Discovery of shared genomic loci using the conditional false discovery rate approach

The Eating Disorders Genetics Initiative 2 (EDGI2): study protocol

BACKGROUND

The Eating Disorders Genetics Initiative 2 (EDGI2) is designed to explore the role of genes and environment in anorexia nervosa, bulimia nervosa, binge-eating disorder, and avoidant/restrictive food intake disorder (ARFID) with a focus on broad population representation and severe and/or longstanding illness.

METHODS

A total of 20,000 new participants (18,700 cases and 1,300 controls) will be ascertained from the United States (US), Mexico (MX), Australia (AU), Aotearoa New Zealand (NZ), Sweden (SE), and Denmark (DK). Comprehensive phenotyping and genotyping will be performed for participants in US, MX, AU, NZ, and SE using the EDGI2 questionnaire battery and participant saliva samples. In DK, case identification and genotyping will be through the National Patient Register and bloodspots archived near birth. Case-control and case-case genome-wide association studies will be conducted within EDGI2 and enhanced via meta-analysis with external data from the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). Additional analyses will explore genetic correlations between eating disorders (EDs) and other psychiatric and metabolic traits, calculate polygenic risk scores (PRS), and leverage functional biology to evaluate clinical outcomes. Moreover, analyzing PRS for patient stratification and linking identified risk loci to clinically relevant phenotypes highlight the potential of EDGI2 for clinical translation.

DISCUSSION

EDGI2 is a global expansion of the EDGI study to increase sample size, increase participant representation across multiple ancestral backgrounds, and to include ARFID. ED genetics research has historically lagged behind other psychiatric disorders, and EDGI2 is designed to rapidly advance the study of the genetics of the major EDs. Exploring EDs at both the diagnostic level and the symptom level will provide an unprecedented look at the genetic architecture underlying EDs.

TRIAL REGISTRATION

EDGI2 is a registered clinical trial: clinicaltrials.gov NCT06594913. https://clinicaltrials.gov/study/NCT06594913 (posted September 19, 2024).

Shared genetic architecture between leukocyte telomere length and Alzheimer's disease

BACKGROUND

Epidemiological and clinical studies have reported an association between leukocyte telomere length (LTL) and Alzheimer's disease (AD). However, genetic association between the two phenotypes remains largely unknown. We aimed to elucidate the potential shared genetic architecture between LTL and AD.

METHODS

Summary statistics from genome-wide association studies were obtained from large-scale biobank in European-ancestry populations for LTL (N = 472,174) and AD (71,880 cases, 383,378 controls). We examined the global and local genetic correlation between LTL and AD using linkage-disequilibrium score regression and ρ-HESS. We applied the bivariate causal mixture model (MiXeR) to calculate the number of shared genetic causal variants, and the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework to identify specific shared loci between LTL and AD. Bidirectional two-sample Mendelian randomization (MR) were used to explore the causal associations between LTL and AD.

RESULTS

We detected a significant genetic correlation between LTL and AD (rg = -0.168). Partitioning the whole genome into 1703 almost independent regions, we observed a significant local genetic correlation for LTL and AD at 19q13.32. MiXeR estimated a total of 360 variants affecting LTL, of which 16 was estimated to influence AD. The condFDR revealed an essential genetic enrichment in LTL conditional on associations with AD, and vice versa. We next identified 8 shared genomic loci between LTL and AD using conjFDR method, of which 4 are novel loci for both the phenotypes. Moreover, 3 shared loci were identified as eQTLs (rs3098168, rs4780338 and rs2680702). All shared loci mapped a subset of 48 credible genes, including USP8, DEXI and APOE. Gene-set analysis identified 18 putative gene sets enriched with the genes mapped to the shared loci. MR analysis suggested that genetically determined AD was causally associated with LTL.

CONCLUSION

Our study identified specific shared loci between LTL and AD, providing new insights for polygenic overlap and molecular mechanisms, and highlighting new opportunities for future experimental validation.

A multilevel study on the genetic relationship between schizophrenia and inflammatory bowel disease

BACKGROUND

Schizophrenia (SCZ) and Inflammatory Bowel Disease (IBD) represent significant clinical challenges, frequently co-morbid and potentially linked by a genetic correlation. However, the precise mechanism underlying this correlation remains elusive.

METHODS

we utilized genome-wide association study (GWAS) data for SCZ and IBD to evaluate their genetic correlation. Initially, we performed an overall assessment using Linkage Disequilibrium Score Regression (LDSC), Genetic Covariance Analysis (GNOVA), and High-Dimensional Likelihood (HDL) methods. Subsequently, we conducted a more detailed local analysis using the Local Analysis of Variant Association (LAVA) method. To quantify the genetic overlap between these traits, we employed the Conditional/Joint False Discovery Rate (cond/conjFDR) statistical framework. Finally, by integrating the conjFDR analysis with Multi-Trait GWAS (MTAG), we successfully identified multiple shared genetic loci, shedding light on the genetic intersection between these two traits.

RESULTS

At the genomic level, three independent methods confirmed the overall genetic correlation between SCZ and IBD, including CD and UC. Local genetic correlations were also observed across multiple chromosomal regions. At the single-nucleotide polymorphism (SNP) level, we performed a conjFDR analysis, which indicated a genetic overlap between the two traits. By integrating conjFDR analysis with MTAG, we successfully identified several shared genetic loci, including SLC39A8, BACH2, ZNF365, NOD2, PLCL1, and KIF21B.

CONCLUSION

The present study provides a novel perspective on the correlation between SCZ and IBD, potentially advancing the understanding of the genetic architecture and mechanisms of co-morbidities in both diseases.

Genome-wide Pleiotropy Analysis Reveals Shared Genetic Associations between Type 2 Diabetes Mellitus and Subcortical Brain Volumes

Type 2 diabetes mellitus (T2DM), a prevalent metabolic disorder marked by insulin resistance and hyperglycemia, has been linked to volumetric changes in subcortical regions, yet the genetic basis of this relationship remains unclear. We analyzed genome-wide association study summary data for T2DM and 14 subcortical volumetric traits, using MiXeR to quantify shared genetic architecture and applying conditional/conjunctional false discovery rate analyses to detect novel and shared genomic loci. Enrichment and gene expression analyses were subsequently performed to explore the biological functions and mechanisms of genes associated with these loci. We observed a substantial proportion of trait-influencing variants shared between T2DM and subcortical structures, with Dice coefficients ranging from 22.4% to 49.6%. Additionally, 70 distinct loci were identified as being jointly associated with T2DM and subcortical volumes, 5 and 22 of which were novel for T2DM and subcortical volumes, respectively. The 769 protein-coding genes mapped to these shared loci are enriched in metabolic and neurodevelopmental pathways and exhibit specific developmental trajectories, with 117 genes showing expression levels linked to both T2DM and subcortical structures. This study uncovered polygenic overlap between T2DM and subcortical structures, deepening our comprehension of the genetic factors linking metabolic disorders and brain health.

Translating Molecular Psychiatry: From Biomarkers to Personalized Therapies-A Narrative Review

In this review, we explore the biomarkers of different psychiatric disorders, such as major depressive disorder, generalized anxiety disorder, schizophrenia, and bipolar disorder. Moreover, we show the interplay between genetic and environmental factors. Novel techniques such as genome-wide association studies (GWASs) have identified numerous risk loci and single-nucleotide polymorphisms (SNPs) implicated in these conditions, contributing to a better understanding of their mechanisms. Moreover, the impact of genetic variations on drug metabolisms, particularly through cytochrome P450 (CYP450) enzymes, highlights the importance of pharmacogenomics in optimizing psychiatric treatment. This review also explores the role of neurotransmitter regulation, immune system interactions, and metabolic pathways in psychiatric disorders. As the technology advances, integrating genetic markers into clinical practice will be crucial in advancing precision psychiatry, improving diagnostic accuracy and therapeutic interventions for individual patients.

