Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N=61294-91644), we investigate whether the polygenic and residual variation of depressive symptoms are modulated by 25 a-priori selected covariate traits: 12 environmental variables, 5 biomarkers and polygenic risk scores for 8 mental health disorders. MRNMs provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. Of the 25 selected covariates, 11 significantly modulate depressive symptoms, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic score, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably non-zero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% CI [0.54,1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

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Genetic basis of falling risk susceptibility in the UK Biobank Study

Communications Biology ◽

10.1038/s42003-020-01256-x ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Katerina Trajanoska ◽

Lotta J. Seppala ◽

Carolina Medina-Gomez ◽

Yi-Hsiang Hsu ◽

Sirui Zhou ◽

...

Keyword(s):

Genetic Correlations ◽

Well Being ◽

Risk Scores ◽

Subjective Well Being ◽

Uk Biobank ◽

Mineral Density ◽

Polygenic Inheritance ◽

Intrinsic Factors ◽

A Genome ◽

The Uk

Abstract Both extrinsic and intrinsic factors predispose older people to fall. We performed a genome-wide association analysis to investigate how much of an individual’s fall susceptibility can be attributed to genetics in 89,076 cases and 362,103 controls from the UK Biobank Study. The analysis revealed a small, but significant SNP-based heritability (2.7%) and identified three novel fall-associated loci (Pcombined ≤ 5 × 10−8). Polygenic risk scores in two independent settings showed patterns of polygenic inheritance. Risk of falling had positive genetic correlations with fractures, identifying for the first time a pathway independent of bone mineral density. There were also positive genetic correlations with insomnia, neuroticism, depressive symptoms, and different medications. Negative genetic correlations were identified with muscle strength, intelligence and subjective well-being. Brain, and in particular cerebellum tissue, showed the highest gene expression enrichment for fall-associated variants. Overall, despite the highly heterogenic nature underlying fall risk, a proportion of the susceptibility can be attributed to genetics.

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Depressive symptoms and neuroticism-related traits are the main factors associated with wellbeing independent of the history of lifetime depression in the UK Biobank

Psychological Medicine ◽

10.1017/s003329172100502x ◽

2021 ◽

pp. 1-9

Author(s):

Chiara Fabbri ◽

Julian Mutz ◽

Cathryn M. Lewis ◽

Alessandro Serretti

Keyword(s):

Depressive Symptoms ◽

Risk Scores ◽

Depression Symptoms ◽

Uk Biobank ◽

Single Episode ◽

History Of ◽

Physical Diseases ◽

Self Harm ◽

The Uk

Abstract Background Wellbeing has a fundamental role in determining life expectancy and major depressive disorder (MDD) is one of the main modulating factors of wellbeing. This study evaluated the modulators of wellbeing in individuals with lifetime recurrent MDD (RMDD), single-episode MDD (SMDD) and no MDD in the UK Biobank. Methods Scores of happiness, meaningful life and satisfaction about functioning were condensed in a functioning-wellbeing score (FWS). We evaluated depression and anxiety characteristics, neuroticism-related traits, physical diseases, lifestyle and polygenic risk scores (PRSs) of psychiatric disorders. Other than individual predictors, we estimated the cumulative contribution to FWS of each group of predictors. We tested the indirect role of neuroticism on FWS through the modulation of depression manifestations using a mediation analysis. Results We identified 47 966, 21 117 and 207 423 individuals with lifetime RMDD, SMDD and no MDD, respectively. Depression symptoms and personality showed the largest impact on FWS (variance explained ~20%), particularly self-harm, worthlessness feelings during the worst depression, chronic depression, loneliness and neuroticism. Personality played a stronger role in SMDD. Anxiety characteristics showed a higher effect in SMDD and no MDD groups. Neuroticism played indirect effects through specific depressive symptoms that modulated FWS. Physical diseases and lifestyle explained only 4–5% of FWS variance. The PRS of MDD showed the largest effect on FWS compared to other PRSs. Conclusions This was the first study to comprehensively evaluate the predictors of wellbeing in relation to the history of MDD. The identified variables are important to identify individuals at risk and promote wellbeing.

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Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank

10.1101/519538 ◽

2019 ◽

Cited By ~ 2

Author(s):

Huanwei Wang ◽

Futao Zhang ◽

Jian Zeng ◽

Yang Wu ◽

Kathryn E. Kemper ◽

...

Keyword(s):

Environmental Factors ◽

Genetic Variants ◽

Phenotypic Variability ◽

Environmental Data ◽

European Ancestry ◽

Environment Interaction ◽

Uk Biobank ◽

Genotype By Environment ◽

A Genome ◽

The Uk

AbstractGenotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank, and identified 75 significant vQTLs with P<2.0×10−9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

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Genome-wide by environment interaction studies (GWEIS) of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland

10.1101/479691 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aleix Arnau-Soler ◽

Erin Macdonald-Dunlop ◽

Mark J. Adams ◽

Toni-Kim Clarke ◽

Donald J. MacIntyre ◽

...

Keyword(s):

Depressive Symptoms ◽

Association Studies ◽

Joint Effect ◽

Risk Scores ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Interaction Studies ◽

Genome Wide ◽

Generation Scotland

ABSTRACTStress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77×10-6). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53kb downstream PIWIL4; p = 4.95×10-9; total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46×10-8; dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00×10-8; dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77×10-6). Polygenic risk scores (PRS) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91×10-3). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions.

