Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways

Gene-set Enrichment with Mathematical Biology (GEMB)

BACKGROUND

Gene-set analyses measure the association between a disease of interest and a "set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further-defining gene contributions based on biophysical properties-by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function.

RESULTS

We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10-4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199).

CONCLUSIONS

Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.

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.

Judging in the genomic era: judges' genetic knowledge, confidence and need for training

Genetic information is increasingly used in many contexts, including health, insurance, policing and sentencing-with numerous potential benefits and risks. Protecting from the related risks requires updates to laws and procedures by justice systems. These updates depend to a large extent on what the key stakeholders-the judiciary-know and think about the use of genetic information. This study used a battery of 25 genetic knowledge items to collect data from 73 supreme court judges from the same country (Romania) on their knowledge of genetic information. Their responses were compared with those of two other groups: lawyers (but not judges; N = 94) and non-lawyers (N = 116) from the same country. The data were collected at approximately the same time from the three groups. The judges' results were also compared to the results obtained from a general population data collection (N = 5310). The results showed that: (1) judges had overall better knowledge of genetics than the other groups, but their knowledge was uneven across different genetic concepts; (2) judges were overall more confident in their knowledge than the other two groups, but their confidence was quite low; and (3) the correlation between knowledge and confidence was moderate for judges, weak for lawyers and not significant for non-lawyers. Finally, 100% of the judges agreed that information on gene-environment processes should be included in judges' training. Increasing genetic expertise of the justice stakeholders is an important step towards achieving adequate legal protection against genetic data misuse.

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.

Genetics and major depressive disorder: clinical implications for disease risk, prognosis and treatment

In the post-genomic era, genetics has led to limited clinical applications in the diagnosis and treatment of major depressive disorder (MDD). Variants in genes coding for cytochrome enzymes are included in guidelines for assisting in antidepressant choice and dosing, but there are no recommendations involving genes responsible for antidepressant pharmacodynamics and no consensus applications for guiding diagnosis or prognosis. However, genetics has contributed to a better understanding of MDD pathogenesis and the mechanisms of antidepressant action, also thanks to recent methodological innovations that overcome the challenges posed by the polygenic architecture of these traits. Polygenic risk scores can be used to estimate the risk of disease at the individual level, which may have clinical relevance in cases with extremely high scores (e.g. top 1%). Genetic studies have also shed light on a wide genetic overlap between MDD and other psychiatric disorders. The relationships between genes/pathways associated with MDD and known drug targets are a promising tool for drug repurposing and identification of new pharmacological targets. Increase in power thanks to larger samples and methods integrating genetic data with gene expression, the integration of common variants and rare variants, are expected to advance our knowledge and assist in personalized psychiatry.

Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services

Objective

Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies.

Methods

We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies. We utilized Spark clusters on both Google Cloud Platform and Amazon Web Services, as well as Hail (http://doi.org/10.5281/zenodo.2646680) for analysis and exploration of genomic variants dataset.

Results

Comparative evaluation of numerous cloud-based cluster configurations demonstrate a successful and unprecedented compromise between speed and cost for performing genome-wide association studies on 4 distinct whole-genome sequencing datasets. Results are consistent across the 2 cloud providers and could be highly useful for accelerating research in genetics.

Conclusions

We present a timely piece for one of the most frequently asked questions when moving to the cloud: what is the trade-off between speed and cost?

Application of antidepressants in depression: A systematic review and meta-analysis

BACKGROUND

The type and quantities of antidepressants are increasing, but the efficacy and safety of first-line and emerging drugs vary between studies. In this article, we estimated the efficacy and safety of first-line and emerging antidepressants (anti-inflammatory drugs and ketamine).

METHOD

ystematic search of EMBASE, ERIC, MEDLINE, psycARTICLES, and psycINFO without language restriction for studies on the depression, depressive symptoms, antidepressants, fluoxetine (Prozac), paroxetine, escitalopram, sertraline, fluvoxamine, venlafaxine, duloxetine, NSAIDs, anti-cytokine drugs or pioglitazone published before May 1st, 2019. Information on study characteristics, depression or depressive symptoms, antidepressants and the descriptive statistics (including efficacy and safety of antidepressants) was extracted independently by 2 investigators. Estimates were pooled using random-effects meta-analysis. Differences by study-level characteristics were estimated using stratified meta-analysis and meta-regression. The response and remission of antidepressants were used as clinical evaluation indicators, and the evaluation criteria were clinical depression scales. OR value of antidepressants as assessed by meta-analysis.

RESULTS

The literature search retrieved 5529 potentially relevant articles of which 49 studies were finally included. We compared the efficacy of antidepressants (seven first-line antidepressants (fluoxetine, paroxetine, escitalopram, sertraline, fluvoxamine, venlafaxine, duloxetine), there kinds of anti-inflammatory drugs(NASIDs, cytokine-inhibitor, pioglitazone) and ketamine) by comparing the OR values.

CONCLUSION

The three drugs with the highest OR value in response were NASID (OR = 3.62(1.58, 8.32)), venlafaxin (OR = 3.50(1.83, 6.70)) and ketamine (OR = 3.28(1.89, 5.68)), while the highest OR value in remission were NASID (OR = 3.17(1.60, 6.29)), ketamine (OR = 2.99(1.58, 5.67)) and venlafaxin (OR = 2.55(1.72, 3.78)). Through reading the literature, we found 69 SNPs associated with depression. Major depression was a debilitating disorder that could ultimately lead to enormous societal and economical challenge [1]. The number of person which affected by depression was up to 16% of the population worldwide. More than 300 million individuals were estimated to suffer depression these days [1,2]. Therefore, it is apparent that safety and effective treatments for depression are necessary. In the 1930 s, the first drug for schizophrenia was discovered. This finding was a landmark for the emerging of biological psychiatry. In the 1950 s, pharmacologists had stumbled upon the antidepressant effect of imipramine. Since then, every 30 years, the use of antidepressants had made a pulsatile leap. Selective serotonin reuptake inhibitors (SSRIs) are the most widely-prescribed psychiatric drugs for the treatment of depression. However, the efficacy was variable and incomplete: 60%-70% of the patients do not experience remission, while 30%-40% do not show a significant response [3,4]. Nevertheless, SSRIs, SNRIs (selective serotonin-norepinephrine reuptake inhibitors, which can block norepinephrine at the same time) and NaSSAs (norepinephrine and selective serotonin receptor agonist), constituted the first-line clinical drugs. Nearly 30 years after the outbreak of SSRIs, antidepressants have ushered in a new chapter. It has been found that anti-inflammatory drugs could also have the small and moderate antidepressant effect and it's widely discussed [5]. More than 40 anti-inflammatory drugs have been certificated to have antidepressant effects in preclinical and clinical studies [6]. The antidepressant that has been approved for use recently is ketamine. There is no comprehensive comparison of the efficacy of all these drugs. In this review, we tried to estimate the efficacy and safety of first-line antidepressants, anti-inflammatory drugs and ketamine. On the other hand, with the development of GWAS, SNPs related to depression have been reported, and the corresponding mechanisms have been elaborated, respectively. However, patients with these SNPs have not been treated with individualized drugs according to the mechanisms. We hope to push this process forward through the summary of this article.

