Jumping To Conclusions, General Intelligence, And Psychosis Liability: Findings From The Multi-Centre EU-GEI Case-Control Study

AbstractBackgroundThe “jumping to conclusions” (JTC) bias is associated with both psychosis and general cognition but their relationship is unclear. In this study, we set out to clarify the relationship between the JTC bias, IQ, psychosis and polygenic liability to schizophrenia and IQ.Methods817 FEP patients and 1294 population-based controls completed assessments of general intelligence (IQ), and JTC (assessed by the number of beads drawn on the probabilistic reasoning “beads” task) and provided blood or saliva samples from which we extracted DNA and computed polygenic risk scores for IQ and schizophrenia.ResultsThe estimated proportion of the total effect of case/control differences on JTC mediated by IQ was 79%. Schizophrenia Polygenic Risk Score (SZ PRS) was non-significantly associated with a higher number of beads drawn (B= 0.47, 95% CI −0.21 to 1.16, p=0.17); whereas IQ PRS (B=0.51, 95% CI 0.25 to 0.76, p<0.001) significantly predicted the number of beads drawn, and was thus associated with reduced JTC bias. The JTC was more strongly associated with higher level of psychotic-like experiences (PLE) in controls, including after controlling for IQ (B= −1.7, 95% CI −2.8 to −0.5, p=0.006), but did not relate to delusions in patients.Conclusionsthe JTC reasoning bias in psychosis is not a specific cognitive deficit but is rather a manifestation or consequence, of general cognitive impairment. Whereas, in the general population, the JTC bias is related to psychotic-like experiences, independent of IQ. The work has potential to inform interventions targeting cognitive biases in early psychosis.

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Jumping to conclusions, general intelligence, and psychosis liability: findings from the multi-centre EU-GEI case-control study

Psychological Medicine ◽

10.1017/s003329171900357x ◽

2020 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Giada Tripoli ◽

Diego Quattrone ◽

Laura Ferraro ◽

Charlotte Gayer-Anderson ◽

Victoria Rodriguez ◽

...

Keyword(s):

Cognitive Biases ◽

Population Based ◽

Case Control ◽

Risk Scores ◽

First Episode ◽

Polygenic Risk Score ◽

General Intelligence ◽

Polygenic Risk ◽

Jumping To Conclusions ◽

Episode Psychosis

Abstract Background The ‘jumping to conclusions’ (JTC) bias is associated with both psychosis and general cognition but their relationship is unclear. In this study, we set out to clarify the relationship between the JTC bias, IQ, psychosis and polygenic liability to schizophrenia and IQ. Methods A total of 817 first episode psychosis patients and 1294 population-based controls completed assessments of general intelligence (IQ), and JTC, and provided blood or saliva samples from which we extracted DNA and computed polygenic risk scores for IQ and schizophrenia. Results The estimated proportion of the total effect of case/control differences on JTC mediated by IQ was 79%. Schizophrenia polygenic risk score was non-significantly associated with a higher number of beads drawn (B = 0.47, 95% CI −0.21 to 1.16, p = 0.17); whereas IQ PRS (B = 0.51, 95% CI 0.25–0.76, p < 0.001) significantly predicted the number of beads drawn, and was thus associated with reduced JTC bias. The JTC was more strongly associated with the higher level of psychotic-like experiences (PLEs) in controls, including after controlling for IQ (B = −1.7, 95% CI −2.8 to −0.5, p = 0.006), but did not relate to delusions in patients. Conclusions Our findings suggest that the JTC reasoning bias in psychosis might not be a specific cognitive deficit but rather a manifestation or consequence, of general cognitive impairment. Whereas, in the general population, the JTC bias is related to PLEs, independent of IQ. The work has the potential to inform interventions targeting cognitive biases in early psychosis.

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Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts

The Lancet Respiratory Medicine ◽

10.1016/s2213-2600(20)30101-6 ◽

2020 ◽

Vol 8 (7) ◽

pp. 696-708 ◽

Cited By ~ 3

Author(s):

Matthew Moll ◽

Phuwanat Sakornsakolpat ◽

Nick Shrine ◽

Brian D Hobbs ◽

Dawn L DeMeo ◽

...

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Population Based ◽

Case Control ◽

Risk Scores ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Polygenic Risk

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SU112DOES POLYGENIC RISK SCORE FOR SCHIZOPHRENIA IMPACT ON JUMPING TO CONCLUSIONS? PRELIMINARY FINDINGS FROM THE EU-GEI CASE-CONTROL STUDY

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2018.08.476 ◽

2019 ◽

Vol 29 ◽

pp. S1326

Author(s):

Giada Tripoli ◽

Diego Quattrone ◽

Charlotte Gayer-Anderson ◽

Victoria Rodríguez ◽

Natasha Benzian-Olsson ◽

...