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Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments

Translational Psychiatry ◽

10.1038/s41398-021-01288-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Josefin Werme ◽

Sophie van der Sluis ◽

Danielle Posthuma ◽

Christiaan A. de Leeuw

Keyword(s):

Exploratory Study ◽

Predictive Ability ◽

Risk Scores ◽

Environment Interaction ◽

Uk Biobank ◽

Gene Sets ◽

Gene Environment ◽

Genome Wide ◽

The Uk ◽

Main Effects

AbstractGene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors (trauma, social support, drug use, physical health). We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset (N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.

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Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank

Science Advances ◽

10.1126/sciadv.aaw3538 ◽

2019 ◽

Vol 5 (8) ◽

pp. eaaw3538 ◽

Cited By ~ 31

Author(s):

Huanwei Wang ◽

Futao Zhang ◽

Jian Zeng ◽

Yang Wu ◽

Kathryn E. Kemper ◽

...

Keyword(s):

Environmental Factors ◽

Genetic Variants ◽

Phenotypic Variability ◽

Environmental Data ◽

European Ancestry ◽

Environment Interaction ◽

Uk Biobank ◽

Genotype By Environment ◽

A Genome ◽

The Uk

Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10−9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

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Estimating the effective sample size in association studies of quantitative traits

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab057 ◽

2021 ◽

Author(s):

Andrey Ziyatdinov ◽

Jihye Kim ◽

Dmitry Prokopenko ◽

Florian Privé ◽

Fabien Laporte ◽

...

Keyword(s):

Statistical Power ◽

Quantitative Traits ◽

Mixed Model ◽

Association Studies ◽

Effective Sample Size ◽

Environment Interaction ◽

Uk Biobank ◽

Gene Environment Interaction ◽

Gene Environment ◽

The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

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Advanced cardiometabolic & inflammatory markers for prediction of cardiovascular disease and cancer

European Heart Journal ◽

10.1093/ehjci/ehaa946.2921 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

D Radenkovic ◽

S.C Chawla ◽

G Botta ◽

A Boli ◽

M.B Banach ◽

...

Keyword(s):

Cardiovascular Disease ◽

Risk Score ◽

Cancer Incidence ◽

Risk Function ◽

Patient Characteristics ◽

Risk Scores ◽

Uk Biobank ◽

Training Set ◽

All Cause Mortality ◽

The Uk

Abstract The two leading causes of mortality worldwide are cardiovascular disease (CVD) and cancer. The annual total cost of CVD and cancer is an estimated $844.4 billion in the US and is projected to double by 2030. Thus, there has been an increased shift to preventive medicine to improve health outcomes and development of risk scores, which allow early identification of individuals at risk to target personalised interventions and prevent disease. Our aim was to define a Risk Score R(x) which, given the baseline characteristics of a given individual, outputs the relative risk for composite CVD, cancer incidence and all-cause mortality. A non-linear model was used to calculate risk scores based on the participants of the UK Biobank (= 502548). The model used parameters including patient characteristics (age, sex, ethnicity), baseline conditions, lifestyle factors of diet and physical activity, blood pressure, metabolic markers and advanced lipid variables, including ApoA and ApoB and lipoprotein(a), as input. The risk score was defined by normalising the risk function by a fixed value, the average risk of the training set. To fit the non-linear model >400,000 participants were used as training set and >45,000 participants were used as test set for validation. The exponent of risk function was represented as a multilayer neural network. This allowed capturing interdependent behaviour of covariates, training a single model for all outcomes, and preserving heterogeneity of the groups, which is in contrast to CoxPH models which are traditionally used in risk scores and require homogeneous groups. The model was trained over 60 epochs and predictive performance was determined by the C-index with standard errors and confidence intervals estimated with bootstrap sampling. By inputing the variables described, one can obtain personalised hazard ratios for 3 major outcomes of CVD, cancer and all-cause mortality. Therefore, an individual with a risk Score of e.g. 1.5, at any time he/she has 50% more chances than average of experiencing the corresponding event. The proposed model showed the following discrimination, for risk of CVD (C-index = 0.8006), cancer incidence (C-index = 0.6907), and all-cause mortality (C-index = 0.7770) on the validation set. The CVD model is particularly strong (C-index >0.8) and is an improvement on a previous CVD risk prediction model also based on classical risk factors with total cholesterol and HDL-c on the UK Biobank data (C-index = 0.7444) published last year (Welsh et al. 2019). Unlike classically-used CoxPH models, our model considers correlation of variables as shown by the table of the values of correlation in Figure 1. This is an accurate model that is based on the most comprehensive set of patient characteristics and biomarkers, allowing clinicians to identify multiple targets for improvement and practice active preventive cardiology in the era of precision medicine. Figure 1. Correlation of variables in the R(x) Funding Acknowledgement Type of funding source: None

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Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

Genes ◽

10.3390/genes12070991 ◽

2021 ◽

Vol 12 (7) ◽

pp. 991

Author(s):

Erik Widen ◽

Timothy G. Raben ◽

Louis Lello ◽

Stephen D. H. Hsu

Keyword(s):

Disease Risk ◽

Smoking Status ◽

Glycated Haemoglobin ◽

European Ancestry ◽

Risk Scores ◽

Common Disease ◽

Atherosclerotic Cardiovascular Disease ◽

Uk Biobank ◽

Lipoprotein A ◽

The Uk

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

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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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