Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits

The human VGLUT3-pT8I mutation elicits uneven striatal DA signaling, food or drug maladaptive consumption in male mice

Cholinergic striatal interneurons (ChIs) express the vesicular glutamate transporter 3 (VGLUT3) which allows them to regulate the striatal network with glutamate and acetylcholine (ACh). In addition, VGLUT3-dependent glutamate increases ACh vesicular stores through vesicular synergy. A missense polymorphism, VGLUT3-p.T8I, was identified in patients with substance use disorders (SUDs) and eating disorders (EDs). A mouse line was generated to understand the neurochemical and behavioral impact of the p.T8I variant. In VGLUT3T8I/T8I male mice, glutamate signaling was unchanged but vesicular synergy and ACh release were blunted. Mutant male mice exhibited a reduced DA release in the dorsomedial striatum but not in the dorsolateral striatum, facilitating habit formation and exacerbating maladaptive use of drug or food. Increasing ACh tone with donepezil reversed the self-starvation phenotype observed in VGLUT3T8I/T8I male mice. Our study suggests that unbalanced dopaminergic transmission in the dorsal striatum could be a common mechanism between SUDs and EDs.

Genetically Informed Study Highlights Income-Independent Effect of Schizophrenia Liability on Mental and Physical Health

BACKGROUND AND HYPOTHESIS

Individuals with schizophrenia (SCZ) suffer from comorbidities that substantially reduce their life expectancy. Socioeconomic inequalities could contribute to many of the negative health outcomes associated with SCZ.

STUDY DESIGN

We investigated genome-wide datasets related to SCZ (52 017 cases and 75 889 controls) from the Psychiatric Genomics Consortium, household income (HI; N = 361 687) from UK Biobank, and 2202 medical endpoints assessed in up to 342 499 FinnGen participants. A phenome-wide genetic correlation analysis of SCZ and HI was performed, also assessing whether SCZ genetic correlations were influenced by the HI effect on SCZ. Additionally, SCZ and HI direct effects on medical endpoints were estimated using multivariable Mendelian randomization (MR).

STUDY RESULTS

SCZ and HI showed overlapping genetic correlations with 70 traits (P < 2.89 × 10-5), including mental health, substance use, gastrointestinal illnesses, reproductive outcomes, liver diseases, respiratory problems, and musculoskeletal phenotypes. SCZ genetic correlations with these traits were not affected by the HI effect on SCZ. Considering Bonferroni multiple testing correction (P < 7.14 × 10-4), MR analysis indicated that SCZ and HI may affect medical abortion (SCZ OR = 1.07; HI OR = 0.78), panic disorder (SCZ OR = 1.20; HI OR = 0.60), personality disorders (SCZ OR = 1.31; HI OR = 0.67), substance use (SCZ OR = 1.2; HI OR = 0.68), and adjustment disorders (SCZ OR = 1.18; HI OR = 0.78). Multivariable MR analysis confirmed that SCZ effects on these outcomes were independent of HI.

CONCLUSIONS

The effect of SCZ genetic liability on mental and physical health may not be strongly affected by socioeconomic differences. This suggests that SCZ-specific strategies are needed to reduce negative health outcomes affecting patients and high-risk individuals.

Risk factors and their association network for young adults' suicidality: a cross-sectional study

BACKGROUND

Understanding the intricate influences of risk factors contributing to suicide among young individuals remains a challenge. The current study employed interpretable machine learning and network analysis to unravel critical suicide-associated factors in Chinese university students.

METHODS

A total of 68,071 students were recruited between Sep 2016 and Sep 2020 in China. Students reported their lifetime experiences with suicidal thoughts and behaviors, categorized as suicide ideation (SI), suicide plan (SP), and suicide attempt (SA). We assessed 36 suicide-associated factors including psychopathology, family environment, life events, and stigma. Local interpretations were provided using Shapley additive explanation (SHAP) interaction values, while a mixed graphical model facilitated a global understanding of their interplay.

RESULTS

Local explanations based on SHAP interaction values suggested that psychoticism and depression severity emerged as pivotal factors for SI, while paranoid ideation strongly correlated with SP and SA. In addition, childhood neglect significantly predicted SA. Regarding the mixed graphical model, a hierarchical structure emerged, suggesting that family factors preceded proximal psychopathological factors, with abuse and neglect retaining unique effects. Centrality indices derived from the network highlighted the importance of subjective socioeconomic status and education in connecting various risk factors.

CONCLUSIONS

The proximity of psychopathological factors to suicidality underscores their significance. The global structures of the network suggested that co-occurring factors influence suicidal behavior in a hierarchical manner. Therefore, prospective prevention strategies should take into account the hierarchical structure and unique trajectories of factors.

Attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD; also known as hyperkinetic disorder) is a common neurodevelopmental condition that affects children and adults worldwide. ADHD has a predominantly genetic aetiology that involves common and rare genetic variants. Some environmental correlates of the disorder have been discovered but causation has been difficult to establish. The heterogeneity of the condition is evident in the diverse presentation of symptoms and levels of impairment, the numerous co-occurring mental and physical conditions, the various domains of neurocognitive impairment, and extensive minor structural and functional brain differences. The diagnosis of ADHD is reliable and valid when evaluated with standard diagnostic criteria. Curative treatments for ADHD do not exist but evidence-based treatments substantially reduce symptoms and/or functional impairment. Medications are effective for core symptoms and are usually well tolerated. Some non-pharmacological treatments are valuable, especially for improving adaptive functioning. Clinical and neurobiological research is ongoing and could lead to the creation of personalized diagnostic and therapeutic approaches for this disorder.

Genetic and shared environmental factors explain the association between adolescent polysubstance use and high school noncompletion

OBJECTIVE

Examine the nature of the relationship between adolescent polysubstance use and high school noncompletion.

METHOD

Among a sample of 9,579 adult Australian twins (58.63% female, Mage = 30.59), we examined the association between the number of substances used in adolescence and high school noncompletion within a discordant twin design and bivariate twin analysis.

RESULTS

In individual-level models controlling for parental education, conduct disorder symptoms, childhood major depression, sex, zygosity, and cohort, each additional substance used in adolescence was associated with a 30% increase in the odds of high school noncompletion (OR = 1.30 [1.18, 1.42]). Discordant twin models found that the potentially causal effect of adolescent use on high school noncompletion was nonsignificant (OR = 1.19 [0.96, 1.47]). Follow-up bivariate twin models suggested genetic (35.4%, 95% CI [24.5%, 48.7%]) and shared environmental influences (27.8%, 95% CI [12.7%, 35.1%]) each contributed to the covariation in adolescent polysubstance use and early school dropout.

CONCLUSIONS

The association between polysubstance use and early school dropout was largely accounted for by genetic and shared environmental factors, with nonsignificant evidence for a potentially causal association. Future research should examine whether underlying shared risk factors reflect a general propensity for addiction, a broader externalizing liability, or a combination of the two. More evidence using finer measurement of substance use is needed to rule out a causal association between adolescent polysubstance use and high school noncompletion. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Distinguishing happiness and meaning in life from depressive symptoms: A GWAS-by-subtraction study in the UK Biobank

Hedonic (happiness) and eudaimonic (meaning in life) well-being are negatively related to depressive symptoms. Genetic variants play a role in this association, reflected in substantial genetic correlations. We investigated the overlap and differences between well-being and depressive symptoms, using results of Genome-Wide Association studies (GWAS) in UK Biobank. Subtracting GWAS summary statistics of depressive symptoms from those of happiness and meaning in life, we obtained GWASs of respectively "pure" happiness (neffective  = 216,497) and "pure" meaning (neffective  = 102,300). For both, we identified one genome-wide significant SNP (rs1078141 and rs79520962, respectively). After subtraction, SNP heritability reduced from 6.3% to 3.3% for pure happiness and from 6.2% to 4.2% for pure meaning. The genetic correlation between the well-being measures reduced from 0.78 to 0.65. Pure happiness and pure meaning became genetically unrelated to traits strongly associated with depressive symptoms, including loneliness, and psychiatric disorders. For other traits, including ADHD, educational attainment, and smoking, the genetic correlations of well-being versus pure well-being changed substantially. GWAS-by-subtraction allowed us to investigate the genetic variance of well-being unrelated to depressive symptoms. Genetic correlations with different traits led to new insights about this unique part of well-being. Our results can be used as a starting point to test causal relationships with other variables, and design future well-being interventions.

Socioeconomic status and severe mental disorders: a bidirectional multivariable Mendelian randomisation study

BACKGROUND

Despite the evidence supporting the relationship between socioeconomic status (SES) and severe mental disorders (SMD), the directionality of the associations between income or education and mental disorders is still poorly understood.

OBJECTIVE

To investigate the potential bidirectional causal relationships between genetic liability to the two main components of SES (income and educational attainment (EA)) on three SMD: schizophrenia, bipolar disorder (BD) and depression.

