Genome-wide meta-analyses of stratified depression in Generation Scotland and UK Biobank

Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D1 and D2 type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index.

A Transcriptomic Signature of Depressive Symptoms in Late Life

Background

Depressive symptoms in late life can impair daily function and accompany cognitive decline. However, the molecular mechanisms that underlie these changes in the brain remain poorly understood.

Methods

Differential expression analysis was performed on bulk-tissue RNA sequencing data generated from dorsolateral prefrontal cortex samples of elderly participants in ROS/MAP (Religious Orders Study and Memory and Aging Project; N = 998, mean age at death = 89.7 years). Bulk tissue RNA sequencing was analyzed against depressive symptoms measured prior to death, controlling for Alzheimer's disease neuropathology, medication status, and lifestyle factors. Sex-stratified models were also tested.

Results

Increased abundance of the Prader-Willi syndrome-associated gene PWAR1 (corrected p = 5.47 × 10-3) and CTDSPL2 (corrected p = .03) were associated with a higher burden of depressive symptoms in the combined sample. An additional 14 genes showed suggestive associations, including several with known links to neuropsychiatric illness (e.g., ACVR2B-AS1, COL19A1). Functional enrichment analysis revealed downregulation of aerobic metabolism and upregulation of both amino acid catabolism and DNA modification processes. Differential expression signatures were poorly correlated between males and females (Pearson r = 0.12; 95% CI, 0.10 to 0.13), and only the male group showed independently significant differential expression. Little overlap was found with previously published analyses of major depressive disorder.

Conclusions

Building on recently published single-nucleus profiling, we present the largest-ever study of transcriptomic correlates of depressive symptoms in late life, revealing new insights into sex-specific regulators. PWAR1 and CTDSPL2 were identified as putative markers of late-life depression in the dorsolateral prefrontal cortex and warrant further study.

Protein associations and protein-metabolite interactions with depressive symptoms and the p-factor

Despite increasing mental health problems among young people, few studies have examined associations between plasma proteins and mental health. Interactions between proteins and metabolites in association with mental health problems remain underexplored. In 730 twins, we quantified associations between plasma proteins measured at age 22 with 21 indicators of either depressive symptoms or the p-factor and tested for interactions with metabolites. Symptoms were collected from questionnaires and interviews completed by different raters (e.g., self-report, teachers) through adolescence to young adulthood (12 to 22 years). We found 47 proteins associated with depressive symptoms or the p-factor (FDR < 0.2), 9 being associated with both. Two proteins, contactin-1 and mast/stem cell growth factor receptor kit, positively interacted with valine levels in explaining p-factor variability. Our study demonstrates strong associations between plasma proteins and mental health and provides evidence for proteome-metabolome interactions in explaining higher levels of mental health problems.

SNP-Associated Differential Methylation in ARHGEF38: Insights into Genetic-Epigenetic Interactions

Objective

Associations have been seen between suicidal behavior and differential DNA methylation of certain genes, with one study showing significant hypomethylation of ARHGEF38 in postmortem brain samples from individuals with bipolar disorder who died by suicide. Our objective was to explore ARHGEF38 methylation in individuals with bipolar disorder and a history of suicide attempt.

Method

With pyrosequencing, we looked at the previously identified region of interest in ARHGEF38. We investigated the methylation levels of 3 CpG sites in 47 individuals with bipolar disorder and a history of suicide attempt, 47 individuals with bipolar disorder without a history of suicide attempt, and 47 non-bipolar disorder controls.

Results

None of the CpG sites measured had an association between groups, although there were distinct clusters of differential methylation in each group. Applying genotypes of SNPs found in the region of interest, rs2121558 and rs1447093, these clusters showed stepwise methylation at each CpG site, regardless of phenotype.

Conclusions

In this relatively small sample size study, differential methylation in ARHGEF38 was not associated with history of suicide attempt, failing to replicate findings from a related outcome, suicide death. However, we did provide evidence of SNP and DNA methylation interplay in this region. This highlights the potential relevance of considering genetics when interrogating epigenetic mechanisms.

Highlights

ARHGEF38 methylation is not associated with bipolar disorder and suicide attempt Methylation of ARHGEF38 is heavily influenced by the presence of SNPs Suicide phenotype, genetics, and sample type impact DNA methylation.

Insights from a methylome-wide association study of antidepressant exposure

This study tests the association of whole-blood DNA methylation and antidepressant exposure in 16,531 individuals from Generation Scotland (GS), using self-report and prescription-derived measures. We identify 8 associations and a high concordance of results between self-report and prescription-derived measures. Sex-stratified analyses observe nominally significant increased effect estimates in females for four CpGs. There is observed enrichment for genes expressed in the Amygdala and annotated to synaptic vesicle membrane ontology. Two CpGs (cg15071067; DGUOK-AS1 and cg26277237; KANK1) show correlation between DNA methylation with the time in treatment. There is a significant overlap in the top 1% of CpGs with another independent methylome-wide association study of antidepressant exposure. Finally, a methylation profile score trained on this sample shows a significant association with antidepressant exposure in a meta-analysis of eight independent external datasets. In this large investigation of antidepressant exposure and DNA methylation, we demonstrate robust associations which warrant further investigation to inform on the design of more effective and tolerated treatments for depression.

Big data in sarcoidosis

PURPOSE OF REVIEW

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine.

RECENT FINDINGS

Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols.

SUMMARY

Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Polygenic risk, childhood abuse and gene x environment interactions with depression development from middle to late adulthood: A U.S. national life-course study

OBJECTIVE

Utilizing national longitudinal data, this study examines how polygenic depression risk and childhood abuse interactively influence the life-course development of depressive conditions from middle to late adulthood.

METHOD

Data from 7512 participants (4323 females and 3189 males) of European ancestry aged 51-90, retrieved from the U.S. Health and Retirement Study (1992-2020), were analyzed. Childhood physical abuse and polygenic depression score were the primary predictors. Depressive symptoms were assessed using the Center for Epidemiologic Studies-Depression (CESD) scale, and clinical depression risk was a binary indicator. Growth-curve linear mixed and logit mixed-effects models were conducted for analysis.

RESULTS

Increasing polygenic depression scores were associated with elevated CES-D levels and potential risks of clinical depression. Males experienced more detrimental effects of childhood abuse on depression development from ages 51 to 90 years. In contract, non-maltreated females generally exhibited higher depressive symptoms and clinical depression risk than males. A significant interactive effect was found between polygenic depression risk and childhood abuse among males. Higher depression levels and clinical risk were observed with increasing polygenic depression score among maltreated males, surpassing those of females with standardized polygenic score ≥0 from age 51 to 90 years.

