The GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring.

MOTIVATION: Polygenic scoring is an approach for estimating an individual's likelihood of a given outcome. Polygenic scores are typically calculated from genome-wide association study (GWAS) summary statistics and individual-level genotype data for the target sample. Going from genotype to interpretable polygenic scores involves many steps and there are many methods available, limiting the accessibility of polygenic scores for research and clinical application. Additional challenges exist for studies in ancestrally diverse populations. We have implemented the leading polygenic scoring methodologies within an easy-to-use pipeline called GenoPred. RESULTS: Here, we present the GenoPred pipeline, an easy-to-use, high-performance, reference-standardized, and reproducible workflow for polygenic scoring. It requires minimal inputs and offers various configuration options to cater to a range of use cases. GenoPred implements a comprehensive set of analyses, including genotype and GWAS quality control, target sample ancestry inference, polygenic score file generation using a range of leading methods, and target sample scoring. GenoPred standardizes the polygenic scoring process using reference genetic data, providing interpretable polygenic scores. The pipeline is applicable to GWAS and targets data from any population within the reference, facilitating studies of diverse ancestry. GenoPred is a Snakemake pipeline with associated Conda software environments, ensuring reproducibility. We apply the pipeline to UK Biobank data demonstrating the pipeline's simplicity, efficiency, and performance. The GenoPred pipeline provides a novel resource for polygenic scoring, integrating a range of complex processes within an easy-to-use framework. GenoPred widens access to the leading polygenic scoring methodology and their application to studies of diverse ancestry. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at https://github.com/opain/GenoPred.

Genome-wide meta-analysis of ascertainment and symptom structures of major depression in case-enriched and community cohorts.

BACKGROUND: Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. METHODS: We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. RESULTS: The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms). CONCLUSION: The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Towards a consensus roadmap for a new diagnostic framework for mental disorders.

Current nosology claims to separate mental disorders into distinct categories that do not overlap with each other. This nosological separation is not based on underlying pathophysiology but on convention-based clustering of qualitative symptoms of disorders which are typically measured subjectively. Yet, clinical heterogeneity and diagnostic overlap in disease symptoms and dimensions within and across different diagnostic categories of mental disorders is huge. While diagnostic categories provide the basis for general clinical management, they do not describe the underlying neurobiology that gives rise to individual symptomatic presentations. The ability to incorporate neurobiology into the diagnostic framework and to stratify patients accordingly will be a critical step forward for the development of new treatments for mental disorders. Furthermore, it will also allow physicians to provide patients with a better understanding of their illness's complexities and management. To realize this ambition, a paradigm shift is needed to build an understanding of how neuropsychiatric conditions can be defined more precisely using quantitative (multimodal) biological processes and markers and thus to significantly improve treatment success. The ECNP New Frontiers Meeting 2024 set out to develop a consensus roadmap for building a new diagnostic framework for mental disorders by discussing its rationale, outlook, and consequences with all stakeholders involved. This framework would instantiate a set of principles and procedures by which research could continuously improve precision diagnostics while moving away from traditional nosology. In this meeting report, the speakers' summaries from their presentations are combined to address three key elements for generating such a roadmap, namely, the application of innovative technologies, understanding the biology of mental illness, and translating biological understanding into new approaches. In general, the meeting indicated a crucial need for a biology-informed framework to establish more precise diagnosis and treatment for mental disorders to facilitate bringing the right treatment to the right patient at the right time.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

Ancestry-aligned polygenic scores combined with conventional risk factors improve prediction of cardiometabolic outcomes in African populations.