METHODS

We performed a bidirectional, two-sample univariable Mendelian randomisation (UVMR) and multivariable Mendelian randomisation (MVMR) study using SES phenotypes (income, n=397 751 and EA, n=766 345) and SMD (schizophrenia, n=127 906; BD, n=51 710 and depression, n=500 119) genome-wide association studies summary-statistics to dissect the potential direct associations of income and EA with SMD.

FINDINGS

UVMR showed that genetic liability to higher income was associated with decreased risk of schizophrenia and depression, with a smaller reverse effect of schizophrenia and depression on income. Effects were comparable after adjusting for EA in the MVMR. UMVR showed bidirectional negative associations between genetic liability to EA and depression and positive associations between genetic liability to EA and BD, with no significant effects on schizophrenia. After accounting for income, MVMR showed a bidirectional positive direction between genetic liability to EA and BD and schizophrenia but not with depression.

CONCLUSIONS

Our results suggest a heterogeneous link pattern between SES and SMD. We found a negative bidirectional association between genetic liability to income and the risk of schizophrenia and depression. On the contrary, we found a positive bidirectional relationship of genetic liability to EA with schizophrenia and BD, which only becomes apparent after adjusting for income in the case of schizophrenia.

CLINICAL IMPLICATIONS

These findings shed light on the directional mechanisms between social determinants and mental disorders and suggest that income and EA should be studied separately in relation to mental illness.

Household income does not affect the pleiotropy of schizophrenia genetic liability with mental and physical health outcomes

Background and Hypothesis

Individuals with schizophrenia (SCZ) suffer from comorbidities that substantially reduce their life expectancy. Socioeconomic inequalities could contribute to many of the negative health outcomes associated with SCZ.

Study Design

We investigated genome-wide datasets related to SCZ (52,017 cases and 75,889 controls) from the Psychiatric Genomics Consortium, household income (HI; N=361,687) from UK Biobank, and 2,202 medical endpoints assessed in up to 342,499 FinnGen participants. A phenome-wide genetic correlation analysis of SCZ and HI was performed, also assessing whether SCZ genetic correlations were influenced by HI effect on SCZ. Additionally, SCZ and HI direct effects on medical endpoints were estimated using multivariable Mendelian randomization (MR).

Study Results

SCZ and HI showed overlapping genetic correlations with 70 traits (p<2.89×10 -5 ), including mental health, substance use, gastrointestinal illnesses, reproductive outcomes, liver diseases, respiratory problems, and musculoskeletal phenotypes. SCZ genetic correlations with these traits were not affected by HI effect on SCZ. Considering Bonferroni multiple testing correction (p<7.14×10 -4 ), MR analysis indicated that SCZ and HI may affect medical abortion (SCZ odds ratio, OR=1.07; HI OR=0.78), panic disorder (SCZ OR=1.20; HI OR=0.60), personality disorders (SCZ OR=1.31; HI OR=0.67), substance use (SCZ OR=1.2; HI OR=0.68), and adjustment disorders (SCZ OR=1.18; HI OR=0.78). Multivariable MR analysis confirmed that SCZ effects on these outcomes were independent of HI.

Conclusions

The effect of SCZ genetic liability on mental and physical health may not be strongly affected by socioeconomic differences. This suggests that SCZ-specific strategies are needed to reduce negative health outcomes affecting patients and high-risk individuals.

Identifying the mediating role of socioeconomic status on the relationship between schizophrenia and major depressive disorder: a Mendelian randomisation analysis

Depressive disorder prevalence in patients with schizophrenia has been reported to be 40%. People with low socioeconomic status (SES) are more likely to suffer from schizophrenia and major depressive disorder (MDD). However, the causal relationship between schizophrenia and depression and the potential mediating role of SES remains unclear. Two-sample Mendelian randomization (MR) analyses were conducted to explore the bidirectional causal relationship between schizophrenia and MDD with the largest sample size of European ancestry from public genome-wide association studies (sample size ranged from 130,644 to 480,359). Inverse variance weighted (IVW) method was used as the primary analysis, and several canonical MR methods were used as validation analyses. The mediating role of SES (educational years, household income, employment status, and Townsend deprivation index) was estimated by the two-step MR method. MR analyses showed that genetically predicted schizophrenia was associated with an increased risk of MDD (IVW odds ratio [OR] = 1.137 [95% CI 1.095, 1.181]). Reversely, MDD was also associated with an increased risk of schizophrenia (IVW OR = 1.323 [95% CI 1.118, 1.565]). The mediation analysis via the two-step MR method revealed that the causal effect of schizophrenia on MDD was partly mediated by the Townsend deprivation index with a proportion of 10.27%, but no significant mediation effect was found of SES on the causal effect of MDD on schizophrenia. These results suggest a robust bidirectional causal effect between schizophrenia and MDD. Patients with schizophrenia could benefit from the early and effective intervention of the Townsend deprivation index.