CONCLUSIONS

The interaction between childhood abuse and genetic factors significantly shaped lifelong depression trajectories in males, while the negative impact of abusive parenting remained constant regardless of polygenic depression risk among females. Individualized prevention and intervention strategies could be crucial in mitigating lifelong depression development, especially for high-genetic-risk males with a history of childhood physical abuse.

Neuroimaging and epigenetic analysis reveal novel epigenetic loci in major depressive disorder

BACKGROUND

Epigenetic modifications, such as DNA methylation, contribute to the pathophysiology of major depressive disorder (MDD). This study aimed to identify novel MDD-associated epigenetic loci using DNA methylation profiles and explore the correlations between epigenetic loci and cortical thickness changes in patients with MDD.

METHODS

A total of 350 patients with MDD and 161 healthy controls (HCs) were included in the epigenome-wide association studies (EWAS). We analyzed methylation, copy number alteration (CNA), and gene network profiles in the MDD group. A total of 234 patients with MDD and 135 HCs were included in neuroimaging methylation analysis. Pearson's partial correlation analysis was used to estimate the correlation between cortical thickness of brain regions and DNA methylation levels of the loci.

RESULTS

In total, 2018 differentially methylated probes (DMPs) and 351 differentially methylated regions (DMRs) were identified. DMP-related genes were enriched in two networks involved in the central nervous system. In neuroimaging analysis, patients with MDD showed cortical thinning in the prefrontal regions and cortical thickening in several occipital regions. Cortical thickness of the left ventrolateral prefrontal cortex (VLPFC, i.e. pars triangularis) was negatively correlated with eight DMPs associated with six genes (EML6, ZFP64, CLSTN3, KCNMA1, TAOK2, and NT5E).

CONCLUSION

Through combining DNA methylation and neuroimaging analyses, negative correlations were identified between the cortical thickness of the left VLPFC and DNA methylation levels of eight DMPs. Our findings could improve our understanding of the pathophysiology of MDD.

Night shift work, genetic risk, and the risk of depression: A prospective cohort study

BACKGROUND

Genetic factors and night shift work both contribute to the risk of depression, but whether the association of night shift work with depression varies by genetic predisposition remains unclear.

OBJECTIVES

To assess whether night shift work is associated with a higher risk of depression regardless of genetic predisposition.

METHODS

We used data from the UK biobank of 247,828 adults aged 38-71 free of depression at baseline from March 13, 2006, to October 1, 2010. Genetic predisposition to depression was assessed using polygenic risk scores (PRS) weighted sums of genetic variant indicator variables and classified as low (lowest tertile), intermediate (tertile 2), and high (highest tertile). Night shift work exposures were collected using a touchscreen questionnaire and were divided into four categories.

RESULTS

After a median follow-up of 12.7 years, 7315 participants developed depression. Compared with day workers, HRs (95 % CIs) of depression were 1.28 (1.19-1.38) for shift work, but never or rarely night shifts, 1.32 (1.20-1.45) for irregular night shifts, and 1.20 (1.07-1.34) for permanent night shifts. Considering lifetime employment and compared with never shift workers, >8 nights/month (HR: 1.40; 95 % CI: 1.19-1.66) and <10 years (HR: 1.30; 95 % CI: 1.09-1.54) of night shift work were associated with a higher risk of depression. In joint effect analyses, compared to participants with low genetic predisposition and day workers, the HRs (95 % CIs) of depression were 1.49 (1.32-1.69) in those with high genetic predisposition and shift work, but never or rarely night shifts, and 1.36 (1.20-1.55) for those with high genetic predisposition and irregular/permanent night shifts. In addition, there was neither multiplicative nor additive interaction between genetic predisposition and night shift work on the risk of depression.

CONCLUSIONS

Night shift work was associated with an increased risk of depression regardless of genetic risk.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study

Importance

Understanding antidepressant mechanisms could help design more effective and tolerated treatments.

Objective

Identify DNA methylation (DNAm) changes associated with antidepressant exposure.

Design

Case-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS.

Setting

Cohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR.

Participants

Participants with DNAm data and self-report/prescription derived antidepressant exposure.

Main Outcomes and Measures

Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, N exposed = 661, N unexposed = 9,575) alongside MBD-Seq in NESDA (N exposed = 398, N unexposed = 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions.

Results

The self-report MWAS (N = 16,536, N exposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, N exposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10 -8 ), respectively. The top locus was cg26277237 ( KANK1, p self-report = 9.3x10 -13 , p prescription = 6.1x10 -3 ). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 ( p adj = 5.0x10 -3 ) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (N studies = 9, N = 10,236, N exposed = 661, f3 = 0.196, p < 1x10 -4 ).

Conclusions and Relevance

Antidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments.

3 Key Points

Question: Is antidepressant exposure associated with differential whole blood DNA methylation?Findings: In this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10 -4 ) in a meta-analysis of external datasets. Meaning: Antidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1 , which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.

Examining the Effect of Genes on Depression as Mediated by Smoking and Modified by Sex

Depression is heritable, differs by sex, and has environmental risk factors such as cigarette smoking. However, the effect of single nucleotide polymorphisms (SNPs) on depression through cigarette smoking and the role of sex is unclear. In order to examine the association of SNPs with depression and smoking in the UK Biobank with replication in the COPDGene study, we used counterfactual-based mediation analysis to test the indirect or mediated effect of SNPs on broad depression through the log of pack-years of cigarette smoking, adjusting for age, sex, current smoking status, and genetic ancestry (via principal components). In secondary analyses, we adjusted for age, sex, current smoking status, genetic ancestry (via principal components), income, education, and living status (urban vs. rural). In addition, we examined sex-stratified mediation models and sex-moderated mediation models. For both analyses, we adjusted for age, current smoking status, and genetic ancestry (via principal components). In the UK Biobank, rs6424532 [LOC105378800] had a statistically significant indirect effect on broad depression through the log of pack-years of cigarette smoking (p = 4.0 × 10-4) among all participants and a marginally significant indirect effect among females (p = 0.02) and males (p = 4.0 × 10-3). Moreover, rs10501696 [GRM5] had a marginally significant indirect effect on broad depression through the log of pack-years of cigarette smoking (p = 0.01) among all participants and a significant indirect effect among females (p = 2.2 × 10-3). In the secondary analyses, the sex-moderated indirect effect was marginally significant for rs10501696 [GRM5] on broad depression through the log of pack-years of cigarette smoking (p = 0.01). In the COPDGene study, the effect of an SNP (rs10501696) in GRM5 on depressive symptoms and medication was mediated by log of pack-years (p = 0.02); however, no SNPs had a sex-moderated mediated effect on depressive symptoms. In the UK Biobank, we found SNPs in two genes [LOC105378800, GRM5] with an indirect effect on broad depression through the log of pack-years of cigarette smoking. In addition, the indirect effect for GRM5 on broad depression through smoking may be moderated by sex. These results suggest that genetic regions associated with broad depression may be mediated by cigarette smoking and this relationship may be moderated by sex.