BACKGROUND: Cardiovascular diseases (CVD) are a major health concern in Africa. Improved identification and treatment of high-risk individuals can reduce adverse health outcomes. Current CVD risk calculators are largely unvalidated in African populations and overlook genetic factors. Polygenic scores (PGS) can enhance risk prediction by measuring genetic susceptibility to CVD, but their effectiveness in genetically diverse populations is limited by a European-ancestry bias. To address this, we developed models integrating genetic data and conventional risk factors to assess the risk of developing cardiometabolic outcomes in African populations. METHODS: We used summary statistics from a genome-wide association meta-analysis (n = 14,126) in African populations to derive novel genome-wide PGS for 14 cardiometabolic traits in an independent African target sample (Africa Wits-INDEPTH Partnership for Genomic Research (AWI-Gen), n = 10,603). Regression analyses assessed relationships between each PGS and corresponding cardiometabolic trait, and seven CVD outcomes (CVD, heart attack, stroke, diabetes mellitus, dyslipidaemia, hypertension, and obesity). The predictive utility of the genetic data was evaluated using elastic net models containing multiple PGS (MultiPGS) and reference-projected principal components of ancestry (PPCs). An integrated risk prediction model incorporating genetic and conventional risk factors was developed. Nested cross-validation was used when deriving elastic net models to enhance generalisability. RESULTS: Our African-specific PGS displayed significant but variable within- and cross- trait prediction (max.R2 = 6.8%, p = 1.86 × 10-173). Significantly associated PGS with dyslipidaemia included the PGS for total cholesterol (logOR = 0.210, SE = 0.022, p = 2.18 × 10-21) and low-density lipoprotein (logOR = - 0.141, SE = 0.022, p = 1.30 × 10-20); with hypertension, the systolic blood pressure PGS (logOR = 0.150, SE = 0.045, p = 8.34 × 10-4); and multiple PGS associated with obesity: body mass index (max. logOR = 0.131, SE = 0.031, p = 2.22 × 10-5), hip circumference (logOR = 0.122, SE = 0.029, p = 2.28 × 10-5), waist circumference (logOR = 0.013, SE = 0.098, p = 8.13 × 10-4) and weight (logOR = 0.103, SE = 0.029, p = 4.89 × 10-5). Elastic net models incorporating MultiPGS and PPCs significantly improved prediction over MultiPGS alone. Models including genetic data and conventional risk factors were more predictive than conventional risk models alone (dyslipidaemia: R2 increase = 2.6%, p = 4.45 × 10-12; hypertension: R2 increase = 2.6%, p = 2.37 × 10-13; obesity: R2 increase = 5.5%, 1.33 × 10-34). CONCLUSIONS: In African populations, CVD and associated cardiometabolic trait prediction models can be improved by incorporating ancestry-aligned PGS and accounting for ancestry. Combining PGS with conventional risk factors further enhances prediction over traditional models based on conventional factors. Incorporating data from target populations can improve the generalisability of international predictive models for CVD and associated traits in African populations.

Metabolic activity of CYP2C19 and CYP2D6 on antidepressant response from 13 clinical studies using genotype imputation: a meta-analysis.

Cytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. CYP2D6 structural variants cannot be imputed from genotype data, limiting the determination of metabolic phenotypes, and precluding testing for association with response. The association of CYP2C19 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR = 1.46, 95% CI [1.03, 2.06], p = 0.033, heterogeneity I2 = 0%, subgroup difference p = 0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression measures, and diverse ancestry studies, would more fully capture the influence of metabolic phenotypes.

Prevalence and correlates of common mental disorders among participants of the Uganda Genome Resource: Opportunities for psychiatric genetics research.