METHODS

Search Strategy and Study Eligibility.

Mood-Stabilizing Antiepileptic Treatment Response in Bipolar Disorder: A Genome-Wide Association Study

Several antiepileptic drugs (AEDs) have US Food and Drug Administration (FDA) approval for use as mood stabilizers in bipolar disorder (BD), but not all BD patients respond to these AED mood stabilizers (AED‐MSs). To identify genetic polymorphisms that contribute to the variability in AED‐MS response, we performed a discovery genome‐wide association study (GWAS) of 199 BD patients from the Mayo Clinic Bipolar Disorder Biobank. Most of these patients had been treated with the AED‐MS valproate/divalproex and/or lamotrigine. AED‐MS response was assessed using the Alda scale, which quantifies clinical improvement while accounting for potential confounding factors. We identified two genome‐wide significant single‐nucleotide polymorphism (SNP) signals that mapped to the THSD7A (rs78835388, P = 7.1E‐09) and SLC35F3 (rs114872993, P = 3.2E‐08) genes. We also identified two genes with statistically significant gene‐level associations: ABCC1 (P = 6.7E‐07; top SNP rs875740, P = 2.0E‐6), and DISP1 (P = 8.9E‐07; top SNP rs34701716, P = 8.9E‐07). THSD7A SNPs were previously found to be associated with risk for several psychiatric disorders, including BD. Both THSD7A and SLC35F3 are expressed in excitatory/glutamatergic and inhibitory/γ‐aminobutyric acidergic (GABAergic) neurons, which are targets of AED‐MSs. ABCC1 is involved in the transport of valproate and lamotrigine metabolites, and the SNPs in ABCC1 and DISP1 with the strongest evidence of association in our GWAS are strong splicing quantitative trait loci in the human gut, suggesting a possible influence on drug absorption. In conclusion, our pharmacogenomic study identified novel genetic loci that appear to contribute to AED‐MS treatment response, and may facilitate precision medicine in BD.

The Latent Genetic Structure of Impulsivity and Its Relation to Internalizing Psychopathology

Factor analyses suggest that impulsivity traits that capture tendencies to act prematurely or take risks tap partially distinct constructs. We applied genomic structure equation modeling to evaluate the genetic factor structure of two well-established impulsivity questionnaires, using published statistics from genome-wide association studies of up to 22,861 participants. We also tested the hypotheses that delay discounting would be genetically separable from other impulsivity factors and that emotionally triggered facets of impulsivity (urgency) would be those most strongly genetically correlated with an internalizing latent factor. A five-factor model best fitted the impulsivity data. Delay discounting was genetically distinct from these five factors. As expected, the two urgency subscales were most strongly related to an internalizing-psychopathology latent factor. These findings provide empirical genetic evidence that impulsivity can be broken down into distinct categories of differential relevance for internalizing psychopathology. They also demonstrate how measured genetic markers can be used to inform theories of psychology and personality.

Insights into the genomics of affective disorders

Affective disorders, or mood disorders, are a group of neuropsychiatric illnesses that are characterized by a disturbance of mood or affect. Most genetic research in this field to date has focused on bipolar disorder and major depression. Symptoms of major depression include a depressed mood, reduced energy, and a loss of interest and enjoyment. Bipolar disorder is characterized by the occurrence of (hypo)manic episodes, which generally alternate with periods of depression. Formal and molecular genetic studies have demonstrated that affective disorders are multifactorial diseases, in which both genetic and environmental factors contribute to disease development. Twin and family studies have generated heritability estimates of 58–85 % for bipolar disorder and 40 % for major depression.

Large genome-wide association studies have provided important insights into the genetics of affective disorders via the identification of a number of common genetic risk factors. Based on these studies, the estimated overall contribution of common variants to the phenotypic variability (single-nucleotide polymorphism [SNP]-based heritability) is 17–23 % for bipolar disorder and 9 % for major depression. Bioinformatic analyses suggest that the associated loci and implicated genes converge into specific pathways, including calcium signaling. Research suggests that rare copy number variants make a lower contribution to the development of affective disorders than to other psychiatric diseases, such as schizophrenia or the autism spectrum disorders, which would be compatible with their less pronounced negative impact on reproduction. However, the identification of rare sequence variants remains in its infancy, as available next-generation sequencing studies have been conducted in limited samples. Future research strategies will include the enlargement of genomic data sets via innovative recruitment strategies; functional analyses of known associated loci; and the development of new, etiologically based disease models. Researchers hope that deeper insights into the biological causes of affective disorders will eventually lead to improved diagnostics and disease prediction, as well as to the development of new preventative, diagnostic, and therapeutic strategies. Pharmacogenetics and the application of polygenic risk scores represent promising initial approaches to the future translation of genomic findings into psychiatric clinical practice.

Genome-wide association study and polygenic risk score analysis of esketamine treatment response

To elucidate the genetic underpinnings of the antidepressant efficacy of S-ketamine (esketamine) nasal spray in major depressive disorder (MDD), we performed a genome-wide association study (GWAS) in cohorts of European ancestry (n = 527). This analysis was followed by a polygenic risk score approach to test for associations between genetic loading for psychiatric conditions, symptom profiles and esketamine efficacy. We identified a genome-wide significant locus in IRAK3 (p = 3.57 × 10^–8, rs11465988, β = − 51.6, SE = 9.2) and a genome-wide significant gene-level association in NME7 (p = 1.73 × 10^–6) for esketamine efficacy (i.e. percentage change in symptom severity score compared to baseline). Additionally, the strongest association with esketamine efficacy identified in the polygenic score analysis was from the genetic loading for depressive symptoms (p = 0.001, standardized coefficient β = − 3.1, SE = 0.9), which did not reach study-wide significance. Pathways relevant to neuronal and synaptic function, immune signaling, and glucocorticoid receptor/stress response showed enrichment among the suggestive GWAS signals.

The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls

BACKGROUND

Mood disorders (including major depressive disorder and bipolar disorder) affect 10% to 20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of risk factors across mood disorders despite their diagnostic distinction.

METHODS

To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N = 609,424).

RESULTS

Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment-the relationship is positive in bipolar disorder but negative in major depressive disorder.

CONCLUSIONS

The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.

Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank

Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094-92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg = 0.24, p = 1.8 × 10-7 versus rg = -0.05, p = 0.39 in individuals not reporting trauma exposure, difference p = 2.3 × 10-4). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.

Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes

Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.

The Potential Effect of Aberrant Testosterone Levels on Common Diseases: A Mendelian Randomization Study

Testosterone has historically been linked to sexual dysfunction; however, it has recently been shown to affect other physical and mental attributes. We attempted to determine whether changes in serum testosterone could play a role in chronic or degenerative diseases. We used two separate genetic instruments comprising of variants from JMJD1C and SHBG regions and conducted a two-sample Mendelian randomization for type II diabetes (T2D), gout, rheumatoid arthritis (RA), schizophrenia, bipolar disorder, Alzheimer's disease and depression. For the JMJD1C locus, one unit increase in log transformed testosterone was significantly associated with RA (OR = 1.69, p = 0.02), gout (OR = 0.469, p = 0.001) and T2D (OR = 0.769, p = 0.048). Similarly, one unit increase in log transformed testosterone using variants from the SHBG locus was associated with depression (OR = 1.02, p < 0.0001), RA (OR = 1.254, p < 0.0001) and T2D (OR = 0.88, p < 0.0001). Our results show that low levels of serum testosterone levels may cause gout and T2D, while higher than normal levels of testosterone may result in RA and depression. Our findings suggest that fluctuations in testosterone levels may have severe consequences that warrant further investigation.

