Associations between polygenic liability for schizophrenia and level of psychosis and mood-incongruence in bipolar disorder

AbstractImportanceBipolar disorder (BD) overlaps schizophrenia in its clinical presentation and genetic liability. Alternative approaches to patient stratification beyond current diagnostic categories are needed to understand the underlying disease processes/mechanisms.ObjectivesTo investigate the relationship between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRS) and psychotic presentations of BD, using clinical descriptions which consider both occurrence and level of mood-incongruent psychotic features.DesignCase-control design: using multinomial logistic regression, to estimate differential associations of PRS across categories of cases and controls.Settings & Participants4399 BDcases, mean [sd] age-at-interview 46[12] years, of which 2966 were woman (67%) from the BD Research Network (BDRN) were included in the final analyses, with data for 4976 schizophrenia cases and 9012 controls from the Type-1 diabetes genetics consortium and Generation Scotland included for comparison.ExposureStandardised PRS, calculated using alleles with an association p-value threshold < 0.05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, adjusted for the first 10 population principal components and genotyping-platform.Main outcome measureMultinomial logit models estimated PRS associations with BD stratified by (1) Research Diagnostic Criteria (RDC) BD subtypes (2) Lifetime occurrence of psychosis.(3) Lifetime mood-incongruent psychotic features and (4) ordinal logistic regression examined PRS associations across levels of mood-incongruence. Ratings were derived from the Schedule for Clinical Assessment in Neuropsychiatry interview (SCAN) and the Bipolar Affective Disorder Dimension Scale (BADDS).ResultsAcross clinical phenotypes, there was an exposure-response gradient with the strongest PRS association for schizophrenia (RR=1.94, (95% C.1.1.86, 2.01)), then schizoaffective BD (RR=1.37, (95% C.I. 1.22, 1.54)), BD I (RR= 1.30, (95% C.I. 1.24, 1.36)) and BD II (RR=1.04, (95% C.1. 0.97, 1.11)). Within BD cases, there was an effect gradient, indexed by the nature of psychosis, with prominent mood-incongruent psychotic features having the strongest association (RR=1.46, (95% C.1.1.36, 1.57)), followed by mood-congruent psychosis (RR= 1.24, (95% C.1. 1.17, 1.33)) and lastly, BD cases with no history of psychosis (RR= 1.09, (95% C.1. 1.04, 1.15)).ConclusionWe show for the first time a polygenic-risk gradient, across schizophrenia and bipolar disorder, indexed by the occurrence and level of mood-incongruent psychotic symptoms.

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Efficient implementation of penalized regression for genetic risk prediction

10.1101/403337 ◽

2018 ◽

Cited By ~ 1

Author(s):

Florian Privé ◽

Hugues Aschard ◽

Michael G.B. Blum

Keyword(s):

Logistic Regression ◽

Genetic Risk ◽

Association Studies ◽

Predictive Performance ◽

Penalized Regression ◽

Risk Scores ◽

P Value ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Penalized Logistic Regression

AbstractPolygenic Risk Scores (PRS) consist in combining the information across many single-nucleotide polymorphisms (SNPs) in a score reflecting the genetic risk of developing a disease. PRS might have a major impact on public health, possibly allowing for screening campaigns to identify high-genetic risk individuals for a given disease. The “Clumping+Thresholding” (C+T) approach is the most common method to derive PRS. C+T uses only univariate genome-wide association studies (GWAS) summary statistics, which makes it fast and easy to use. However, previous work showed that jointly estimating SNP effects for computing PRS has the potential to significantly improve the predictive performance of PRS as compared to C+T.In this paper, we present an efficient method to jointly estimate SNP effects, allowing for practical application of penalized logistic regression (PLR) on modern datasets including hundreds of thousands of individuals. Moreover, our implementation of PLR directly includes automatic choices for hyper-parameters. The choice of hyper-parameters for a predictive model is very important since it can dramatically impact its predictive performance. As an example, AUC values range from less than 60% to 90% in a model with 30 causal SNPs, depending on the p-value threshold in C+T.We compare the performance of PLR, C+T and a derivation of random forests using both real and simulated data. PLR consistently achieves higher predictive performance than the two other methods while being as fast as C+T. We find that improvement in predictive performance is more pronounced when there are few effects located in nearby genomic regions with correlated SNPs; for instance, AUC values increase from 83% with the best prediction of C+T to 92.5% with PLR. We confirm these results in a data analysis of a case-control study for celiac disease where PLR and the standard C+T method achieve AUC of 89% and of 82.5%.In conclusion, our study demonstrates that penalized logistic regression can achieve more discriminative polygenic risk scores, while being applicable to large-scale individual-level data thanks to the implementation we provide in the R package bigstatsr.

