Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation

Pleiotropic and sex-specific genetic mechanisms of circulating metabolic markers

Metabolites in plasma form biosignatures of a range of common complex human diseases. Discovering variants with pleiotropic effects across metabolites can reveal underlying biological mechanisms. We therefore performed uni- and multivariate genome-wide association studies (GWAS) on 249 circulating metabolic markers across 328,006 UK Biobank and Estonian Biobank participants. We investigated rare variation through whole exome sequencing gene burden tests, analysed the role of body mass index through Mendelian randomization, and performed genome-wide interaction analyses with sex. We discovered 15,585 loci summed over the univariate GWAS, with high pleiotropy across markers, linked to a wide range of disorders. Findings from common and rare variant gene tests converged on lipid homeostasis pathways. 31 loci interacted with sex, mapped to genes involved in cholesterol processing. The findings offer insights into the genetic architecture of circulating metabolites, revealing pleiotropic loci, highlighting the role of rare variation, and uncovering sex-specific molecular mechanisms of lipid metabolism.

Sex-specific genetics underlie increased chronic pain risk in women: genome-wide association studies from the UK Biobank

BACKGROUND

Chronic pain disproportionately affects women, but the reasons for this disparity are unclear.

METHODS

We investigated this from a genetic perspective using data from the UK Biobank, focusing on multi-site chronic pain, which is highly heritable and manifests a sex bias.

RESULTS

Genome-wide association studies (GWAS) revealed that women have approximately 4500 sex-specific causal loci for overlapping pains-four times more than men-accounting for their higher heritability. Heritability partitioning indicated that pain-related loci are primarily enriched in brain regions, but only in women. Additionally, 200 imaging-derived brain phenotypes were significantly associated with pain in women, compared with only six in men. GWAS of these brain phenotypes showed stronger genetic correlations with pain in women, particularly regarding cortical thickness and striatal volume. When disentangling pleiotropy from causation in genetically correlated pairs of brain- and pain-related traits, we found that the genetics of brain phenotypes are more often causally implicated with the presence of chronic pain in women.

CONCLUSIONS

Our findings suggest that genetics play a crucial role in the increased risk of chronic pain observed in women.

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.

Deciphering the influence of socioeconomic status on brain structure: insights from Mendelian randomization

Socioeconomic status (SES) influences physical and mental health, however its relation with brain structure is less well documented. Here, we examine the role of SES on brain structure using Mendelian randomisation. First, we conduct a multivariate genome-wide association study of SES using educational attainment, household income, occupational prestige, and area-based social deprivation, with an effective sample size of N = 947,466. We identify 554 loci associated with SES and distil these loci into those that are common across those four traits. Second, using an independent sample of ~35,000 we provide evidence to suggest that SES is protective against white matter hyperintensities as a proportion of intracranial volume (WMHicv). Third, we find that differences in SES still afford a protective effect against WMHicv, independent of that made by cognitive ability. Our results suggest that SES is a modifiable risk factor, causal in the maintenance of cognitive ability in older-age.

Genetic Architecture and Risk of Childhood Maltreatment Across 5 Psychiatric Diagnoses

Importance

Childhood maltreatment (CM) is associated with psychiatric disorders. The underlying mechanisms are complex and involve genetics.

Objective

To investigate the polygenic architecture of CM-exposed individuals across psychiatric conditions and if genetics modulates absolute CM risk in the presence of high-impact risk factors such as parental psychiatric diagnoses.

Design, Setting, and Participants

The population-based case-cohort iPSYCH was used to analyze 13 polygenic scores (PGS) in CM-exposed individuals across 5 psychiatric International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnoses benchmarked against controls. Individuals were stratified into PGS quantiles, and absolute CM risk was calculated using Cox regression. Sex-specific analyses were also performed. Data were analyzed from June 2022 to December 2024.

Exposures

PGS of phenotypes of psychiatric disorders, CM, educational attainment, and substance use.

Main Outcomes and Measures

PGSs were generated using summary statistics from genome-wide association studies of phenotypes representing psychiatric disorders, CM, educational attainment, and substance use and tested for their association with CM across psychiatric disorders.