Genetics research has potential to alleviate the burden of mental disorders in low- and middle-income-countries through identification of new mechanistic pathways which can lead to efficacious drugs or new drug targets. However, there is currently limited genetics data from Africa. The Uganda Genome Resource provides opportunity for psychiatric genetics research among underrepresented people from Africa. We aimed at determining the prevalence and correlates of major depressive disorder (MDD), suicidality, post-traumatic stress disorder (PTSD), alcohol abuse, generalised anxiety disorder (GAD) and probable attention-deficit hyperactivity disorder (ADHD) among participants of the Uganda Genome Resource. Standardised tools assessed for each mental disorder. Prevalence of each disorder was calculated with 95% confidence intervals. Multivariate logistic regression models evaluated the association between each mental disorder and associated demographic and clinical factors. Among 985 participants, prevalence of the disorders were: current MDD 19.3%, life-time MDD 23.3%, suicidality 10.6%, PTSD 3.1%, alcohol abuse 5.7%, GAD 12.9% and probable ADHD 9.2%. This is the first study to determine the prevalence of probable ADHD among adult Ugandans from a general population. We found significant association between sex and alcohol abuse (adjusted odds ratio [AOR] = 0.26 [0.14,0.45], p < 0.001) and GAD (AOR = 1.78 [1.09,2.49], p = 0.019) respectively. We also found significant association between body mass index and suicidality (AOR = 0.85 [0.73,0.99], p = 0.041), alcohol abuse (AOR = 0.86 [0.78,0.94], p = 0.003) and GAD (AOR = 0.93 [0.87,0.98], p = 0.008) respectively. We also found a significant association between high blood pressure and life-time MDD (AOR = 2.87 [1.08,7.66], p = 0.035) and probable ADHD (AOR = 1.99 [1.00,3.97], p = 0.050) respectively. We also found a statistically significant association between tobacco smoking and alcohol abuse (AOR = 3.2 [1.56,6.67], p = 0.002). We also found ever been married to be a risk factor for probable ADHD (AOR = 2.12 [0.88,5.14], p = 0.049). The Uganda Genome Resource presents opportunity for psychiatric genetics research among underrepresented people from Africa.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

The impact of major depressive disorder on glycaemic control in type 2 diabetes: a longitudinal cohort study using UK Biobank primary care records.

BACKGROUND: This study evaluates longitudinal associations between glycaemic control, measured by mean and within-patient variability of glycated haemaglobin (HbA1c) levels, and major depressive disorder (MDD) in individuals with type 2 diabetes (T2D), focusing on the timings of these diagnoses. METHODS: In UK Biobank, T2D was defined using self-report and linked health outcome data, then validated using polygenic scores. Repeated HbA1c measurements (mmol/mol) over the 10 years following T2D diagnosis were outcomes in mixed effects models, with disease duration included using restricted cubic splines. Four MDD exposures were considered: MDD diagnosis prior to T2D diagnosis (pre-T2D MDD), time between pre-T2D MDD diagnosis and T2D, new MDD diagnosis during follow-up (post-T2D MDD) and time since post-T2D MDD diagnosis. Models with and without covariate adjustment were considered. RESULTS: T2D diagnostic criteria were robustly associated with T2D polygenic scores. In 11,837 T2D cases (6.9 years median follow-up), pre-T2D MDD was associated with a 0.92 increase in HbA1c (95% CI: [0.00, 1.84]), but earlier pre-T2D MDD diagnosis correlated with lower HbA1c. These pre-T2D MDD effects became non-significant after covariate adjustment. Post-T2D MDD individuals demonstrated increasing HbA1c with years since MDD diagnosis ( ß = 0.51 , 95% CI: [0.17, 0.86]). Retrospectively, across study follow-up, within-patient variability in HbA1c was 1.16 (95% CI: 1.13-1.19) times higher in post-T2D MDD individuals. CONCLUSIONS: The timing of MDD diagnosis is important for understanding glycaemic control in T2D. Poorer control was observed in MDD diagnosed post-T2D, highlighting the importance of depression screening in T2D, and closer monitoring for individuals who develop MDD after T2D.

The duration of lithium use and biological ageing: telomere length, frailty, metabolomic age and all-cause mortality.