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The use of Polygenic Risk Scores to Inform Aetiology of Mood and Psychotic Disorders

European Psychiatry ◽

10.1016/j.eurpsy.2017.01.2051 ◽

2017 ◽

Vol 41 (S1) ◽

pp. S166-S166

Author(s):

J. Harrison ◽

S. Mistry

Keyword(s):

Bipolar Disorder ◽

Genetic Basis ◽

Psychotic Disorders ◽

Mental Illnesses ◽

Risk Scores ◽

Psychotic Symptoms ◽

Polygenic Risk ◽

Genetic Overlap ◽

Competing Interest

IntroductionPolygenic risk scores (PRS) incorporate many small genetic markers that are associated with conditions. This technique was first used to investigate mental illnesses in 2009. Since then, it has been widely used.ObjectivesWe wanted to explore how PRS have been used to the study the aetiology of psychosis, schizophrenia, bipolar disorder and depression.AimsWe aimed to conduct a systematic review, identifying studies that have examined associations between PRS for bipolar disorder, schizophrenia/psychosis and depression and psychopathology-related outcome measures.MethodsWe searched EMBASE, Medline and PsychInfo from 06/08/2009 to 14/03/2016. We hand-searched the reference lists of related papers.ResultsAfter removing duplicates, the search yielded 1043 publications. When irrelevant articles were excluded, 33 articles remained. We found 24 studies using schizophrenia PRS, three using bipolar PRS and nine using depression PRS. Many studies successfully used PRS to predict case/control status. Some studies showed associations between PRS and diagnostic sub-categories. A range of clinical phenotypes and symptoms has been explored. For example, specific PRS are associated with cognitive performance in schizophrenia, psychotic symptoms in bipolar disorder, and frequency of episodes of depression. PRS have also demonstrated genetic overlap between mental illnesses. It was difficult to assess the quality of some studies as not all reported sufficient methodological detail.ConclusionsPRS have enabled us to explore the polygenic architecture of mental illness and demonstrate a genetic basis for some observed features. However, they have yet to give insights into the biology, which underpin mental illnesses.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

Psychological Medicine ◽

10.1017/s0033291721001082 ◽

2021 ◽

pp. 1-12

Author(s):

Simon Schmitt ◽

Tina Meller ◽

Frederike Stein ◽

Katharina Brosch ◽

Kai Ringwald ◽

...

Keyword(s):

Bipolar Disorder ◽

Mental Disorders ◽

Cortical Thickness ◽

Genetic Risk ◽

Right Hemisphere ◽

Autism Spectrum ◽

Risk Scores ◽

Grey Matter Volume ◽

Polygenic Risk ◽

Risk For Depression

Abstract Background MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. Methods We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. Results The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. Conclusions Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

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Polygenic Risk Scores Derived From Largest Schizophrenia Gwas Are Associated With The Presence Of Psychosis And Level Of Mood Incongruent Psychosis In Bipolar Disorder

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2016.09.511 ◽

2017 ◽

Vol 27 ◽

pp. S445-S446

Author(s):

Judith Allardyce ◽

Ganna Leonenko ◽

Marian Hamshere ◽

Sarah Knott ◽

Liz Forty ◽

...

Keyword(s):

Bipolar Disorder ◽

Risk Scores ◽

Polygenic Risk

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Who Responds to Prophylactic Lithium Therapy?

The British Journal of Psychiatry ◽

10.1192/s0007125000292441 ◽

1993 ◽

Vol 163 (S21) ◽

pp. 20-26 ◽

Cited By ~ 24

Author(s):

M. T. Abou-Saleh

Keyword(s):

Bipolar Disorder ◽

Family History ◽

Bipolar Affective Disorder ◽

Positive Family History ◽

Specific Treatment ◽

Predictors Of Outcome ◽

Powerful Predictor ◽

Bipolar Illness ◽

History Of ◽

Psychotic Features

The search for predictors of outcome has not been particularly rewarding, and the use of lithium remains empirical: a trial of lithium is the most powerful predictor of outcome. However, lithium is a highly specific treatment for bipolar disorder. In non-bipolar affective disorder, factors of interest are correlates of bipolar disorder: mood-congruent psychotic features, retarded-endogenous profile, cyclothymic personality, positive family history of bipolar illness, periodicity, and normality between episodes of illness.

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Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank

PLoS Medicine ◽

10.1371/journal.pmed.1003782 ◽

2021 ◽

Vol 18 (10) ◽

pp. e1003782

Author(s):

Michael Wainberg ◽

Samuel E. Jones ◽

Lindsay Melhuish Beaupre ◽

Sean L. Hill ◽

Daniel Felsky ◽

...

Keyword(s):

Bipolar Disorder ◽

Sleep Duration ◽

Sleep Efficiency ◽

Risk Scores ◽

Psychiatric Diagnoses ◽

Uk Biobank ◽

Major Depressive ◽

Cross Sectional ◽

Polygenic Risk ◽

The Uk

Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

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Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores

10.1101/2020.03.31.20044800 ◽

2020 ◽

Author(s):

Brandon J. Coombes ◽

Matej Markota ◽

J. John Mann ◽

Colin Colby ◽

Eli Stahl ◽

...