School achievement in adolescence and the risk of mental disorders in early adulthood: a Finnish nationwide register study

School grades in adolescence have been linked to later psychiatric outcomes, but large-scale nationwide studies across the spectrum of mental disorders are scarce. In the present study, we examined the risk of a wide array of mental disorders in adulthood, as well as the risk of comorbidity, associated with school achievement in adolescence. We used population-based cohort data comprising all individuals born in Finland over the period 1980-2000 (N = 1,070,880) who were followed from age 15 or 16 until a diagnosis of mental disorder, emigration, death, or December 2017, whichever came first. Final grade average from comprehensive school was the exposure, and the first diagnosed mental disorder in a secondary healthcare setting was the outcome. The risks were assessed with Cox proportional hazards models, stratified Cox proportional hazard models within strata of full-siblings, and multinomial regression models. The cumulative incidence of mental disorders was estimated using competing risks regression. Better school achievement was associated with a smaller risk of all subsequent mental disorders and comorbidity, except for eating disorders, where better school achievement was associated with a higher risk. The largest associations were observed between school achievement and substance use disorders. Overall, individuals with school achievement more than two standard deviations below average had an absolute risk of 39.6% of a later mental disorder diagnosis. By contrast, for individuals with school achievement more than two standard deviations above average, the absolute risk of a later mental disorder diagnosis was 15.7%. The results show that the largest mental health burden accumulates among those with the poorest school achievement in adolescence.

Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program

Smoking behaviors and alcohol use disorder (AUD), both moderately heritable traits, commonly co-occur in the general population. Single-trait genome-wide association studies (GWAS) have identified multiple loci for smoking and AUD. However, GWASs that have aimed to identify loci contributing to co-occurring smoking and AUD have used small samples and thus have not been highly informative. Applying multi-trait analysis of GWASs (MTAG), we conducted a joint GWAS of smoking and AUD with data from the Million Veteran Program (N = 318,694). By leveraging GWAS summary statistics for AUD, MTAG identified 21 genome-wide significant (GWS) loci associated with smoking initiation and 17 loci associated with smoking cessation compared to 16 and 8 loci, respectively, identified by single-trait GWAS. The novel loci for smoking behaviors identified by MTAG included those previously associated with psychiatric or substance use traits. Colocalization analysis identified 10 loci shared by AUD and smoking status traits, all of which achieved GWS in MTAG, including variants on SIX3, NCAM1, and near DRD2. Functional annotation of the MTAG variants highlighted biologically important regions on ZBTB20, DRD2, PPP6C, and GCKR that contribute to smoking behaviors. In contrast, MTAG of smoking behaviors and alcohol consumption (AC) did not enhance discovery compared with single-trait GWAS for smoking behaviors. We conclude that using MTAG to augment the power of GWAS enables the identification of novel genetic variants for commonly co-occuring phenotypes, providing new insights into their pleiotropic effects on smoking behavior and AUD.

Multivariate genome-wide association meta-analysis of over 1 million subjects identifies loci underlying multiple substance use disorders

Genetic liability to substance use disorders can be parsed into loci that confer general or substance-specific addiction risk. We report a multivariate genome-wide association meta-analysis that disaggregates general and substance-specific loci for published summary statistics of problematic alcohol use, problematic tobacco use, cannabis use disorder, and opioid use disorder in a sample of 1,025,550 individuals of European descent and 92,630 individuals of African descent. Nineteen independent SNPs were genome-wide significant (P < 5e-8) for the general addiction risk factor (addiction-rf), which showed high polygenicity. Across ancestries, PDE4B was significant (among other genes), suggesting dopamine regulation as a cross-substance vulnerability. An addiction-rf polygenic risk score was associated with substance use disorders, psychopathologies, somatic conditions, and environments associated with the onset of addictions. Substance-specific loci (9 for alcohol, 32 for tobacco, 5 for cannabis, 1 for opioids) included metabolic and receptor genes. These findings provide insight into genetic risk loci for substance use disorders that could be leveraged as treatment targets.

The Genetically Informed Neurobiology of Addiction (GINA) model

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

Commentary on Lannoy et al.: The continued value of within-family designs in addiction and psychiatric research

Family-based designs continue to be valuable for establishing evidence of causal relationships in studies of substance use disorders and other mental health conditions. In the era of genome-wide, measured genetic variation, addiction genetics researchers should not overlook the value of co-relative designs for moving beyond correlation.