Alcohol use disorder and body mass index show genetic pleiotropy and shared neural associations

Despite neurobiological overlap, alcohol use disorder (AUD) and body mass index (BMI) show minimal genetic correlation (rg), possibly due to mixed directions of shared variants. Here we applied MiXeR to investigate shared genetic architecture between AUD and BMI, conjunctional false discovery rate to detect shared loci and their directional effect, local analysis of (co)variant association for local rg, functional mapping and annotation to identify lead single-nucleotide polymorphisms, Genotype-Tissue Expression (GTEx) to examine tissue enrichment and BrainXcan to assess associations with brain phenotypes. MiXeR indicated 82.2% polygenic overlap, despite an rg of -0.03. The conjuctional false discovery rate method identified 132 shared lead single-nucleotide polymorphisms, with 53 novel, showing both concordant and discordant effects. GTEx analyses identified overexpression in multiple brain regions. Amygdala and caudate nucleus volumes were associated with AUD and BMI. Opposing variant effects explain the minimal rg between AUD and BMI, with implicated brain regions involved in executive function and reward, clarifying their polygenic overlap and neurobiological mechanisms.

Polygenic overlap with granulocyte counts identifies novel loci for clozapine metabolism and clozapine-induced agranulocytosis

While clozapine is the most effective antipsychotic drug, its use is limited due to hematological adverse effects involving the reduction of granulocyte counts with potential life-threatening agranulocytosis. It is not yet possible to predict or prevent the risk of agranulocytosis, and the mechanisms are unknown but likely related to clozapine metabolism. Genome-wide association studies (GWASs) of clozapine metabolism and clozapine-induced agranulocytosis have identified few genetic loci. We used the largest available GWAS summary statistics of clozapine metabolism (clozapine-to-norclozapine ratio) and clozapine-induced agranulocytosis, applying the conditional false discovery rate (condFDR) method to increase power for genetic discovery by conditioning on granulocyte counts variants. To investigate potential causal effects of shared loci, we performed Mendelian Randomization analyses. After conditioning on granulocyte counts, we identified two novel loci associated with clozapine-to-norclozapine ratio. These loci were significantly associated with clozapine metabolism in a validation sample of 392 clozapine-treated individuals. For clozapine-induced agranulocytosis, five loci were identified after conditioning on granulocyte counts. These five loci were significantly associated with reduced granulocyte counts in a small independent sample of clozapine-treated individuals. Genetic liability to slow clozapine metabolism (high clozapine-to-norclozapine ratio) showed evidence of a causal effect on reduced neutrophil counts, and genetic liability to low neutrophil counts exhibited weak evidence of a causal effect on clozapine-induced agranulocytosis. Our findings of shared genetic variants associated with clozapine metabolism and granulocyte counts may form the basis for developing prediction models for clozapine-induced agranulocytosis.

Identification of genetic architecture shared between schizophrenia and Alzheimer's disease

Both schizophrenia (SCZ) and Alzheimer's disease (AD) are highly heritable brain disorders. Despite of the observed comorbidity and shared psychosis and cognitive decline between the two disorders, the genetic risk architecture shared by SCZ and AD remains largely unknown. Based on summary statistics of the currently available largest genome-wide association studies for SCZ (n = 130,644) and AD (n = 455,258) in individuals of European ancestry, we conducted conditional/conjunctional false discovery rate (FDR) analysis to enhance the statistical power for discovering more genetic associations with SCZ or AD and to detect the common genetic variants shared by both disorders. We found shared genetic architecture in SCZ conditioned on AD and vice versa across different levels of significance, indicating polygenic overlap. We found 268 (78 novel) SCZ-only and 125 (55 novel) AD-only SNPs at conditional FDR < 0.01, and 16 lead SNPs shared by SCZ and AD at conjunctional FDR < 0.05. Only half of the shared SNPs showed concordant effect direction, which was consistent with the modest genetic correlation (r = 0.097; P = 0.026) between the two disorders. This study provides evidence for polygenic overlap between SCZ and AD, suggesting the existence of the shared molecular genetic mechanisms, which may inform therapeutic targets that are applicable for both disorders.

Multigene overlap analysis of bipolar disorder subtypes and educational attainment

OBJECTIVE

Bipolar disorder subtypes (BIP-I and BIP-II) differ in clinical presentation and genetic basis, yet their patterns of genetic association with educational attainment (EA) remain poorly understood. This study investigated the genetic overlap between BIP subtypes and EA, along with their underlying molecular mechanisms.

METHODS

Using genome-wide association study (GWAS) data for BIP-I (n = 25,060), BIP-II (n = 6781), and EA (n = 765,283), we estimated genetic overlap using bivariate causal mixed models (MiXeR) and identified shared gene loci through the joint false discovery rate (conjFDR) method.

RESULTS

MiXeR analysis revealed approximately 7.4 K single nucleotide polymorphisms (SNPs) shared between BIP-I and EA, accounting for 97.4 % of SNPs influencing BIP-I and 56.5 % of those affecting EA. ConjFDR identified 264 loci commonly associated with BIP-I and EA, including 168 novel loci for both traits. Among the 312 lead SNPs at these loci, 219 exhibited consistent effects, while 93 demonstrated opposing effects. In contrast, only two loci were co-associated between BIP-II and EA. Functional annotation and enrichment analyses showed that most loci shared by BIP-I and EA were located in intronic and intergenic regions, with associated genes enriched in processes such as protein binding and nervous system development.

CONCLUSIONS

This study highlights the distinct degrees and patterns of genetic association between BIP subtypes and EA, offering insights into the heterogeneity of BIP and a potential genetic basis for clinical subtyping and personalized treatment strategies.

Investigating the shared genetic architecture between schizophrenia and sex hormone traits

Sex hormones are involved in schizophrenia pathogenesis; however, their direction and genetic overlap remain unknown. By leveraging summary statistics from large-scale genome-wide association studies, we quantified the shared genetic architecture between schizophrenia and four sex hormone traits. Linkage disequilibrium score regression and bivariate causal mixture modeling strategies showed significant positive correlations between sex hormone-binding globulin (SHBG), total testosterone, and schizophrenia, while bioavailable testosterone and schizophrenia were negatively correlated. Estradiol showed a weak positive correlation with schizophrenia, with little polygenic overlap. The conjunctional false discovery rate method identified 303 lead single-nucleotide polymorphisms (SNPs) in jointly shared genomic loci between schizophrenia and SHBG, with 130, 52, and 9 SNPs shared between schizophrenia and total testosterone, bioavailable testosterone, and estradiol, respectively. Functional annotation suggests that mitotic sister chromatid segregation and N-glycan biosynthesis may be involved in common mechanisms underlying sex hormone regulation and schizophrenia onset. In conclusion, this study clarified the inherent relationships between schizophrenia and sex hormone traits, highlighted the roles of mitotic sister chromatid segregation and N-glycan biosynthesis in the pathogenesis of schizophrenia, and delivered potential targets for further validation.