Effects of sex and gender on the etiologies and presentation of select internalizing psychopathologies

The internalizing spectrum encompasses a subset of psychopathologies characterized by emotional liability, anhedonia, anxiousness, distress, and fear, and includes, among others, diagnoses of major depressive disorder (MDD), generalized anxiety disorder (GAD), and posttraumatic stress disorder (PTSD). In this review, we describe the vast body of work highlighting a role for sex and gender in the environment, symptom onset, genetic liability, and disorder progression and comorbidities of MDD, GAD, and PTSD. We also point the reader to different language used in diverse fields to describe sexual and gender minorities that may complicate the interpretation of emerging literature from the social sciences, psychiatric and psychological sciences, and genetics. Finally, we identify several gaps in knowledge that we hope serve as launch-points for expanding the scope of psychiatric studies beyond binarized sex-stratification. Despite being under-represented in genomics studies, placing emphasis on inclusion of sexual and gender diverse participants in these works will hopefully improve our understanding of disorder etiology using genetics as one tool to inform how biology (e.g., hormone concentration) and environmental variables (e.g., exposure to traumatic events) contribute to differences in symptom onset, pattern, and long-term trajectory.

Integrating serum metabolomics and network analysis to explore the antidepressant activity of crocin in rats with chronic unexpected mild stress-induced depression

CONTEXT

Crocin exhibits anti-depressant properties. However, its underlying mechanisms and its relationship with metabolomics remain unclear.

OBJECTIVE

This study elucidates the mechanism of action and potential targets of crocin in treating chronic unexpected mild stress (CUMS)-induced depression in rats.

MATERIALS AND METHODS

Male Sprague-Dawley (SD) rats underwent 4 weeks of CUMS to establish the depression model. The normal control (distilled water), crocin (25 mg/kg), and fluoxetine (5.4 mg/kg) groups were orally administered for 4-weeks. Behavioural tests evaluated the effects of crocin, while liquid chromatography-mass spectrometry metabolomics identified differential metabolites and their associated metabolic pathways. Subsequently, network pharmacology was utilized to predict the targets of crocin.

RESULTS

Crocin significantly increased body weight (from 319.16 ± 4.84 g to 325.67 ± 2.84 g), sucrose preference (from 0.46 ± 0.09 to 0.70 ± 0.09), vertical activity (from 2.83 ± 1.94 to 8 ± 2.36), horizontal activity (from 1 ± 0.63 to 4.5 ± 3.08) and decreased immobilization time (from 13.16 ± 2.69 to 3.97 ± 3.00). Metabolomics analysis identified 7 metabolites and 5 associated metabolic pathways. From the combined analysis of network pharmacology and metabolomics, three targets (PRMT1, CYP3A4, and GLB1) are the overlapping targets and the two most important metabolic pathways are tryptophan metabolism and glycerolipid metabolism.

DISCUSSION AND CONCLUSIONS

This study provides insights into the antidepressant therapeutic effect of crocin and its underlying mechanisms. The findings contribute to a better understanding of the metabolic mechanism involved in the anti-depressant effect of crocin, establishing a strong foundation for future research in this area.

The genetic basis of major depressive disorder

The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

Gender differences in the association of polygenic risk and divergent depression trajectories from mid to late life: a national longitudinal study

Our research fills a critical gap in the depression literature by utilizing a life course perspective to examine gender-gene interactions in association with depression trajectories over time. Using data from the Health and Retirement Study, we estimated multi-level negative binomial and logistic mixed models to analyze gender-specific trajectories of depressive symptoms (CESD-8) and potential clinical depression risk from middle to late adulthood in relation to gender-by-polygenic-risk (PRS) interactions. We found increasingly greater female-male gaps in the CESD-8 scale and a higher probability of clinical depression risk with increasing polygenic risk scores. Furthermore, females' higher genetic vulnerabilities to depressive conditions than males vary from ages 51 to 90 years, with most salient larger differences at oldest old ages at 76-85 (e.g. 0.28 higher CESD-8 scale for females at ages 76-85 years than for similar-aged males; higher 3.44% probability of depression risk for females at ages 81-85 compared to similar-aged males) followed by old ages at 61-70  years (e.g. about 2.40% higher probability of depression risk for females at ages 61-70 years than for similar-aged males) in comparison to younger ages during middle adulthood. This study contributes to new knowledge of how gender-by-polygenic-risk interactions are associated with depression trajectories across the life course.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

Genetic heterogeneity and subtypes of major depression

Major depression (MD) is a heterogeneous disorder; however, the extent to which genetic factors distinguish MD patient subgroups (genetic heterogeneity) remains uncertain. This study sought evidence for genetic heterogeneity in MD. Using UK Biobank cohort, the authors defined 16 MD subtypes within eight comparison groups (vegetative symptoms, symptom severity, comorbid anxiety disorder, age at onset, recurrence, suicidality, impairment, and postpartum depression; N ~ 3000-47000). To compare genetic component of these subtypes, subtype-specific genome-wide association studies were performed to estimate SNP-heritability, and genetic correlations within subtype comparison and with other related disorders/traits. The findings indicated that MD subtypes were divergent in their SNP-heritability, and genetic correlations both within subtype comparisons and with other related disorders/traits. Three subtype comparisons (vegetative symptoms, age at onset, and impairment) showed significant differences in SNP-heritability; while genetic correlations within subtype comparisons ranged from 0.55 to 0.86, suggesting genetic profiles are only partially shared among MD subtypes. Furthermore, subtypes that are more clinically challenging, e.g., early-onset, recurrent, suicidal, more severely impaired, had stronger genetic correlations with other psychiatric disorders. MD with atypical-like features showed a positive genetic correlation (+0.40) with BMI while a negative correlation (-0.09) was found in those without atypical-like features. Novel genomic loci with subtype-specific effects were identified. These results provide the most comprehensive evidence to date for genetic heterogeneity within MD, and suggest that the phenotypic complexity of MD can be effectively reduced by studying the subtypes which share partially distinct etiologies.

