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

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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Faculty Opinions recommendation of Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: Meta-analysis of three treatment cohorts.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.734054356.793563008 ◽

2019 ◽

Author(s):

John Nurnberger

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Meta Analysis ◽

Risk Scores ◽

Antidepressant Response ◽

Major Depressive ◽

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

PLoS ONE ◽

10.1371/journal.pone.0203896 ◽

2018 ◽

Vol 13 (9) ◽

pp. e0203896 ◽

Cited By ~ 15

Author(s):

Joey Ward ◽

Nicholas Graham ◽

Rona J. Strawbridge ◽

Amy Ferguson ◽

Gregory Jenkins ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Meta Analysis ◽

Risk Scores ◽

Antidepressant Response ◽

Major Depressive ◽

Polygenic Risk

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Effect of polygenic risk scores on depression in childhood trauma

The British Journal of Psychiatry ◽

10.1192/bjp.bp.113.143081 ◽

2014 ◽

Vol 205 (2) ◽

pp. 113-119 ◽

Cited By ~ 105

Author(s):

Wouter J. Peyrot ◽

Yuri Milaneschi ◽

Abdel Abdellaoui ◽

Patrick F. Sullivan ◽

Jouke J. Hottenga ◽

...

Keyword(s):

Childhood Trauma ◽

Meta Analysis ◽

Genetic Effect ◽

Risk Scores ◽

Environment Interaction ◽

Polygenic Risk ◽

Gene Environment Interaction ◽

Gene Environment ◽

Genome Wide ◽

Direct Genetic Effect

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

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2019.08.082 ◽

2019 ◽

Vol 29 ◽

pp. S155

Author(s):

Eirini Zartaloudi ◽

Johan Thygesen ◽

Aritz Irizar ◽

Karoline Kuchenbaecker ◽

Stella Calafato ◽

...

Keyword(s):

Prospective Study ◽

Meta Analysis ◽

Risk Scores ◽

Polygenic Risk

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29.2 CAN POLYGENIC RISK SCORES PREDICT THE DEVELOPMENT OF MOOD DISORDERS IN YOUTH?

Journal of the American Academy of Child & Adolescent Psychiatry ◽

10.1016/j.jaac.2021.07.720 ◽

2021 ◽

Vol 60 (10) ◽

pp. S302-S303

Author(s):

Maura DiSalvo

Keyword(s):

Mood Disorders ◽

Risk Scores ◽

Polygenic Risk

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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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