Enhanced insights into the genetic architecture of 3D cranial vault shape using pleiotropy-informed GWAS

Large-scale GWAS studies have uncovered hundreds of genomic loci linked to facial and brain shape variation, but only tens associated with cranial vault shape, a largely overlooked aspect of the craniofacial complex. Surrounding the neocortex, the cranial vault plays a central role during craniofacial development and understanding its genetics are pivotal for understanding craniofacial conditions. Experimental biology and prior genetic studies have generated a wealth of knowledge that presents opportunities to aid further genetic discovery efforts. Here, we use the conditional FDR method to leverage GWAS data of facial shape, brain shape, and bone mineral density to enhance SNP discovery for cranial vault shape. This approach identified 120 independent genomic loci at 1% FDR, nearly tripling the number discovered through unconditioned analysis and implicating crucial craniofacial transcription factors and signaling pathways. These results significantly advance our genetic understanding of cranial vault shape and craniofacial development more broadly.

COVID-19 infection and longevity: an observational and mendelian randomization study

BACKGROUND

Studies have indicated that COVID-19 infection may accelerate the aging process in organisms. However, it remains unknown whether contracting COVID-19 affects life expectancy. Furthermore, the underlying biological mechanisms behind these findings are still unclear.

METHODS

We conducted a prospective cohort study on 56,504 participants of European ancestry from the UK Biobank who reported the time and number of COVID-19 infection between January 2020 and September 2023. The parental average longevity was used as a proxy for their own longevity. Linear regression was used to assess the relationship between COVID-19 infection and longevity. Furthermore, we investigated the shared genetic basis between COVID-19 and longevity using large-scale genome-wide association studies (GWAS) for COVID-19 (122,616 cases and 2,475,240 controls) and longevity (3,484 cases and 25,483 controls). Mendelian randomization (MR) and mediation analysis were utilized to assess causal relationships and potential mediators between COVID-19 susceptibility and longevity. Shared genetic loci between the two phenotypes were identified using conjunctional false discovery rate (conjFDR) statistical frameworks.

RESULTS

After controlling for relevant covariates, COVID-19 infection might not be significantly correlated with longevity. In all MR methods, generalized summary-data-based Mendelian randomization (GSMR) analysis revealed a significant decrease in longevity due to severe COVID-19 infection (OR = 0.91, 95%CI: 0.84-0.98, P = 0.015). Mediation analysis identified stroke and myocardial infarction as potential mediators between COVID-19 susceptibility and reduced longevity. At conjFDR < 0.05, we identified rs62062323 (KANSL1) and rs9530111 (PIBF1) as shared loci between COVID-19 and longevity.

CONCLUSION

Together, our findings provided preliminary evidence for the shared genetic basics between COVID-19 and aging. This discovery may have implications for personalized medicine and preventive strategies, helping identify individuals who may be more vulnerable to severe outcomes from COVID-19 due to their genetic makeup.

An evolutionary perspective on the genetics of anorexia nervosa

Anorexia nervosa (AN) typically emerges around adolescence and predominantly affects females. Recent progress has been made in identifying biological correlates of AN, but more research is needed to pinpoint the specific mechanisms that lead to its development and maintenance. There is a known phenotypic link between AN, growth and sexual maturation, yet the genetic overlap between these phenotypes remains enigmatic. One may hypothesize that shared factors between AN, energy metabolism and reproductive functions may have been under recent evolutionary selection. Here, we characterize the genetic overlap between AN, BMI and age at menarche, and aimed to reveal recent evolutionary factors that may help explain the origin of AN. We obtained publicly available GWAS summary statistics of AN, BMI and age at menarche and studied the polygenic overlap between them. Next, we used Neandertal Selective Sweep scores to explore recent evolutionary selection. We found 22 loci overlapping between AN and BMI, and 9 loci between AN and age at menarche, with 7 of these not previously associated with AN. We found that loci associated with AN may have been under particular evolutionary dynamic. Chronobiology appeared relevant to the studied genetic overlaps and prone to recent evolutionary selection, offering a promising avenue for future research. Taken together, our findings contribute to the understanding of the genetic underpinning of AN. Ultimately, better knowledge of the biological origins of AN may help to target specific biological processes and facilitate early intervention in individuals who are most at risk.

Genome-wide analysis identifies novel shared loci between depression and white matter microstructure

Depression, a complex and heritable psychiatric disorder, is associated with alterations in white matter microstructure, yet their shared genetic basis remains largely unclear. Utilizing the largest available genome-wide association study (GWAS) datasets for depression (N = 674,452) and white matter microstructure (N = 33,224), assessed through diffusion tensor imaging metrics such as fractional anisotropy (FA) and mean diffusivity (MD), we employed linkage disequilibrium score regression method to estimate global genetic correlations, local analysis of [co]variant association approach to pinpoint genomic regions with local genetic correlations, and conjunctional false discovery rate analysis to identify shared variants. Our findings revealed that depression showed significant local genetic correlations with FA in 37 genomic regions and with MD in 59 regions, while global genetic correlations were weak. Variant-level analysis identified 78 distinct loci jointly associated with depression (25 novel loci) and FA (35 novel loci), and 41 distinct loci associated with depression (17 novel loci) and MD (25 novel loci). Further analyses showed that these shared loci exhibited both concordant and discordant effect directions between depression and white matter traits, as well as distinct yet overlapping hemispheric patterns in their genetic architecture. Enrichment analysis of these shared loci implicated biological processes related to metabolism and regulation. This study provides evidence of a mixed-direction shared genetic architecture between depression and white matter microstructure. The identification of specific loci and pathways offers potential insights for developing targeted interventions to improve white matter integrity and alleviate depressive symptoms.

Mapping the Genetic Landscape of Psychiatric Disorders With the MiXeR Toolset

Psychiatric disorders have complex genetic architectures with substantial genetic overlap across conditions, which may partially explain their high levels of comorbidity. This presents significant challenges to research. Genome-wide association studies (GWASs) have uncovered hundreds of loci associated with single disorders, but the genetic landscape of psychiatric disorders has remained largely obscure. Moving beyond the conventional infinitesimal model, uni-, bi-, and trivariate MiXeR tools, applied to GWAS summary statistics, has enabled us to more comprehensively describe the genetic architecture of complex disorders and traits and their overlap. Furthermore, the GSA-MiXeR tool improves biological interpretation of GWAS findings to better elucidate causal mechanisms. Here, we outline the methodology that underlies the MiXeR tools together with instructions for their optimal use. We review results from studies that have investigated the genetic architecture of psychiatric disorders and their overlap using the MiXeR toolset. These studies have revealed generally high polygenicity and low discoverability among psychiatric disorders, particularly in contrast to somatic disorders. There is also pervasive genetic overlap across psychiatric disorders and behavioral traits, while their overlap with somatic traits is smaller, consistent with differences in polygenicity. Finally, GSA-MiXeR has quantified the contribution of gene sets to the heritability of psychiatric disorders, prioritizing small, biologically coherent gene sets. Together, these findings have implications for our understanding of the complex relationships between psychiatric disorders and related traits. MiXeR tools have provided new insights into the genetic architecture of psychiatric disorders, generating a better understanding of their underlying biological mechanisms and potential for clinical utility.