Socioeconomic differences in major depressive disorder onset among adults are partially explained by lifestyle factors: A longitudinal analysis of the Lifelines Cohort Study

BACKGROUND

Major depressive disorder (MDD) onset varies by socioeconomic position (SEP), this could be explained by lifestyle factors, but little is known about this pathway. Our study aims to disentangle the interplay between SEP measures (i.e., education, income and occupational prestige) and MDD onset and to examine to what extent these associations are mediated by lifestyle (i.e., occupational- and leisure time physical activity, smoking, alcohol intake, diet quality, sleep and central adiposity).

METHODS

A subsample (n = 76,045) of the Lifelines Cohort Study without MDD at baseline was included. MDD onset was measured after a median follow-up time of 3.8 years with the Mini International Neuropsychiatric Interview (MINI). Direct associations between SEP, lifestyle and MDD onset were estimated using logistic regression analyses. Mediating percentages were estimated using the Karlson-Holm-Breen method.

RESULTS

1864 participants (2.5 %) showed MDD at follow-up. SEP was inversely associated with MDD onset, with education showing the strongest association. Educational, income and occupational differences in MDD onset were for 18.7 %, 5.9 % and 21.7 % explained by lifestyle factors (mainly smoking, alcohol intake and central adiposity).

LIMITATIONS

SEP and lifestyle factors were measured simultaneously at baseline. MDD status (only based on a screening tool) was only measured at baseline and 3.8 years later.

CONCLUSIONS

Compared to their lower SEP counterparts, higher SEP individuals had a lower risk of MDD onset. This was partially explained by a healthier lifestyle (mainly less smoking, alcohol intake and central adiposity) of the higher SEP individuals.

What next for eating disorder genetics? Replacing myths with facts to sharpen our understanding

Substantial progress has been made in the understanding of anorexia nervosa (AN) and eating disorder (ED) genetics through the efforts of large-scale collaborative consortia, yielding the first genome-wide significant loci, AN-associated genes, and insights into metabo-psychiatric underpinnings of the disorders. However, the translatability, generalizability, and reach of these insights are hampered by an overly narrow focus in our research. In particular, stereotypes, myths, assumptions and misconceptions have resulted in incomplete or incorrect understandings of ED presentations and trajectories, and exclusion of certain patient groups from our studies. In this review, we aim to counteract these historical imbalances. Taking as our starting point the Academy for Eating Disorders (AED) Truth #5 "Eating disorders affect people of all genders, ages, races, ethnicities, body shapes and weights, sexual orientations, and socioeconomic statuses", we discuss what we do and do not know about the genetic underpinnings of EDs among people in each of these groups, and suggest strategies to design more inclusive studies. In the second half of our review, we outline broad strategic goals whereby ED researchers can expand the diversity, insights, and clinical translatability of their studies.

Pairwise genetic meta-analyses between schizophrenia and substance dependence phenotypes reveals novel association signals with pharmacological significance

Almost half of individuals diagnosed with schizophrenia also present with a substance use disorder, however, little is known about potential molecular mechanisms underlying this comorbidity. We used genetic analyses to enhance our understanding of the molecular overlap between these conditions. Our analyses revealed a positive genetic correlation between schizophrenia and the following dependence phenotypes: alcohol (r_g = 0.368, SE = 0.076, P = 1.61 × 10^-6), cannabis use disorder (r_g = 0.309, SE = 0.033, P = 1.97 × 10^-20) and nicotine (r_g = 0.117, SE = 0.043, P = 7.0 × 10^-3), as well as drinks per week (r_g = 0.087, SE = 0.021, P = 6.36 × 10^-5), cigarettes per day (r_g = 0.11, SE = 0.024, P = 4.93 × 10^-6) and life-time cannabis use (r_g = 0.234, SE = 0.029, P = 3.74 × 10^-15). We further constructed latent causal variable (LCV) models to test for partial genetic causality and found evidence for a potential causal relationship between alcohol dependence and schizophrenia (GCP = 0.6, SE = 0.22, P = 1.6 × 10^-3). This putative causal effect with schizophrenia was not seen using a continuous phenotype of drinks consumed per week, suggesting that distinct molecular mechanisms underlying dependence are involved in the relationship between alcohol and schizophrenia. To localise the specific genetic overlap between schizophrenia and substance use disorders (SUDs), we conducted a gene-based and gene-set pairwise meta-analysis between schizophrenia and each of the four individual substance dependence phenotypes in up to 790,806 individuals. These bivariate meta-analyses identified 44 associations not observed in the individual GWAS, including five shared genes that play a key role in early central nervous system development. The results from this study further supports the existence of underlying shared biology that drives the overlap in substance dependence in schizophrenia, including specific biological systems related to metabolism and neuronal function.