Prefrontal cortex eQTLs/mQTLs enriched in genetic variants associated with alcohol use disorder and other diseases

Aim: This study aimed to investigate the function of genome-wide association study (GWAS)-identified variants associated with alcohol use disorder (AUD)/comorbid psychiatric disorders. Materials & methods: Genome-wide genotype, transcriptome and DNA methylome data were obtained from postmortem prefrontal cortex (PFC) of 48 Caucasians (24 AUD cases/24 controls). Expression/methylation quantitative trait loci (eQTL/mQTL) were identified and their enrichment in GWAS signals for the above disorders were analyzed. Results: PFC cis-eQTLs (923 from cases+controls, 27 from cases and 98 from controls) and cis-mQTLs (9,932 from cases+controls, 264 from cases and 695 from controls) were enriched in GWAS-identified genetic variants for the above disorders. Cis-eQTLs from AUD cases were mapped to morphine addiction-related genes. Conclusion: PFC cis-eQTLs/cis-mQTLs influence gene expression/DNA methylation patterns, thus increasing the disease risk.

Association of FKBP5 polymorphisms with patient susceptibility to coronary artery disease comorbid with depression

BACKGROUND

Coronary artery disease (CAD) and depression cause great burden to society and frequently co-occur. The exact mechanisms of this comorbidity are unclear. FK506-binding protein 51 (FKBP51) is correlated with cardiovascular disease and depression. The aim of this study was to determine the role of the seven single nucleotide polymorphisms (SNPs) of FKBP5 that code FKBP51, namely, rs1360780 (C>T), rs2817032 (T>C), rs2817035 (G>A), rs9296158 (G>A), rs9470079 (G>A), rs4713902 (T>C), and rs3800373 (C>T) in a patient's susceptibility to comorbid CAD and depression.

METHODS

We enrolled 271 Northern Chinese Han patients with CAD, including 123 patients with depression and 147 patients without depression. We also included 113 healthy controls that match the patients' sex and age. Genomic DNA from whole blood was extracted, and seven SNPs were assessed using MassArray method. Patient Health Questionnaire-9 was applied to access the depression.

RESULTS

The GA genotype for rs9470079 was associated with a significantly decreased risk of CAD (odds ratio = 0.506, 95% confidence interval = 0.316-0.810, P = 0.005) when the GG genotype was used as reference. A statistically significant difference was observed among females but not among males in the rs9470079 genotype and allele frequency. Patients with CAD were further divided into CAD+D and CAD-D groups according to the presence of comorbid depression and were compared with the controls. Significant differences were found regarding the genotype and allele frequency of rs2817035 and rs9470079 in CAD+H groups compared with the control subjects in all groups and the female groups (P < 0.05).

CONCLUSIONS

The current study found a remarkable association between FKBP5 gene variations and the risk of comorbid CAD and depression in a north Chinese population. rs9470079 may be a potential gene locus for the incidence of comorbid CAD and depression.

Cohort profile: the Australian genetics of depression study

PURPOSE

Depression is the most common psychiatric disorder and the largest contributor to global disability. The Australian Genetics of Depression study was established to recruit a large cohort of individuals who have been diagnosed with depression at some point in their lifetime. The purpose of establishing this cohort is to investigate genetic and environmental risk factors for depression and response to commonly prescribed antidepressants.

PARTICIPANTS

A total of 20 689 participants were recruited through the Australian Department of Human Services and a media campaign, 75% of whom were female. The average age of participants was 43 years±15 years. Participants completed an online questionnaire that consisted of a compulsory module that assessed self-reported psychiatric history, clinical depression using the Composite Interview Diagnostic Interview Short Form and experiences of using commonly prescribed antidepressants. Further voluntary modules assessed a wide range of traits of relevance to psychopathology. Participants who reported they were willing to provide a DNA sample (75%) were sent a saliva kit in the mail.

FINDINGS TO DATE

95% of participants reported being given a diagnosis of depression by a medical practitioner and 88% met the criteria for a lifetime depressive episode. 68% of the sample report having been diagnosed with another psychiatric disorder in addition to depression. In line with findings from clinical trials, only 33% of the sample report responding well to the first antidepressant they were prescribed.

FUTURE PLANS

A number of analyses to investigate the genetic architecture of depression and common comorbidities will be conducted. The cohort will contribute to the global effort to identify genetic variants that increase risk to depression. Furthermore, a thorough investigation of genetic and psychosocial predictors of antidepressant response and side effects is planned.

Genomic analysis of the natural history of attention-deficit/hyperactivity disorder using Neanderthal and ancient Homo sapiens samples

Attention-deficit/hyperactivity disorder (ADHD) is an impairing neurodevelopmental condition highly prevalent in current populations. Several hypotheses have been proposed to explain this paradox, mainly in the context of the Paleolithic versus Neolithic cultural shift but especially within the framework of the mismatch theory. This theory elaborates on how a particular trait once favoured in an ancient environment might become maladaptive upon environmental changes. However, given the lack of genomic data available for ADHD, these theories have not been empirically tested. We took advantage of the largest GWAS meta-analysis available for this disorder consisting of over 20,000 individuals diagnosed with ADHD and 35,000 controls, to assess the evolution of ADHD-associated alleles in European populations using archaic, ancient and modern human samples. We also included Approximate Bayesian computation coupled with deep learning analyses and singleton density scores to detect human adaptation. Our analyses indicate that ADHD-associated alleles are enriched in loss of function intolerant genes, supporting the role of selective pressures in this early-onset phenotype. Furthermore, we observed that the frequency of variants associated with ADHD has steadily decreased since Paleolithic times, particularly in Paleolithic European populations compared to samples from the Neolithic Fertile Crescent. We demonstrate this trend cannot be explained by African admixture nor Neanderthal introgression, since introgressed Neanderthal alleles are enriched in ADHD risk variants. All analyses performed support the presence of long-standing selective pressures acting against ADHD-associated alleles until recent times. Overall, our results are compatible with the mismatch theory for ADHD but suggest a much older time frame for the evolution of ADHD-associated alleles compared to previous hypotheses.

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.

Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective

Genome-wide association studies (GWAS) have been performed for many psychiatric disorders and revealed a complex polygenic architecture linking mental and physical health phenotypes. Psychiatric diagnoses are often heterogeneous, and several layers of trait heterogeneity may contribute to detection of genetic risks per disorder or across multiple disorders. In this review, we discuss these heterogeneities and their consequences on the discovery of risk loci using large-scale genetic data. We primarily highlight the ways in which sex and diagnostic complexity contribute to risk locus discovery in schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, major depressive disorder, obsessive-compulsive disorder, Tourette’s syndrome and chronic tic disorder, anxiety disorders, suicidality, feeding and eating disorders, and substance use disorders. Genetic data also have facilitated discovery of clinically relevant subphenotypes also described here. Collectively, GWAS of psychiatric disorders revealed that the understanding of heterogeneity, polygenicity, and pleiotropy is critical to translate genetic findings into treatment strategies.

A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

Depression is a leading cause of worldwide disability but there remains considerable uncertainty regarding its neural and behavioural associations. Here, using non-overlapping Psychiatric Genomics Consortium (PGC) datasets as a reference, we estimate polygenic risk scores for depression (depression-PRS) in a discovery (N = 10,674) and replication (N = 11,214) imaging sample from UK Biobank. We report 77 traits that are significantly associated with depression-PRS, in both discovery and replication analyses. Mendelian Randomisation analysis supports a potential causal effect of liability to depression on brain white matter microstructure (β: 0.125 to 0.868, p_FDR < 0.043). Several behavioural traits are also associated with depression-PRS (β: 0.014 to 0.180, p_FDR: 0.049 to 1.28 × 10⁻¹⁴) and we find a significant and positive interaction between depression-PRS and adverse environmental exposures on mental health outcomes. This study reveals replicable associations between depression-PRS and white matter microstructure. Our results indicate that white matter microstructure differences may be a causal consequence of liability to depression.

Depression is correlated with many brain-related traits. Here, Shen et al. perform phenome-wide association studies of a depression polygenic risk score (PRS) and find associations with 51 behavioural and 26 neuroimaging traits which are further followed up on using Mendelian randomization and mediation analyses.

The Rearing Environment and Risk for Major Depression: A Swedish National High-Risk Home-Reared and Adopted-Away Co-Sibling Control Study

OBJECTIVE

The authors sought to clarify the role of rearing environment in the etiology of major depression.

METHODS

Defining high risk as having at least one biological parent with major depression, the authors identified a Swedish National Sample of 666 high-risk full sibships and 2,596 high-risk half sibships containing at least one home-reared and one adopted-away sibling. Major depression was assessed from national medical registries.

RESULTS

Controlling for sex, parental age at birth, and, for half siblings, history of major depression in the nonshared parent, the risk for major depression in the matched adopted compared with home-reared full and half siblings was reduced by 23% (95% CI=7-36) and by 19% (95% CI=10-38), respectively. This protective rearing effect was not influenced by the relative educational status of the biological and adoptive parents. However, in both full and half sibships, the protective effect of adoption disappeared when an adoptive parent or stepsibling had major depression or the adoptive home was disrupted by parental death or divorce.

CONCLUSIONS

In matched full and half sibships at high risk for major depression, compared with individuals raised in their home environment, those reared in adoptive homes (homes selected in Sweden for their high-quality rearing environment) had a significantly reduced risk for major depression. This protective effect disappeared if an adoptive parent had major depression or if the adoptive home experienced parental death or divorce during childhood/adolescence. The rearing environment has a meaningful impact on risk for major depression, and this effect is likely mediated both by parental depression and the continuity or disruption of the home environment.

Toward Circuit Mechanisms of Pathophysiology in Depression

The search for more effective treatments for depression is a long-standing primary objective in both psychiatry and translational neuroscience. From initial models centered on neurochemical deficits, such as the monoamine hypothesis, research toward this goal has shifted toward a focus on network and circuit models to explain how key nodes in the limbic system and beyond interact to produce persistent shifts in affective states. To build these models, researchers have turned to two complementary approaches: neuroimaging studies in human patients (and their healthy counterparts) and neurophysiology studies in animal models, facilitated in large part by optogenetic and chemogenetic techniques. As the authors discuss, functional neuroimaging studies in humans have included largely task-oriented experiments, which have identified brain regions differentially activated during processing of affective stimuli, and resting-state functional MRI experiments, which have identified brain-wide networks altered in depressive states. Future work in this area will build on a multisite approach, assembling large data sets across diverse populations, and will also leverage the statistical power afforded by longitudinal imaging studies in patient samples. Translational studies in rodents have used optogenetic and chemogenetic tools to identify not just nodes but also connections within the networks of the limbic system that are both critical and permissive for the expression of motivated behavior and affective phenotypes. Future studies in this area will exploit mesoscale imaging and multisite electrophysiology recordings to construct network models with cell-type specificity and high statistical power, identifying candidate circuit and molecular pathways for therapeutic intervention.

Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning

BACKGROUND

Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge.

METHODS

In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia.

RESULTS

Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits.

CONCLUSIONS

These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.

Minimal phenotyping yields genome-wide association signals of low specificity for major depression

Minimal phenotyping refers to the reliance on the use of a small number of self-reported items for disease case identification, increasingly used in genome-wide association studies (GWAS). Here we report differences in genetic architecture between depression defined by minimal phenotyping and strictly defined major depressive disorder (MDD): the former has a lower genotype-derived heritability that cannot be explained by inclusion of milder cases and a higher proportion of the genome contributing to this shared genetic liability with other conditions than for strictly defined MDD. GWAS based on minimal phenotyping definitions preferentially identifies loci that are not specific to MDD, and, although it generates highly predictive polygenic risk scores, the predictive power can be explained entirely by large sample sizes rather than by specificity for MDD. Our results show that reliance on results from minimal phenotyping may bias views of the genetic architecture of MDD and impede the ability to identify pathways specific to MDD.

Effects of tryptophan, serotonin, and kynurenine on ischemic heart diseases and its risk factors: a Mendelian Randomization study

BACKGROUND/OBJECTIVES

Tryptophan is an essential amino acid that must be obtained from dietary items, such as dairy products, eggs, nuts, legumes, and grains, which are rich in tryptophan. It has also been suggested as a dietary supplement to improve mental health. Observationally plasma tryptophan is inversely associated with ischemic heart disease (IHD), however, its main metabolites, serotonin, and kynurenine are positively associated with IHD, which makes the effects of tryptophan difficult to infer. This study aimed to obtain less-confounded estimates of the associations of tryptophan and physiologically related factors (serotonin and kynurenine) with IHD, its risk factors and depression.

SUBJECTS/METHODS

We used a two-sample Mendelian Randomization study design. We used genetic instruments independently associated with tryptophan, serotonin, and kynurenine metabolites applied to a meta-analysis of the UK Biobank SOFT CAD study with the CARDIoGRAMplusC4D consortium (cases n ≤ 76,014 and controls n ≤ 264,785), and other consortia for risk factors including diabetes, lipids, and blood pressure, as well as for depression. We combined genetic variant-specific estimates using inverse variance weighting, with MR-Egger, the weighted median and MR-PRESSO as sensitivity analyses.

RESULTS

Tryptophan and serotonin were not associated with IHD. Kynurenine was nominally and positively associated with IHD (odds ratio 1.57 per standard deviation, 95% confidence interval 1.05-2.33) but not after correction for multiple comparisons. Associations with IHD risk factors and depression were null.

CONCLUSIONS

We cannot exclude the possibility that one of the main metabolites of tryptophan, kynurenine, might be positively associated with IHD. Further studies are needed to confirm any association and underlying mechanism.

Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression

BACKGROUND

The prevalence of depression is higher in individuals with autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Shared genetic etiology is a plausible explanation for the overlap, and in this study we tested whether genetic variation in the major histocompatibility complex (MHC), which is associated with risk for autoimmune diseases, is also associated with risk for depression.

METHODS

We fine-mapped the classical MHC (chr6: 29.6-33.1 Mb), imputing 216 human leukocyte antigen (HLA) alleles and 4 complement component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium Major Depressive Disorder Working Group and the UK Biobank. The total sample size was 45,149 depression cases and 86,698 controls. We tested for association between depression status and imputed MHC variants, applying both a region-wide significance threshold (3.9 × 10-6) and a candidate threshold (1.6 × 10-4).

RESULTS

No HLA alleles or C4 haplotypes were associated with depression at the region-wide threshold. HLA-B*08:01 was associated with modest protection for depression at the candidate threshold for testing in HLA genes in the meta-analysis (odds ratio = 0.98, 95% confidence interval = 0.97-0.99).

CONCLUSIONS

We found no evidence that an increased risk for depression was conferred by HLA alleles, which play a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia. These results suggest that any HLA or C4 variants associated with depression either are rare or have very modest effect sizes.

CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research

The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18-30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.

Divergence of an association between depressive symptoms and a dopamine polygenic score in Caucasians and Asians

A recent study reported a negative association between a putatively functional dopamine (DA) polygenic score, indexing higher levels of DA signaling, and depressive symptoms. We attempted to replicate this association using data from the Duke Neurogenetics Study. Our replication attempt was made in a subsample of 520 non-Hispanic Caucasian volunteers (277 women, mean age 19.78 ± 1.24 years). The DA polygenic score was based on the following five loci: rs27072 (SLC6A3/DAT1), rs4532 (DRD1), rs1800497 (DRD2/ANKK1), rs6280 (DRD3), and rs4680 (COMT). Because the discovery sample in the original study consisted mostly of Asian participants, we also conducted a post hoc analysis in a smaller subsample of Asian volunteers (N = 316, 179 women, mean age 19.61 ± 1.32 years). In the primary sample of non-Hispanic Caucasians, a linear regression analysis controlling for sex, age, socioeconomic status (SES), body mass index, genetic ancestry, and both early and recent life stress, revealed that higher DA polygenic scores were associated with higher self-reported symptoms of depression. This was in contrast to the original association of higher DA polygenic scores and lower depressive symptoms. However, the direction of the association in our Asian subsample was consistent with this original finding. Our results also suggested that compared to the Asian subsample, the non-Hispanic Caucasian subsample was characterized by higher SES, lower early and recent life stress, and lower depressive symptoms. These differences may have contributed to the observed divergence in associations. Collectively, the current findings add to evidence that specific genetic associations may differ between populations and further encourage explicit modeling of race/ethnicity in examining the polygenic nature of depressive symptoms and depression.

Measurement and genetic architecture of lifetime depression in the Netherlands as assessed by LIDAS (Lifetime Depression Assessment Self-report)

BACKGROUND

Major depressive disorder (MDD) is a common mood disorder, with a heritability of around 34%. Molecular genetic studies made significant progress and identified genetic markers associated with the risk of MDD; however, progress is slowed down by substantial heterogeneity as MDD is assessed differently across international cohorts. Here, we used a standardized online approach to measure MDD in multiple cohorts in the Netherlands and evaluated whether this approach can be used in epidemiological and genetic association studies of depression.

METHODS

Within the Biobank Netherlands Internet Collaboration (BIONIC) project, we collected MDD data in eight cohorts involving 31 936 participants, using the online Lifetime Depression Assessment Self-report (LIDAS), and estimated the prevalence of current and lifetime MDD in 22 623 unrelated individuals. In a large Netherlands Twin Register (NTR) twin-family dataset (n ≈ 18 000), we estimated the heritability of MDD, and the prediction of MDD in a subset (n = 4782) through Polygenic Risk Score (PRS).

RESULTS

Estimates of current and lifetime MDD prevalence were 6.7% and 18.1%, respectively, in line with population estimates based on validated psychiatric interviews. In the NTR heritability estimates were 0.34/0.30 (s.e. = 0.02/0.02) for current/lifetime MDD, respectively, showing that the LIDAS gives similar heritability rates for MDD as reported in the literature. The PRS predicted risk of MDD (OR 1.23, 95% CI 1.15-1.32, R2 = 1.47%).

CONCLUSIONS

By assessing MDD status in the Netherlands using the LIDAS instrument, we were able to confirm previously reported MDD prevalence and heritability estimates, which suggests that this instrument can be used in epidemiological and genetic association studies of depression.

Twin study designs as a tool to identify new candidate genes for depression: A systematic review of DNA methylation studies

Monozygotic (MZ) twin studies constitute a key resource for the dissection of environmental and biological risk factors for human complex disorders. Given that epigenetic differences accumulate throughout the lifespan, the assessment of MZ twin pairs discordant for depression offers a genetically informative design to explore DNA methylation while accounting for the typical confounders of the field, shared by co-twins of a pair. In this review, we systematically evaluate all twin studies published to date assessing DNA methylation in association with depressive phenotypes. However, difficulty to recruit large numbers of MZ twin pairs fails to provide enough sample size to develop genome-wide approaches. Alternatively, region and pathway analysis revealed an enrichment for nervous system related functions; likewise, evidence supports an accumulation of methylation variability in affected subjects when compared to their co-twins. Nevertheless, longitudinal studies incorporating known risk factors for depression such as childhood trauma are required for understanding the role that DNA methylation plays in the etiology of depression.

Analyzing the genes and pathways related to major depressive disorder via a systems biology approach

INTRODUCTION

Major depressive disorder (MDD) is a mental disorder caused by the combination of genetic, environmental, and psychological factors. Over the years, a number of genes potentially associated with MDD have been identified. However, in many cases, the role of these genes and their relationship in the etiology and development of MDD remains unclear. Under such situation, a systems biology approach focusing on the function correlation and interaction of the candidate genes in the context of MDD will provide useful information on exploring the molecular mechanisms underlying the disease.

METHODS

We collected genes potentially related to MDD by screening the human genetic studies deposited in PubMed (https://www.ncbi.nlm.nih.gov/pubmed). The main biological themes within the genes were explored by function and pathway enrichment analysis. Then, the interaction of genes was analyzed in the context of protein-protein interaction network and a MDD-specific network was built by Steiner minimal tree algorithm.

RESULTS

We collected 255 candidate genes reported to be associated with MDD from available publications. Functional analysis revealed that biological processes and biochemical pathways related to neuronal development, endocrine, cell growth and/or survivals, and immunology were enriched in these genes. The pathways could be largely grouped into three modules involved in biological procedures related to nervous system, the immune system, and the endocrine system, respectively. From the MDD-specific network, 35 novel genes potentially associated with the disease were identified.

CONCLUSION

By means of network- and pathway-based methods, we explored the molecular mechanism underlying the pathogenesis of MDD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular features of MDD.