Sensitive period-regulating genetic pathways and exposure to adversity shape risk for depression

Animal and human studies have documented the existence of developmental windows (or sensitive periods) when experience can have lasting effects on brain structure or function, behavior, and disease. Although sensitive periods for depression likely arise through a complex interplay of genes and experience, this possibility has not yet been explored in humans. We examined the effect of genetic pathways regulating sensitive periods, alone and in interaction with common childhood adversities, on depression risk. Guided by a translational approach, we: (1) performed association analyses of three gene sets (60 genes) shown in animal studies to regulate sensitive periods using summary data from a genome-wide association study of depression (n = 807,553); (2) evaluated the developmental expression patterns of these genes using data from BrainSpan (n = 31), a transcriptional atlas of postmortem brain samples; and (3) tested gene-by-development interplay (dGxE) by analyzing the combined effect of common variants in sensitive period genes and time-varying exposure to two types of childhood adversity within a population-based birth cohort (n = 6254). The gene set regulating sensitive period opening associated with increased depression risk. Notably, 6 of the 15 genes in this set showed developmentally regulated gene-level expression. We also identified a statistical interaction between caregiver physical or emotional abuse during ages 1-5 years and genetic risk for depression conferred by the opening genes. Genes involved in regulating sensitive periods are differentially expressed across the life course and may be implicated in depression vulnerability. Our findings about gene-by-development interplay motivate further research in large, more diverse samples to further unravel the complexity of depression etiology through a sensitive period lens.

The Role of Sex in Genetic Association Studies of Depression

Depression is the most common mental illness in the U.S. affecting nearly 40 million adults age 18 years and older. Depression has both genetic and environmental influences. In addition, women are more likely to be affected by depression than men. However, the relationship between genes and depression is complex and may be influenced by sex. Understanding the genetic basis of sex-specific differences for depression has the potential to lead to new biological understanding of the etiology of depression in females compared to males and to promote the development of novel and more effective pharmacotherapies. This review examines the role of sex in genetic associations with depression for both genome-wide association and candidate gene studies. While the genetic association signals of depression differ by sex, the role of sex in the heritability of depression is complex and warrants further investigation.

Different genes may be involved in distal and local sensitisation: a genome-wide gene-based association study and meta-analysis

Background

Neuropathic pain symptoms and signs of increased pain sensitization in osteoarthritis (OA) patients may explain persistent pain after total joint replacement (TJR). Therefore, identifying genetic markers associated with pain sensitization and neuropathic‐like pain phenotypes could be clinically important in identifying targets for early intervention.

Methods

We performed a genome‐wide gene‐based association study (GWGAS) using pressure pain detection thresholds (PPTs) from distal pain‐free sites (anterior tibia), a measure of distal sensitization, and from proximal pain‐affected sites (lateral joint line), a measure of local sensitization, in 320 knee OA participants from the Knee Pain and related health in the Community (KPIC) cohort. We next performed gene‐based fixed‐effects meta‐analysis of PPTs and a neuropathic‐like pain phenotype using genome‐wide association study (GWAS) data from KPIC and from an independent cohort of 613 post‐TJR participants, respectively.

Results

The most significant genes associated with distal and local sensitization were OR5B3 and BRDT, respectively. We also found previously identified neuropathic pain‐associated genes—KCNA1, MTOR, ADORA1 and SCN3B—associated with PPT at the anterior tibia and an inflammatory pain gene—PTAFR—associated with PPT at the lateral joint line. Meta‐analysis results of anterior tibia and neuropathic‐like pain phenotypes revealed genes associated with bone morphogenesis, neuro‐inflammation, obesity, type 2 diabetes, cardiovascular disease and cognitive function.

Conclusions

Overall, our results suggest that different biological processes might be involved in distal and local sensitization, and common genetic mechanisms might be implicated in distal sensitization and neuropathic‐like pain. Future studies are needed to replicate these findings.

Significance

To the best of our knowledge, this is the first GWAS for pain sensitization and the first gene‐based meta‐analysis of pain sensitization and neuropathic‐like pain. Higher pain sensitization and neuropathic pain symptoms are associated with persistent pain after surgery hence, identifying genetic biomarkers and molecular pathways associated with these traits is clinically relevant.

Role of glyoxalase 1 in methylglyoxal detoxification-the broad player of psychiatric disorders

Methylglyoxal (MG) is a highly reactive α-ketoaldehyde formed endogenously as a byproduct of the glycolytic pathway. To remove MG, various detoxification systems work together in vivo, including the glyoxalase system, which enzymatically degrades MG using glyoxalase 1 (GLO1) and GLO2. Recently, numerous reports have shown that GLO1 expression and MG accumulation in the brain are involved in the pathogenesis of psychiatric disorders, such as anxiety disorder, depression, autism, and schizophrenia. Furthermore, it has been reported that GLO1 inhibitors may be promising drugs for the treatment of psychiatric disorders. In this review, we discuss the recent findings of the effects of altered GLO1 function on mental behavior, especially focusing on results obtained from animal models.

The genetic basis of major depression

Major depressive disorder (MDD) is a common, debilitating, phenotypically heterogeneous disorder with heritability ranges from 30% to 50%. Compared to other psychiatric disorders, its high prevalence, moderate heritability, and strong polygenicity have posed major challenges for gene-mapping in MDD. Studies of common genetic variation in MDD, driven by large international collaborations such as the Psychiatric Genomics Consortium, have confirmed the highly polygenic nature of the disorder and implicated over 100 genetic risk loci to date. Rare copy number variants associated with MDD risk were also recently identified. The goal of this review is to present a broad picture of our current understanding of the epidemiology, genetic epidemiology, molecular genetics, and gene-environment interplay in MDD. Insights into the impact of genetic factors on the aetiology of this complex disorder hold great promise for improving clinical care.

Exploring the Use of Genomic and Routinely Collected Data: Narrative Literature Review and Interview Study

Background

Advancing the use of genomic data with routinely collected health data holds great promise for health care and research. Increasing the use of these data is a high priority to understand and address the causes of disease.

Objective

This study aims to provide an outline of the use of genomic data alongside routinely collected data in health research to date. As this field prepares to move forward, it is important to take stock of the current state of play in order to highlight new avenues for development, identify challenges, and ensure that adequate data governance models are in place for safe and socially acceptable progress.

Methods

We conducted a literature review to draw information from past studies that have used genomic and routinely collected data and conducted interviews with individuals who use these data for health research. We collected data on the following: the rationale of using genomic data in conjunction with routinely collected data, types of genomic and routinely collected data used, data sources, project approvals, governance and access models, and challenges encountered.

Results

The main purpose of using genomic and routinely collected data was to conduct genome-wide and phenome-wide association studies. Routine data sources included electronic health records, disease and death registries, health insurance systems, and deprivation indices. The types of genomic data included polygenic risk scores, single nucleotide polymorphisms, and measures of genetic activity, and biobanks generally provided these data. Although the literature search showed that biobanks released data to researchers, the case studies revealed a growing tendency for use within a data safe haven. Challenges of working with these data revolved around data collection, data storage, technical, and data privacy issues.

Conclusions

Using genomic and routinely collected data holds great promise for progressing health research. Several challenges are involved, particularly in terms of privacy. Overcoming these barriers will ensure that the use of these data to progress health research can be exploited to its full potential.

