations between genetic variants and phenotypes of interest have been discovered. Polygenic risk scores (PRS), which are being widely explored in precision medicine, use the results of association studies to predict the genetic component of disease risk by accumulating risk alleles weighted by their effect sizes. However, limited studies have thoroughly investigated best practices for PRS in global populations across different diseases. In this study, we utilize data from the Global-Biobank Meta-analysis Initiative (GBMI), which consists of individuals from diverse ancestries and across continents, to explore methodological considerations and PRS prediction performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRS using heuristic (pruning and thresholding, P+T) and Bayesian (PRS-CS) methods. We found that the genetic architecture, such as SNP-based heritability and polygenicity, varied greatly among endpoints. For both PRS construction methods, using a European ancestry LD reference panel resulted in comparable or higher prediction accuracy compared to several other non-European based panels; this is largely attributable to European descent populations still comprising the majority of GBMI participants. PRS-CS overall outperformed the classic P+T method, especially for endpoints with higher SNP-based heritability. For example, substantial improvements are observed in East-Asian ancestry (EAS) using PRS-CS compared to P+T for heart failure (HF) and chronic obstructive pulmonary disease (COPD). Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma which has known variation in disease prevalence across global populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using the GBMI and highlight the importance of best practices for PRS in the biobank-scale genomics era.

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P.0882 A meta-analysis of polygenic risk scores for mood disorders, neuroticism, and schizophrenia in antidepressant response

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2021.10.738 ◽

2021 ◽

Vol 53 ◽

pp. S646-S647

Author(s):

G. Fanelli ◽

C. Fabbri ◽

K. Domschke ◽

A. Minelli ◽

M. Gennarelli ◽

...

Keyword(s):

Mood Disorders ◽

Meta Analysis ◽

Risk Scores ◽

Antidepressant Response ◽

Polygenic Risk

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Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: meta-analysis of three treatment cohorts

10.1101/295717 ◽

2018 ◽

Cited By ~ 2

Author(s):

Joey Ward ◽

Nicholas Graham ◽

Rona Strawbridge ◽

Amy Ferguson ◽

Gregory Jenkins ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Meta Analysis ◽

Antidepressant Medication ◽

Research Network ◽

Risk Scores ◽

Antidepressant Response ◽

Major Depressive ◽

Polygenic Risk ◽

Therapeutic Drugs

AbstractThere are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 783). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10-5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.

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Association between substance use disorder and polygenic liability to schizophrenia

10.1101/129288 ◽

2017 ◽

Author(s):

Sarah M. Hartz ◽

Amy Horton ◽

Mary Oehlert ◽

Caitlin E. Carey ◽

Arpana Agrawal ◽

...

Keyword(s):

Substance Use ◽

Substance Use Disorder ◽

Meta Analysis ◽

Risk Scores ◽

Polygenic Risk Score ◽

Individual Substance ◽

Polygenic Risk ◽

Genome Wide ◽

Individual Dataset ◽

Shared Genetic

AbstractBackgroundThere are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity.MethodsHere, we test the hypothesis that shared genetic liability contributes to the high rates of comorbidity between schizophrenia and substance use disorder. To do this, polygenic risk scores for schizophrenia derived from a large meta-analysis by the Psychiatric Genomics Consortium were computed in three substance use disorder datasets: COGEND (ascertained for nicotine dependence n=918 cases, 988 controls), COGA (ascertained for alcohol dependence n=643 cases, 384 controls), and FSCD (ascertained for cocaine dependence n=210 cases, 317 controls). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to 1000 Genomes reference panel.ResultsIn each individual dataset and in the mega-analysis, strong associations were observed between any substance use disorder diagnosis and the polygenic risk score for schizophrenia (mega-analysis pseudo R2 range 0.8%-3.7%, minimum p=4×10-23).ConclusionsThese results suggest that comorbidity between schizophrenia and substance use disorder is partially attributable to shared polygenic liability. This shared liability is most consistent with a general risk for substance use disorder rather than specific risks for individual substance use disorders and adds to increasing evidence of a blurred boundary between schizophrenia and substance use disorder.

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Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients

Translational Psychiatry ◽

10.1038/s41398-021-01702-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Klaus Oliver Schubert ◽

Anbupalam Thalamuthu ◽

Azmeraw T. Amare ◽

Joseph Frank ◽

Fabian Streit ◽

...

Keyword(s):

Bipolar Disorder ◽

Treatment Response ◽

Molecular Mechanisms ◽

Lithium Treatment ◽

Meta Analysis ◽

Risk Scores ◽

Poor Responders ◽

Genetic Trait ◽

Polygenic Risk ◽

Lithium Response

AbstractLithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium’s therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.

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Breast cancer polygenic risk scores: a 12-month prospective study of patient reported outcomes and risk management behavior

Genetics in Medicine ◽

10.1038/s41436-021-01288-6 ◽

2021 ◽

Author(s):

Tatiane Yanes ◽

Bettina Meiser ◽

Rajneesh Kaur ◽

Mary-Anne Young ◽

Philip B. Mitchell ◽

...

Keyword(s):

Breast Cancer ◽

Risk Management ◽

Prospective Study ◽

Patient Reported Outcomes ◽

Risk Scores ◽

Management Behavior ◽

Polygenic Risk ◽

Patient Reported

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Polygenic risk scores for schizophrenia are associated with oculomotor endophenotypes

Psychological Medicine ◽

10.1017/s0033291721003251 ◽

2021 ◽

pp. 1-9

Author(s):

Annabell Coors ◽

Mohammed-Aslam Imtiaz ◽

Meta M. Boenniger ◽

N. Ahmad Aziz ◽

Monique M. B. Breteler ◽

...

Keyword(s):

General Population ◽

Smooth Pursuit ◽

Association Studies ◽

Meta Analysis ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Pursuit Velocity ◽

Oculomotor Function ◽

Multivariable Regression Models

Abstract Background Schizophrenia is a heterogeneous disorder with substantial heritability. The use of endophenotypes may help clarify its aetiology. Measures from the smooth pursuit and antisaccade eye movement tasks have been identified as endophenotypes for schizophrenia in twin and family studies. However, the genetic basis of the overlap between schizophrenia and these oculomotor markers is largely unknown. Here, we tested whether schizophrenia polygenic risk scores (PRS) were associated with oculomotor performance in the general population. Methods Analyses were based on the data of 2956 participants (aged 30–95) of the Rhineland Study, a community-based cohort study in Bonn, Germany. Genotyping was performed on Omni-2.5 exome arrays. Using summary statistics from a recent meta-analysis based on the two largest schizophrenia genome-wide association studies to date, we quantified genetic risk for schizophrenia by creating PRS at different p value thresholds for genetic markers. We examined associations between PRS and oculomotor performance using multivariable regression models. Results Higher PRS were associated with higher antisaccade error rate and latency, and lower antisaccade amplitude gain. PRS showed inconsistent patterns of association with smooth pursuit velocity gain and were not associated with saccade rate during smooth pursuit or performance on a prosaccade control task. Conclusions There is an overlap between genetic determinants of schizophrenia and oculomotor endophenotypes. Our findings suggest that the mechanisms that underlie schizophrenia also affect oculomotor function in the general population.

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