The Australian Genetics of Depression Study: New Risk Loci and Dissecting Heterogeneity Between Subtypes

BACKGROUND

Major depressive disorder (MDD) is a common and highly heterogeneous psychiatric disorder, but little is known about the genetic characterization of this heterogeneity. Understanding the genetic etiology of MDD can be challenging because large sample sizes are needed for gene discovery-often achieved with a trade-off in the depth of phenotyping.

METHODS

The Australian Genetics of Depression Study is the largest stand-alone depression cohort with both genetic data and in-depth phenotyping and comprises a total of 15,792 participants of European ancestry, 92% of whom met diagnostic criteria for MDD. We leveraged the unique nature of this cohort to conduct a meta-analysis with the largest publicly available depression genome-wide association study to date and subsequently used polygenic scores to investigate genetic heterogeneity across various clinical subtypes of MDD.

RESULTS

We increased the number of known genome-wide significant variants associated with depression from 103 to 126 and found evidence of association of novel genes implicated in neuronal development. We found that a polygenic score for depression explained 5.7% of variance in MDD liability in our sample. Finally, we found strong support for genetic heterogeneity in depression with differential associations of multiple psychiatric and comorbid traits with age of onset, longitudinal course, and various subtypes of MDD.

CONCLUSIONS

Until now, this degree of detailed phenotyping in such a large sample of MDD cases has not been possible. Along with the discovery of novel loci, we provide support for differential pathways to illness models that recognize the overlap with other common psychiatric disorders as well as pathophysiological differences.

Genome-wide association study of cerebellar volume provides insights into heritable mechanisms underlying brain development and mental health

Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson's disease, Alzheimer's disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health.

Interoceptive pathways to understand and treat mental health conditions

An increasing recognition that brain and body are dynamically coupled has enriched our scientific understanding of mental health conditions. Peripheral signals interact centrally to influence how we think and feel, generating our sense of the internal condition of the body, a process known as interoception. Disruptions to this interoceptive system may contribute to clinical conditions, including anxiety, depression, and psychosis. After reviewing the nature of interoceptive disturbances in mental health conditions, this review focuses on interoceptive pathways of existing and putative mental health treatments. Emerging clinical interventions may target novel peripheral treatment mechanisms. Future treatment development requires forward- and back-translation to uncover and target specific interoceptive processes in mental health to elucidate their efficacy relative to interventions targeting other factors.

Genetics, leadership position, and well-being: An investigation with a large-scale GWAS

Our study presents the largest whole-genome investigation of leadership phenotypes to date. We identified genome-wide significant loci for leadership phenotypes, which are overlapped with top hits for bipolar disorder, schizophrenia, and intelligence. Our study demonstrated the polygenetic nature of leadership, the positive genetic correlations between leadership traits and a broad range of well-being indicators, and the unique association of leadership with well-being after accounting for genetic influences related to other socioeconomic status measures. Our findings offer insights into the biological underpinnings of leadership.

Twin studies document leadership role occupancy (e.g., whether one holds formal supervisory or management positions) as a heritable trait. However, previous studies have been underpowered in identifying specific genes associated with this trait, which has limited our understanding of the genetic correlations between leadership and one’s well-being. We conducted a genome-wide association study (GWAS) on individuals’ leadership phenotypes that were derived from supervisory/managerial positions and demands among 248,640 individuals of European ancestry from the UK Biobank data. Among the nine genome-wide significant loci, the identified top regions are pinpointed to previously reported GWAS loci for bipolar disorder (miR-2113/POUSF2 and LINC01239) and schizophrenia loci (ZSWIM6). We found positive genetic correlations between leadership position and several positive well-being and health indicators, including high levels of subjective well-being, and low levels of anxiety and depression (|r_g| > 0.2). Intriguingly, we observed positive genetic correlations between leadership position and some negative well-being indicators, including high levels of bipolar disorder and alcohol intake frequency. We also observed positive genetic correlations between leadership position and shortened longevity, cardiovascular diseases, and body mass index after partialing out the genetic variance attributed to either educational attainment or income. The positive genetic correlation between leadership and bipolar disorder seems potentially more pronounced for those holding senior leadership positions (r_g: 0.10 to 0.24), partially due to shared genetic variants with educational attainment. Our findings provide insights into the polygenic nature of leadership and shared genetic underpinnings between the leadership position and one’s health and well-being. We caution against simplistic interpretations of our findings as advocating genetic determinism.