Self-reported medication use as an alternate phenotyping method for anxiety and depression in the UK Biobank

The requirement for large sample sizes for psychiatric genetic analyses necessitates novel approaches to derive cases. Anxiety and depression show substantial genetic overlap and share pharmacological treatments. Data on prescribed medication could be effective for inferring case status when other indicators of mental health are unavailable. We investigated self-reported current medication use in UK Biobank participants of European ancestry. Medication Status cases reported using antidepressant or anxiolytic medication (n = 22,218), controls did not report psychotropic medication use (n = 168,959). A subset, "Medication Only," additionally did not meet criteria for any other mental health indicator (case n = 2,643, control n = 107,029). We assessed genetic overlap between these phenotypes and two published genetic association studies of anxiety and depression, and an internalizing disorder trait derived from symptom-based questionnaires in UK Biobank. Genetic correlations between Medication Status and the three anxiety and depression phenotypes were significant (r_g  = 0.60-0.73). In the Medication Only subset, the genetic correlation with depression was significant (r_g  = 0.51). The three polygenic scores explained 0.33% - 0.80% of the variance in Medication Status and 0.07% - 0.19% of the variance in Medication Only. This study provides evidence that self-reported current medication use offers an alternate or supplementary anxiety or depression phenotype in genetic studies where diagnostic information is sparse or unavailable.

A genome-wide association study finds genetic variants associated with neck or shoulder pain in UK Biobank

Background

Common types of musculoskeletal conditions include pain in the neck and shoulder areas. This study seeks to identify the genetic variants associated with neck or shoulder pain based on a genome-wide association approach using 203 309 subjects from the UK Biobank cohort and look for replication evidence from the Generation Scotland: Scottish Family Health Study (GS:SFHS) and TwinsUK.

Methods

A genome-wide association study was performed adjusting for age, sex, BMI and nine population principal components. Significant and independent genetic variants were then sent to GS:SFHS and TwinsUK for replication.

Results

We identified three genetic loci that were associated with neck or shoulder pain in the UK Biobank samples. The most significant locus was in an intergenic region in chromosome 17, rs12453010, having P = 1.66 × 10⁻¹¹. The second most significant locus was located in the FOXP2 gene in chromosome 7 with P = 2.38 × 10⁻¹⁰ for rs34291892. The third locus was located in the LINC01572 gene in chromosome 16 with P = 4.50 × 10⁻⁸ for rs62053992. In the replication stage, among four significant and independent genetic variants, rs2049604 in the FOXP2 gene and rs62053992 in the LINC01572 gene were weakly replicated in GS:SFHS (P = 0.0240 and P = 0.0202, respectively).

Conclusions

We have identified three loci associated with neck or shoulder pain in the UK Biobank cohort, two of which were weakly supported in a replication cohort. Further evidence is needed to confirm their roles in neck or shoulder pain.

Analysis of Major Depression Risk Genes Reveals Evolutionary Conservation, Shared Phenotypes, and Extensive Genetic Interactions

Major depressive disorder (MDD) affects around 15% of the population at some stage in their lifetime. It can be gravely disabling and it is associated with increased risk of suicide. Genetics play an important role; however, there are additional environmental contributions to the pathogenesis. A number of possible risk genes that increase liability for developing symptoms of MDD have been identified in genome-wide association studies (GWAS). The goal of this study was to characterize the MDD risk genes with respect to the degree of evolutionary conservation in simpler model organisms such as Caenorhabditis elegans and zebrafish, the phenotypes associated with variation in these genes and the extent of network connectivity. The MDD risk genes showed higher conservation in C. elegans and zebrafish than genome-to-genome comparisons. In addition, there were recurring themes among the phenotypes associated with variation of these risk genes in C. elegans. The phenotype analysis revealed enrichment for essential genes with pleiotropic effects. Moreover, the MDD risk genes participated in more interactions with each other than did randomly-selected genes from similar-sized gene sets. Syntenic blocks of risk genes with common functional activities were also identified. By characterizing evolutionarily-conserved counterparts to the MDD risk genes, we have gained new insights into pathogenetic processes relevant to the emergence of depressive symptoms in man.

Deep feature extraction of single-cell transcriptomes by generative adversarial network

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) offers the opportunity to dissect heterogeneous cellular compositions and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition designs.

RESULTS

Here, we present a single-cell Generative Adversarial Network (scGAN) to simultaneously acquire patterns from raw data while minimizing the confounding effect driven by technical artifacts or other factors inherent to the data. Specifically, scGAN models the data likelihood of the raw scRNA-seq counts by projecting each cell onto a latent embedding. Meanwhile, scGAN attempts to minimize the correlation between the latent embeddings and the batch labels across all cells. We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder.

AVAILABILITYAND IMPLEMENTATION

The scGAN code and the information for the public scRNA-seq datasets are available at https://github.com/li-lab-mcgill/singlecell-deepfeature.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.

Computational approaches and machine learning for individual-level treatment predictions

RATIONALE

The impact of neuroscience-based approaches for psychiatry on pragmatic clinical decision-making has been limited. Although neuroscience has provided insights into basic mechanisms of neural function, these insights have not improved the ability to generate better assessments, prognoses, diagnoses, or treatment of psychiatric conditions.

OBJECTIVES

To integrate the emerging findings in machine learning and computational psychiatry to address the question: what measures that are not derived from the patient's self-assessment or the assessment by a trained professional can be used to make more precise predictions about the individual's current state, the individual's future disease trajectory, or the probability to respond to a particular intervention?

RESULTS

Currently, the ability to use individual differences to predict differential outcomes is very modest possibly related to the fact that the effect sizes of interventions are small. There is emerging evidence of genetic and neuroimaging-based heterogeneity of psychiatric disorders, which contributes to imprecise predictions. Although the use of machine learning tools to generate clinically actionable predictions is still in its infancy, these approaches may identify subgroups enabling more precise predictions. In addition, computational psychiatry might provide explanatory disease models based on faulty updating of internal values or beliefs.

CONCLUSIONS

There is a need for larger studies, clinical trials using machine learning, or computational psychiatry model parameters predictions as actionable outcomes, comparing alternative explanatory computational models, and using translational approaches that apply similar paradigms and models in humans and animals.