Lithium is an established first-line treatment for bipolar disorder. Beyond its therapeutic effect as a mood stabiliser, lithium exhibits potential anti-ageing effects. This study aimed to examine the relationship between the duration of lithium use, biological ageing and mortality. The UK Biobank is an observational study of middle-aged and older adults. We tested associations between the duration of lithium use (number of prescriptions, total duration of use and duration of the first prescription period) and telomere length, frailty, metabolomic age (MileAge) delta, pulse rate and all-cause mortality. Five hundred ninety-one individuals (mean age = 57.49 years; 55% females) had been prescribed lithium. There was no evidence that the number of prescriptions (ß = - 0.022, 95% CI - 0.081 to 0.037, p = 0.47), the total duration of use (ß = - 0.005, 95% CI - 0.023 to 0.013, p = 0.57) or the duration of the first prescription period (ß = - 0.018, 95% CI - 0.051 to 0.015, p = 0.29) correlated with telomere length. There was also no evidence that the duration of lithium use correlated with frailty or MileAge delta. However, a higher prescription count and a longer duration of use was associated with a lower pulse rate. The duration of lithium use did not predict all-cause mortality. We observed no evidence of associations between the duration of lithium use and biological ageing markers, including telomere length. Our findings suggest that the potential anti-ageing effects of lithium do not differ by the duration of use.

Polygenic risk scores for mood and related disorders and environmental factors: Interaction effects on wellbeing in the UK biobank.

Mood disorders have a genetic and environmental component and interactions (GxE) on the risk of psychiatric diseases have been investigated. The same GxE interactions may affect wellbeing measures, which go beyond categorical diagnoses and reflect the health-disease continuum. We evaluated GxE effects in the UK Biobank, considering as outcomes subjective wellbeing (feeling good and functioning well) and objective measures (education and income). We estimated the polygenic risk scores (PRSs) of major depressive disorder, bipolar disorder, schizophrenia, and attention deficit hyperactivity disorder. Stressful/traumatic events during adulthood or childhood were considered as E variables, as well as social support. The addition of the PRSxE interaction to PRS and E variables was tested in linear or multinomial regression models, adjusting for confounders. We included 33 k-380 k participants, depending on the variables considered. Most PRSs and E factors showed additive effects on outcomes, with effect sizes generally 3-5 times larger for E variables than PRSs. We found some interaction effects, particularly when considering recent stress, history of a long illness/disability/infirmity, and social support. Higher PRSs increased the negative effects of stress on wellbeing, but they also increased the positive effects of social support, with interaction effects particularly for the outcomes health satisfaction, loneliness, and income (p < Bonferroni corrected threshold of 1.92e-4). PRSxE terms usually added ∼0.01-0.02% variance explained to the corresponding additive model. PRSxE effects on wellbeing involve both positive and negative E factors. Despite small variance explained at the population level, preventive/therapeutic interventions that modify E factors could be beneficial at the individual level.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Background: Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD). Aims: Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis. Methods: Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses. Results: While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD. Conclusions: We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease.

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.

Stratifying early-onset emotional disorders: using genetics to assess persistence in young people of European and South Asian ancestry.

BACKGROUND: Depression and anxiety are the most common mental health problems in young people. Currently, clinicians are advised to wait before initiating treatment for young people with these disorders as many spontaneously remit. However, others develop recurrent disorder but this subgroup cannot be identified at the outset. We examined whether psychiatric polygenic scores (PGS) could help inform stratification efforts to predict those at higher risk of recurrence. METHODS: Probable emotional disorder was examined in two UK population cohorts using the emotional symptoms subscale of the Strengths and Difficulties Questionnaire (SDQ). Those with emotional disorder at two or more time points between ages 5 and 25 years were classed as 'recurrent emotional disorder' (n = 1,643) and those with emotional disorder at one time point as having 'single episode emotional disorder' (n = 1,435, controls n = 8,715). We first examined the relationship between psychiatric PGS and emotional disorders in childhood and adolescence. Second, we tested whether psychiatric PGS added to predictor variables of known association with emotional disorder (neurodevelopmental comorbidity, special educational needs, family history of depression and socioeconomic status) when discriminating between single-episode and recurrent emotional disorder. Analyses were conducted separately in individuals of European and South Asian ancestry. RESULTS: Probable emotional disorder was associated with higher PGS for major depressive disorder (MDD), anxiety, broad depression, ADHD and autism spectrum disorder (ASD) in those of European ancestry. Higher MDD and broad depression PGS were associated with emotional disorder in people of South Asian ancestry. Recurrent, compared to single-episode, emotional disorder was associated with ASD and parental psychiatric history. PGS were not associated with episode recurrence, and PGS did not improve discrimination of recurrence when combined with clinical predictors. CONCLUSIONS: Our findings do not support the use of PGS as a tool to assess the likelihood of recurrence in young people experiencing their first episode of emotional disorder.