Dissecting the shared genetic architecture between nonalcoholic fatty liver disease and type 2 diabetes

Observational studies have reported a bidirectional correlation between nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D), but the shared genetic basis between the two conditions remains unclear. Using genome-wide association study (GWAS) summary data from European-ancestry populations, we examined the cross-trait genetic correlation and identified genomic overlaps and shared risk loci. We employed a latent causal variable model and Mendelian randomization (MR) analysis to infer causal relationships. Colocalization analysis and conditional/conjunctional false discovery rate (condFDR/conjFDR) were used to identify genomic overlaps and shared risk loci. Two-step MR analysis was utilized to identify potential mediators. We observed a strong positive genomic correlation between NAFLD and T2D (rg = 0.652, P = 5.67 × 10-6) and identified tissue-specific transcriptomic correlations in the pancreas, liver, skeletal muscle, subcutaneous adipose, and blood. Genetic enrichment was observed in NAFLD conditional on associations with T2D and vice versa, indicating significant polygenic overlaps. We found robust evidence for the causal effect of NAFLD on T2D, particularly insulin-related T2D, rather than vice versa. Colocalization analysis identified shared genomic regions between NAFLD and T2D, including GCKR, FTO, MAU2-TM6SF2, and PNPLA3-SAMM50. High-density lipoprotein cholesterol and insulin were partly mediated the association between NAFLD and T2D. These findings unveil a close genetic link between NAFLD and T2D, shedding light on the biological mechanisms connecting NAFLD progression to T2D.

Exploring genetic structures and shared sites between alcohol, cheese intake, and inflammatory bowel disease

Background

An association has been observed between alcohol and cheese intake and the onset of inflammatory bowel disease (IBD), necessitating further exploration from a genetic structural perspective.

Methods

The present analysis was focused on the intake of alcohol and cheese in conjunction with IBD genome-wide association study (GWAS) data, with the objective of exploring genetic correlations and identifying common loci. Initially, overall genetic correlations were assessed employing two methodologies: linkage disequilibrium score regression (LDSC) and genetic covariance analyzer (GNOVA). Subsequently, local correlations were examined through the SUPERGNOVA method. A genetic overlap analysis between various traits was then conducted based on the statistical theory of conditional/conjunctional false discovery rate (cond/conjFDR). Ultimately, shared loci between the two traits were identified via conjFDR analysis and multi-trait analysis of GWAS (MTAG).

Results

Substantial overall correlations were noted at the genome-wide level between alcohol and cheese intake and both IBD and Crohn's disease (CD), whereas the association with ulcerative colitis (UC) was of lesser significance. In the local genetic analysis, chromosome 16 emerged as a key region implicated in the relationship between alcohol and cheese intake and IBD (including both CD and UC). The conjFDR analysis confirmed the genetic overlap between the two diseases. Furthermore, both conjFDR and MTAG analyses identified multiple shared genetic loci, with nine genes (Y_RNA, DENND1B, GCKR, KPNA7, CLN3, SLC39A8, FUT2, ERAP2, and SMAD3) being.

Conclusion

The present study provides genetic evidence supporting the comorbidity of alcohol and cheese intake with IBD, offering novel insights into potential strategies for the prevention and treatment of IBD through the modulation of alcohol and cheese consumption.

Shared polygenic susceptibility to treatment response in severe affective and psychotic disorders: Evidence from GWAS data sets

While schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) genetically correlate, the pleiotropy underlying response/resistance to drugs used in these disorders has not been investigated. The aim of this study is to analyze the genetic relationship between treatment-resistant schizophrenia (TRS), response to lithium in BD (respLi) and response to antidepressants in MDD (respAD) using the conditional/conjunctional false discovery rate (cond/conjFDR) methodology, based on the hypothesis that shared mechanisms related to a common psychopathology factor underlie these phenotypes. A cross-trait polygenic enrichment for TRS conditioned on associations with respLi was observed. The conjFDR analysis identified rs11631065 (chr15:66654304) as a shared locus between them. One of the genes at this locus is MAP2K1, previously reported as associated with TRS after conditioning on body mass index genome-wide association study (GWAS). The set of genes at TRS-respLi conjFDR < 0.95 showed enrichment in response to psychotropic drugs in severe mental disorders from GWAS Catalog as well as in neurodevelopment and synaptic pathways. In conclusion, our study constitutes the first evidence of a transdiagnostic genetic signal associated with response to different pharmacological treatments in psychotic and affective disorders. It is necessary to confirm these results when larger GWAS of these phenotypes are available.

Large-scale brainstem neuroimaging and genetic analyses provide new insights into the neuronal mechanisms of hypertension

While brainstem regions are central regulators of blood pressure, the neuronal mechanisms underlying their role in hypertension remain poorly understood. Here, we investigated the structural and genetic relationships between global and regional brainstem volumes and blood pressure. We used magnetic resonance imaging data from n = 32,666 UK Biobank participants, and assessed the association of volumes of the whole brainstem and its main regions with blood pressure. We applied powerful statistical genetic tools, including bivariate causal mixture modeling (MiXeR) and conjunctional false discovery rate (conjFDR), to non-overlapping genome-wide association studies of brainstem volumes (n = 27,034) and blood pressure (n = 321,843) in the UK Biobank cohort. We observed negative associations between the whole brainstem and medulla oblongata volumes and systolic blood and pulse pressure, and positive relationships between midbrain and pons volumes and blood pressure traits when adjusting for the whole brainstem volume (all partial correlation coefficients ∣r∣ effects between 0.03 and 0.05, p ≤ 0.0042). We observed the largest genetic overlap for the whole brainstem, sharing 77% of its trait-influencing variants with blood pressure. We identified 65 shared loci between brainstem volumes and blood pressure traits and mapped these to 71 genes, implicating molecular pathways linked to sympathetic nervous system development, metal ion transport, and vascular homeostasis. The present findings support a link between brainstem structures and blood pressure and provide insights into their shared genetic underpinnings. The overlapping genetic architectures and mapped genes offer mechanistic information about the roles of brainstem regions in hypertension.

Identification of shared genetic etiology of cardiovascular and cerebrovascular diseases through common cardiometabolic risk factors

Cardiovascular diseases (CVDs) and cerebrovascular diseases (CeVDs) are closely related vascular diseases, sharing common cardiometabolic risk factors (RFs). Although pleiotropic genetic variants of these two diseases have been reported, their underlying pathological mechanisms are still unclear. Leveraging GWAS summary data and using genetic correlation, pleiotropic variants identification, and colocalization analyses, we identified 11 colocalized loci for CVDs-CeVDs-BP (blood pressure), CVDs-CeVDs-LIP (lipid traits), and CVDs-CeVDs-cIMT (carotid intima-media thickness) triplets. No shared causal loci were found for CVDs-CeVDs-T2D (type 2 diabetes) or CVDs-CeVDs-BMI (body mass index) triplets. The 11 loci were mapped to 12 genes, namely CASZ1, CDKN1A, TWIST1, CDKN2B, ABO, SWAP70, SH2B3, LRCH1, FES, GOSR2, RPRML, and LDLR, where both GOSR2 and RPRML were mapped to one locus. They were enriched in pathways related to cellular response to external stimulus and regulation of the phosphate metabolic process and were highly expressed in endothelial cells, epithelial cells, and smooth muscle cells. Multi-omics analysis revealed methylation of two genes (CASZ1 and LRCH1) may play a causal role in the genetic pleiotropy. Notably, these pleiotropic loci are highly enriched in the targets of antihypertensive drugs, which further emphasizes the role of the blood pressure regulation pathway in the shared etiology of CVDs and CeVDs.

Shared genetic architecture of cortical thickness alterations in major depressive disorder and schizophrenia

BACKGROUND

Major depressive disorder (MDD) and schizophrenia (SCZ) are heritable brain disorders characterized by alterations in cortical thickness. However, the shared genetic basis for cortical thickness changes in these disorders remains unclear.

METHODS

We conducted a systematic literature search on cortical thickness in MDD and SCZ through PubMed and Web of Science. A coordinate-based meta-analysis was performed to identify cortical thickness changes. Additionally, utilizing summary statistics from the largest genome-wide association studies for depression (Ncase = 268,615, Ncontrol = 667,123) and SCZ (Ncase = 53,386, Ncontrol = 77,258), we explored shared genomic loci using conjunctional false discovery rate (conjFDR) analysis. Transcriptome-neuroimaging association analysis was then employed to identify shared genes associated with cortical thickness alterations, and enrichment analysis was finally carried out to elucidate the biological significance of these genes.