Multiple dimensions of stress vs. genetic effects on depression

Many psychiatric disorders including depression involve complex interactions of genetics and environmental stressors. Environmental influence is challenging to measure objectively and account for in genetic studies because the necessary large population samples in these studies involve individuals with varying cultures and life experiences, clouding genetic findings. In a unique population with relative sociocultural homogeneity and a narrower range of types of stress experiences, we quantitatively assessed multiple stress dimensions and measured their potential influence in biasing the heritability estimate of depression. We quantified depressive symptoms, major lifetime stressors, current perceived stress, and a culturally specific community stress measure in individuals with depression-related diagnoses and community controls in Old Order Amish and Mennonite populations. Results showed that lifetime stressors measured by lifetime stressor inventory (R²= 0.06, p = 2 × 10^-5) and current stress measured by Perceived Stress Scale (R²= 0.13, p < 1 × 10^-6) were both associated with current depressive symptoms quantified by Beck Depression Inventory in community controls, but current stress was the only measure associated with current depressive symptoms in individuals with a depression diagnosis, and to a greater degree (R² = 0.41, p < 1 × 10^-6). A novel, culturally specific community stress measure demonstrated internal reliability and was associated with current stress but was not significantly related to depression. Heritability (h²) for depression diagnosis (0.46 ± 0.14) and quantitative depression severity as measured by Beck Depression Inventory (0.45 ± 0.12) were significant, but h² for depression diagnosis decreased to 0.25 ± 0.14 once stressors were accounted for in the model. This quantifies and demonstrates the importance of accounting for environmental influence in reducing phenotypic heterogeneity of depression and improving the power and replicability of genetic association findings that can be better translated to patient groups.

Can Molecular Biology Propose Reliable Biomarkers for Diagnosing Major Depression?

BACKGROUND

Modern medicine has provided considerable knowledge of the pathophysiology of mental disorders at the body, systemic, organ and neurochemical levels of the biological organization of the body. Modern clinical diagnostics of depression have some problems, that is why psychiatric society makes use of diagnostics and taxonomy of different types of depression by implemention of modern molecular biomarkers in diagnostic procedures. But up to now, there are no reliable biomarkers of major depressive disorder (MDD) and other types of depression.

OBJECTIVE

The purpose of this review is to find fundamentals in pathological mechanisms of depression, which could be a basis for development of molecular and genetic biomarkers, being the most feasible for clinical use.

METHOD

This review summarizes the published data using PubMed, Science Direct, Google Scholar and Scopus.

RESULTS

In this review, we summarized and discussed findings in molecular biology, genetics, neuroplasticity, neurotransmitters, and neuroimaging that could increase our understanding of the biological foundations of depression and show new directions for the development of reliable biomarkers. We did not find any molecular and genetic biomarker approved for the clinic. But the Genome-Wide Association Study method promises some progress in the development of biomarkers based on SNP in the future. Epigenetic factors also are a promising target for biomarkers. We have found some differences in the etiology of different types of atypical and melancholic depression. This knowledge could be the basis for development of biomarkers for clinical practice in diagnosis, prognosis and selection of treatment.

CONCLUSION

Depression is not a monoetiological disease. Many pathological mechanisms are involved in depression, thus up to now, there is no approved and reliable biomarker for diagnosis, prognosis and correction of treatment of depression. The structural and functional complexity of the brain, the lack of invasive technology, poor correlations between genetic and clinical manifestation of depression, imperfect psychiatric classification and taxonomy of subtypes of disease are the main causes of this situation. One of the possible ways to come over this situation can be to pay attention to the trigger mechanism of disease and its subtypes. Researchers and clinicians should focus their efforts on searching the trigger mechanism of depression and different types of it . HPA axis can be a candidate for such trigger in depression caused by stress, because it influences the main branches of disease: neuroinflammation, activity of biogenic amines, oxidative and nitrosative stress, epigenetic factors, metabolomics, etc. But before we shall find any trigger mechanism, we need to create complex biomarkers reflecting genetic, epigenetic, metabolomics and other pathological changes in different types of depression. Recently the most encouraging results have been obtained from genetics and neuroimaging. Continuing research in these areas should be forced by using computational, statistical and systems biology approaches, which can allow to obtain more knowledge about the neurobiology of depression. In order to obtain clinically useful tests, search for biomarkers should use appropriate research methodologies with increasing samples and identifying more homogeneous groups of depressed patients.

Polygenic risk score, healthy lifestyles, and risk of incident depression

Genetic factors increase the risk of depression, but the extent to which this can be offset by modifiable lifestyle factors is unknown. We investigated whether a combination of healthy lifestyles is associated with lower risk of depression regardless of genetic risk. Data were obtained from the UK Biobank and consisted of 339,767 participants (37-73 years old) without depression between 2006 and 2010. Genetic risk was categorized as low, intermediate, or high according to polygenic risk score for depression. A combination of healthy lifestyles factors-including no current smoking, regular physical activity, a healthy diet, moderate alcohol intake and a body mass index <30 kg/m²-was categorized into favorable, intermediate, and unfavorable lifestyles. The risk of depression was 22% higher among those at high genetic risk compared with those at low genetic risk (HR = 1.22, 95% CI: 1.14-1.30). Participants with high genetic risk and unfavorable lifestyle had a more than two-fold risk of incident depression compared with low genetic risk and favorable lifestyle (HR = 2.18, 95% CI: 1.84-2.58). There was no significant interaction between genetic risk and lifestyle factors (P for interaction = 0.69). Among participants at high genetic risk, a favorable lifestyle was associated with nearly 50% lower relative risk of depression than an unfavorable lifestyle (HR = 0.51, 95% CI: 0.43-0.60). We concluded that genetic and lifestyle factors were independently associated with risk of incident depression. Adherence to healthy lifestyles may lower the risk of depression regardless of genetic risk.

Mendelian randomization provides no evidence for a causal role in the bidirectional relationship between depression and multiple sclerosis

BACKGROUND

Major depressive disorder (MDD) is common in multiple sclerosis (MS) and its incidence rises before MS diagnosis. However, the causality and direction of this association remain unclear.

OBJECTIVE

The objective is to investigate the bidirectional relationship between MS and MDD using Mendelian randomization (MR).

METHODS

We selected genetic instruments associated with risk of MDD (n = 660,937 cases; 1,453,489 controls) and MS (n = 47,429 cases; 68,374 controls). Using two-sample MR, we examined putative causal effects in either direction, with sensitivity analyses to assess pleiotropy. Also, we adjusted for body mass index (BMI) in multivariable MR.

RESULTS

We found no effect of genetic liability to MDD on the odds of MS (OR = 1.07/doubling in odds, 95% CI = 0.90-1.28). Similarly, our findings did not support a causal effect of genetic liability to MS on MDD (OR = 1.00/doubling in odds, 95% CI = 0.99-1.01). Despite heterogeneity, sensitivity analyses indicated that bias from pleiotropy was unlikely. Conversely, genetic predisposition toward higher BMI increased the odds of MS (OR = 1.34/SD increase, 95% CI = 1.09-1.65) and MDD (OR = 1.08, 95% CI = 1.01-1.15).

CONCLUSION

This study does not support a causal association between MDD genetic liability and MS susceptibility, and vice versa. Genetic evidence suggesting commonality of obesity to both conditions may partly explain the increased incidence of depression pre-MS diagnosis.