Targeted exome sequencing identifies five novel loci at genome-wide significance for modulating antidepressant response in patients with major depressive disorder

In order to determine the role of single nucleotide variants (SNVs) in modulating antidepressant response, we conducted a study, consisting of 929 major depressive disorder (MDD) patients, who were treated with antidepressant drugs (drug-only) or in combination with a repetitive transcranial magnetic stimulation (plus-rTMS), followed by targeted exome sequencing analysis. We found that the "plus-rTMS" patients presented a more effective response to the treatment when compared to the 'drug-only' group. Our data firstly demonstrated that the SNV burden had a significant impact on the antidepressant response presented in the "drug-only" group, but was limited in the "plus-rTMS" group. Further, after controlling for overall SNV burden, seven single nucleotide polymorphisms (SNPs) at five loci, IL1A, GNA15, PPP2CB, PLA2G4C, and GBA, were identified as affecting the antidepressant response at genome-wide significance (P < 5 × 10^-08). Additional multiple variants achieved a level of correction for multiple testing, including GNA11, also shown as a strong signal for MDD risk. Our study showed some promising evidence on genetic variants that could be used as individualized therapeutic guides for MDD patients.

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.

Bivariate genome-wide association analyses of the broad depression phenotype combined with major depressive disorder, bipolar disorder or schizophrenia reveal eight novel genetic loci for depression

Although a genetic basis of depression has been well established in twin studies, identification of genome-wide significant loci has been difficult. We hypothesized that bivariate analyses of findings from a meta-analysis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive disorder (MDD), bipolar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for depression. LD score regression analyses were first used to estimate the genetic correlations of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia. Then, we performed four bivariate GWAS analyses. The genetic correlations (r_g ± SE) of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia were 0.79 ± 0.07, 0.24 ± 0.08, 0.53 ± 0.09 and 0.57 ± 0.05, respectively. From a total of 20 independent genome-wide significant loci, 13 loci replicated of which 8 were novel for depression. These were MUC21 for the broad depression phenotype with self-reported MDD and ZNF804A, MIR3143, PSORS1C2, STK19, SPATA31D1, RTN1 and TCF4 for the broad depression phenotype with schizophrenia. Post-GWAS functional analyses of these loci revealed their potential biological involvement in psychiatric disorders. Our results emphasize the genetic similarities among different psychiatric disorders and indicate that cross-disorder analyses may be the best way forward to accelerate gene finding for depression, or psychiatric disorders in general.

Association Between FoxO1, A2M, and TGF-β1, Environmental Factors, and Major Depressive Disorder

INTRODUCTION

Investigations of gene-environment (G×E) interactions in major depressive disorder (MDD) have been limited to hypothesis testing of candidate genes while poly-gene-environmental causation has not been adequately address. To this end, the present study analyzed the association between three candidate genes, two environmental factors, and MDD using a hypothesis-free testing approach.

METHODS

A logistic regression model was used to analyze interaction effects; a hierarchical regression model was used to evaluate the effects of different genotypes and the dose-response effects of the environment; genetic risk score (GRS) was used to estimate the cumulative contribution of genetic factors to MDD; and protein-protein interaction (PPI) analyses were carried out to evaluate the relationship between candidate genes and top MDD susceptibility genes.

RESULTS

Allelic association analyses revealed significant effects of the interaction between the candidate genes Forkhead box (Fox)O1, α2-macroglobulin (A2M), and transforming growth factor (TGF)-β1 genes and the environment on MDD. Gene-gene (G×G) and gene-gene-environment (G×G×E) interactions in MDD were also included in the model. Hierarchical regression analysis showed that the effect of environmental factors on MDD was greater in homozygous than in heterozygous mutant genotypes of the FoxO1 and TGF-β1 genes; a dose-response effect between environment and MDD on genotypes was also included in this model. Haplotype analyses revealed significant global and individual effects of haplotypes on MDD in the whole sample as well as in subgroups. There was a significant association between GRS and MDD (P = 0.029) and a GRS and environment interaction effect on MDD (P = 0.009). Candidate and top susceptibility genes were connected in PPI networks.

CONCLUSIONS

FoxO1, A2M, and TGF-β1 interact with environmental factors and with each other in MDD. Multi-factorial G×E interactions may be responsible for a higher explained variance and may be associated with causal factors and mechanisms that could inform new diagnosis and therapeutic strategies, which can contribute to the personalized medicine of MDD.

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.

Targeted exon sequencing in deceased schizophrenia patients in Denmark

Schizophrenia patients have higher mortality rates and lower life expectancy than the general population. However, forensic investigations of their deaths often fail to determine the cause of death, hindering prevention. As schizophrenia is a highly heritable condition and given recent advances in our understanding of the genetics of schizophrenia, it is now possible to investigate how genetic factors may contribute to mortality. We made use of findings from genome-wide association studies (GWAS) to design a targeted panel (PsychPlex) for sequencing of exons of 451 genes near index single nucleotide polymorphisms (SNPs) identified with GWAS. We sequenced the DNA of 95 deceased schizophrenia patients included in SURVIVE, a prospective, autopsy-based study of mentally ill persons in Denmark. We compared the allele frequencies of 1039 SNPs in these cases with the frequencies of 2000 Danes without psychiatric diseases and calculated their deleteriousness (CADD) scores. For 81 SNPs highly associated with schizophrenia and CADD scores above 15, expression profiles in the Genotype-Tissue Expression (GTEx) Project indicated that these variants were in exons, whose expressions are increased in several types of brain tissues, particularly in the cerebellum. Molecular pathway analysis indicated the involvement of 163 different pathways. As for rare SNP variants, most variants were scored as either benign or likely benign with an average of 17 variants of unknown significance per individual and no pathogenic variant. Our results highlight the potential of DNA sequencing of an exon panel to discover genetic factors that may be involved in the development of schizophrenia.

White Matter Microstructure and Its Relation to Longitudinal Measures of Depressive Symptoms in Mid- and Late Life

BACKGROUND

Studies of white matter microstructure in depression typically show alterations in individuals with depression, but they are frequently limited by small sample sizes and the absence of longitudinal measures of depressive symptoms. Depressive symptoms are dynamic, however, and understanding the neurobiology of different trajectories could have important clinical implications.

METHODS

We examined associations between current and longitudinal measures of depressive symptoms and white matter microstructure (fractional anisotropy and mean diffusivity [MD]) in the UK Biobank Imaging Study. Depressive symptoms were assessed on two to four occasions over 5.9 to 10.7 years (n = 18,959 individuals on at least two occasions, n = 4444 on four occasions), from which we derived four measures of depressive symptomatology: cross-sectional measure at the time of scan and three longitudinal measures, namely trajectory and mean and intrasubject variance over time.

RESULTS

Decreased white matter microstructure in the anterior thalamic radiation demonstrated significant associations across all four measures of depressive symptoms (MD: βs = .020-.029, pcorr < .030). The greatest effect sizes were seen between white matter microstructure and longitudinal progression (MD: βs = .030-.040, pcorr < .049). Cross-sectional symptom severity was particularly associated with decreased white matter integrity in association fibers and thalamic radiations (MD: βs = .015-.039, pcorr < .041). Greater mean and within-subject variance were mainly associated with decreased white matter microstructure within projection fibers (MD: βs = .019-.029, pcorr < .044).