RESULTS

Our search yielded 34 MDD (Ncase = 1621, Ncontrol = 1507) and 19 SCZ (Ncase = 1170, Ncontrol = 1043) neuroimaging studies for cortical thickness meta-analysis. Specific alterations in the left supplementary motor area were observed in MDD, while SCZ exhibited widespread reductions in various brain regions, particularly in the frontal and temporal areas. The conjFDR approach identified 357 genomic loci jointly associated with MDD and SCZ. Within these loci, 55 genes were found to be associated with cortical thickness alterations in both disorders. Enrichment analysis revealed their involvement in nervous system development, apoptosis, and cell communication.

CONCLUSION

This study revealed the shared genetic architecture underlying cortical thickness alterations in MDD and SCZ, providing insights into common neurobiological pathways. The identified genes and pathways may serve as potential transdiagnostic markers, informing precision medicine approaches in psychiatric care.

Alcohol use disorder and body mass index show genetic pleiotropy and shared neural associations

Despite neurobiological overlap, alcohol use disorder (AUD) and body mass index (BMI) show minimal genetic correlation (rg), possibly due to mixed directions of shared variants. We applied MiXeR to investigate shared genetic architecture between AUD and BMI, conjunctional false discovery rate (conjFDR) to detect shared loci and their directional effect, Local Analysis of (co)Variant Association (LAVA) for local rg, Functional Mapping and Annotation (FUMA) to identify lead single nucleotide polymorphisms (SNPs), Genotype-Tissue Expression (GTEx) to examine tissue enrichment, and BrainXcan to assess associations with brain phenotypes. MiXeR indicated 82.2% polygenic overlap, despite a rg of -.03. ConjFDR identified 132 shared lead SNPs, with 53 novel, showing both concordant and discordant effects. GTEx analyses identified overexpression in multiple brain regions. Amygdala and caudate nucleus volumes were associated with AUD and BMI. Opposing variant effects explain the minimal rg between AUD and BMI, with implicated brain regions involved in executive function and reward, clarifying their polygenic overlap and neurobiological mechanisms.

Identification of novel genomic loci for anxiety symptoms and extensive genetic overlap with psychiatric disorders

AIMS

Anxiety disorders are prevalent and anxiety symptoms (ANX) co-occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

METHODS

We included a genome-wide association study of ANX (meta-analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders.

RESULTS

Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k-11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MDn = 47 , BIPn = 33 , SCZn = 71 , ADHDn = 20 , and ASDn = 5 . Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci.

CONCLUSIONS

Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets.

Charting the shared genetic architecture of Alzheimer's disease, cognition, and educational attainment, and associations with brain development

The observation that the risk of developing Alzheimer's disease is reduced in individuals with high premorbid cognitive functioning, higher educational attainment, and occupational status has led to the 'cognitive reserve' hypothesis. This hypothesis suggests that individuals with greater cognitive reserve can tolerate a more significant burden of neuropathological changes before the onset of cognitive decline. The underpinnings of cognitive reserve remain poorly understood, although a shared genetic basis between measures of cognitive reserve and Alzheimer's disease has been suggested. Using the largest samples to date and novel statistical tools, we aimed to investigate shared genetic variants between Alzheimer's disease, and measures of cognitive reserve; cognition and educational attainment to identify molecular and neurobiological foundations. We applied the causal mixture model (MiXeR) to estimate the number of trait-influencing variants shared between Alzheimer's disease, cognition, and educational attainment, and condFDR/conjFDR to identify shared loci. To provide biological insights loci were functionally characterized. Subsequently, we constructed a Structural Equation Model (SEM) to determine if the polygenic foundation of cognition has a direct impact on Alzheimer's disease risk, or if its effect is mediated through established risk factors for the disease, using a case-control sample from the UK Biobank. Univariate MiXeR analysis (after excluding chromosome 19) revealed that Alzheimer's disease was substantially less polygenic (450 trait-influencing variants) compared to cognition (11,100 trait-influencing variants), and educational attainment (12,700 trait-influencing variants). Bivariate MiXeR analysis estimated that Alzheimer's disease shared approximately 70 % of trait-influencing variants with cognition, and approximately 40 % with educational attainment, with mixed effect directions. Using condFDR analysis, we identified 18 loci jointly associated with Alzheimer's disease and cognition and 6 loci jointly associated with Alzheimer's disease and educational attainment. Genes mapped to shared loci were associated with neurodevelopment, expressed in early life, and implicated the dendritic tree and phosphatidylinositol phosphate binding mechanisms. Spatiotemporal gene expression analysis of the identified genes showed that mapped genes were highly expressed during the mid-fetal period, further suggesting early neurodevelopmental stages as critical periods for establishing cognitive reserve which affect the risk of Alzheimer's disease in old age. Furthermore, our SEM analysis showed that genetic variants influencing cognition had a direct effect on the risk of developing Alzheimer's disease, providing evidence in support of the neurodevelopmental hypothesis of the disease.

Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide "eras" of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and omics approaches. The evolution of psychiatric genetic research offers valuable insights that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.

Unraveling the shared genetics of common epilepsies and general cognitive ability

PURPOSE

Cognitive impairment is prevalent among individuals with epilepsy, and increasing evidence indicates that genetic factors can underlie this relationship. However, the extent to which epilepsy subtypes differ in their genetic relationship with cognitive function, and information about the specific genetic variants involved remain largely unknown.

METHODS

We investigated the genetic relationship between epilepsies and general cognitive ability (COG) using complementary statistical tools, including linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR). We analyzed genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes 'all epilepsy', focal epilepsies and genetic generalized epilepsies (GGE), as well as specific subtypes. We functionally annotated the identified loci using several biological resources and validated the results in independent samples.

RESULTS

Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than 'all epilepsy', GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k - 2.9k variants). The other epilepsy phenotypes were insufficiently powered for MiXeR analysis. We quantified extensive genetic overlap between COG and epilepsy types, but with varying negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and 'all epilepsy', and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), 'all epilepsy' (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 3.62 × 10-7; 'all epilepsy': p = 2.58 × 10-3).

CONCLUSION

Our study further dissects the substantial genetic basis shared between epilepsies and COG and identifies novel shared loci. An improved understanding of the genetic relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.

Genetic overlap between inflammatory bowel disease and iridocyclitis: insights from a genome-wide association study in a European population

BACKGROUND

Inflammatory bowel disease (IBD) is occasionally associated with ophthalmic diseases, including iridocyclitis (IC). The co-occurrence of IBD and IC has been increasingly observed, possibly due to shared genetic structures.

METHODS

A three-part analysis was executed utilizing genome-wide association study (GWAS) data on IBD and IC. First, the overall genetic correlation between the two traits was observed using linkage disequilibrium score regression (LDSC). Subsequent to this, a local genetic correlation analysis was conducted utilizing the heritability estimation from summary statistics (HESS) methodology. Finally, the conditional/conjunctional false discovery rate (cond/conjFDR) statistical framework was utilized to ascertain the degree of genetic overlap between the two traits.

