Investigation of glycaemic traits in psychiatric disorders using Mendelian randomisation revealed a causal relationship with anorexia nervosa

Modifiable lifestyle factors influencing psychiatric disorders mediated by plasma proteins: A systemic Mendelian randomization study

BACKGROUND

Psychiatric disorders are emerging as a serious public health hazard, influencing an increasing number of individuals worldwide. However, the effect of modifiable lifestyle factors on psychiatric disorders remains unclear.

METHODS

Genome-wide association studies (GWAS) summary statistics were obtained mainly from Psychiatric Genomics Consortium and UK Biobank, with sample sizes varying between 10,000 and 1,200,000. The two-sample Mendelian randomization (MR) method was applied to investigate the causal associations between 45 lifestyle factors and 13 psychiatric disorders, and screen potential mediator proteins from 2992 candidate plasma proteins. We implemented a four-step framework with step-by-step screening incorporating two-step, univariable, and multivariable MR.

RESULTS

We found causal effects of strenuous sports or other exercise on Tourette's syndrome (OR [95%CI]: 0.0047 [5.24E-04-0.042]); lifelong smoking index on attention-deficit hyperactivity disorder (10.53 [6.96-15.93]), anxiety disorders (3.44 [1.95-6.05]), bipolar disorder (BD) (2.25 [1.64-3.09]), BD II (2.89 [1.81-4.62]), and major depressive disorder (MDD) (2.47 [1.90-3.20]); and educational years on anorexia nervosa (AN) (1.47 [1.22-1.76]), and MDD (0.74 [0.66-0.83]). Five proteins were found to have causal associations with psychiatric disorders, namely ADH1B, GHDC, STOM, CD226, and TP63. STOM, a membrane protein deficient in the erythrocytes of hereditary stomatocytosis patients, may mediate the effect of educational attainment on AN.

LIMITATIONS

The mechanisms underlying the effects of lifestyle factors on psychiatric disorders require further investigation.

CONCLUSIONS

These findings could help assess the risk of psychiatric disorders based on lifestyle factors and also support lifestyle interventions as a prevention strategy for mental illness.

Anorexia nervosa: diagnostic, therapeutic, and risk biomarkers in clinical practice

In anorexia nervosa (AN), measurable biological parameters can inform the process of treating patients. Such biomarkers include established laboratory parameters as well as a range of potential future biomarkers, including genetic, metabolomic, microbiomic, endocrine, immunological, hematological, electrophysiological, and neuroimaging parameters. In this opinion article we discuss how these biomarkers can support diagnosic and therapeutic processes at specific steps during the AN treatment cycle, that is, the diagnosis, diagnostic specification, risk management, choice of therapy, therapy monitoring, and treatment review. History-taking, physical and neuropsychological examination, clinical observation, and judgment about treatment success by the patient, their carers, and members of the multidisciplinary team are essential to interpret laboratory and imaging data appropriately and to assess the full clinical picture.

High-throughput screening of glucocorticoid-induced enhancer activity reveals mechanisms of stress-related psychiatric disorders

Exposure to stressful life events increases the risk for psychiatric disorders. Mechanistic insight into the genetic factors moderating the impact of stress can increase our understanding of disease processes. Here, we test 3,662 single nucleotide polymorphisms (SNPs) from preselected expression quantitative trait loci in massively parallel reporter assays to identify genetic variants that modulate the activity of regulatory elements sensitive to glucocorticoids, important mediators of the stress response. Of the tested SNP sequences, 547 were located in glucocorticoid-responsive regulatory elements of which 233 showed allele-dependent activity. Transcripts regulated by these functional variants were enriched for those differentially expressed in psychiatric disorders in the postmortem brain. Phenome-wide Mendelian randomization analysis in 4,439 phenotypes revealed potentially causal associations specifically in neurobehavioral traits, including major depression and other psychiatric disorders. Finally, a functional gene score derived from these variants was significantly associated with differences in the physiological stress response, suggesting that these variants may alter disease risk by moderating the individual set point of the stress response.

Investigating the association between glycaemic traits and colorectal cancer in the Japanese population using Mendelian randomisation

Observational studies suggest that abnormal glucose metabolism and insulin resistance contribute to colorectal cancer; however, the causal association remains unknown, particularly in Asian populations. A two-sample Mendelian randomisation analysis was performed to determine the causal association between genetic variants associated with elevated fasting glucose, haemoglobin A1c (HbA1c), and fasting C-peptide and colorectal cancer risk. In the single nucleotide polymorphism (SNP)-exposure analysis, we meta-analysed study-level genome-wide associations of fasting glucose (~ 17,289 individuals), HbA1c (~ 52,802 individuals), and fasting C-peptide (1,666 individuals) levels from the Japanese Consortium of Genetic Epidemiology studies. The odds ratios of colorectal cancer were 1.01 (95% confidence interval [CI], 0.99-1.04, P = 0.34) for fasting glucose (per 1 mg/dL increment), 1.02 (95% CI, 0.60-1.73, P = 0.95) for HbA1c (per 1% increment), and 1.47 (95% CI, 0.97-2.24, P = 0.06) for fasting C-peptide (per 1 log increment). Sensitivity analyses, including Mendelian randomisation-Egger and weighted-median approaches, revealed no significant association between glycaemic characteristics and colorectal cancer (P > 0.20). In this study, genetically predicted glycaemic characteristics were not significantly related to colorectal cancer risk. The potential association between insulin resistance and colorectal cancer should be validated in further studies.

Schizophrenia and cardiometabolic abnormalities: A Mendelian randomization study

Background: Individuals with a diagnosis of schizophrenia are known to be at high risk of premature mortality due to poor physical health, especially cardiovascular disease, diabetes, and obesity. The reasons for these physical health outcomes within this patient population are complex. Despite well-documented cardiometabolic adverse effects of certain antipsychotic drugs and lifestyle factors, schizophrenia may have an independent effect. Aims: To investigate if there is evidence that schizophrenia is causally related to cardiometabolic traits (blood lipids, anthropometric traits, glycaemic traits, blood pressure) and vice versa using bi-directional two-sample Mendelian randomization (MR) analysis. Methods: We used 185 genetic variants associated with schizophrenia from the latest Psychiatric Genomics Consortium GWAS (n = 130,644) in the forward analysis (schizophrenia to cardiometabolic traits) and genetic variants associated with the cardiometabolic traits from various consortia in the reverse analysis (cardiometabolic traits to schizophrenia), both at genome-wide significance (5 × 10-8). The primary method was inverse-variance weighted MR, supported by supplementary methods such as MR-Egger, as well as median and mode-based methods. Results: In the forward analysis, schizophrenia was associated with slightly higher low-density lipoprotein (LDL) cholesterol levels (0.013 SD change in LDL per log odds increase in schizophrenia risk, 95% CI, 0.001-0.024 SD; p = 0.027) and total cholesterol levels (0.013 SD change in total cholesterol per log odds increase in schizophrenia risk, 95% CI, 0.002-0.025 SD; p = 0.023). However, these associations did not survive multiple testing corrections. There was no evidence of a causal effect of cardiometabolic traits on schizophrenia in the reverse analysis. Discussion: Dyslipidemia and obesity in schizophrenia patients are unlikely to be driven primarily by schizophrenia itself. Therefore, lifestyle, diet, antipsychotic drugs side effects, as well as shared mechanisms for metabolic dysfunction and schizophrenia such as low-grade systemic inflammation could be possible reasons for the apparent increased risk of metabolic disease in people with schizophrenia. Further research is needed to examine the shared immune mechanism hypothesis.

Mapping anorexia nervosa genes to clinical phenotypes

BACKGROUND

Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes.

METHODS

Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations.

RESULTS

Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex.

CONCLUSIONS

Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.

Examining the shared etiology of psychopathology with genome-wide association studies

Genome-wide association studies (GWASs) have ushered in a new era of reproducible discovery in psychiatric genetics. The field has now identified hundreds of common genetic variants that are associated with mental disorders, and many of them influence more than one disorder. By advancing the understanding of causal biology underlying psychopathology, GWAS results are poised to inform the development of novel therapeutics, stratification of at-risk patients, and perhaps even the revision of top-down classification systems in psychiatry. Here, we provide a concise review of GWAS findings with an emphasis on findings that have elucidated the shared genetic etiology of psychopathology, summarizing insights at three levels of analysis: 1) genome-wide architecture; 2) networks, pathways, and gene sets; and 3) individual variants/genes. Three themes emerge from these efforts. First, all psychiatric phenotypes are heritable, highly polygenic, and influenced by many pleiotropic variants with incomplete penetrance. Second, GWAS results highlight the broad etiological roles of neuronal biology, system-wide effects over localized effects, and early neurodevelopment as a critical period. Third, many loci that are robustly associated with multiple forms of psychopathology harbor genes that are involved in synaptic structure and function. Finally, we conclude our review by discussing the implications that GWAS results hold for the field of psychiatry, as well as expected challenges and future directions in the next stage of psychiatric genetics.

Multiomic prioritisation of risk genes for anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a psychiatric disorder associated with marked morbidity. Whilst AN genetic studies could identify novel treatment targets, integration of functional genomics data, including transcriptomics and proteomics, would assist to disentangle correlated signals and reveal causally associated genes.

METHODS

We used models of genetically imputed expression and splicing from 14 tissues, leveraging mRNA, protein, and mRNA alternative splicing weights to identify genes, proteins, and transcripts, respectively, associated with AN risk. This was accomplished through transcriptome, proteome, and spliceosome-wide association studies, followed by conditional analysis and finemapping to prioritise candidate causal genes.

RESULTS

We uncovered 134 genes for which genetically predicted mRNA expression was associated with AN after multiple-testing correction, as well as four proteins and 16 alternatively spliced transcripts. Conditional analysis of these significantly associated genes on other proximal association signals resulted in 97 genes independently associated with AN. Moreover, probabilistic finemapping further refined these associations and prioritised putative causal genes. The gene WDR6, for which increased genetically predicted mRNA expression was correlated with AN, was strongly supported by both conditional analyses and finemapping. Pathway analysis of genes revealed by finemapping identified the pathway regulation of immune system process (overlapping genes = MST1, TREX1, PRKAR2A, PROS1) as statistically overrepresented.

CONCLUSIONS

We leveraged multiomic datasets to genetically prioritise novel risk genes for AN. Multiple-lines of evidence support that WDR6 is associated with AN, whilst other prioritised genes were enriched within immune related pathways, further supporting the role of the immune system in AN.

Association Between Glycemic Traits and Primary Open-Angle Glaucoma: A Mendelian Randomization Study in the Japanese Population

PURPOSE

A meta-analysis suggests a relationship between abnormal glucose metabolism and primary open-angle glaucoma (POAG); however, the causal association between them remains controversial. We therefore conducted a Mendelian randomization (MR) study to assess the causal association between genetically predicted glycemic traits and the risk of POAG.

DESIGN

Two-sample MR design.

METHODS

We examined the genetically predicted measures of fasting glucose, hemoglobin A1c (HbA1c), and fasting C-peptide, in relation to POAG. For the single nucleotide polymorphism (SNP)-exposure analyses, we meta-analyzed the study-level genome-wide associations of fasting glucose levels (n = 17,289; n of SNPs = 34), HbA1c (n = 52,802; n of SNPs = 43), and fasting C-peptide levels (n=1666; n of SNPs = 17) from the Japanese Consortium of Genetic Epidemiology studies. We used summary statistics from the BioBank Japan projects (n = 3980 POAG cases and 18,815 controls) for the SNP-outcome association.

RESULTS

We observed no association of genetically predicted HbA1c and fasting C-peptide with POAG. The MR inverse-variance-weighted (IVW) odds ratios (ORs) were 1.44 (95% confidence interval [CI], 0.78-2.65; P = .25) for HbA1c (per 1% increment) and 0.92 (95% CI, 0.56-1.53; P = .76) for fasting C-peptide (per 2-fold increment). A significant association between fasting glucose (per 10 mg/dL-increment) and POAG was observed according to the MR IVW analysis (OR = 1.48 [95% CI, 1.10-1.79, P = .009]); however, sensitivity analyses, including MR-Egger and weighted-median methods, did not support this association (P > .10).

CONCLUSIONS

We did not observe strong evidence to support the association between genetically predicted glycemic traits and POAG in the Japanese population.

Genetics and neurobiology of eating disorders

Eating disorders (anorexia nervosa, bulimia nervosa and binge-eating disorder) are a heterogeneous class of complex illnesses marked by weight and appetite dysregulation coupled with distinctive behavioral and psychological features. Our understanding of their genetics and neurobiology is evolving thanks to global cooperation on genome-wide association studies, neuroimaging, and animal models. Until now, however, these approaches have advanced the field in parallel, with inadequate cross-talk. This review covers overlapping advances in these key domains and encourages greater integration of hypotheses and findings to create a more unified science of eating disorders. We highlight ongoing and future work designed to identify implicated biological pathways that will inform staging models based on biology as well as targeted prevention and tailored intervention, and will galvanize interest in the development of pharmacologic agents that target the core biology of the illnesses, for which we currently have few effective pharmacotherapeutics.

Genetic estimates of correlation and causality between blood-based biomarkers and psychiatric disorders

There is a long-standing interest in exploring the relationship between blood-based biomarkers and psychiatric disorders, despite their causal role being difficult to resolve in observational studies. In this study, we leverage genome-wide association study data for a large panel of heritable serum biochemical traits to refine our understanding of causal effect in biochemical-psychiatric trait pairings. We observed widespread positive and negative genetic correlation between psychiatric disorders and biochemical traits. Causal inference was then implemented to distinguish causation from correlation, with strong evidence that C-reactive protein (CRP) exerts a causal effect on psychiatric disorders. Notably, CRP demonstrated both protective and risk-increasing effects on different disorders. Multivariable models that conditioned CRP effects on interleukin-6 signaling and body mass index supported that the CRP-schizophrenia relationship was not driven by these factors. Collectively, these data suggest that there are shared pathways that influence both biochemical traits and psychiatric illness.

Advancing the use of genome-wide association studies for drug repurposing

Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.