CONCLUSIONS

These findings indicate shared and differential neurobiological associations with severity, course, and intrasubject variability of depressive symptoms. This enriches our understanding of the neurobiology underlying dynamic features of the disorder.

Chaihu-Shugan-San Reinforces CYP3A4 Expression via Pregnane X Receptor in Depressive Treatment of Liver-Qi Stagnation Syndrome

Backgrounds. Chaihu-Shugan-San (CSS) is a classic traditional Chinese herbal prescription for treating depression. However, the underlying mechanism of the Chinese syndrome-specific efficacy of CSS is poorly understood. Aim of the Study. From traditional Chinese medicine and pharmacogenetics perspectives, the present study aimed to investigate the antidepressant effects of CSS on a mouse model of Liver-Qi Stagnation (LQS) syndrome and its underlying mechanisms. Methods and Materials. We used two main mouse models of depressive syndromes in the study, including LQS and liver stagnation and spleen deficiency (LSSD) syndrome. Tail suspension and forced swimming tests were used to evaluate the effects of CSS on animal behaviour. The expression level of the CYP450 enzyme from liver microsomes was analysed by western blot (WB) analysis and quantitative real-time polymerase chain reaction (qRT-PCR). More specifically, we analysed the key compounds of CSS that are responsible for CYP450 regulation via bioinformatics. Ultimately, luciferase assays were employed to confirm the prediction in vitro. Results. CSS remarkably reduced the immobile time in LQS rather than in LSSD mice. Although CSS significantly upregulated CYP2C9 in mice with both syndromes, activated translation of CYP3A4 induced by CSS was only observed in the LQS group. Bioinformatics analysis revealed that the unique regulation of CYP3A4 was responsible for the effects of glycyrrhetinic acid (GA) from CSS. Further luciferase assays confirmed the enhancement of CYP3A4 expression via the pregnane X receptor (PXR) pathway in vitro. Conclusions. CSS specifically upregulates the translation of CYP3A4 via the PXR pathway in depressed LQS mice. GA, a bioactive compound that originates from CSS, contributes to this activation. This work provides novel insight into Chinese syndrome-based therapy for depression.

Genomic prediction of depression risk and resilience under stress

Advancing ability to predict who is likely to develop depression holds great potential in reducing the disease burden. Here, we utilize the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the depression polygenic risk score (MDD-PRS) derived from the most recent PGC2/UKB/23andMe GWAS to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (beta=0.095, p=4.7×10⁻¹⁶) and that MDD-PRS was more strongly associated with depression under stress than at baseline (MDD-PRSxstress interaction beta=0.036, p=0.005). Further, known risk factors accounted for substantially less of the association between MDD-PRS and depression at under stress than at baseline, suggesting that MDD-PRS adds unique predictive power in depression prediction. Finally, we found that low MDD-PRS may have particular utility in identifying individuals with high resilience. Together, these findings suggest that MDD-PRS holds promise in furthering our ability to predict vulnerability and resilience under stress.

Genome-wide association analysis of autism identified multiple loci that have been reported as strong signals for neuropsychiatric disorders

Autism is a common neurodevelopmental disorder with a moderate to a high degree of heritability, but only a few common genetic variants that explain the heritability have been associated. We performed a genome-wide transmission disequilibrium test analysis of a newly genotyped autism case-parent triad samples (127 trios) in Han Chinese, identified top association signals at multiple single nucleotide polymorphisms (SNPs), including rs9839376 (OR = 2.59, P = 1.27 × 10^-05 ) at KCNMB2, rs6044680 (OR = 0.319, P = 4.82 × 10^-05 ) and rs7274133 (OR = 0.313, P = 3.22 × 10^-05 ) at PCSK2, and rs310619 (OR = 2.40, P = 7.44 × 10^-05 ) at EEF1A2. Furthermore, a genome-wide combined P-value of individual SNPs in two independent case-parent triad samples (total 402 triads, n = 1,206) identified SNPs at EGFLAM, ZDHHC2, AGBL1, and SNX29 as additional association signals for autism. While none of these signals achieved a genome-wide significance in the two samples of our study, they have been reported in a previous genome-wide association study of neuropsychiatric disorders, and the majority of these SNP have a significant cis-regulatory association with mRNA in human tissues (false discovery rate (FDR) < 0.05). Our study warrants further study or replication with additional sample for association with autism and other neuropsychiatric disorders. Autism Res 2020, 13: 382-396. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism is a common neurodevelopmental disorder, heritable, but only a few common genetic variants that explain the heritability have been associated. We conducted a genome-wide association study with two cohorts of autism case-parent triad samples in Han Chinese and identified multiple single nucleotide polymorphisms that were reported as strong association signals in a previous genome-wide association study of other neuropsychiatric disorders or related traits. Our study provides evidence for shared genetic variants among autism and other neuropsychiatric disorders.

Three Novel Loci for Infant Head Circumference Identified by a Joint Association Analysis

As an important trait at birth, infant head circumference (HC) is associated with a variety of intelligence- and mental-related conditions. Despite being dominated by genetics, the mechanism underlying the variation of HC is poorly understood. Aiming to uncover the genetic basis of HC, we performed a genome-wide joint association analysis by integrating the genome-wide association summary statistics of HC with that of its two related traits, birth length and birth weight, using a recently developed integrative method, multitrait analysis of genome-wide association (MTAG), and performed in silico replication in an independent sample of intracranial volume (N = 26,577). We then conducted a series of bioinformatic investigations on the identified loci. Combining the evidence from both the MTAG analysis and the in silico replication, we identified three novel loci at the genome-wide significance level (α = 5.0 × 10^-8): 3q23 [lead single nucleotide polymorphism (SNP) rs9846396, p _MTAG = 3.35 × 10^-8, p _replication = 0.01], 7p15.3 (rs12534093, p _MTAG = 2.00 × 10^-8, p _replication = 0.004), and 9q33.3 (rs7048271 p _MTAG = 9.23 × 10^-10, p _replication = 1.14 × 10^-4). Each of the three lead SNPs was associated with at least one of eight brain-related traits including intelligence and educational attainment. Credible risk variants, defined as those SNPs located within 500 kb of the lead SNP and with p values within two orders of magnitude of the lead SNP, were enriched in DNase I hypersensitive site region in brain. Nine candidate genes were prioritized at the three novel loci using multiple sources of information. Gene set enrichment analysis identified one associated pathway GO:0048009, which participates in the development of nervous system. Our findings provide useful insights into the genetic basis of HC and the relationship between brain growth and mental health.

Impact of Polygenic Risk for Schizophrenia on Cortical Structure in UK Biobank

BACKGROUND

Schizophrenia is a neurodevelopmental disorder with many genetic variants of individually small effect contributing to phenotypic variation. Lower cortical thickness (CT), surface area, and cortical volume have been demonstrated in people with schizophrenia. Furthermore, a range of obstetric complications (e.g., lower birth weight) are consistently associated with an increased risk for schizophrenia. We investigated whether a high polygenic risk score for schizophrenia (PGRS-SCZ) is associated with CT, surface area, and cortical volume in UK Biobank, a population-based sample, and tested for interactions with birth weight.

