Genetic studies in psychiatry have primarily focused on the effects of common genetic variants, but few have investigated the role of rare genetic variants, particularly for major depression. In ...order to explore the role of rare variants in the gap between estimates of single nucleotide polymorphism (SNP) heritability and twin study heritability, we examined the contribution of common and rare genetic variants to latent traits underlying psychiatric disorders using high-quality imputed genotype data from the UK Biobank. Using a pre-registered analysis, we used items from the UK Biobank Mental Health Questionnaire relevant to three psychiatric disorders: major depression (N = 134,463), bipolar disorder (N = 117,376) and schizophrenia (N = 130,013) and identified a general hierarchical factor for each that described participants' responses. We calculated participants' scores on these latent traits and conducted single-variant genetic association testing (MAF > 0.05%), gene-based burden testing and pathway association testing associations with these latent traits. We tested for enrichment of rare variants (MAF 0.05-1%) in genes that had been previously identified by common variant genome-wide association studies, and genes previously associated with Mendelian disorders having relevant symptoms. We found moderate genetic correlations between the latent traits in our study and case-control phenotypes in previous genome-wide association studies, and identified one common genetic variant (rs72657988, minor allele frequency = 8.23%, p = 1.01 × 10
) associated with the general factor of schizophrenia, but no other single variants, genes or pathways passed significance thresholds in this analysis, and we did not find enrichment in previously identified genes.
Major depressive disorder (MDD) is defined differently across genetic research studies and this may be a key source of heterogeneity. While previous literature highlights differences between minimal ...and strict phenotypes, the components contributing to this heterogeneity have not been identified. Using the cardinal symptoms (depressed mood/anhedonia) as a baseline, we build MDD phenotypes using five components-(1) five or more symptoms, (2) episode duration, (3) functional impairment, (4) episode persistence, and (5) episode recurrence-to determine the contributors to such heterogeneity. Thirty-two depression phenotypes which systematically incorporate different combinations of MDD components were created using the mental health questionnaire data within the UK Biobank. SNP-based heritabilities and genetic correlations with three previously defined major depression phenotypes were calculated (Psychiatric Genomics Consortium (PGC) defined depression, 23andMe self-reported depression and broad depression) and differences between estimates analysed. All phenotypes were heritable (h
range: 0.102-0.162) and showed substantial genetic correlations with other major depression phenotypes (Rg range: 0.651-0.895 (PGC); 0.652-0.837 (23andMe); 0.699-0.900 (broad depression)). The strongest effect on SNP-based heritability was from the requirement for five or more symptoms (1.4% average increase) and for a long episode duration (2.7% average decrease). No significant differences were noted between genetic correlations. While there is some variation, the two cardinal symptoms largely reflect the genetic aetiology of phenotypes incorporating more MDD components. These components may index severity, however, their impact on heterogeneity in genetic results is likely to be limited.
Major depression (MD) is often characterised as a categorical disorder; however, observational studies comparing sub-threshold and clinical depression suggest MD is continuous. Many of these studies ...do not explore the full continuum and are yet to consider genetics as a risk factor. This study sought to understand if polygenic risk for MD could provide insight into the continuous nature of depression.
Factor analysis on symptom-level data from the UK Biobank (
= 148 957) was used to derive continuous depression phenotypes which were tested for association with polygenic risk scores (PRS) for a categorical definition of MD (
= 119 692).
Confirmatory factor analysis showed a five-factor hierarchical model, incorporating 15 of the original 18 items taken from the PHQ-9, GAD-7 and subjective well-being questionnaires, produced good fit to the observed covariance matrix (CFI = 0.992, TLI = 0.99, RMSEA = 0.038, SRMR = 0.031). MD PRS associated with each factor score (standardised
range: 0.057-0.064) and the association remained when the sample was stratified into case- and control-only subsets. The case-only subset had an increased association compared to controls for all factors, shown via a significant interaction between lifetime MD diagnosis and MD PRS (
value range: 2.23 × 10
-3.94 × 10
).
An association between MD PRS and a continuous phenotype of depressive symptoms in case- and control-only subsets provides support against a purely categorical phenotype; indicating further insights into MD can be obtained when this within-group variation is considered. The stronger association within cases suggests this variation may be of particular importance.
Abstract Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates ...relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries ( N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.
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 variance components of depressive symptoms are modulated by 17 a priori selected covariate traits—12 environmental variables and 5 biomarkers. MRNMs, a mixed‐effects modelling approach, provide unbiased polygenic–covariate interaction estimates for a quantitative trait by controlling for outcome‐covariate correlations and residual–covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs—one for each covariate trait. Each MRNM had a fixed‐effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic–covariate and residual–covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, 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 component, to influence depressive symptoms. Only average sleep duration has a polygenic–covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: 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.
Progress towards understanding the aetiology of major depression is compromised by its clinical heterogeneity. The variety of contexts underlying the development of a major depressive episode may ...contribute to such heterogeneity.
To compare risk factor profiles for three subgroups of major depression according to episode context.