Keyword(s):

Bipolar Disorder ◽

Psychiatric Disorders ◽

Suicide Attempts ◽

Risk Scores ◽

Rapid Cycling ◽

Clinical Heterogeneity ◽

Polygenic Risk ◽

Clinical Observations ◽

History Of ◽

Dsm Iv

AbstractBipolar disorder (BD) has high clinical heterogeneity, frequent psychiatric comorbidities, and elevated suicide risk. To determine genetic differences between common clinical sub-phenotypes of BD, we performed a systematic PRS analysis using multiple polygenic risk scores (PRSs) from a range of psychiatric, personality, and lifestyle traits to dissect differences in BD sub-phenotypes in two BD cohorts: the Mayo Clinic BD Biobank (N = 968) and Genetic Association Information Network (N = 1001). Participants were assessed for history of psychosis, early-onset BD, rapid cycling (defined as four or more episodes in a year), and suicide attempts using questionnaires and the Structured Clinical Interview for DSM-IV. In a combined sample of 1969 bipolar cases (45.5% male), those with psychosis had higher PRS for SCZ (OR = 1.3 per S.D.; p = 3e-5) but lower PRSs for anhedonia (OR = 0.87; p = 0.003) and BMI (OR = 0.87; p = 0.003). Rapid cycling cases had higher PRS for ADHD (OR = 1.23; p = 7e-5) and MDD (OR = 1.23; p = 4e-5) and lower BD PRS (OR = 0.8; p = 0.004). Cases with a suicide attempt had higher PRS for MDD (OR = 1.26; p = 1e-6) and anhedonia (OR = 1.22; p = 2e-5) as well as lower PRS for educational attainment (OR = 0.87; p = 0.003). The observed novel PRS associations with sub-phenotypes align with clinical observations such as rapid cycling BD patients having a greater lifetime prevalence of ADHD. Our findings confirm that genetic heterogeneity underlies the clinical heterogeneity of BD and consideration of genetic contribution to psychopathologic components of psychiatric disorders may improve genetic prediction of complex psychiatric disorders.

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Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

10.1101/810713 ◽

2019 ◽

Cited By ~ 4

Author(s):

Zijie Zhao ◽

Yanyao Yi ◽

Yuchang Wu ◽

Xiaoyuan Zhong ◽

Yupei Lin ◽

...

Keyword(s):

Association Studies ◽

Fine Tuning ◽

Risk Scores ◽

Training Dataset ◽

Validation Dataset ◽

P Value ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Polygenic Risk ◽

Model Tuning

AbstractPolygenic risk scores (PRSs) have wide applications in human genetics research. Notably, most PRS models include tuning parameters which improve predictive performance when properly selected. However, existing model-tuning methods require individual-level genetic data as the training dataset or as a validation dataset independent from both training and testing samples. These data rarely exist in practice, creating a significant gap between PRS methodology and applications. Here, we introduce PUMAS (Parameter-tuning Using Marginal Association Statistics), a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform a variety of model-tuning procedures (e.g. cross-validation) using GWAS summary statistics and can effectively benchmark and optimize PRS models under diverse genetic architecture. On average, PUMAS improves the predictive R2 by 205.6% and 62.5% compared to PRSs with arbitrary p-value cutoffs of 0.01 and 1, respectively. Applied to 211 neuroimaging traits and Alzheimer’s disease, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis. We believe our method resolves a fundamental problem without a current solution and will greatly benefit genetic prediction applications.

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Cigarette smoking and psychotic symptoms in bipolar affective disorder

The British Journal of Psychiatry ◽

10.1192/bjp.179.1.35 ◽

2001 ◽

Vol 179 (1) ◽

pp. 35-38 ◽

Cited By ~ 50

Author(s):

Aiden Corvin ◽

Ed O'Mahony ◽

Myra O'Regan ◽

Claire Comerford ◽

Robert O'Connell ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Affective Disorder ◽

Bipolar Affective Disorder ◽

Psychotic Symptoms ◽

Heavy Smoking ◽

Ordinal Logistic Regression Analysis ◽

History Of ◽

Categorical Diagnosis ◽

The Relationship

BackgroundAn association exists between smoking and schizophrenia, independent of other factors and related to psychotic symptomatology.AimsTo determine whether smoking is associated with psychosis in bipolar affective disorder.MethodSmoking data were collected from 92 unrelated patients with bipolar affective disorder. An ordinal logistic regression analysis tested the relationship between smoking severity and psychotic symptomatology, allowing for potential confounders.ResultsA significant relationship was detected between smoking/heavy smoking and history of psychosis (68.7%, n=44). Smoking was less prevalent in patients who were less symptomatic (56.5%, n=13) than in patients with a more severe psychosis (75.7%, n=31). Prevalence and severity of smoking predicted severity of psychotic symptoms (P=0.001), a relationship independent of other variables (P=0.0272).ConclusionA link between smoking and psychosis exists in bipolar affective disorder and may be independent of categorical diagnosis.

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