Results

This study included 102 856 individuals (mean [SD] age, 22.6 [7.1] years; 54 918 male [53.4%]) 8 to 35 years old. A total of 2179 CM-exposed individuals were analyzed across individuals with attention-deficit/hyperactivity disorder (ADHD; n = 22 674), autism (n = 18 941), schizophrenia (n = 6103), bipolar disorder (n = 3061), depression (n = 28 896), and controls (n = 34 689). PGSs for ADHD and educational attainment were associated with CM across all psychiatric diagnoses. The absolute CM risk was increased in the highest PGS groups, eg, for ADHD, the absolute CM risk was 5.6% in the highest ADHD-PGS quartile whereas it was only 3.3% in the lowest ADHD-PGS quartile (hazard rate ratio quantile 4 vs quantile 1 = 1.81; 95% CI, 1.47-2.22). CM risk was more than twice as high for children with parents with a psychiatric diagnosis (5.7%) than for children with parents without a psychiatric diagnosis (2.5%), but even in the presence of this risk factor, individuals could still be stratified into risk groups based on their genetics. No genetic differences between CM-exposed males and females were observed, but there were striking sex differences in absolute CM risk, which reached 5.6% for females in the highest ADHD-PGS quartile and 2.0% for males.

Conclusions and Relevance

Results of this case-control study suggest that individuals with high ADHD-PRS and/or low educational attainment-PRS had an associated elevated risk of CM. Extra attention should be given to individuals at high risk for CM across all 5 psychiatric diagnoses, ie, females with a high ADHD-PGS and/or a parent diagnosed with a psychiatric disorder.

Mapping the genetic landscape of immune-mediated disorders: potential implications for classification and therapeutic strategies

Objectives

Based on clinical, biomarker, and genetic data, McGonagle and McDermott suggested that autoimmune and autoinflammatory disorders can be classified as a disease continuum from purely autoimmune to autoinflammatory with mixed diseases in between. However, the genetic architecture of this spectrum has not been systematically described. Here, we investigate the continuum of polygenic immune-mediated disorders using genome-wide association studies (GWAS) and statistical genetics methods.

Methods

We mapped the genetic landscape of 15 immune-mediated disorders using GWAS summary statistics and methods including genomic structural equation modeling (genomic SEM), linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and Gaussian causal mixture modeling (MiXeR). We performed enrichment analyses of tissues and biological gene sets using MAGMA.

Results

Genomic SEM suggested a continuum structure with four underlying latent factors from autoimmune diseases at one end to autoinflammatory on the opposite end. Across disorders, we observed a balanced mixture of negative and positive local genetic correlations within the major histocompatibility complex, while outside this region, local genetic correlations were predominantly positive. MiXeR analysis showed large genetic overlap in accordance with the continuum landscape. MAGMA analysis implicated genes associated with known monogenic immune diseases for prominent autoimmune and autoinflammatory component.

Conclusions

Our findings support a polygenic continuum across immune-mediated disorders, with four genetic clusters. The "polygenic autoimmune" and "polygenic autoinflammatory" clusters reside on margins of this continuum. These findings provide insights and lead us to hypothesize that the identified clusters could inform future therapeutical strategies, with patients in the same clusters potentially responding similarly to specific therapies.

Integrating HiTOP and RDoC frameworks Part I: Genetic architecture of externalizing and internalizing psychopathology

BACKGROUND

There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP).

METHODS

We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in individuals genetically similar to European reference panels (EUR-like; n = 16,400 to 1,074,629). Traits included clinical (e.g. major depressive disorder, alcohol use disorder) and subclinical measures (e.g. risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors.

RESULTS

A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r = 0.37, SE = 0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE = 3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits.

CONCLUSIONS

The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.