Genetic Risk for Smoking: Disentangling Interplay Between Genes and Socioeconomic Status

This study aims to disentangle the contribution of genetic liability, educational attainment (EA), and their overlap and interaction in lifetime smoking. We conducted genome-wide association studies (GWASs) in UK Biobank (N = 394,718) to (i) capture variants for lifetime smoking, (ii) variants for EA, and (iii) variants that contribute to lifetime smoking independently from EA ('smoking-without-EA'). Based on the GWASs, three polygenic scores (PGSs) were created for individuals from the Netherlands Twin Register (NTR, N = 17,805) and the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2, N = 3090). We tested gene-environment (G × E) interactions between each PGS, neighborhood socioeconomic status (SES) and EA on lifetime smoking. To assess if the PGS effects were specific to smoking or had broader implications, we repeated the analyses with measures of mental health. After subtracting EA effects from the smoking GWAS, the SNP-based heritability decreased from 9.2 to 7.2%. The genetic correlation between smoking and SES characteristics was reduced, whereas overlap with smoking traits was less affected by subtracting EA. The PGSs for smoking, EA, and smoking-without-EA all predicted smoking. For mental health, only the PGS for EA was a reliable predictor. There were suggestions for G × E for some relationships, but there were no clear patterns per PGS type. This study showed that the genetic architecture of smoking has an EA component in addition to other, possibly more direct components. PGSs based on EA and smoking-without-EA had distinct predictive profiles. This study shows how disentangling different models of genetic liability and interplay can contribute to our understanding of the etiology of smoking.

A scoping review and comparison of approaches for measuring genetic heterogeneity in psychiatric disorders

An improved understanding of genetic etiological heterogeneity in a psychiatric condition may help us (a) isolate a neurophysiological 'final common pathway' by identifying its upstream genetic origins and (b) facilitate characterization of the condition's phenotypic variation. This review aims to identify existing genetic heterogeneity measurements in the psychiatric literature and provides a conceptual review of their mechanisms, limitations, and assumptions. The Scopus database was searched for studies that quantified genetic heterogeneity or correlation of psychiatric phenotypes with human genetic data. Ninety studies were included. Eighty-seven reports quantified genetic correlation, five applied genomic structural equation modelling, three evaluated departure from the Hardy-Weinberg equilibrium at one or more loci, and two applied a novel approach known as MiXeR. We found no study that rigorously measured genetic etiological heterogeneity across a large number of markers. Developing such approaches may help better characterize the biological diversity of psychopathology.

Genome-wide association study of musical beat synchronization demonstrates high polygenicity

Moving in synchrony to the beat is a fundamental component of musicality. Here we conducted a genome-wide association study to identify common genetic variants associated with beat synchronization in 606,825 individuals. Beat synchronization exhibited a highly polygenic architecture, with 69 loci reaching genome-wide significance (P < 5 × 10^-8) and single-nucleotide-polymorphism-based heritability (on the liability scale) of 13%-16%. Heritability was enriched for genes expressed in brain tissues and for fetal and adult brain-specific gene regulatory elements, underscoring the role of central-nervous-system-expressed genes linked to the genetic basis of the trait. We performed validations of the self-report phenotype (through separate experiments) and of the genome-wide association study (polygenic scores for beat synchronization were associated with patients algorithmically classified as musicians in medical records of a separate biobank). Genetic correlations with breathing function, motor function, processing speed and chronotype suggest shared genetic architecture with beat synchronization and provide avenues for new phenotypic and genetic explorations.

Discordant associations of educational attainment with ASD and ADHD implicate a polygenic form of pleiotropy

Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are complex co-occurring neurodevelopmental conditions. Their genetic architectures reveal striking similarities but also differences, including strong, discordant polygenic associations with educational attainment (EA). To study genetic mechanisms that present as ASD-related positive and ADHD-related negative genetic correlations with EA, we carry out multivariable regression analyses using genome-wide summary statistics (N = 10,610-766,345). Our results show that EA-related genetic variation is shared across ASD and ADHD architectures, involving identical marker alleles. However, the polygenic association profile with EA, across shared marker alleles, is discordant for ASD versus ADHD risk, indicating independent effects. At the single-variant level, our results suggest either biological pleiotropy or co-localisation of different risk variants, implicating MIR19A/19B microRNA mechanisms. At the polygenic level, they point to a polygenic form of pleiotropy that contributes to the detectable genome-wide correlation between ASD and ADHD and is consistent with effect cancellation across EA-related regions.

