Polygenic risk, lifestyle, and cardiovascular mortality: a prospective population-based UK Biobank study

AbstractOBJECTIVETo assess the prognostic ability of polygenic risk scores (PRSs) for coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) for cardiovascular (CV) mortality, independent of traditional risk factors, and further investigate the additive effect between lifestyle behavior and PRS on CV mortality.DESIGNProspective population-based cohort study.SETTINGUK Biobank.PARTICIPANTSA total 377,909 unrelated participants of white British descent were included in the analyses from the UK Biobank cohort.MAIN OUTCOME MEASURESGenome-wide PRSs were constructed using >6 million genetic variants. We stratified patients into four PRS risk groups: low (0 to 19th percentile), intermediate (20 to 79th percentile), high (80 to 98th percentile), and very high (99th percentile). We defined a favorable and unfavorable lifestyle with four modifiable lifestyle components, including smoking, obesity, physical activity, and diet. Cox proportional hazard models were used to analyze the relationship between PRS and CV mortality with stratification by age, sex, disease status, and lifestyle behavior.RESULTSOf 377,909 UK Biobank participants having European ancestry, 3,210 (0.8%) died due to CV disease during a median follow-up of 8.9 years. CV mortality risk was significantly associated with CAD PRS (low vs. very high genetic risk groups, CAD PRS hazard ratio [HR] 2.61 [2.02 to 3.36]) and T2DM PRS (HR 2.08 [1.58 to 2.73]), respectively. These relationships remained significant even after adjustment for a comprehensive range of demographic and clinical factors. In the very high genetic risk group, adherence to an unfavorable lifestyle was further associated with a substantially increased risk of CV mortality (favorable versus unfavorable lifestyle with very high genetic risk for CAD PRS, HR 8.31 [5.12 to 13.49]; T2DM PRS, HR 5.84 [3.39 to 10.04]). Across all genetic risk groups, 32.1% of CV mortality was attributable to lifestyle behavior (population attributable fraction [PAF] 32.1% [95% CI 28.8 to 35.3%]) and 14.1% was attributable to smoking (PAF 14.1% [95% CI 12.4 to 15.7%]). There was no evidence of significant interaction between PRSs and age, sex, or lifestyle behavior in predicting the risk of CV mortality.CONCLUSIONPRSs for CAD or T2DM and lifestyle behaviors are independent predictive factors for future CV mortality in the white, middle-aged population. PRS-based risk assessment could be useful to identify individuals who need intensive behavioral or therapeutic interventions to reduce the risk of CV mortality.Summary BoxWhat is already known on this topicPolygenic risk scores quantify the inherited risk conferred by the cumulative impact of common variants into a quantitative risk estimate.Previous studies primarily targeted the ability of polygenic risk scores to predict a specific disease, and only a few studies have investigated the association between genetic risk scores and cardiovascular mortality.The majority of previous analyses calculated polygenic risk scores from only a small number of genetic variants or adjusted for only a few risk factors, and no studies have examined whether the association of polygenic risk score with cardiovascular mortality differs by lifestyle behavior.What this study addsGenetic risk and lifestyle are independent predictive factors for cardiovascular mortality, even after adjustment for a comprehensive range of demographic and clinical factors.A healthy lifestyle is associated with relative risk reduction for cardiovascular mortality across all genetic risk categories, a finding that indicates the potential benefit of intensive lifestyle modification in overcoming genetic risk for cardiovascular mortality.

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Exploring Various Polygenic Risk Scores for Skin Cancer in the Phenomes of the Michigan Genomics Initiative and the UK Biobank with a Visual Catalog:PRSWeb

10.1101/384909 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lars G. Fritsche ◽

Lauren J. Beesley ◽

Peter VandeHaar ◽

Robert B. Peng ◽

Maxwell Salvatore ◽

...

Keyword(s):