RESULTS

Positive overall correlations were observed among IBD, ulcerative colitis (UC), and IC, encompassing both acute/subacute and chronic IC presentations. While a significant correlation was identified between Crohn's disease (CD) and IC, it was not evident for acute/subacute or chronic IC (P > 0.05). Notably, IBD (encompassing CD and UC) demonstrated local genetic correlations with IC and acute/subacute IC, with pronounced enrichment notably on chromosomes 1 and 6, though such correlations were not observed with chronic IC. The conjFDR analysis confirmed the genetic overlap between the two diseases. The shared genes overlapping between IBD (encompassing CD and UC) and IC were IL23R, GPR35, and ERAP1. For acute/subacute IC and chronic IC, there were six overlapping genes (GPR35, RPL23AP12, IL23R, SNAPC4, ERAP1, and INAVA) and one overlapping gene (INAVA), respectively.

CONCLUSION

This study confirms the existence of a shared genetic structure between IBD and IC, providing a biological basis for their comorbidity. Additionally, this finding has significant implications for preventing and treating these two diseases.

Unveiling the shared genetic architecture between testosterone and polycystic ovary syndrome

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation.

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

The genetic landscape of basal ganglia and implications for common brain disorders

The basal ganglia are subcortical brain structures involved in motor control, cognition, and emotion regulation. We conducted univariate and multivariate genome-wide association analyses (GWAS) to explore the genetic architecture of basal ganglia volumes using brain scans obtained from 34,794 Europeans with replication in 4,808 white and generalization in 5,220 non-white Europeans. Our multivariate GWAS identified 72 genetic loci associated with basal ganglia volumes with a replication rate of 55.6% at P < 0.05 and 87.5% showed the same direction, revealing a distributed genetic architecture across basal ganglia structures. Of these, 50 loci were novel, including exonic regions of APOE, NBR1 and HLAA. We examined the genetic overlap between basal ganglia volumes and several neurological and psychiatric disorders. The strongest genetic overlap was between basal ganglia and Parkinson's disease, as supported by robust LD-score regression-based genetic correlations. Mendelian randomization indicated genetic liability to larger striatal volume as potentially causal for Parkinson's disease, in addition to a suggestive causal effect of greater genetic liability to Alzheimer's disease on smaller accumbens. Functional analyses implicated neurogenesis, neuron differentiation and development in basal ganglia volumes. These results enhance our understanding of the genetic architecture and molecular associations of basal ganglia structure and their role in brain disorders.

Investigating the shared genetic basis of inflammatory bowel disease and systemic lupus erythematosus using genetic overlap analysis

BACKGROUND

Inflammatory bowel disease (IBD) and systemic lupus erythematosus (SLE) are autoimmune diseases that often coexist clinically. This phenomenon might be due to shared genetic components.

METHODS

Genome-wide association study (GWAS) data for IBD and SLE were analyzed to determine both global and local genetic correlations using three methodologies: linkage disequilibrium score regression (LDSC), genetic covariance analyzer (GNOVA), and SUPERGNOVA. The genetic overlap and risk loci were subsequently examined using the conditional/conjunctional false discovery rate (cond/conjFDR) statistical framework. Furthermore, a multi-trait analysis of MTAG was employed to validate the loci, followed by an LDSC analysis focusing on tissue-specific gene expression.

RESULTS

GWAS findings demonstrated a marked global genetic correlation between IBD (including Crohn's disease and ulcerative colitis) and SLE. Locally, SLE showed a strong association with IBD and Crohn's disease on chromosomes 10, 19, and 22. ConjFDR analysis confirmed the genetic overlap and identified relevant genetic risk loci. MTAG further validated several shared susceptibility genes. Additionally, the LDSC-SEG analysis results indicate that IBD (including CD and UC) and SLE are jointly enriched in the tissues of Spleen and Whole Blood.

CONCLUSION

This study confirms a genetic overlap between IBD and SLE, identifying marked comorbid genes and offering new insights for treating these diseases.

Sex differences in shared genetic determinants between severe mental disorders and metabolic traits

High rates of metabolic risk factors contribute to premature mortality in patients with severe mental disorders, but the molecular underpinnings of this association are largely unknown. We performed the first analysis on shared genetic factors between severe mental disorders and metabolic traits considering the effect of sex. We applied an integrated analytical pipeline on the largest sex-stratified genome-wide association datasets available for bipolar disorder (BD), major depressive disorder (MDD), schizophrenia (SZ), and for body mass index (BMI) and waist-to-hip ratio (WHR) (all including participants of European origin). We observed extensive genetic overlap between all severe mental disorders and variants associated with BMI in women or men and identified several genetic loci shared between BD, or SZ and BMI in women (24 and 91, respectively) or men (13 and 208, respectively), with mixed directions of effect. A large part of the identified genetic variants showed sex differences in terms of location, genes modulated in adipose tissue and/or brain regions, and druggable targets. By providing a complete picture of disorder specific and cross-disorder shared genetic determinants, our results highlight potential sex differences in the genetic liability to metabolic comorbidities in patients with severe mental disorders.

Multitrait Analysis to Decipher the Intertwined Genetic Architecture of Neuroanatomical Phenotypes and Psychiatric Disorders

BACKGROUND

There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven to be challenging, and new approaches are needed to infer the genetic structures that may underlie those phenotypes. Multivariate analyses are a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches.

METHODS

First, we conducted univariate and multivariate genome-wide association studies for 9 MRI-derived brain volume phenotypes in 20,000 UK Biobank participants. Next, we performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches could distinguish disorder-associated and non-disorder-associated variants from 6 psychiatric disorders: bipolar disorder, attention-deficit/hyperactivity disorder, autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized k-medoids approach.

RESULTS

A univariate MRI genome-wide association study revealed only negligible genetic correlations with psychiatric disorders, while a multitrait genome-wide association study identified multiple new associations and showed significant enrichment for variants related to both attention-deficit/hyperactivity disorder and schizophrenia. Clustering analyses also detected 2 clusters that showed not only enrichment for association with attention-deficit/hyperactivity disorder and schizophrenia but also a consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophin pathways in both MRI phenotypes and schizophrenia.

CONCLUSIONS

Our results show that multitrait association signature can be used to infer genetically driven latent MRI variables associated with psychiatric disorders, thereby opening paths for future biomarker development.

Genetic association and causal effects between inflammatory bowel disease and conjunctivitis

Background

Inflammatory bowel disease (IBD) is often clinically associated with conjunctivitis, which may result from genetic associations and causal effects.

Methods

Genetic correlations were investigated through the genome-wide association study (GWAS) data on IBD and conjunctivitis using the linkage disequilibrium score regression (LDSC) and heritability estimated in summary statistics (HESS). The causal effect analysis was performed using four methods of Mendelian randomization (MR) and the genetic risk loci common to both diseases were identified by the statistical method of conditional/conjoint false discovery rate (cond/conjFDR), followed by genetic overlap analysis. Finally, a multi-trait GWAS analysis (MTAG) was performed to validate the identified shared loci.

Results

IBD (including CD and UC) and conjunctivitis showed a significant overall correlation at the genomic level; however, the local correlation of IBD and CD with conjunctivitis was significant and limited to chromosome 11. MR analysis suggested a significant positive and non-significant negative correlation between IBD (including CD and UC) and conjunctivitis. The conjFDR analysis confirmed the genetic overlap between the two diseases. Additionally, MTAG was employed to identify and validate multiple genetic risk loci.

Conclusion

The present study provides evidence of genetic structure and causal effects for the co-morbidity between IBD (both CD and UC) and conjunctivitis, expanding the epidemiologic understanding of the two diseases.

Shared Genetic Architecture Between Schizophrenia and Anorexia Nervosa: A Cross-trait Genome-Wide Analysis

BACKGROUND AND HYPOTHESIS

Schizophrenia (SCZ) and anorexia nervosa (AN) are 2 severe and highly heterogeneous disorders showing substantial familial co-aggregation. Genetic factors play a significant role in both disorders, but the shared genetic etiology between them is yet to be investigated.