METHODS

Data were available for 2864 participants (n_male/n_female = 1382/1482; mean age = 62.35 years, SD = 7.40). Linear mixed models were used to test for associations among PGRS-SCZ and cortical volume, surface area, and CT and between PGRS-SCZ and birth weight. Interaction effects of these variables on cortical structure were also tested.

RESULTS

We found a significant negative association between PGRS-SCZ and global CT; a higher PGRS-SCZ was associated with lower CT across the whole brain. We also report a significant negative association between PGRS-SCZ and insular lobe CT. PGRS-SCZ was not associated with birth weight and no PGRS-SCZ × birth weight interactions were found.

CONCLUSIONS

These results suggest that individual differences in CT are partly influenced by genetic variants and are most likely not due to factors downstream of disease onset. This approach may help to elucidate the genetic pathophysiology of schizophrenia. Further investigation in case-control and high-risk samples could help identify any localized effects of PGRS-SCZ, and other potential schizophrenia risk factors, on CT as symptoms develop.

Impact of major depression on cardiovascular outcomes for individuals with hypertension: prospective survival analysis in UK Biobank

OBJECTIVES

To assess whether a history of major depressive disorder (MDD) in middle-aged individuals with hypertension influences first-onset cardiovascular disease outcomes.

DESIGN

Prospective cohort survival analysis using Cox proportional hazards regression with a median follow-up of 63 months (702 902 person-years). Four mutually exclusive groups were compared: hypertension only (n=56 035), MDD only (n=15 098), comorbid hypertension plus MDD (n=12 929) and an unaffected (no hypertension, no MDD) comparison group (n=50 798).

SETTING

UK Biobank.

PARTICIPANTS

UK Biobank participants without cardiovascular disease aged 39-70 who completed psychiatric questions relating International Classification of Diseases-10 Revision (ICD-10) diagnostic criteria on a touchscreen questionnaire at baseline interview in 2006-2010 (n=134 860).

PRIMARY AND SECONDARY OUTCOME MEASURES

First-onset adverse cardiovascular outcomes leading to hospital admission or death (ICD-10 codes I20-I259, I60-69 and G45-G46), adjusted in a stepwise manner for sociodemographic, health and lifestyle features. Secondary analyses were performed looking specifically at stroke outcomes (ICD-10 codes I60-69 and G45-G46) and in gender-separated models.

RESULTS

Relative to controls, adjusted HRs for adverse cardiovascular outcomes were increased for the hypertension only group (HR 1.36, 95% CI 1.22 to 1.52) and were higher still for the comorbid hypertension plus MDD group (HR 1.66, 95% CI 1.45 to 1.9). HRs for the comorbid hypertension plus MDD group were significantly raised compared with hypertension alone (HR 1.22, 95% CI 1.1 to 1.35). Interaction measured using relative excess risk due to interaction (RERI) and likelihood ratios (LRs) were identified at baseline (RERI 0.563, 95% CI 0.189 to 0.938; LR p=0.0116) but not maintained during the follow-up.

LIMITATIONS

Possible selection bias in UK Biobank and inability to assess for levels of medication adherence.

CONCLUSIONS

Comorbid hypertension and MDD conferred greater hazard than hypertension alone for adverse cardiovascular outcomes, although evidence of interaction between hypertension and MDD was inconsistent over time. Future cardiovascular risk prediction tools may benefit from the inclusion of questions about prior history of depressive disorders.

Multivariable two-sample Mendelian randomization estimates of the effects of intelligence and education on health

Intelligence and education are predictive of better physical and mental health, socioeconomic position (SEP), and longevity. However, these associations are insufficient to prove that intelligence and/or education cause these outcomes. Intelligence and education are phenotypically and genetically correlated, which makes it difficult to elucidate causal relationships. We used univariate and multivariable Mendelian randomization to estimate the total and direct effects of intelligence and educational attainment on mental and physical health, measures of socioeconomic position, and longevity. Both intelligence and education had beneficial total effects. Higher intelligence had positive direct effects on income and alcohol consumption, and negative direct effects on moderate and vigorous physical activity. Higher educational attainment had positive direct effects on income, alcohol consumption, and vigorous physical activity, and negative direct effects on smoking, BMI and sedentary behaviour. If the Mendelian randomization assumptions hold, these findings suggest that both intelligence and education affect health.

Epigenome-wide association study of depression symptomatology in elderly monozygotic twins

Depression is a severe and debilitating mental disorder diagnosed by evaluation of affective, cognitive and physical depression symptoms. Severity of these symptoms strongly impacts individual's quality of life and is influenced by a combination of genetic and environmental factors. One of the molecular mechanisms allowing for an interplay between these factors is DNA methylation, an epigenetic modification playing a pivotal role in regulation of brain functioning across lifespan. The aim of this study was to investigate if there are DNA methylation signatures associated with depression symptomatology in order to identify molecular mechanisms contributing to pathophysiology of depression. We performed an epigenome-wide association study (EWAS) of continuous depression symptomatology score measured in a cohort of 724 monozygotic Danish twins (346 males, 378 females). Through EWAS analyses adjusted for sex, age, flow-cytometry based blood cell composition, and twin relatedness structure in the data we identified depression symptomatology score to be associated with blood DNA methylation levels in promoter regions of neuropsin (KLK8, p-value = 4.7 × 10^-7) and DAZ associated protein 2 (DAZAP2, p-value = 3.13 × 10^-8) genes. Other top associated probes were located in gene bodies of MAD1L1 (p-value = 5.16 × 10^-6), SLC29A2 (p-value = 6.15 × 10^-6) and AKT1 (p-value = 4.47 × 10^-6), all genes associated before with development of depression. Additionally, the following three measures (a) DNAmAge (calculated with Horvath and Hannum epigenetic clock estimators) adjusted for chronological age, (b) difference between DNAmAge and chronological age, and (c) DNAmAge acceleration were not associated with depression symptomatology score in our cohort. In conclusion, our data suggests that depression symptomatology score is associated with DNA methylation levels of genes implicated in response to stress, depressive-like behaviors, and recurrent depression in patients, but not with global DNA methylation changes across the genome.

Quantifying between-cohort and between-sex genetic heterogeneity in major depressive disorder

Major depressive disorder (MDD) is clinically heterogeneous with prevalence rates twice as high in women as in men. There are many possible sources of heterogeneity in MDD most of which are not measured in a sufficiently comparable way across study samples. Here, we assess genetic heterogeneity based on two fundamental measures, between-cohort and between-sex heterogeneity. First, we used genome-wide association study (GWAS) summary statistics to investigate between-cohort genetic heterogeneity using the 29 research cohorts of the Psychiatric Genomics Consortium (PGC; N cases = 16,823, N controls = 25,632) and found that some of the cohort heterogeneity can be attributed to ascertainment differences (such as recruitment of cases from hospital vs. community sources). Second, we evaluated between-sex genetic heterogeneity using GWAS summary statistics from the PGC, Kaiser Permanente GERA, UK Biobank, and the Danish iPSYCH studies but did not find convincing evidence for genetic differences between the sexes. We conclude that there is no evidence that the heterogeneity between MDD data sets and between sexes reflects genetic heterogeneity. Larger sample sizes with detailed phenotypic records and genomic data remain the key to overcome heterogeneity inherent in assessment of MDD.