Investigating the role of the relaxin-3/RXFP3 system in neuropsychiatric disorders and metabolic phenotypes: A candidate gene approach.

The relaxin-3/RXFP3 system has been implicated in the modulation of depressive- and anxiety-like behaviour in the animal literature; however, there is a lack of human studies investigating this signalling system. We seek to bridge this gap by leveraging the large UK Biobank study to retrospectively assess genetic risk variants linked with this neuropeptidergic system. Specifically, we conducted a candidate gene study in the UK Biobank to test for potential associations between a set of functional, candidate single nucleotide polymorphisms (SNPs) pertinent to relaxin-3 signalling, determined using in silico tools, and several outcomes, including depression, atypical depression, anxiety and metabolic syndrome. For each outcome, we used several rigorously defined phenotypes, culminating in subsample sizes ranging from 85,881 to 386,769 participants. Across all outcomes, there were no associations between any candidate SNP and any outcome phenotype, following corrections for multiple testing burden. Regression models comprising several SNPs per relevant candidate gene as exploratory variables further exhibited no prediction of outcome. Our findings corroborate conclusions from previous literature about the limitations of candidate gene approaches, even when based on firm biological hypotheses, in the domain of genetic research for neuropsychiatric disorders.

Estimating the impact of transmitted and non-transmitted psychiatric and neurodevelopmental polygenic scores on youth emotional problems.

Anxiety and depression (emotional disorders) are familial and heritable, especially when onset is early. However, other cross-generational studies suggest transmission of youth emotional problems is explained by mainly environmental risks. We set out to test the contribution of parental non-transmitted genetic liability, as indexed by psychiatric/neurodevelopmental common polygenic liability, to youth emotional problems using a UK population-based cohort: the Millennium Cohort Study. European (N = 6328) and South Asian (N = 814) ancestries were included, as well as a subset with genomic data from both parents (European: N = 2809; South Asian: N = 254). We examined the association of transmitted (PGST) and non-transmitted polygenic scores (PGSNT) for anxiety, depression, bipolar disorder and neurodevelopmental disorders (attention-deficit/hyperactivity disorder [ADHD], autism spectrum disorder [ASD], schizophrenia) with youth emotional disorder and symptom scores, measured using the parent- and self-reported Strengths and Difficulties Questionnaire emotional subscale at 6 timepoints between ages 3-17 years. In the European sample, PGST for anxiety and depression, but not bipolar disorder, were associated with emotional disorder and symptom scores across all ages, except age 3, with strongest association in adolescence. ADHD and ASD PGST also showed association across ages 11-17 years. In the South Asian sample, evidence for associations between all PGST and outcome measures were weaker. There was weak evidence of association between PGSNT for anxiety and depression and age 17 symptom scores in the South Asian sample, but not in the European sample for any outcome. Overall, PGST for depression, anxiety, ADHD and ASD contributed to youth emotional problems, with stronger associations in adolescence. There was limited support for non-transmitted genetic effects: these findings do not support the hypothesis that parental polygenic psychiatric/neurodevelopmental liability confer risk to offspring emotional problems through non-transmitted rearing/nurture effects.

Telomere Length Associations With Clinical Diagnosis, Age, and Polygenic Risk Scores for Anxiety Disorder, Depression, and Bipolar Disorder.