STUDY DESIGN

Using summary statistics from recent large genome-wide association studies on SCZ (Ncases = 53 386) and AN (Ncases = 16 992), a 2-sample Mendelian randomization analysis was conducted to explore the causal relationship between SCZ and AN. MiXeR was employed to quantify their polygenic overlap. A conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was adopted to identify loci jointly associated with both disorders. Functional annotation and enrichment analyses were performed on the shared loci.

STUDY RESULTS

We observed a cross-trait genetic enrichment, a suggestive bidirectional causal relationship, and a considerable polygenic overlap (Dice coefficient = 62.2%) between SCZ and AN. The proportion of variants with concordant effect directions among all shared variants was 69.9%. Leveraging overlapping genetic associations, we identified 6 novel loci for AN and 33 novel loci for SCZ at condFDR <0.01. At conjFDR <0.05, we identified 10 loci jointly associated with both disorders, implicating multiple genes highly expressed in the cerebellum and pituitary and involved in synapse organization. Particularly, high expression of the shared genes was observed in the hippocampus in adolescence and orbitofrontal cortex during infancy.

CONCLUSIONS

This study provides novel insights into the relationship between SCZ and AN by revealing a shared genetic component and offers a window into their complex etiology.

Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence

The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

Genome-wide association analysis of hypertension and epigenetic aging reveals shared genetic architecture and identifies novel risk loci

Hypertension is a disease associated with epigenetic aging. However, the pathogenic mechanism underlying this relationship remains unclear. We aimed to characterize the shared genetic architecture of hypertension and epigenetic aging, and identify novel risk loci. Leveraging genome-wide association studies (GWAS) summary statistics of hypertension (129,909 cases and 354,689 controls) and four epigenetic clocks (N = 34,710), we investigated genetic architectures and genetic overlap using bivariate casual mixture model and conditional/conjunctional false discovery rate methods. Functional gene-sets pathway analyses were performed by functional mapping and gene annotation (FUMA) protocol. Hypertension was polygenic with 2.8 K trait-influencing genetic variants. We observed cross-trait genetic enrichment and genetic overlap between hypertension and all four measures of epigenetic aging. Further, we identified 32 distinct genomic loci jointly associated with hypertension and epigenetic aging. Notably, rs1849209 was shared between hypertension and three epigenetic clocks (HannumAge, IEAA, and PhenoAge). The shared loci exhibited a combination of concordant and discordant allelic effects. Functional gene-set analyses revealed significant enrichment in biological pathways related to sensory perception of smell and nervous system processes. We observed genetic overlaps with mixed effect directions between hypertension and all four epigenetic aging measures, and identified 32 shared distinct loci with mixed effect directions, 25 of which were novel for hypertension. Shared genes enriched in biological pathways related to olfaction.

Genetic architecture of brain morphology and overlap with neuropsychiatric traits

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.

Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer

Genome-wide association studies (GWAS) have accelerated the exploration of genotype-phenotype associations, facilitating the discovery of replicable genetic markers associated with specific traits or complex diseases. This narrative review explores the statistical methodologies developed using GWAS data to investigate relationships between various phenotypes, focusing on endometrial cancer, the most prevalent gynecological malignancy in developed nations. Advancements in analytical techniques such as genetic correlation, colocalization, cross-trait locus identification, and causal inference analyses have enabled deeper exploration of associations between different phenotypes, enhancing statistical power to uncover novel genetic risk regions. These analyses have unveiled shared genetic associations between endometrial cancer and many phenotypes, enabling identification of novel endometrial cancer risk loci and furthering our understanding of risk factors and biological processes underlying this disease. The current status of research in endometrial cancer is robust; however, this review demonstrates that further opportunities exist in statistical genetics that hold promise for advancing the understanding of endometrial cancer and other complex diseases.

Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia

Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.

Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology

Background and Objectives

Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences.

Methods

We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples.

Results

The epilepsies (2.4 k-2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples.

Discussion

Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.

Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits

Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.

Identification of Transcripts with Shared Roles in the Pathogenesis of Postmenopausal Osteoporosis and Cardiovascular Disease

Epidemiological evidence suggests existing comorbidity between postmenopausal osteoporosis (OP) and cardiovascular disease (CVD), but identification of possible shared genes is lacking. The skeletal global transcriptomes were analyzed in trans-iliac bone biopsies (n = 84) from clinically well-characterized postmenopausal women (50 to 86 years) without clinical CVD using microchips and RNA sequencing. One thousand transcripts highly correlated with areal bone mineral density (aBMD) were further analyzed using bioinformatics, and common genes overlapping with CVD and associated biological mechanisms, pathways and functions were identified. Fifty genes (45 mRNAs, 5 miRNAs) were discovered with established roles in oxidative stress, inflammatory response, endothelial function, fibrosis, dyslipidemia and osteoblastogenesis/calcification. These pleiotropic genes with possible CVD comorbidity functions were also present in transcriptomes of microvascular endothelial cells and cardiomyocytes and were differentially expressed between healthy and osteoporotic women with fragility fractures. The results were supported by a genetic pleiotropy-informed conditional False Discovery Rate approach identifying any overlap in single nucleotide polymorphisms (SNPs) within several genes encoding aBMD- and CVD-associated transcripts. The study provides transcriptional and genomic evidence for genes of importance for both BMD regulation and CVD risk in a large collection of postmenopausal bone biopsies. Most of the transcripts identified in the CVD risk categories have no previously recognized roles in OP pathogenesis and provide novel avenues for exploring the mechanistic basis for the biological association between CVD and OP.

Dissecting the genetic overlap between severe mental disorders and markers of cellular aging: Identification of pleiotropic genes and druggable targets

Patients with severe mental disorders such as bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) show a substantial reduction in life expectancy, increased incidence of comorbid medical conditions commonly observed with advanced age and alterations of aging hallmarks. While severe mental disorders are heritable, the extent to which genetic predisposition might contribute to accelerated cellular aging is not known. We used bivariate causal mixture models to quantify the trait-specific and shared architecture of mental disorders and 2 aging hallmarks (leukocyte telomere length [LTL] and mitochondrial DNA copy number), and the conjunctional false discovery rate method to detect shared genetic loci. We integrated gene expression data from brain regions from GTEx and used different tools to functionally annotate identified loci and investigate their druggability. Aging hallmarks showed low polygenicity compared with severe mental disorders. We observed a significant negative global genetic correlation between MDD and LTL (rg = -0.14, p = 6.5E-10), and no significant results for other severe mental disorders or for mtDNA-cn. However, conditional QQ plots and bivariate causal mixture models pointed to significant pleiotropy among all severe mental disorders and aging hallmarks. We identified genetic variants significantly shared between LTL and BD (n = 17), SCZ (n = 55) or MDD (n = 19), or mtDNA-cn and BD (n = 4), SCZ (n = 12) or MDD (n = 1), with mixed direction of effects. The exonic rs7909129 variant in the SORCS3 gene, encoding a member of the retromer complex involved in protein trafficking and intracellular/intercellular signaling, was associated with shorter LTL and increased predisposition to all severe mental disorders. Genetic variants underlying risk of SCZ or MDD and shorter LTL modulate expression of several druggable genes in different brain regions. Genistein, a phytoestrogen with anti-inflammatory and antioxidant effects, was an upstream regulator of 2 genes modulated by variants associated with risk of MDD and shorter LTL. While our results suggest that shared heritability might play a limited role in contributing to accelerated cellular aging in severe mental disorders, we identified shared genetic determinants and prioritized different druggable targets and compounds.