Keyword(s):

Risk Score ◽

Case Control Study ◽

Case Control ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Jumping To Conclusions ◽

Control Study ◽

The Eu

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Follicular lymphoma polygenic risk score is associated with increased disease risk but improved overall survival among women in a population based case-control in Los Angeles County California

Cancer Epidemiology ◽

10.1016/j.canep.2020.101688 ◽

2020 ◽

Vol 65 ◽

pp. 101688

Author(s):

Charlie Zhong ◽

Chun R. Chao ◽

Joo Y. Song ◽

Dennis D. Weisenburger ◽

Jianning Luo ◽

...

Keyword(s):

Overall Survival ◽

Los Angeles ◽

Follicular Lymphoma ◽

Risk Score ◽

Los Angeles County ◽

Disease Risk ◽

Population Based ◽

Case Control ◽

Polygenic Risk Score ◽

Polygenic Risk

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Leveraging correlations between polygenic risk score predictors to detect heterogeneity in GWAS cohorts

10.1101/827162 ◽

2019 ◽

Author(s):

Jie Yuan ◽

Henry Xing ◽

Alexandre Lamy ◽

Todd Lencz ◽

Itsik Pe’er ◽

...

Keyword(s):

Autoimmune Diseases ◽

Clinical Observation ◽

Case Control ◽

Risk Scores ◽

P Value ◽

Polygenic Risk Score ◽

Significant Heterogeneity ◽

Weighted Sum ◽

Polygenic Risk ◽

Study Designs

AbstractEvidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 5% of variance on the liability scale and cohorts comprise 50, 000 − 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54e-4), as well as in individual cohorts meta-analyzed using Fisher’s method (p-value 0.03), based on significantly associated variants.

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A machine learning case–control classifier for schizophrenia based on DNA methylation in blood

Translational Psychiatry ◽

10.1038/s41398-021-01496-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chathura J. Gunasekara ◽

Eilis Hannon ◽

Harry MacKay ◽

Cristian Coarfa ◽

Andrew McQuillin ◽

...

Keyword(s):

Dna Methylation ◽

Population Based ◽

Case Control ◽

Training Data ◽

Polygenic Risk Score ◽

Reverse Causality ◽

Cell Type Specificity ◽

Data Set ◽

Human Genomic ◽

Genomic Regions

AbstractEpigenetic dysregulation is thought to contribute to the etiology of schizophrenia (SZ), but the cell type-specificity of DNA methylation makes population-based epigenetic studies of SZ challenging. To train an SZ case–control classifier based on DNA methylation in blood, therefore, we focused on human genomic regions of systemic interindividual epigenetic variation (CoRSIVs), a subset of which are represented on the Illumina Human Methylation 450K (HM450) array. HM450 DNA methylation data on whole blood of 414 SZ cases and 433 non-psychiatric controls were used as training data for a classification algorithm with built-in feature selection, sparse partial least squares discriminate analysis (SPLS-DA); application of SPLS-DA to HM450 data has not been previously reported. Using the first two SPLS-DA dimensions we calculated a “risk distance” to identify individuals with the highest probability of SZ. The model was then evaluated on an independent HM450 data set on 353 SZ cases and 322 non-psychiatric controls. Our CoRSIV-based model classified 303 individuals as cases with a positive predictive value (PPV) of 80%, far surpassing the performance of a model based on polygenic risk score (PRS). Importantly, risk distance (based on CoRSIV methylation) was not associated with medication use, arguing against reverse causality. Risk distance and PRS were positively correlated (Pearson r = 0.28, P = 1.28 × 10−12), and mediational analysis suggested that genetic effects on SZ are partially mediated by altered methylation at CoRSIVs. Our results indicate two innate dimensions of SZ risk: one based on genetic, and the other on systemic epigenetic variants.

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Symptom dimensions of major depression in a large community-based cohort

Psychological Medicine ◽

10.1017/s0033291721001707 ◽

2021 ◽

pp. 1-8

Author(s):

Michael Wainberg ◽

Peter Zhukovsky ◽

Sean L. Hill ◽

Daniel Felsky ◽

Aristotle Voineskos ◽

...

Keyword(s):