Genome-wide association meta-analysis of age at onset of walking in over 70,000 infants of European ancestry

Age at onset of walking is an important early childhood milestone which is used clinically and in public health screening. In this genome-wide association study meta-analysis of age at onset of walking (N = 70,560 European-ancestry infants), we identified 11 independent genome-wide significant loci. SNP-based heritability was 24.13% (95% confidence intervals = 21.86-26.40) with ~11,900 variants accounting for about 90% of it, suggesting high polygenicity. One of these loci, in gene RBL2, co-localized with an expression quantitative trait locus (eQTL) in the brain. Age at onset of walking (in months) was negatively genetically correlated with ADHD and body-mass index, and positively genetically correlated with brain gyrification in both infant and adult brains. The polygenic score showed out-of-sample prediction of 3-5.6%, confirmed as largely due to direct effects in sib-pair analyses, and was separately associated with volume of neonatal brain structures involved in motor control. This study offers biological insights into a key behavioural marker of neurodevelopment.

Relationship Between Problematic Alcohol Use and Various Psychiatric Disorders: A Genetically Informed Study

OBJECTIVE

Problematic alcohol use (PAU) adversely affects the clinical course of psychiatric disorders. Genetic studies have suggested that genetic factors underlie the co-occurrence of PAU with psychiatric disorders. This study aimed to elucidate shared genetic architectures, prioritizing genes that disorders may have in common.

METHODS

Using genome-wide association data of PAU including 435,563 samples from people of European ancestry, this study investigated the genetic relationship between PAU and 11 psychiatric disorders using a bivariate causal mixture model (MiXeR). Local genetic correlation and colocalization analyses were conducted to identify the genomic regions significantly associated with PAU and each psychiatric disorder. Postanalysis included the false discovery rate (FDR) and transcriptome-wide association studies (TWASs), as well as summary-data-based Mendelian randomization to prioritize shared genes by integrating brain transcriptome data.

RESULTS

MiXeR analysis revealed a substantial polygenic overlap (39%-73%) between PAU and psychiatric disorders. Four bivariate genomic regions with high correlations suggest shared causal variants of PAU with major depression and schizophrenia. Within these regions, four and six genes for the PAU-major depression and PAU-schizophrenia pairs, respectively, were mapped by conjunctional FDR analysis. Furthermore, TTC12 and ANKK1 were identified as potential causal genes for PAU and these disorders. The findings were replicated in multi-ancestry analyses of colocalization and TWASs.

CONCLUSIONS

Despite the varying degrees of genetic overlap and directions of shared genetic effect correlations, these results imply the presence of shared genetic factors influencing the comorbidity of PAU and psychiatric disorders. Additionally, TTC12 and ANKK1, located near DRD2, may be causally associated with comorbid conditions.

Sex-stratified genome-wide association meta-analysis of Major Depressive Disorder

There are striking sex differences in the prevalence and symptomology of Major Depressive Disorder (MDD). We conducted the largest sex-stratified genome wide association and genotype-by-sex interaction meta-analyses of MDD to date (Females: 130,471 cases, 159,521 controls. Males: 64,805 cases, 132,185 controls). We found 16 and eight independent genome-wide significant SNPs in females and males, respectively, including one novel variant on the X chromosome. MDD in females and males shows substantial genetic overlap with a large proportion of MDD variants displaying similar effect sizes across sexes. However, we also provide evidence for a higher burden of genetic risk in females which could be due to female-specific variants. Additionally, sex-specific pleiotropic effects may contribute to the higher prevalence of metabolic symptoms in females with MDD. These findings underscore the importance of considering sex-specific genetic architectures in the study of health conditions, including MDD, paving the way for more targeted treatment strategies.

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.