Skin Cancer ◽

Cell Carcinoma ◽

Genetic Risk ◽

Population Based ◽

Risk Scores ◽

Uk Biobank ◽

Polygenic Risk ◽

Cancer Subtypes ◽

Construction Methods ◽

The Uk

AbstractPolygenic risk scores (PRS) are designed to serve as a single summary measure, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. In this paper, we consider several choices for constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict outcomes derived from electronic health records (EHR). Weexamine the three most common skin cancer subtypes in the USA: basal cellcarcinoma, cutaneous squamous cell carcinoma, and melanoma. The genetic risk profiles of subtypes may consist of both shared and unique elements and we construct PRS to understand the common versus distinct etiology. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop an accompanying visual catalog calledPRSwebthat provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.Author summaryIn the study of genetically complex diseases, polygenic risk scores synthesize information from multiple genetic risk factors to provide insight into a patient’s risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in the electronic medical records. They may be associated with diseases that may be related to or precursors of the underlying disease of interest. Limited work is available guiding risk score construction when the goal is to identify associations across the medical phenome. In this paper, we compare different polygenic risk score construction methods in terms of their relationships with the medical phenome. We further propose methods for using these risk scores to decouple the shared and unique genetic profiles of related diseases and to explore related diseases’ shared and unique secondary associations. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the larger population-based UK Biobank study, we investigated the performance of genetic risk profiling methods for the three most common types of skin cancer: melanoma, basal cell carcinoma and squamous cell carcinoma.

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Sleep patterns, genetic susceptibility, and incident cardiovascular disease: a prospective study of 385 292 UK biobank participants

European Heart Journal ◽

10.1093/eurheartj/ehz849 ◽

2019 ◽

Vol 41 (11) ◽

pp. 1182-1189 ◽

Cited By ~ 12

Author(s):

Mengyu Fan ◽

Dianjianyi Sun ◽

Tao Zhou ◽

Yoriko Heianza ◽

Jun Lv ◽

...

Keyword(s):

Cardiovascular Disease ◽

Prospective Study ◽

Genetic Susceptibility ◽

Genetic Risk ◽

Risk Groups ◽

Sleep Pattern ◽

Risk Scores ◽

Poor Sleep ◽

Uk Biobank ◽

High Genetic Risk

Abstract Aims To quantify the association of combined sleep behaviours and genetic susceptibility with the incidence of cardiovascular disease (CVD). Methods and results This study included 385 292 participants initially free of CVD from UK Biobank. We newly created a healthy sleep score according to five sleep factors and defined the low-risk groups as follows: early chronotype, sleep 7–8 h per day, never/rarely insomnia, no snoring, and no frequent excessive daytime sleepiness. Weighted genetic risk scores of coronary heart disease (CHD) or stroke were calculated. During a median of 8.5 years of follow-up, we documented 7280 incident CVD cases including 4667 CHD and 2650 stroke cases. Compared to those with a sleep score of 0–1, participants with a score of 5 had a 35% (19–48%), 34% (22–44%), and 34% (25–42%) reduced risk of CVD, CHD, and stroke, respectively. Nearly 10% of cardiovascular events in this cohort could be attributed to poor sleep pattern. Participants with poor sleep pattern and high genetic risk showed the highest risk of CHD and stroke. Conclusion In this large prospective study, a healthy sleep pattern was associated with reduced risks of CVD, CHD, and stroke among participants with low, intermediate, or high genetic risk.

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Two schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population

10.1101/2022.01.07.22268854 ◽

2022 ◽

Author(s):

Ganesh B Chand ◽

Pankhuri Singhal ◽

Dominic B Dwyer ◽

Junhao Wen ◽

Guray Erus ◽

...

Keyword(s):

Genetic Risk ◽

Gray Matter Volume ◽

Control Sample ◽

Population Level ◽

Population Based ◽

Risk Scores ◽

Support Vector ◽

Cross Sectional ◽

Polygenic Risk ◽

Independent Population

The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume, and signature 2 with increased striatal volume, could be replicated in an independent schizophrenia sample, and investigate whether expression of these signatures can be detected at the population-level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (n=347; age 16-57 years) for replication of imaging signatures, and then examined two independent population-level datasets: Philadelphia Neurodevelopmental Cohort [PNC; n=359 typically developing (TD) and psychosis-spectrum symptoms (PS) youth] and UK Biobank (UKBB; n=836; age 44-50 years) adults. We quantified signature expression using support-vector machine learning, and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youth with PS than TD youth, whereas signature 2 frequency was similar. In both youth and adults, signature 1 had worse cognitive performance than signature 2. Compared to adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. We successfully replicate two neuroanatomical signatures of schizophrenia, and describe their prevalence in population-based samples of youth and adults. We further demonstrate distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

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Polygenic prediction of breast cancer: comparison of genetic predictors and implications for screening

10.1101/448597 ◽

2018 ◽

Author(s):

Kristi Läll ◽

Maarja Lepamets ◽

Marili Palover ◽

Tõnu Esko ◽

Andres Metspalu ◽

...