Primary Care ◽

Major Depression ◽

Risk Scores ◽

Polygenic Risk Score ◽

Psychiatric Diagnoses ◽

Uk Biobank ◽

Community Based ◽

Symptom Dimensions ◽

Polygenic Risk ◽

Large Community

Abstract Background Our understanding of major depression is complicated by substantial heterogeneity in disease presentation, which can be disentangled by data-driven analyses of depressive symptom dimensions. We aimed to determine the clinical portrait of such symptom dimensions among individuals in the community. Methods This cross-sectional study consisted of 25 261 self-reported White UK Biobank participants with major depression. Nine questions from the UK Biobank Mental Health Questionnaire encompassing depressive symptoms were decomposed into underlying factors or ‘symptom dimensions’ via factor analysis, which were then tested for association with psychiatric diagnoses and polygenic risk scores for major depressive disorder (MDD), bipolar disorder and schizophrenia. Replication was performed among 655 self-reported non-White participants, across sexes, and among 7190 individuals with an ICD-10 code for MDD from linked inpatient or primary care records. Results Four broad symptom dimensions were identified, encompassing negative cognition, functional impairment, insomnia and atypical symptoms. These dimensions replicated across ancestries, sexes and individuals with inpatient or primary care MDD diagnoses, and were also consistent among 43 090 self-reported White participants with undiagnosed self-reported depression. Every dimension was associated with increased risk of nearly every psychiatric diagnosis and polygenic risk score. However, while certain psychiatric diagnoses were disproportionately associated with specific symptom dimensions, the three polygenic risk scores did not show the same specificity of associations. Conclusions An analysis of questionnaire data from a large community-based cohort reveals four replicable symptom dimensions of depression with distinct clinical, but not genetic, correlates.

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Polygenic Risk Score of Longevity Predicts Longer Survival Across an Age Continuum

The Journals of Gerontology Series A ◽

10.1093/gerona/glaa289 ◽

2020 ◽

Author(s):

Niccolo’ Tesi ◽

Sven J van der Lee ◽

Marc Hulsman ◽

Iris E Jansen ◽

Najada Stringa ◽

...

Keyword(s):

Older Adults ◽

Cellular Response ◽

Molecular Mechanisms ◽

Association Studies ◽

Risk Scores ◽

Human Longevity ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Polygenic Risk ◽

Age Related

Abstract Studying the genome of centenarians may give insights into the molecular mechanisms underlying extreme human longevity and the escape of age-related diseases. Here, we set out to construct polygenic risk scores (PRSs) for longevity and to investigate the functions of longevity-associated variants. Using a cohort of centenarians with maintained cognitive health (N = 343), a population-matched cohort of older adults from 5 cohorts (N = 2905), and summary statistics data from genome-wide association studies on parental longevity, we constructed a PRS including 330 variants that significantly discriminated between centenarians and older adults. This PRS was also associated with longer survival in an independent sample of younger individuals (p = .02), leading up to a 4-year difference in survival based on common genetic factors only. We show that this PRS was, in part, able to compensate for the deleterious effect of the APOE-ε4 allele. Using an integrative framework, we annotated the 330 variants included in this PRS by the genes they associate with. We find that they are enriched with genes associated with cellular differentiation, developmental processes, and cellular response to stress. Together, our results indicate that an extended human life span is, in part, the result of a constellation of variants each exerting small advantageous effects on aging-related biological mechanisms that maintain overall health and decrease the risk of age-related diseases.

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Abstract P256: Genetic Determinants Of Blood Pressure Associate With Apparent Treatment Resistant Hypertension

Hypertension ◽

10.1161/hyp.78.suppl_1.p256 ◽

2021 ◽

Vol 78 (Suppl_1) ◽

Author(s):

Joseph H Breeyear ◽

Megan M Shuey ◽

Todd L Edwards ◽

Jacklyn Hellwege

Keyword(s):

Blood Pressure ◽

Risk Scores ◽

Uncontrolled Hypertension ◽

Polygenic Risk Score ◽

Genetic Determinants ◽

Treatment Resistant ◽

Polygenic Risk ◽

Blood Pressures ◽

The Uk ◽

Hispanic White

Hypertension is estimated to affect more than 49.6% of US adults 20 years and older. Of those individuals with hypertension, more than ten million are classified as apparent treatment resistant hypertensive (aTRH). The attributable risk of uncontrolled hypertension was estimated to be 49% for cardiovascular disease and 62% for stroke. We developed a polygenic risk score (PRS) for systolic (SBP) and diastolic (DBP) blood pressure to examine the association between the genetic determinants of blood pressure and aTRH with the goal of identifying high risk individuals. The meta-analyzed transethnic results of Giri et al., Biobank Japan, and Liang et al. were used to generate a PRS with PRS-CS followed by p -value thresholding, and validation in the UK Biobank (n max =341,930). Associations were modeled with logistic regression adjusted for age, age-squared, BMI, sex, and ten principal components of ancestry in BioVU’s transethnic population (n max =37,978), as well as non-Hispanic Black (n max =5,026) and non-Hispanic White (n max =28,545) subsets. The SBP PRS was significantly associated with an increased aTRH risk in the non-Hispanic White subset (1.08 (1.04 - 1.12), p = 0.00037) and transethnic (1.08 (1.04 - 1.13), p = 0.00020) populations, but not the non-Hispanic Black subset. The DBP PRS was not associated with aTRH in any population. Our findings present evidence that individuals with a higher genetic predisposition towards hypertension are at higher risk of aTRH. By integrating polygenic risk scores and clinical covariates in prediction of aTRH, individuals’ therapeutic regimens may be tailored to help maintain stable blood pressures, therefore reducing their risk of comorbidities.

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