Using self-report questionnaires and administrative records from the UK Biobank, we characterised three contextual subgroups of major depression: postpartum depression (3581 cases), depression following diagnosis of a chronic disease (409 cases) and a more typical (named heterogeneous) major depression phenotype excluding the two other contexts (34 699 cases). Controls with the same exposure were also defined. We tested each subgroup for association with the polygenic risk scores (PRS) for major depression and with other risk factors previously associated with major depression (bipolar disorder PRS, neuroticism, reported trauma in childhood and adulthood, socioeconomic status, family history of depression, education).
Major depression PRS was associated with all subgroups, but postpartum depression cases had higher PRS than heterogeneous major depression cases (OR = 1.06, 95% CI 1.02-1.10). Relative to heterogeneous depression, postpartum depression was more weakly associated with adulthood trauma and neuroticism. Depression following diagnosis of a chronic disease had weaker association with neuroticism and reported trauma in adulthood and childhood relative to heterogeneous depression.
The observed differences in risk factor profiles according to the context of a major depressive episode help provide insight into the heterogeneity of depression. Future studies dissecting such heterogeneity could help reveal more refined aetiological insights.
The UK Biobank contains data with varying degrees of reliability and completeness for assessing depression. A third of participants completed a Mental Health Questionnaire (MHQ) containing the ...gold-standard Composite International Diagnostic Interview (CIDI) criteria for assessing mental health disorders.
To investigate whether multiple observations of depression from sources other than the MHQ can enhance the validity of major depressive disorder (MDD).
In participants who did not complete the MHQ, we calculated the number of other depression measures endorsed, for example from hospital episode statistics and interview data. We compared cases defined this way with CIDI-defined cases for several estimates: the variance explained by polygenic risk scores (PRS), area under the curve attributable to PRS, single nucleotide polymorphisms (SNPs)-based heritability and genetic correlations with summary statistics from the Psychiatric Genomics Consortium MDD genome-wide association study.
The strength of the genetic contribution increased with the number of measures endorsed. For example, SNP-based heritability increased from 7% in participants who endorsed only one measure of depression, to 21% in those who endorsed four or five measures of depression. The strength of the genetic contribution to cases defined by at least two measures approximated that for CIDI-defined cases. Most genetic correlations between UK Biobank and the Psychiatric Genomics Consortium MDD study exceeded 0.7, but there was variability between pairwise comparisons.
Multiple measures of depression can serve as a reliable approximation for case status where the CIDI measure is not available, indicating sample size can be optimised using the entire suite of UK Biobank data.
There are currently no disease-modifying treatments for Alzheimer's disease (AD), and an understanding of preclinical causal biomarkers to help target disease pathogenesis in the earliest phases ...remains elusive. Here, we investigated whether 19 metabolites previously associated with midlife cognition-a preclinical predictor of AD-translate to later clinical risk, using Mendelian randomization (MR) to tease out AD-specific causal relationships. Summary statistics from the largest genome-wide association studies (GWASs) for AD and metabolites were used to perform bidirectional univariable MR. Bayesian model averaging (BMA) was additionally performed to address high correlation between metabolites and identify metabolite combinations that may be on the AD causal pathway. Univariable MR indicated four extra-large high-density lipoproteins (XL.HDL) on the causal pathway to AD: free cholesterol (XL.HDL.FC: 95% CI = 0.78 to 0.94), total lipids (XL.HDL.L: 95% CI = 0.80 to 0.97), phospholipids (XL.HDL.PL: 95% CI = 0.81 to 0.97), and concentration of XL.HDL particles (95% CI = 0.79 to 0.96), significant at an adjusted
< 0.009. MR-BMA corroborated XL.HDL.FC to be among the top three causal metabolites, in addition to total cholesterol in XL.HDL (XL.HDL.C) and glycoprotein acetyls (GP). Both XL.HDL.C and GP demonstrated suggestive univariable evidence of causality (
< 0.05), and GP successfully replicated within an independent dataset. This study offers insight into the causal relationship between metabolites demonstrating association with midlife cognition and AD. It highlights GP in addition to several XL.HDLs-particularly XL.HDL.FC-as causal candidates warranting further investigation. As AD pathology is thought to develop decades prior to symptom onset, expanding on these findings could inform risk reduction strategies.
Hereditary factors, including single genetic variants and family history, can be used for targeting colorectal cancer (CRC) screening, but limited data exist on the impact of polygenic risk scores ...(PRS) on risk-based CRC screening.
Using longitudinal health and genomics data on 453,733 Finnish individuals including 8801 CRC cases, we estimated the impact of a genome-wide CRC PRS on CRC screening initiation age through population-calibrated incidence estimation over the life course in men and women.
Compared to the cumulative incidence of CRC at age 60 in Finland (the current age for starting screening in Finland), a comparable cumulative incidence was reached 5 and 11 years earlier in persons with high PRS (80-99% and >99%, respectively), while those with a low PRS (< 20%) reached comparable incidence 7 years later. The PRS was associated with increased risk of post-colonoscopy CRC after negative colonoscopy (hazard ratio 1.76 per PRS SD, 95% CI 1.54-2.01). Moreover, the PRS predicted colorectal adenoma incidence and improved incident CRC risk prediction over non-genetic risk factors.
Our findings demonstrate that a CRC PRS can be used for risk stratification of CRC, with further research needed to optimally integrate the PRS into risk-based screening.
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