Gender Differences in Depression: Evidence From Genetics

Compared with men, female accounts for a larger proportion of patients with depression. Behavioral genetics researches find gender differences in genetic underpinnings of depression. We found that gender differences exist in heritability and the gene associated with depression after reviewing relevant research. Both genes and gene-environment interactions contribute to the risk of depression in a gender-specific manner. We detailed the relationships between serotonin transporter gene-linked promoter region (5-HTTLPR) and depression. However, the results of these studies are very different. We explored the reasons for the contradictory conclusions and provided some suggestions for future research on the gender differences in genetic underpinnings of depression.

Genetic heterogeneity in self-reported depressive symptoms identified through genetic analyses of the PHQ-9

BACKGROUND

Depression is a clinically heterogeneous disorder. Previous large-scale genetic studies of depression have explored genetic risk factors of depression case-control status or aggregated sums of depressive symptoms, ignoring possible clinical or genetic heterogeneity.

METHODS

We analyse data from 148 752 subjects of white British ancestry in the UK Biobank who completed nine items of a self-rated measure of current depressive symptoms: the Patient Health Questionnaire (PHQ-9). Genome-Wide Association analyses were conducted for nine symptoms and two composite measures. LD Score Regression was used to calculate SNP-based heritability (h2SNP) and genetic correlations (rg) across symptoms and to investigate genetic correlations with 25 external phenotypes. Genomic structural equation modelling was used to test the genetic factor structure across the nine symptoms.

RESULTS

We identified nine genome-wide significant genomic loci (8 novel), with no overlap in loci across symptoms. h2SNP ranged from 6% (concentration problems) to 9% (appetite changes). Genetic correlations ranged from 0.54 to 0.96 (all p < 1.39 × 10-3) with 30 of 36 correlations being significantly smaller than one. A two-factor model provided the best fit to the genetic covariance matrix, with factors representing 'psychological' and 'somatic' symptoms. The genetic correlations with external phenotypes showed large variation across the nine symptoms.

CONCLUSIONS

Patterns of SNP associations and genetic correlations differ across the nine symptoms, suggesting that current depressive symptoms are genetically heterogeneous. Our study highlights the value of symptom-level analyses in understanding the genetic architecture of a psychiatric trait. Future studies should investigate whether genetic heterogeneity is recapitulated in clinical symptoms of major depression.

Systemic neuro-dysregulation in depression: Evidence from genome-wide association

Depression is the world's leading cause of disability. Greater understanding of the neurobiological basis of depression is necessary for developing novel treatments with improved efficacy and acceptance. Recently, major advances have been made in the search for genetic variants associated with depression which may help to elucidate etiological mechanisms. The present review has two major objectives. First, we offer a brief review of two major biological systems with strong evidence for involvement in depression pathology: neurotransmitter systems and the stress response. Secondly, we provide a synthesis of the functions of the 269 genes implicated by the most recent genome-wide meta-analysis, supporting the importance of these systems in depression and providing insights into other possible mechanisms involving neurodevelopment, neurogenesis, and neurodegeneration. Our goal is to undertake a broad, preliminary stock-taking of the most recent hypothesis-free findings and examine the weight of the evidence supporting these existing theories and highlighting novel directions. This qualitative review and accompanying gene function table provides a valuable resource and guide for basic and translational researchers, with suggestions for future mechanistic research, leveraging genetics to prioritize studies on the neurobiological processes involved in depression etiology and treatment.

Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies

With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely 'depressions' rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.

Immune dysregulation in depression: Evidence from genome-wide association

A strong body of evidence supports a role for immune dysregulation across many psychiatric disorders including depression, the leading cause of global disability. Recent progress in the search for genetic variants associated with depression provides the opportunity to strengthen our current understanding of etiological factors contributing to depression and generate novel hypotheses. Here, we provide an overview of the literature demonstrating a role for immune dysregulation in depression, followed by a detailed discussion of the immune-related genes identified by the most recent genome-wide meta-analysis of depression. These genes represent strong evidence-based targets for future basic and translational research which aims to understand the role of the immune system in depression pathology and identify novel points for therapeutic intervention.

Do gene expression findings from mouse models of cocaine use recapitulate human cocaine use disorder in reward circuitry?

Animal models of drug use have investigated possible mechanisms governing human substance use traits for over 100 years. Most cross-species research on drug use/addiction examines behavioral overlap, but studies assessing neuromolecular (e.g. RNA) correspondence are lacking. Our study utilized transcriptome-wide data from the hippocampus and ventral tegmental area (VTA)/midbrain from a total of 35 human males with cocaine use disorder/controls and 49 male C57BL/6J cocaine/saline administering/exposed mice. We hypothesized differential expressed genes and systems of co-expressed genes (gene networks) would show appreciable overlap across mouse cocaine self-administration and human cocaine use disorder. We found modest, but significant relationships between differentially expressed genes associated with cocaine self-administration (short access) and cocaine use disorder within reward circuitry. Differentially expressed genes underlying models of acute cocaine exposure (cocaine), context re-exposure and cocaine + context re-exposure were not consistently associated with human CUD across brain regions. Investigating systems of co-expressed genes, we found several validated gene networks with weak to moderate conservation between cocaine/saline self-administering mice and disordered cocaine users/controls. The most conserved hippocampal and VTA gene networks demonstrated substantial overlap (2029 common genes) and included both novel and previously implicated targets for cocaine use/addiction. Lastly, we conducted (expression-based) phenome-wide association studies of the nine common hub genes across conserved gene networks. Common hub genes were associated with dopamine/serotonin function, cocaine self-administration and other relevant mouse traits. Overall, our study pinpointed and characterized conserved brain-related RNA patterns across mouse cocaine self-administration and human cocaine use disorder. We offer recommendations for future research and add to the dialogue surrounding pre-clinical animal research for human disease.

Using the tools of genetic epidemiology to understand sex differences in neuropsychiatric disorders

Many neuropsychiatric disorders exhibit differences in prevalence, age of onset, symptoms or course of illness between males and females. For the most part, the origins of these differences are not well understood. In this article, we provide an overview of sex differences in psychiatric disorders including autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), anxiety, depression, alcohol and substance abuse, schizophrenia, eating disorders and risk of suicide. We discuss both genetic and nongenetic mechanisms that have been hypothesized to underlie these differences, including ascertainment bias, environmental stressors, X- or Y-linked risk loci, and differential liability thresholds in males and females. We then review the use of twin, family and genome-wide association approaches to study potential genetic mechanisms of sex differences and the extent to which these designs have been employed in studies of psychiatric disorders. We describe the utility of genetic epidemiologic study designs, including classical twin and family studies, large-scale studies of population registries, derived recurrence risks, and molecular genetic analyses of genome-wide variation that may enhance our understanding sex differences in neuropsychiatric disorders.