Background: Accelerated biological aging might contribute to the lower life expectancy of individuals with mental disorders. The aim of this study was to characterize telomere length, a biological hallmark of aging, in individuals with mental disorders. Methods: The UK Biobank is a multicenter community-based observational study that recruited >500,000 middle-aged and older adults. Average leukocyte telomere length (telomere repeat copy number/single-copy gene ratio) was measured using quantitative polymerase chain reaction. Polygenic risk scores (PRSs) were calculated for individuals of European ancestry. We estimated differences in telomere length between individuals with anxiety disorder, depression, or bipolar disorder and people without mental disorders and examined associations with psychotropic medication use, age, and PRSs for these 3 disorders. Results: The analyses included up to 308,725 participants. Individuals with depression had shorter telomeres than people without mental disorders (ß = -0.011, 95% CI, -0.019 to -0.004, Bonferroni-corrected p = .027). Associations between bipolar disorder and telomere length differed by lithium use. There was limited evidence that individuals with an anxiety disorder had shorter telomeres. There was no evidence that associations between age and telomere length differed between individuals with and without these disorders. PRSs for depression, but not anxiety disorder or bipolar disorder, were associated with shorter telomeres (ß = -0.006, 95% CI, -0.010 to -0.003, Bonferroni-corrected p = .001). Conclusions: Differences in telomere length were observed primarily for individuals with depression or bipolar disorder and in individuals with a higher PRS for depression. There was no evidence that the association between age and telomere length differed between individuals with and without an anxiety disorder, depression, or bipolar disorder.

Polygenic risk scores of lithium response and treatment resistance in major depressive disorder.

Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate the genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) and non-TRD within ~4500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores (PRS) of antidepressants and lithium response for individuals with MDD and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1778 ECT-treated MDD cases, nearly all (94%) used antidepressants before their first ECT and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We did not observe a significant difference in the mean PRS of antidepressant response between TRD and non-TRD; however, we found that TRD cases had a significantly higher PRS of lithium response compared to non-TRD cases (OR = 1.10-1.12 under various definitions). The results support the evidence of heritable components in treatment-related phenotypes and highlight the overall genetic profile of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.

Genome-wide association study of esophageal squamous cell cancer identifies shared and distinct risk variants in African and Chinese populations.

Esophageal squamous cell carcinoma (ESCC) has a high disease burden in sub-Saharan Africa and has a very poor prognosis. Genome-wide association studies (GWASs) of ESCC in predominantly East Asian populations indicate a substantial genetic contribution to its etiology, but no genome-wide studies have been done in populations of African ancestry. Here, we report a GWAS in 1,686 African individuals with ESCC and 3,217 population-matched control individuals to investigate its genetic etiology. We identified a genome-wide-significant risk locus on chromosome 9 upstream of FAM120A (rs12379660, p = 4.58 × 10-8, odds ratio = 1.28, 95% confidence interval = 1.22-1.34), as well as a potential African-specific risk locus on chromosome 2 (rs142741123, p = 5.49 × 10-8) within MYO1B. FAM120A is a component of oxidative stress-induced survival signals, and the associated variants at the FAM120A locus co-localized with highly significant cis-eQTLs in FAM120AOS in both esophageal mucosa and esophageal muscularis tissue. A trans-ethnic meta-analysis was then performed with the African ESCC study and a Chinese ESCC study in a combined total of 3,699 ESCC-affected individuals and 5,918 control individuals, which identified three genome-wide-significant loci on chromosome 9 at FAM120A (rs12379660, pmeta = 9.36 × 10-10), chromosome 10 at PLCE1 (rs7099485, pmeta = 1.48 × 10-8), and chromosome 22 at CHEK2 (rs1033667, pmeta = 1.47 × 10-9). This indicates the existence of both shared and distinct genetic risk loci for ESCC in African and Asian populations. Our GWAS of ESCC conducted in a population of African ancestry indicates a substantial genetic contribution to ESCC risk in Africa.