Keyword(s):

Breast Cancer ◽

Genetic Risk ◽

Odds Ratio ◽

Population Based ◽

Risk Scores ◽

Full Potential ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Risk Scores ◽

The Uk

AbstractBackgroundPublished genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aims to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.MethodsFour different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) are compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors are studied in both cohorts.ResultsThe metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) has the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponds to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10-135) in the UK Biobank and accounting for family history marginally attenuates the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 9.1*10-129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% is 4.2 (95% CI 2.8 to 6.2, p = 8.1*10-13). The different GRSs are only moderately correlated with each other and are associated with different known predictors of BC. The classification of genetic risk for the same individual may vary considerably depending on the chosen GRS.ConclusionsWe have shown that metaGRS2 that combines on the effects of more than 900 SNPs provides best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

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Abstract P879: Differences in Statistical Performance of Polygenic Risk Scores for Cardiovascular Disease Across Different Race/Ethnicities

Stroke ◽

10.1161/str.52.suppl_1.p879 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Julian N Acosta ◽

Cameron Both ◽

Natalia Szejko ◽

Stacy Brown ◽

Kevin N Sheth ◽

...

Keyword(s):

Cardiovascular Disease ◽

Logistic Regression ◽

Genetic Risk ◽

Regression Models ◽

Risk Scores ◽

Uk Biobank ◽

Polygenic Risk ◽

Logistic Regression Models ◽

The Uk ◽

Significant Health

Introduction: Genome-wide association studies have identified numerous genetic risk variants for stroke and myocardial infarction (MI) in Europeans. However, the limited applicability of these results to non-Europeans due to racial/ethnic differences in the genetic architecture of cardiovascular disease (CVD), coupled with the limited availability of genomic data in non-Europeans, may create significant health disparities now that genomic-based precision medicine is a reality. We tested the hypothesis that the performance of polygenic risk scores (PRS) for CVD differ in Europeans versus non-Europeans. Methods: We conducted a nested study within the UK Biobank, a prospective, population-based study that enrolled ~500,000 participants across the UK. For this study, we identified self-reported black participants and randomly matched them 1:1 by age and sex with white participants. We created a PRS using previously discovered loci for stroke and MI. We then tested whether this PRS representing the aggregate polygenic susceptibility to CVD yielded similar precision in black versus white participants in logistic regression models. Results: Of the 502,536 participants enrolled in the UK Biobank, 8,061 were self-reported blacks, with 7,644 having available data for our analyses. We randomly matched these participants with white individuals, leading to a total sample size of 15,288 (mean age 51.9 [SD 8.1], female 8,722 [57%]). The total number of events was 741 overall, with 363 happening in blacks and 378 happening in whites. In logistic regression models including age, sex, and 5 principal components, the statistical precision (e.g. narrower confidence intervals) for the PRS was substantially higher for whites (OR 1.22, 95%CI 1.08 - 1.37; p<0.0001) compared to blacks (OR 1.24, 95%CI 1.05-1.47; p=0.01). Secondary analyses using genetically-determined ancestry yielded similar results. Conclusion: Because CVD-related PRSs are derived mainly using genetic risk factors identified in populations of European ancestry, their statistical performance is lower in non-European populations. This asymmetry can lead to significant health disparities now that these tools are being evaluated in multiple precision medicine approaches.

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Improving prediction of atrial fibrillation: the impact of polygenic risk scores over conventional risk factors amongst 270,000 individuals in UK Biobank

European Heart Journal ◽

10.1093/ehjci/ehaa946.0491 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

A Von Ende ◽

B Casadei ◽

J.C Hopewell

Keyword(s):