CONVEX APPROACHES TO ISOLATE THE SHARED AND DISTINCT GENETIC STRUCTURES OF SUBPHENOTYPES IN HETEROGENEOUS COMPLEX TRAITS

Groups of complex diseases, such as coronary heart diseases, neuropsychiatric disorders, and cancers, often display overlapping clinical symptoms and pharmacological treatments. The shared associations of genetic variants across diseases has the potential to explain their underlying biological processes, but this remains poorly understood. To address this, we model the matrix of summary statistics of trait-associated genetic variants as the sum of a low-rank component - representing shared biological processes - and a sparse component, representing unique processes and arbitrarily corrupted or contaminated components. We introduce Clorinn, an open-source Python software that uses convex optimization algorithms to recover these components by minimizing a weighted combination of the nuclear norm and of the L1 norm. Among others, Clorinn provides two significant benefits: (a) Convex optimization guarantees reproducibility of the components, and (b) The low-rank "uncorrupted" matrix allows robust singular value decomposition (SVD) and principal component analysis (PCA), which are otherwise highly sensitive to outliers and noise in the input matrix. In extensive simulations, we observe that Clorinn outperforms state-of-the-art approaches in capturing the shared latent factors across phenotypes. We apply Clorinn to estimate 200 latent factors from GWAS summary data of 2,110 phenotypes measured in European-ancestry Pan-UK BioBank individualsN = 420 , 531 and 14 psychiatric disorders.

Genome-wide analyses identify 25 infertility loci and relationships with reproductive traits across the allele frequency spectrum

Genome-wide association studies (GWASs) may help inform the etiology of infertility. Here, we perform GWAS meta-analyses across seven cohorts in up to 42,629 cases and 740,619 controls and identify 25 genetic risk loci for male and female infertility. We additionally identify up to 269 genetic loci associated with follicle-stimulating hormone, luteinizing hormone, estradiol and testosterone through sex-specific GWAS meta-analyses (n = 6,095-246,862). Exome sequencing analyses reveal that women carrying testosterone-lowering rare variants in some genes are at risk of infertility. However, we find no local or genome-wide genetic correlation between female infertility and reproductive hormones. While infertility is genetically correlated with endometriosis and polycystic ovary syndrome, we find limited genetic overlap between infertility and obesity. Finally, we show that the evolutionary persistence of infertility-risk alleles may be explained by directional selection. Taken together, we provide a comprehensive view of the genetic determinants of infertility across multiple diagnostic criteria.

Polygenic overlap between subjective well-being and psychiatric disorders and cross-ancestry validation

Subjective well-being (SWB) is important for understanding human behaviour and health. Although the connection between SWB and psychiatric disorders has been studied, common genetic mechanisms remain unclear. This study aimed to explore the genetic relationship between SWB and psychiatric disorders. Bivariate causal mixture modelling (MiXeR), polygenic risk score (PRS) and Mendelian randomization (MR) analyses showed substantial polygenic overlap and associations between SWB and the psychiatric disorders. Subsequent replication studies in East Asian populations confirmed the polygenic overlap between schizophrenia and SWB. The conditional and conjunctional false discovery rate analyses identified additional or shared genetic loci associated with SWB or psychiatric disorders. Functional annotation revealed enrichment of specific brain tissues and genes associated with SWB. The identified genetic loci showed cross-ancestry transferability between the European and Korean populations. Our findings provide valuable insights into the common genetic mechanisms underlying SWB and psychiatric disorders.

Gene discovery and pleiotropic architecture of chronic pain in a genome-wide association study of >1.2 million individuals

Chronic pain is highly prevalent worldwide, and genome-wide association studies (GWAS) have identified a growing number of chronic pain loci. To further elucidate its genetic architecture, we leveraged data from 1,235,695 European ancestry individuals across three biobanks. In a meta-analytic GWAS, we identified 343 independent loci for chronic pain, 92 of which were new. Sex-specific meta-analyses revealed 115 independent loci (12 of which were new) for males (N = 583,066) and 12 loci (two of which were new) for females (N = 241,266). Multi-omics gene prioritization analyses highlighted 490 genes associated with chronic pain through their effects on brain- and blood-specific regulation. Loci associated with increased risk for chronic pain were also associated with increased risk for multiple other traits, with Mendelian randomization analyses showing that chronic pain was causally associated with psychiatric disorders, substance use disorders, and C-reactive protein levels. Chronic pain variants also exhibited pleiotropic associations with cortical area brain structures. This study expands our knowledge of the genetics of chronic pain and its pathogenesis, highlighting the importance of its pleiotropy with multiple disorders and elucidating its multi-omic pathophysiology.

Genetic and neural mechanisms shared by schizophrenia and depression

Schizophrenia (SCZ) and depression are two prevalent mental disorders characterized by comorbidity and overlapping symptoms, yet the underlying genetic and neural mechanisms remain largely elusive. Here, we investigated the genetic variants and neuroimaging changes shared by SCZ and depression in Europeans and then extended our investigation to cross-ancestry (Europeans and East Asians) populations. Using conditional and conjunctional analyses, we found 213 genetic variants shared by SCZ and depression in Europeans, of which 82.6% were replicated in the cross-ancestry population. The shared risk variants exhibited a higher degree of deleteriousness than random and were enriched for synapse-related functions, among which fewer than 3% of shared variants showed horizontal pleiotropy between the two disorders. Mendelian randomization analyses indicated reciprocal causal effects between SCZ and depression. Using multiple trait genetic colocalization analyses, we pinpointed 13 volume phenotypes shared by SCZ and depression. Particularly noteworthy were the shared volume reductions in the left insula and planum polare, which were validated through large-scale meta-analyses of previous studies and independent neuroimaging datasets of first-episode drug-naïve patients. These findings suggest that the shared genetic risk variants, synapse dysfunction, and brain structural changes may underlie the comorbidity and symptom overlap between SCZ and depression.

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.

Novel mechanistic insights into the comorbidity of anemia and rheumatoid arthritis: Identification of therapeutic targets

OBJECTIVES

To investigate the mechanisms underlying the comorbidity of anemia and rheumatoid arthritis (RA) and identify promising therapeutic targets.

METHODS

We assessed the phenotypic linkage between anemia and RA. Using the largest genome-wide association studies (GWAS) summary statistics of European populations, we scrutinized the causal association and shared genetic architecture between the two conditions using multiple complementary approaches.

RESULTS

Logistic regression analysis confirmed a strong clinical association between anemia and RA. Using GWAS data, we identified a significant causal effect of RA on anemia and positive global genetic correlations between the two conditions (rg (genotype) = 0.28, P = 9.6 × 10-7; rg (gene expression) = 0.45, P = 2 × 10-3). After dividing the genome into 2495 independent regions, we identified 15 significant regions associated with both conditions, with 14 showing concordant effects. Fine-mapping at the SNP level revealed 72 % of RA-associated SNPs overlapped with anemia, most with concordant effects. Stratified Q-Q plots visualized the shared genetic enrichment, showing a 12-fold enrichment for RA conditional on anemia and 100-fold enrichment for anemia conditional on RA. Further analysis using conjFDR method pinpointed 14 pleiotropic loci, including several novel loci. Gene mapping identified 33 shared genes, with BLK and FAM167A further prioritized as the top two genes by SMR analysis. Enrichment analysis highlighted pathways related to inflammation, immune response, and iron metabolism. Blood and T cells showed significant tissue- and cell-type-specific enrichment.

CONCLUSIONS

This study provides novel insights into anemia-RA comorbidity mechanisms and identifies new drug targets for RA.

Polygenic overlap of substance use behaviors and disorders with externalizing and internalizing problems independent of genetic correlations

BACKGROUND

Externalizing and internalizing pathways may lead to the development of substance use behaviors (SUBs) and substance use disorders (SUDs), which are all heritable phenotypes. Genetic correlation studies have indicated differences in the genetic susceptibility between SUBs and SUDs. We investigated whether these substance use phenotypes are differently related to externalizing and internalizing problems at a genetic level.

METHODS

We analyzed data from genome-wide association studies (GWAS) of four SUBs and SUDs, five externalizing traits, and five internalizing traits using the bivariate causal mixture model (MiXeR) to estimate genetic overlap beyond genetic correlation.

RESULTS

Two distinct patterns were found. SUBs demonstrated high genetic overlap but low genetic correlation of shared variants with internalizing traits, suggesting a pattern of mixed effect directions of shared genetic variants. Conversely, SUDs and externalizing traits exhibited considerable genetic overlap with moderate to high positive genetic correlation of shared variants, suggesting concordant effect direction of shared risk variants.

CONCLUSIONS

These results highlight the importance of the externalizing pathway in SUDs as well as the limited role of the internalizing pathway in SUBs. As MiXeR is not intended for the identification of specific genes, further studies are needed to reveal the underlying shared mechanisms of these traits.

Novel Multimodal Precision Medicine Approaches and the Relevance of Developmental Trajectories in Bipolar Disorder

There is a pressing need to establish objective measures to improve diagnosis, prediction, prevention, and treatment of bipolar disorder (BD). Multimodal artificial intelligence (AI) tools could provide these means by incorporating various layers of data orthogonally related to BD, including genomics and other omics, environmental exposures, imaging measures, electronic health records, cognition, sensing devices, and clinical variables. These rapidly evolving AI models hold promise to capture the multidimensional complexity of BD and delineate clinically relevant developmental trajectories that could guide clinical care and therapeutic strategies. In this review, we describe the potential of mapping developmental trajectories that underlie BD, outline how novel multimodal models could improve the prediction of BD and related outcomes, and discuss specific clinical use cases and key ethical and practical challenges regarding the development and potential implementation of these multimodal AI solutions to advance precision medicine approaches in BD.

Genetic analysis reveals the shared genetic architecture between breast cancer and atrial fibrillation

Background

Epidemiological studies have observed an association between atrial fibrillation (AF) and breast cancer (BC). However, the underlying mechanisms linking these two conditions remain unclear. This study aims to systematically explore the genetic association between AF and BC.

Methods

We utilized the largest available genome-wide association study (GWAS) datasets for European individuals, including summary data for AF (N = 1,030,836) and BC (N = 247,173). Multiple approaches were employed to systematically investigate the genetic relationship between AF and BC from the perspectives of pleiotropy and causality.

Results

Global genetic analysis using LDSC and HDL revealed a genetic correlation between AF and BC (rg = 0.0435, P = 0.039). Mixer predicted genetic overlap between non-MHC regions of the two conditions (n = 125, rg = 0.05). Local genetic analyses using LAVA and GWAS-PW identified 22 regions with potential genetic sharing. Cross-trait meta-analysis by CPASSOC identified one novel pleiotropic SNP and 14 pleiotropic SNPs, which were subsequently annotated. Eight of these SNPs passed Bayesian colocalization tests, including one novel pleiotropic SNP. Further fine-mapping analysis identified a set of causal SNPs for each significant SNP. TWAS analyses using JTI and FOCUS models jointly identified 10 pleiotropic genes. Phenome-wide association study (PheWAS) of novel pleiotropic SNPs identified two eQTLs (PELO, ITGA1). Gene-based PheWAS results showed strong associations with BMI, height, and educational attainment. PCGA methods combining GTEx V8 tissue data and single-cell RNA data identified 16 co-enriched tissue types (including cardiovascular, reproductive, and digestive systems) and 5 cell types (including macrophages and smooth muscle cells). Finally, univariable and multivariable bidirectional Mendelian randomization analyses excluded a causal relationship between AF and BC.

Conclusion

This study systematically investigated the shared genetic overlap between AF and BC. Several pleiotropic SNPs and genes were identified, and co-enriched tissue and cell types were revealed. The findings highlight common mechanisms from a genetic perspective rather than a causal relationship. This study provides new insights into the AF-BC association and suggests potential experimental targets and directions for future research. Additionally, the results underscore the importance of monitoring the potential risk of one disease in patients diagnosed with the other.

Unraveling the genetic links between depression and type 2 diabetes

BACKGROUND

Type 2 diabetes (T2D) is a chronic metabolic disorder that has high comorbidity with mental disorders. The genetic relationships between T2D and depression are far from being well understood.

METHODS

We performed genetic correlation, polygenic overlap, Mendelian randomization (MR) analyses, cross-trait meta-analysis, and Bayesian colocalization analysis to assess genetic relationships between T2D and depression, in the forms of major depressive disorder (MDD) and depressed affect (DAF). Then, the summary data-based MR (SMR) analysis was performed to prioritize genes contributing to MDD and to T2D from functional perspective. MDD-driven signaling pathways were constructed to understand the influence of MDD on T2D at the molecular level.

RESULTS

T2D has positive genetic correlations both with MDD (rg = 0.14) and with DAF (rg = 0.19). The polygenic overlap analysis showed that about 60 % of causal variants for T2D are shared with MDD and DAF. The MR analysis indicated that genetic liabilities to both MDD (OR: 1.24, 95 % CI: 1.11-1.38) and DAF (OR: 1.48, 95 % CI: 1.23-1.78) are associated with an increased risk for T2D, while genetic liability to T2D is not associated with the risk for MDD (OR: 1.00, 95 % CI: 0.99-1.01) or DAF (OR: 1.01, 95 % CI: 1.00-1.02). The cross-trait meta-analysis identified 271 genomic loci, of which 29 were novel. Genetic predisposition to MDD and T2D shares six overlapping loci, involving some well-characterized genes, such as TCF4 and NEGR1. Colocalization analysis revealed three shared chromosome regions between MDD and T2D, which covers mediator genes including SCYL1, DENND1A, and MAD1L1. Molecular pathway analysis suggests mechanisms that promote the development of T2D through inflammatory pathways overactive in patients with MDD. The SMR analysis and the meta-analysis highlighted seven genes with functional implications for both MDD and T2D, including TNKS2, CCDC92, FADS1, ERI1, THUMPD3, NUCKS1, and PM20D1.

CONCLUSIONS

Our study points out that depression, in the forms of MDD and DAF, may increase the risk of T2D. Analysis of underlying genetic variation and the molecular pathways, connecting depression and T2D, indicate that the pathophysiological foundations of these two conditions have a notable overlap.

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.

Immune-developmental processes contribute to schizophrenia risk: insights from a genetic overlap study with height

BACKGROUND

Shorter stature has been phenotypically linked to increased prevalence of schizophrenia (SCZ), but the nature of this association is unknown.

METHODS

Using genome-wide genetic data, we studied the SCZ-height relationship on a genetic level. Applying novel genetic methods and tools, we analyzed gene-sets, tissue-types, cell-types, local genetic correlation, conditional genetic analyses, and fine-mapping of effector-genes to scrutinize the SCZ-height relationship.

RESULTS

We identified 142 genes statistically associated with both SCZ and height and found enrichment in 3 functional gene-sets. Genetic annotations implicated the pituitary and specifically mesenchymal stem cells for height and thyrotropic cells for SCZ. While the global SCZ-height genetic correlation was nonsignificant, 9 genomic regions showed robust local genetic correlations (7 negative, 6 in the MHC-region). The shared genetic signal for SCZ and height within the 6 MHC-regions was partially explained by mutual genetic overlap with white blood cell count, particularly lymphocytes. Fine-mapping prioritized 3 shared effector-genes (GIGYF2, HLA-C, and LIN28B) involved in immune response sensitivity and development of immune and pituitary cell-types.

CONCLUSIONS

Overall, our findings suggest an involvement during height-development of thyrotropic cells and immune response sensitivity contributing towards risk of SCZ.

Gene discovery and pleiotropic architecture of Chronic Pain in a Genome-wide Association Study of >1.2 million Individuals

Chronic pain is highly prevalent worldwide, and genome-wide association studies (GWAS) have identified a growing number of chronic pain loci. To further elucidate its genetic architecture, we leveraged data from 1,235,695 European ancestry individuals across three biobanks. In a meta-analytic GWAS, we identified 343 independent loci for chronic pain, 92 of which were new. Sex-specific meta-analyses revealed 115 independent loci (12 of which were new) for males (N = 583,066) and 12 loci (two of which were new) for females (N = 241,266). Multi-omics gene prioritization analyses highlighted 490 genes associated with chronic pain through their effects on brain- and blood-specific regulation. Loci associated with increased risk for chronic pain were also associated with increased risk for multiple other traits, with Mendelian randomization analyses showing that chronic pain was causally associated with psychiatric disorders, substance use disorders, and C-reactive protein levels. Chronic pain variants also exhibited pleiotropic associations with cortical area brain structures. This study expands our knowledge of the genetics of chronic pain and its pathogenesis, highlighting the importance of its pleiotropy with multiple disorders and elucidating its multi-omic pathophysiology.