Polygenic Risk Scores Derived From Varying Definitions of Depression and Risk of Depression

IMPORTANCE

Genetic studies with broad definitions of depression may not capture genetic risk specific to major depressive disorder (MDD), raising questions about how depression should be operationalized in future genetic studies.

OBJECTIVE

To use a large, well-phenotyped single study of MDD to investigate how different definitions of depression used in genetic studies are associated with estimation of MDD and phenotypes of MDD, using polygenic risk scores (PRSs).

DESIGN, SETTING, AND PARTICIPANTS

In this case-control polygenic risk score analysis, patients meeting diagnostic criteria for a diagnosis of MDD were drawn from the Australian Genetics of Depression Study, a cross-sectional, population-based study of depression, and controls and patients with self-reported depression were drawn from QSkin, a population-based cohort study. Data analyzed herein were collected before September 2018, and data analysis was conducted from September 10, 2020, to January 27, 2021.

MAIN OUTCOME AND MEASURES

Polygenic risk scores generated from genome-wide association studies using different definitions of depression were evaluated for estimation of MDD in and within individuals with MDD for an association with age at onset, adverse childhood experiences, comorbid psychiatric and somatic disorders, and current physical and mental health.

RESULTS

Participants included 12 106 (71% female; mean age, 42.3 years; range, 18-88 years) patients meeting criteria for MDD and 12 621 (55% female; mean age, 60.9 years; range, 43-87 years) control participants with no history of psychiatric disorders. The effect size of the PRS was proportional to the discovery sample size, with the largest study having the largest effect size with the odds ratio for MDD (1.75; 95% CI, 1.73-1.77) per SD of PRS and the PRS derived from ICD-10 codes documented in hospitalization records in a population health cohort having the lowest odds ratio (1.14; 95% CI, 1.12-1.16). When accounting for differences in sample size, the PRS from a genome-wide association study of patients meeting diagnostic criteria for MDD and control participants was the best estimator of MDD, but not in those with self-reported depression, and associations with higher odds ratios with childhood adverse experiences and measures of somatic distress.

CONCLUSIONS AND RELEVANCE

These findings suggest that increasing sample sizes, regardless of the depth of phenotyping, may be most informative for estimating risk of depression. The next generation of genome-wide association studies should, like the Australian Genetics of Depression Study, have both large sample sizes and extensive phenotyping to capture genetic risk factors for MDD not identified by other definitions of depression.

Novel characterization of the multivariate genetic architecture of internalizing psychopathology and alcohol use

Genetic correlations suggest that the genetic relationship of alcohol use with internalizing psychopathology depends on the measure of alcohol use. Problematic alcohol use (PAU) is positively genetically correlated with internalizing psychopathology, whereas alcohol consumption ranges from not significantly correlated to moderately negatively correlated with internalizing psychopathology. To explore these different genetic relationships of internalizing psychopathology with alcohol use, we performed a multivariate genome-wide association study of four correlated factors (internalizing psychopathology, PAU, quantity of alcohol consumption, and frequency of alcohol consumption) and then assessed genome-wide and local genetic covariance between these factors. We identified 14 significant regions of local, largely positive, genetic covariance between PAU and internalizing psychopathology and 12 regions of significant local genetic covariance (including both positive and negative genetic covariance) between consumption factors and internalizing psychopathology. Partitioned genetic covariance among functional annotations suggested that brain tissues contribute significantly to positive genetic covariance between internalizing psychopathology and PAU but not to the genetic covariance between internalizing psychopathology and quantity or frequency of alcohol consumption. We hypothesize that genome-wide genetic correlations between alcohol use and psychiatric traits may not capture the more complex shared or divergent genetic architectures at the locus or tissue specific level. This study highlights the complexity of genetic architectures of alcohol use and internalizing psychopathology, and the differing shared genetics of internalizing disorders with PAU compared to consumption.

Dissecting polygenic signals from genome-wide association studies on human behaviour

Genome-wide association studies on human behavioural traits are producing large amounts of polygenic signals with significant predictive power and potentially useful biological clues. Behavioural traits are more distal and are less directly under biological control compared with physical characteristics, which makes the associated genetic effects harder to interpret. The results of genome-wide association studies for human behaviour are likely made up of a composite of signals from different sources. While sample sizes continue to increase, we outline additional steps that need to be taken to better delineate the origin of the increasingly stronger polygenic signals. In addition to genetic effects on the traits themselves, the major sources of polygenic signals are those that are associated with correlated traits, environmental effects and ascertainment bias. Advances in statistical approaches that disentangle polygenic effects from different traits as well as extending data collection to families and social circles with better geographical coverage will probably contribute to filling the gap of knowledge between genetic effects and behavioural outcomes.