Genetic stratification of depression in UK Biobank

Depression is a common and clinically heterogeneous mental health disorder that is frequently comorbid with other diseases and conditions. Stratification of depression may align sub-diagnoses more closely with their underling aetiology and provide more tractable targets for research and effective treatment. In the current study, we investigated whether genetic data could be used to identify subgroups within people with depression using the UK Biobank. Examination of cross-locus correlations were used to test for evidence of subgroups using genetic data from seven other complex traits and disorders that were genetically correlated with depression and had sufficient power (>0.6) for detection. We found no evidence for subgroups within depression for schizophrenia, bipolar disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, anorexia nervosa, inflammatory bowel disease or obesity. This suggests that for these traits, genetic correlations with depression were driven by pleiotropic genetic variants carried by everyone rather than by a specific subgroup.

Toward a Risk-Utility Data Governance Framework for Research Using Genomic and Phenotypic Data in Safe Havens: Multifaceted Review

BACKGROUND

Research using genomic data opens up new insights into health and disease. Being able to use the data in association with health and administrative record data held in safe havens can multiply the benefits. However, there is much discussion about the use of genomic data with perceptions of particular challenges in doing so safely and effectively.

OBJECTIVE

This study aimed to work toward a risk-utility data governance framework for research using genomic and phenotypic data in an anonymized form for research in safe havens.

METHODS

We carried out a multifaceted review drawing upon data governance arrangements in published research, case studies of organizations working with genomic and phenotypic data, public views and expectations, and example studies using genomic and phenotypic data in combination. The findings were contextualized against a backdrop of legislative and regulatory requirements and used to create recommendations.

RESULTS

We proposed recommendations toward a risk-utility model with a flexible suite of controls to safeguard privacy and retain data utility for research. These were presented as overarching principles aligned to the core elements in the data sharing framework produced by the Global Alliance for Genomics and Health and as practical control measures distilled from published literature and case studies of operational safe havens to be applied as required at a project-specific level.

CONCLUSIONS

The recommendations presented can be used to contribute toward a proportionate data governance framework to promote the safe, socially acceptable use of genomic and phenotypic data in safe havens. They do not purport to eradicate risk but propose case-by-case assessment with transparency and accountability. If the risks are adequately understood and mitigated, there should be no reason that linked genomic and phenotypic data should not be used in an anonymized form for research in safe havens.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

Genetics of resilience: Implications from genome-wide association studies and candidate genes of the stress response system in posttraumatic stress disorder and depression

Resilience is the ability to cope with critical situations through the use of personal and socially mediated resources. Since a lack of resilience increases the risk of developing stress-related psychiatric disorders such as posttraumatic stress disorder (PTSD) and major depressive disorder (MDD), a better understanding of the biological background is of great value to provide better prevention and treatment options. Resilience is undeniably influenced by genetic factors, but very little is known about the exact underlying mechanisms. A recently published genome-wide association study (GWAS) on resilience has identified three new susceptibility loci, DCLK2, KLHL36, and SLC15A5. Further interesting results can be found in association analyses of gene variants of the stress response system, which is closely related to resilience, and PTSD and MDD. Several promising genes, such as the COMT (catechol-O-methyltransferase) gene, the serotonin transporter gene (SLC6A4), and neuropeptide Y (NPY) suggest gene × environment interaction between genetic variants, childhood adversity, and the occurrence of PTSD and MDD, indicating an impact of these genes on resilience. GWAS on PTSD and MDD provide another approach to identifying new disease-associated loci and, although the functional significance for disease development for most of these risk genes is still unknown, they are potential candidates due to the overlap of stress-related psychiatric disorders and resilience. In the future, it will be important for genetic studies to focus more on resilience than on pathological phenotypes, to develop reasonable concepts for measuring resilience, and to establish international cooperations to generate sufficiently large samples.

Driven by Pain, Not Gain: Computational Approaches to Aversion-Related Decision Making in Psychiatry

Although it is well known that "losses loom larger than gains," computational approaches to aversion-related decision making (ARDM) for psychiatric disorders is an underdeveloped area. Computational models of ARDM have been implemented primarily as state-dependent reinforcement learning models with bias parameters to quantify Pavlovian associations, and differential learning rates to quantify instrumental updating have been shown to depend on context, involve complex cost calculations, and include the consideration of counterfactual outcomes. Little is known about how individual differences influence these models relevant to anxiety-related conditions or addiction-related dysfunction. It is argued that model parameters reflecting 1) Pavlovian biases in the context of reinforcement learning or 2) hyperprecise prior beliefs in the context of active inference play an important role in the emergence of dysfunctional avoidance behaviors. The neural implementation of ARDM includes brain areas that are important for valuation (ventromedial prefrontal cortex) and positive reinforcement-related prediction errors (ventral striatum), but also aversive processing (insular cortex and cerebellum). Computational models of ARDM will help to establish a quantitative explanatory account of the development of anxiety disorders and addiction, but such models also face several challenges, including limited evidence for stability of individual differences, relatively low reliability of tasks, and disorder heterogeneity. Thus, it will be necessary to develop robust, reliable, and model-based experimental probes; recruit larger sample sizes; and use single case experimental designs for better pragmatic and explanatory biological models of psychiatric disorders.

Genetic Intersections of Language and Neuropsychiatric Conditions

PURPOSE OF REVIEW

To better understand the shared basis of language and mental health, this review examines the behavioral and neurobiological features of aberrant language in five major neuropsychiatric conditions. Special attention is paid to genes implicated in both language and neuropsychiatric disorders, as they reveal biological domains likely to underpin the processes controlling both.

RECENT FINDINGS

Abnormal language and communication are common manifestations of neuropsychiatric conditions, and children with impaired language are more likely to develop psychiatric disorders than their peers. Major themes in the genetics of both language and psychiatry include master transcriptional regulators, like FOXP2; key developmental regulators, like AUTS2; and mediators of neurotransmission, like GRIN2A and CACNA1C.

Genetic Advance in Depressive Disorder

Major depressive disorder (MDD) and bipolar disorder (BPD) are both chronic, severe mood disorder with high misdiagnosis rate, leading to substantial health and economic burdens to patients around the world. There is a high misdiagnosis rate of bipolar depression (BD) just based on symptomology in depressed patients whose previous manic or mixed episodes have not been well recognized. Therefore, it is important for psychiatrists to identify these two major psychiatric disorders. Recently, with the accumulation of clinical sample sizes and the advances of methodology and technology, certain progress in the genetics of major depression and bipolar disorder has been made. This article reviews the candidate genes for MDD and BD, genetic variation loci, chromosome structural variation, new technologies, and new methods.

Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies

To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression.