Meta-Analyses of Genome-Wide Association Studies for Postpartum Depression.

OBJECTIVE: Postpartum depression (PPD) is a common subtype of major depressive disorder (MDD) that is more heritable, yet is understudied in psychiatric genetics. The authors conducted meta-analyses of genome-wide association studies (GWASs) to investigate the genetic architecture of PPD. METHOD: Meta-analyses were conducted on 18 cohorts of European ancestry (17,339 PPD cases and 53,426 controls), one cohort of East Asian ancestry (975 cases and 3,780 controls), and one cohort of African ancestry (456 cases and 1,255 controls), totaling 18,770 PPD cases and 58,461 controls. Post-GWAS analyses included 1) single-nucleotide polymorphism (SNP)-based heritability ([Formula: see text]), 2) genetic correlations between PPD and other phenotypes, and 3) enrichment of the PPD GWAS findings in 27 human tissues and 265 cell types from the mouse central and peripheral nervous system. RESULTS: No SNP achieved genome-wide significance in the European or the trans-ancestry meta-analyses. The [Formula: see text] of PPD was 0.14 (SE=0.02). Significant genetic correlations were estimated for PPD with MDD, bipolar disorder, anxiety disorders, posttraumatic stress disorder, insomnia, age at menarche, and polycystic ovary syndrome. Cell-type enrichment analyses implicate inhibitory neurons in the thalamus and cholinergic neurons within septal nuclei of the hypothalamus, a pattern that differs from MDD. CONCLUSIONS: While more samples are needed to reach genome-wide levels of significance, the results presented confirm PPD as a polygenic and heritable phenotype. There is also evidence that despite a high correlation with MDD, PPD may have unique genetic components. Cell enrichment results suggest GABAergic neurons, which converge on a common mechanism with the only medication approved by the U.S. Food and Drug Administration for PPD (brexanolone).

Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases.

Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestries has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping, which builds on the single-population fine-mapping framework, Sum of Single Effects (SuSiE). SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and LD patterns, accounts for multiple causal variants in a genomic region, and can be applied to GWAS summary statistics. We comprehensively evaluated SuSiEx using simulations, a range of quantitative traits measured in both UK Biobank and Taiwan Biobank, and schizophrenia GWAS across East Asian and European ancestries. In all evaluations, SuSiEx fine-mapped more association signals, produced smaller credible sets and higher posterior inclusion probability (PIP) for putative causal variants, and captured population-specific causal variants.

Evaluation of the causal relationship between smoking and schizophrenia in East Asia.

Cigarette smoking has been suggested to be associated with the risk of schizophrenia in observational studies. A significant causal effect of smoking on schizophrenia has been reported in European populations using the Mendelian randomization approach; however, no evidence of causality was found in participants from East Asia. Using Taiwan Biobank (TWBB), we conducted genome-wide association studies (GWAS) to identify susceptibility loci for smoking behaviors, including smoking initiation (N = 79,989) and the onset age (N = 15,582). We then meta-analyzed GWAS from TWBB and Biobank Japan (BBJ) with the total sample size of 245,425 for smoking initiation and 46,000 for onset age of smoking. The GWAS for schizophrenia was taken from the East Asia Psychiatric Genomics Consortium, which included 22,778 cases and 35,362 controls. We performed a two-sample Mendelian randomization to estimate the causality of smoking behaviors on schizophrenia in East Asia. In TWBB, we identified one locus that met genome-wide significance for onset age. In a meta-analysis of TWBB and BBJ, we identified two loci for smoking initiation. In Mendelian randomization, genetically predicted smoking initiation (odds ratio (OR) = 4.00, 95% confidence interval (CI) = 0.89-18.01, P = 0.071) and onset age (OR for a per-year increase = 0.96, 95% CI = 0.91-1.01, P = 0.098) were not significantly associated with schizophrenia; the direction of effect was consistent with European Ancestry samples, which had higher statistical power. These findings provide tentative evidence consistent with a causal role of smoking on the development of schizophrenia in East Asian populations.

Psychiatric manifestations of rare variation in medically actionable genes: a PheWAS approach.

BACKGROUND: As genomic sequencing moves closer to clinical implementation, there has been an increasing acceptance of returning incidental findings to research participants and patients for mutations in highly penetrant, medically actionable genes. A curated list of genes has been recommended by the American College of Medical Genetics and Genomics (ACMG) for return of incidental findings. However, the pleiotropic effects of these genes are not fully known. Such effects could complicate genetic counseling when returning incidental findings. In particular, there has been no systematic evaluation of psychiatric manifestations associated with rare variation in these genes. RESULTS: Here, we leveraged a targeted sequence panel and real-world electronic health records from the eMERGE network to assess the burden of rare variation in the ACMG-56 genes and two psychiatric-associated genes (CACNA1C  and TCF4) across common mental health conditions in 15,181 individuals of European descent. As a positive control, we showed that this approach replicated the established association between rare mutations in LDLR and hypercholesterolemia with no visible inflation from population stratification. However, we did not identify any genes significantly enriched with rare deleterious variants that confer risk for common psychiatric disorders after correction for multiple testing. Suggestive associations were observed between depression and rare coding variation in PTEN (P = 1.5 × 10-4), LDLR (P = 3.6 × 10-4), and CACNA1S (P = 5.8 × 10-4). We also observed nominal associations between rare variants in KCNQ1 and substance use disorders (P = 2.4 × 10-4), and APOB and tobacco use disorder (P = 1.1 × 10-3). CONCLUSIONS: Our results do not support an association between psychiatric disorders and incidental findings in medically actionable gene mutations, but power was limited with the available sample sizes. Given the phenotypic and genetic complexity of psychiatric phenotypes, future work will require a much larger sequencing dataset to determine whether incidental findings in these genes have implications for risk of psychopathology.

Genome-wide risk prediction of common diseases across ancestries in one million people.

Polygenic risk scores (PRS) measure genetic disease susceptibility by combining risk effects across the genome. For coronary artery disease (CAD), type 2 diabetes (T2D), and breast and prostate cancer, we performed cross-ancestry evaluation of genome-wide PRSs in six biobanks in Europe, the United States, and Asia. We studied transferability of these highly polygenic, genome-wide PRSs across global ancestries, within European populations with different health-care systems, and local population substructures in a population isolate. All four PRSs had similar accuracy across European and Asian populations, with poorer transferability in the smaller group of individuals of African ancestry. The PRSs had highly similar effect sizes in different populations of European ancestry, and in early- and late-settlement regions with different recent population bottlenecks in Finland. Comparing genome-wide PRSs to PRSs containing a smaller number of variants, the highly polygenic, genome-wide PRSs generally displayed higher effect sizes and better transferability across global ancestries. Our findings indicate that in the populations investigated, the current genome-wide polygenic scores for common diseases have potential for clinical utility within different health-care settings for individuals of European ancestry, but that the utility in individuals of African ancestry is currently much lower.

Causality of abdominal obesity on cognition: a trans-ethnic Mendelian randomization study.

BACKGROUND: Obesity has been associated with cognition in observational studies; however, whether its effect is confounding or a reverse causality remains inconclusive. This study aimed to investigate the causal relationships of overall obesity, measured by body mass index (BMI), and abdominal adiposity, measured by waist-hip ratio adjusted for BMI (WHRadjBMI), and cognition across European and Asian populations using Mendelian randomization (MR) analysis. METHODS: We used publicly available genome-wide association study (GWAS) summary data of European ancestry, including BMI (n = 322,154) and WHRadjBMI (n = 210,088) from the GIANT consortium, and cognition performance (n = 257,828) from the UK Biobank and COGENT consortium. Data for individuals of Asian ancestry were retrieved from Taiwan Biobank to perform GWAS for BMI (n = 65,689), WHRadjBMI (n = 65,683), and Mini-Mental State Examination (MMSE, n = 21,273). MR analysis was carried out using the inverse-variance weighted method for the main results. Further, we examined the overall pleiotropy by MR-Egger intercept, and detected and adjusted for possible outliers using MR PRESSO. RESULTS: No causal effect of BMI on cognition performance (beta [95% CI] = 0.00 [-0.07, 0.07], p value = 0.91) was found for Europeans; however, a 1-SD increase in WHRadjBMI was associated with a 0.07 standardized score decrease in cognition performance (beta [95% CI] = -0.07 [-0.12, -0.02], p value = 0.006). Further, no causal effect of BMI on MMSE (beta [95% CI] = 0.01 [-0.08, 0.10], p = 0.91) was found for Asians; however, a 1-SD increase in WHRadjBMI was associated with a 0.17 standardized score decrease in MMSE (beta [95% CI] = -0.17 [-0.30, -0.03], p = 0.02). In both populations, overall pleiotropy was not detected, and outliers did not affect the robustness of the main findings. CONCLUSIONS: This trans-ethnic MR study reveals that abdominal adiposity, as measured by WHR adjusted for BMI, impairs cognition, whereas weak evidence suggests that BMI impairs cognition.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Use of the PsycheMERGE Network to Investigate the Association Between Depression Polygenic Scores and White Blood Cell Count.

Importance: Although depression is a common psychiatric disorder, its underlying biological basis remains poorly understood. Pairing depression polygenic scores with the results of clinical laboratory tests can reveal biological processes involved in depression etiology and in the physiological changes resulting from depression. Objective: To characterize the association between depression polygenic scores and an inflammatory biomarker, ie, white blood cell count. Design, Setting, and Participants: This genetic association study was conducted from May 19, 2019, to June 5, 2021, using electronic health record data from 382 452 patients across 4 health care systems. Analyses were conducted separately in each health care system and meta-analyzed across all systems. Primary analyses were conducted in Vanderbilt University Medical Center's biobank. Replication analyses were conducted across 3 other PsycheMERGE sites: Icahn School of Medicine at Mount Sinai, Mass General Brigham, and the Million Veteran Program. All patients with available genetic data and recorded white blood cell count measurements were included in the analyses. Primary analyses were conducted in individuals of European descent and then repeated in a population of individuals of African descent. Exposures: Depression polygenic scores. Main Outcomes and Measures: White blood cell count. Results: Across the 4 PsycheMERGE sites, there were 382 452 total participants of European ancestry (18.7% female; median age, 57.9 years) and 12 383 participants of African ancestry (61.1% female; median age, 39.0 [range, birth-90.0 years]). A laboratory-wide association scan revealed a robust association between depression polygenic scores and white blood cell count (ß, 0.03; SE, 0.004; P = 1.07 × 10-17), which was replicated in a meta-analysis across the 4 health care systems (ß, 0.03; SE, 0.002; P = 1.03 × 10-136). Mediation analyses suggested a bidirectional association, with white blood cell count accounting for 2.5% of the association of depression polygenic score with depression diagnosis (95% CI, 2.2%-20.8%; P = 2.84 × 10-70) and depression diagnosis accounting for 9.8% of the association of depression polygenic score with white blood cell count (95% CI, 8.4%-11.1%; P = 1.78 × 10-44). Mendelian randomization provided additional support for an association between increased white blood count and depression risk, but depression modeled as the exposure showed no evidence of an influence on white blood cell counts. Conclusions and Relevance: This genetic association study found that increased depression polygenic scores were associated with increased white blood cell count, and suggests that this association may be bidirectional. These findings highlight the potential importance of the immune system in the etiology of depression and may motivate future development of clinical biomarkers and targeted treatment options for depression.

Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort.

INTRODUCTION: Disruption of lipid metabolism is implicated in gestational diabetes (GDM). However, prospective studies on lipidomics and GDM risk in race/ethnically diverse populations are sparse. Here, we aimed to (1) identify lipid networks in early pregnancy to mid-pregnancy that are associated with subsequent GDM risk and (2) examine the associations of lipid networks with glycemic biomarkers to understand the underlying mechanisms. RESEARCH DESIGN AND METHODS: This study included 107 GDM cases confirmed using the Carpenter and Coustan criteria and 214 non-GDM matched controls from the National Institute of Child Health and Human Development Fetal Growth Studies-Singleton cohort, untargeted lipidomics data of 420 metabolites (328 annotated and 92 unannotated), and information on glycemic biomarkers in maternal plasma at visit 0 (10-14 weeks) and visit 1 (15-26 weeks). We constructed lipid networks using weighted correlation network analysis technique. We examined prospective associations of lipid networks and individual lipids with GDM risk using linear mixed effect models. Furthermore, we calculated Pearson's partial correlation for GDM-related lipid networks and individual lipids with plasma glucose, insulin, C-peptide and glycated hemoglobin at both study visits. RESULTS: Lipid networks primarily characterized by elevated plasma diglycerides and short, saturated/low unsaturated triglycerides and lower plasma cholesteryl esters, sphingomyelins and phosphatidylcholines were associated with higher risk of developing GDM (false discovery rate (FDR) <0.05). Among individual lipids, 58 metabolites at visit 0 and 96 metabolites at visit 1 (40 metabolites at both time points) significantly differed between women who developed GDM and who did not (FDR <0.05). Furthermore, GDM-related lipid networks and individual lipids showed consistent correlations with maternal glycemic markers particularly in early pregnancy at visit 0. CONCLUSIONS: Plasma lipid metabolites in early pregnancy both individually and interactively in distinct networks were associated with subsequent GDM risk in race/ethnically diverse US women. Future research is warranted to assess lipid metabolites as etiologic markers of GDM.

Evidence in the UK Biobank for the underdiagnosis of erythropoietic protoporphyria.

PURPOSE: Erythropoietic protoporphyria (EPP), characterized by painful cutaneous photosensitivity, results from pathogenic variants in ferrochelatase (FECH). For 96% of patients, EPP results from coinheriting a rare pathogenic variant in trans of a common hypomorphic variant c.315-48T>C (minor allele frequency 0.05). The estimated prevalence of EPP derived from the number of diagnosed individuals in Europe is 0.00092%, but this may be conservative due to underdiagnosis. No study has estimated EPP prevalence using large genetic data sets. METHODS: Disease-associated FECH variants were identified in the UK Biobank, a data set of 500,953 individuals including 49,960 exome sequences. EPP prevalence was then estimated. The association of FECH variants with EPP-related traits was assessed. RESULTS: Analysis of pathogenic FECH variants in the UK Biobank provides evidence that EPP prevalence is 0.0059% (95% confidence interval [CI]: 0.0042-0.0076%), 1.7-3.0 times more common than previously thought in the UK. In homozygotes for the common c.315-48T>C FECH variant, there was a novel decrement in both erythrocyte mean corpuscular volume (MCV) and hemoglobin. CONCLUSION: The prevalence of EPP has been underestimated secondary to underdiagnosis. The common c.315-48T>C allele is associated with both MCV and hemoglobin, an association that could be important both for those with and without EPP.

Pleiotropy and Cross-Disorder Genetics Among Psychiatric Disorders.

Genome-wide analyses of common and rare genetic variations have documented the heritability of major psychiatric disorders, established their highly polygenic genetic architecture, and identified hundreds of contributing variants. In recent years, these studies have illuminated another key feature of the genetic basis of psychiatric disorders: the important role and pervasive nature of pleiotropy. It is now clear that a substantial fraction of genetic influences on psychopathology transcend clinical diagnostic boundaries. In this review, we summarize evidence in psychiatry for pleiotropy at multiple levels of analysis: from overall genome-wide correlation to biological pathways and down to the level of individual loci. We examine underlying mechanisms of observed pleiotropy, including genetic effects on neurodevelopment, diverse actions of regulatory elements, mediated effects, and spurious associations of genomic variation with multiple phenotypes. We conclude with an exploration of the implications of pleiotropy for understanding the genetic basis of psychiatric disorders, informing nosology, and advancing the aims of precision psychiatry and genomic medicine.

Estimating cell-type-specific DNA methylation effects in heterogeneous cellular populations.

Aim: To develop a method for estimating cell-specific effects in epigenomic association studies in the presence of cell type heterogeneity. Materials & methods: We utilized Monte Carlo Expectation-Maximization algorithm with Metropolis-Hastings sampler to reconstruct the 'missing' cell-specific methylations and to estimate their associations with phenotypes free of confounding by cell type proportions. Results: Simulations showed reliable performance of the method under various settings including when the cell type is rare. Application to a real dataset recapitulated the directly measured cell-specific methylation pattern in whole blood. Conclusion: This work provides a framework to identify important cell groups and account for cell type composition useful for studying the role of epigenetic changes in human traits and diseases.