Insights into the genetic architecture of cerebellar lobules derived from the UK Biobank

In this work we endeavor to further understand the genetic architecture of the cerebellum by examining the genetic underpinnings of the different cerebellar lob(ul)es, identifying their genetic relation to cortical and subcortical regions, as well as to psychiatric disorders, as well as traces of their evolutionary trajectories. We confirm the moderate heritability of cerebellar volumes, and reveal genetic clustering and variability across their different substructures, which warranted a detailed analysis using this higher structural resolution. We replicated known genetic correlations with several subcortical volumes, and report new cortico-cerebellar genetic correlations, including negative genetic correlations between anterior cerebellar lobules and cingulate, and positive ones between lateral Crus I and lobule VI with cortical measures in the fusiform region. Heritability partitioning for evolutionary annotations highlighted that the vermis of Crus II has depleted heritability in genomic regions of "archaic introgression deserts", but no enrichment/depletion of heritability in any other cerebellar regions. Taken together, these findings reveal novel insights into the genetic underpinnings of the different cerebellar lobules.

Shared genetic aetiology of Alzheimer's disease and age-related macular degeneration by APOC1 and APOE genes

Background

Alzheimer's disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights into the underlying pathogenesis and inform integrated prevention and treatment for both diseases.

Methods

A stratified quantile-quantile (QQ) plot was constructed to detect the pleiotropy among AD and AMD based on genome-wide association studies data from 17 008 patients with AD and 30 178 patients with AMD. A Bayesian conditional false discovery rate-based (cFDR) method was used to identify pleiotropic genes. UK Biobank was used to verify the pleiotropy analysis. Biological network and enrichment analysis were conducted to explain the biological reason for pleiotropy phenomena. A diagnostic test based on gene expression data was used to predict biomarkers for AD and AMD based on pleiotropic genes and their regulators.

Results

Significant pleiotropy was found between AD and AMD (significant leftward shift on QQ plots). APOC1 and APOE were identified as pleiotropic genes for AD-AMD (cFDR <0.01). Network analysis revealed that APOC1 and APOE occupied borderline positions on the gene co-expression networks. Both APOC1 and APOE genes were enriched on the herpes simplex virus 1 infection pathway. Further, machine learning-based diagnostic tests identified that APOC1, APOE (areas under the curve (AUCs) >0.65) and their upstream regulators, especially ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and AMD (AUCs >0.8).

Conclusion

In this study, we confirmed the genetic pleiotropy between AD and AMD and identified APOC1 and APOE as pleiotropic genes. Further, the integration of multiomics data identified ZNF131, ADNP2 and HINFP as novel diagnostic biomarkers for AD and AMD.

Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases

BACKGROUND

Despite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD.

METHODS

Based on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets.

RESULTS

Compared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15-1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure.

CONCLUSIONS

This study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.

Identification of Novel Genomic Loci for Anxiety and Extensive Genetic Overlap with Psychiatric Disorders

Background

Anxiety disorders are prevalent and anxiety symptoms co-occur with many psychiatric disorders. We aimed to identify genomic risk loci associated with anxiety, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

Methods

We used the GWAS of anxiety symptoms, schizophrenia, bipolar disorder, major depression, and attention deficit hyperactivity disorder (ADHD). We employed MiXeR and LAVA to characterize the genetic architecture and genetic overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of genomic loci associated with anxiety and those shared with psychiatric disorders. Gene annotation and gene set analyses were conducted using OpenTargets and FUMA, respectively.

Results

Anxiety was polygenic with 12.9k estimated genetic risk variants and overlapped extensively with psychiatric disorders (4.1-11.4k variants). MiXeR and LAVA revealed predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 114 novel loci for anxiety by conditioning on the psychiatric disorders. We also identified loci shared between anxiety and major depression (n = 47), bipolar disorder (n = 33), schizophrenia (n = 71), and ADHD (n = 20). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci.

Conclusions

Anxiety is a highly polygenic phenotype with extensive genetic overlap with psychiatric disorders. These genetic overlaps enabled the identification of novel loci for anxiety. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified genetic loci implicate molecular pathways that may lead to potential drug targets.

Genetic overlap between schizophrenia and cognitive performance

Schizophrenia (SCZ), a highly heritable mental disorder, is characterized by cognitive impairment, yet the extent of the shared genetic basis between schizophrenia and cognitive performance (CP) remains poorly understood. Therefore, we aimed to explore the polygenic overlap between SCZ and CP. Specifically, the bivariate causal mixture model (MiXeR) was employed to estimate the extent of genetic overlap between SCZ (n = 130,644) and CP (n = 257,841), and conjunctional false discovery rate (conjFDR) approach was used to identify shared genetic loci. Subsequently, functional annotation and enrichment analysis were carried out on the identified genomic loci. The MiXeR analyses revealed that 9.6 K genetic variants are associated with SCZ and 10.9 K genetic variants for CP, of which 9.5 K variants are shared between these two traits (Dice coefficient = 92.8%). By employing conjFDR, 236 loci were identified jointly associated with SCZ and CP, of which 139 were novel for the two traits. Within these shared loci, 60 exhibited consistent effect directions, while 176 had opposite effect directions. Functional annotation analysis indicated that the shared genetic loci were mainly located in intronic and intergenic regions, and were found to be involved in relevant biological processes such as nervous system development, multicellular organism development, and generation of neurons. Together, our findings provide insights into the shared genetic architecture between SCZ and CP, suggesting common pathways and mechanisms contributing to both traits.

Pleiotropy with sex-specific traits reveals genetic aspects of sex differences in Parkinson's disease

Parkinson's disease is an age-related neurodegenerative disorder with a higher incidence in males than females. The causes for this sex difference are unknown. Genome-wide association studies (GWAS) have identified 90 Parkinson's disease risk loci, but the genetic studies have not found sex-specific differences in allele frequency on autosomal chromosomes or sex chromosomes. Genetic variants, however, could exert sex-specific effects on gene function and regulation of gene expression. To identify genetic loci that might have sex-specific effects, we studied pleiotropy between Parkinson's disease and sex-specific traits. Summary statistics from GWASs were acquired from large-scale consortia for Parkinson's disease (n cases = 13 708; n controls = 95 282), age at menarche (n = 368 888 females) and age at menopause (n = 69 360 females). We applied the conditional/conjunctional false discovery rate (FDR) method to identify shared loci between Parkinson's disease and these sex-specific traits. Next, we investigated sex-specific gene expression differences in the superior frontal cortex of both neuropathologically healthy individuals and Parkinson's disease patients (n cases = 61; n controls = 23). To provide biological insights to the genetic pleiotropy, we performed sex-specific expression quantitative trait locus (eQTL) analysis and sex-specific age-related differential expression analysis for genes mapped to Parkinson's disease risk loci. Through conditional/conjunctional FDR analysis we found 11 loci shared between Parkinson's disease and the sex-specific traits age at menarche and age at menopause. Gene-set and pathway analysis of the genes mapped to these loci highlighted the importance of the immune response in determining an increased disease incidence in the male population. Moreover, we highlighted a total of nine genes whose expression or age-related expression in the human brain is influenced by genetic variants in a sex-specific manner. With these analyses we demonstrated that the lack of clear sex-specific differences in allele frequencies for Parkinson's disease loci does not exclude a genetic contribution to differences in disease incidence. Moreover, further studies are needed to elucidate the role that the candidate genes identified here could have in determining a higher incidence of Parkinson's disease in the male population.