Risk Factors ◽

Atrial Fibrillation ◽

Risk Score ◽

Genetic Risk ◽

Genetic Information ◽

Risk Scores ◽

Funding Source ◽

Uk Biobank ◽

Polygenic Risk ◽

Conventional Risk Factors

Abstract Background Previous studies have suggested only modest benefits of adding genetic information to conventional risk factors for prediction of atrial fibrillation (AF). However, these studies have been based on limited numbers of AF cases and pre-date recent AF genetic discoveries. Purpose To examine the independent relevance of common genetic risk factors over and above established non-genetic risk factors for predicting AF amongst 270,000 participants from UK Biobank, and to determine potential clinical utility. Methods UK Biobank (UKB) is a large prospective study of over 500,000 British individuals aged 40 to 69 years at recruitment. Incident AF was ascertained using hospital episode statistics and death registry data. The CHARGE-AF score, which combines the relevance of age, height, weight, blood pressure, use of antihypertensives, diabetes, heart failure, and myocardial infarction (MI) was used to estimate 5-year risk of AF at baseline. A polygenic risk score (PRS) was constructed based on 142 independent variants previously associated with AF in a genome-wide meta-analysis of 60,620 AF cases from the AFGen Consortium, weighted by their published effect sizes. A total of 270,254 individuals were analysed after exclusions for genetic QC, non-White British ancestry, and prevalent AF. Cox proportional hazard models were used to estimate associations between risk scores (based on standard deviation [SD] units) and incident AF. Standard methods were used to assess predictive value. Results During a median follow-up of 8.1 years, 12,407 incident AF cases were identified. The CHARGE-AF risk score strongly predicted incident AF in UK Biobank, and was associated with a ∼3-fold higher risk of AF per SD (Hazard ratio [HR]=2.88; 95% CI: 2.82–2.94). The PRS was associated with a 54% higher risk of AF per SD (HR=1.54; 95% CI: 1.51–1.57). The independent impact of the PRS, after adjusting for the CHARGE-AF score, was unchanged and remained strongly predictive (HR=1.57, 95% CI: 1.54–1.60), with participants in the upper tertile of the PRS having more than a 2.5-fold higher risk (HR=2.59, 95% CI: 2.47–2.71) when compared with those in the lower tertile. The addition of the PRS improved the C-statistic from 0.758 (CHARGE-AF alone) to 0.783 (Δ=0.025) and correctly reclassified 8.7% of cases and 2.6% of controls at 5 years. Both non-genetic and genetic risk scores were well-calibrated in the UK Biobank participants, and sensitivity of the results to alternative PRS selection approaches and age at risk were also examined. Conclusion In a large prospective cohort, genetic determinants of AF were independent of conventional risk factors and significantly improved prediction over a well-validated clinical risk algorithm. This illustrates the potential added benefit of genetic information to identify higher-risk individuals who may benefit from earlier monitoring and personalised risk management strategies. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): British Heart Foundation

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Dissection of Major Depressive Disorder using polygenic risk scores for Schizophrenia in two independent cohort

10.1101/054973 ◽

2016 ◽

Author(s):

HC Whalley ◽

MJ Adams ◽

LS Hall ◽

T-K Clarke ◽

AM Fernandez-Pujals ◽

...

Keyword(s):

Major Depressive Disorder ◽

Psychological Distress ◽

Depressive Disorder ◽

Genetic Risk ◽

Large Population ◽

Case Control ◽

Risk Scores ◽

Uk Biobank ◽

Major Depressive ◽

Polygenic Risk

AbstractMajor depressive disorder (MDD) is known for its substantial clinical and suspected causal heterogeneity. It is characterised by low mood, psychomotor slowing, and increased levels of the personality trait neuroticism; factors which are also associated with schizophrenia (SCZ). It is possible that some cases of MDD may have a substantial genetic loading for SCZ. A sign of the presence of SCZ-like MDD sub-groups would be indicated by an interaction between MDD status and polygenic risk of SCZ on cognitive, personality and mood measures. In the current study, we hypothesised that higher SCZ-polygenic risk would define larger MDD case-control differences in cognitive ability, and smaller differences in distress and neuroticism. Polygenic risk scores (PGRS) for SCZ and their association with cognitive variables, neuroticism, mood, and psychological distress were estimated in a large population-based cohort (Generation Scotland: Scottish Family Health Study, GS:SFHS). Individuals were divided into those with, and without, depression (n=2587 & n=16,764 respectively) to test whether there was an interaction between MDD status and schizophrenia risk. Replication was sought in UK Biobank (n=33,525). In both GS:SFHS and UK Biobank we found significant interactions between SCZ-PGRS and MDD status for measures of psychological distress and neuroticism. In both cohorts there was a reduction of case-control differences on a background of higher genetic risk of SCZ. These findings suggest that depression on a background of high genetic risk for SCZ may show attenuated associations with distress and neuroticism. This may represent a causally distinct form of MDD more closely related to SCZ.

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Associations Between Major Psychiatric Disorder Polygenic Risk Scores and Blood-Based Markers in UK Biobank

10.1101/2020.11.06.20227066 ◽

2020 ◽

Author(s):

Michael D.E. Sewell ◽

Xueyi Shen ◽

Lorena Jiménez-Sánchez ◽

Amelia J. Edmondson-Stait ◽

Claire Green ◽

...

Keyword(s):

Genetic Risk ◽

Smoking Status ◽

Hdl Cholesterol ◽

Risk Scores ◽

Uk Biobank ◽

Linear Regression Models ◽

Polygenic Risk ◽

Bbb Permeability ◽

The Uk ◽

Peripheral Markers

AbstractBackgroundMajor depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BD) have both shared and discrete genetic risk factors and abnormalities in blood-based measures of inflammation and blood-brain barrier (BBB) permeability. The relationships between such genetic architectures and blood-based markers are however unclear. We investigated relationships between polygenic risk scores for these disorders and peripheral biomarkers in the UK Biobank cohort.MethodsWe calculated polygenic risk scores (PRS) for samples of n = 367,329 (MDD PRS), n = 366,465 (SCZ PRS), and n = 366,383 (BD PRS) individuals from the UK Biobank cohort. We examined associations between each disorder PRS and 62 blood markers, using two generalized linear regression models: ‘minimally adjusted’ controlling for variables including age and sex, and ‘fully adjusted’ including additional lifestyle covariates such as alcohol and smoking status.Results12/62, 13/62 and 9/62 peripheral markers were significantly associated with MDD, SCZ and BD PRS respectively for both models. Most associations were disorder PRS-specific, including several immune-related markers for MDD and SCZ. We also identified several BBB-permeable marker associations, including vitamin D for all three disorder PRS, IGF-1 and triglycerides for MDD PRS, testosterone for SCZ PRS, and HDL cholesterol for BD PRS.ConclusionsThis study suggests that MDD, SCZ and BD have shared and distinct peripheral markers associated with disorder-specific genetic risk. The results implicate BBB permeability disruptions in all three disorders and inflammatory dysfunction in MDD and SCZ, and enrich our understanding of potential underlying pathophysiological mechanisms in major psychiatric disorders.

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Subthreshold psychosis symptoms associated with molecular genetic risk in a population-based cohort: Findings from Generation Scotland

10.1101/821041 ◽

2019 ◽

Author(s):

A.R. Docherty ◽

Andrey A. Shabalin ◽

Daniel E. Adkins ◽

Frank Mann ◽

Robert F. Krueger ◽

...

Keyword(s):

Sex Differences ◽

General Population ◽

Genetic Risk ◽

Molecular Genetic ◽

Population Based ◽

Risk Scores ◽

Factor Scores ◽

Polygenic Risk ◽

Genome Wide ◽

Genetic Risks

AbstractImportanceSubthreshold psychosis symptoms in the general population may be associated with genetic risk for schizophrenia. In this analysis, empirically-derived symptom factor scores led to a detection of significant and robust polygenic signal.ObjectiveThis study sought to optimize genetic association with data-driven symptom factor scores, accounting for cohort factor structure and sex differences.DesignEFA-derived symptom factor scores were regressed onto PRS for schizophrenia in models accounting for age and genetic ancestry principal components. Follow-up examination of symptom factor score associations with other related genetic risks included ADHD, autism, bipolar disorder, major depression, and neuroticism.ParticipantsThis study examined the newly expanded symptom dataset from the Northern European ancestry cohort, Generation Scotland: Scottish Family Health Study (N = 9,105 individuals 18-65 years of age) comprising common variant and subthreshold psychosis symptom data. A total of 5,391 females and 3,713 males with age M[SD] = 45.2 [13] were included in the final analyses.Main Outcome and MeasureSubthreshold psychosis symptoms were measured using the Schizotypal Personality Questionnaire-Brief (SPQ-B). Primary phenotypic factor scores and genome-wide polygenic risk scores (PRS) reflected weighted sum scores and were examined as continuous measures. Polygenic risk scores were calculated from genome-wide association summary statistics using 7,358,674 imputed common genetic variants passing quality control.ResultsIn males, symptom factor scores were positively associated with polygenic risk for schizophrenia alone and implicated primarily interpersonal/negative symptoms. In females, symptom factor scores were positively associated with polygenic risks for ADHD and autism but not schizophrenia. Scores were robustly associated with genetic risk for neuroticism across both males and females.Conclusions and RelevanceThis study detected a significant association of subthreshold psychosis symptoms with genetic risk for schizophrenia and neuroticism in a population-based sample. Furthermore, important sex differences suggest a need for better understanding of schizophrenia risk assessment in females.Key PointsQuestionWhat molecular genetic risks are associated with subthreshold psychosis symptoms in the general population?FindingsIn a large population-based cohort (N = 9,084), significant associations of polygenic risks with symptoms were observed. Symptoms were associated with genetic risk for schizophrenia in males, for ADHD and autism spectrum disorder in females, and for neuroticism across both males and females.MeaningAssociations of genetic risk with symptoms in the general population are highly significant and suggest important sex differences.

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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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