Genetic overlap between schizophrenia and constipation: insights from a genome-wide association study in a European population

BACKGROUND

Patients with schizophrenia (SCZ) experience constipation at significantly higher rates compared with the general population. This relationship suggests a potential genetic overlap between these two conditions.

METHODS

We analyzed genome-wide association study (GWAS) data for both SCZ and constipation using a five-part approach. The first and second parts assessed the overall and local genetic correlations using methods such as linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (HESS). The third part investigated the causal association between the two traits using Mendelian randomization (MR). The fourth part employed conditional/conjunctional false discovery rate (cond/conjFDR) to analyze the genetic overlap with different traits based on the statistical theory. Finally, an LDSC-specifically expressed gene (LDSC-SEG) analysis was conducted to explore the tissue-level associations.

RESULTS

Our analyses revealed both overall and specific genetic correlations between SCZ and constipation at the genomic level. The MR analysis suggests a positive causal relationship between SCZ and constipation. The ConjFDR analysis confirms the genetic overlap between the two conditions and identifies two genetic risk loci (rs7583622 and rs842766) and seven mapped genes (GPR75-ASB3, ASB3, CHAC2, ERLEC1, GPR75, PSME4, and ACYP2). Further investigation into the functions of these genes could provide valuable insights. Interestingly, disease-related tissue analysis revealed associations between SCZ and constipation in eight brain regions (substantia nigra, anterior cingulate cortex, hypothalamus, cortex, hippocampus, cortex, amygdala, and spinal cord).

CONCLUSION

This study provides the first genetic evidence for the comorbidity of SCZ and constipation, enhancing our understanding of the pathophysiology of both conditions.

Genomics yields biological and phenotypic insights into bipolar disorder

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

Elucidating shared genetic association between female body mass index and preeclampsia

The prevalence of obesity is steadily rising and poses a significant challenge to women's health. Preeclampsia (PE), a leading cause of maternal and fetal mortality, is significantly linked to a high body mass index (BMI). However, the shared genetic architecture underlying these conditions remains poorly understood. In this study, we used summary-level data from large-scale genome-wide association studies of BMI (N = 434,794) and PE (Ncases = 8185; Ncontrols = 234,147) to assess the shared genetic architecture between them. Our findings revealed a significant genetic correlation between BMI and PE, with an estimated sample overlap of approximately 0.8%. We identified roughly 1100 shared genetic variants, with the most notable region of local genetic correlation located in 16q12.2. Enrichment analyses highlighted endothelial dysfunction as a key biological mechanism linking BMI and PE. Additionally, RABEP2 was identified as a novel shared risk gene. Mendelian randomization analysis demonstrated a bidirectional causal relationship between BMI and PE, with blood pressure identified as a key mediator. We identified the shared genetic foundation between BMI and PE, providing valuable insights into the comorbidity of these conditions and offering a new framework for future research into comorbidity.

Dissecting Schizophrenia Biology Using Pleiotropy With Cognitive Genomics

BACKGROUND

Given the increasingly large number of loci discovered by psychiatric genome-wide association studies (GWASs), specification of the key biological pathways that underlie these loci has become a priority for the field. We have previously leveraged the pleiotropic genetic relationships between schizophrenia (SCZ) and 2 cognitive phenotypes (educational attainment and cognitive task performance) to differentiate 2 subsets of illness-relevant single nucleotide polymorphisms (SNPs): 1) those with concordant alleles, which are associated with reduced cognitive performance and educational attainment and increased SCZ risk, and 2) those with discordant alleles, which are linked to reduced educational and/or cognitive levels but lower SCZ susceptibility.

METHODS

In the current study, we extended our prior work, utilizing larger input GWAS datasets and a more powerful statistical approach to pleiotropic meta-analysis, the pleiotropic locus exploration and interpretation using optimal test (PLEIO).

RESULTS

Our pleiotropic meta-analysis of SCZ and the 2 cognitive phenotypes revealed 768 significant pleiotropic loci (166 novel). Among these, 347 loci harbored concordant SNPs, 270 encompassed discordant SNPs, and 151 dual loci contained concordant and discordant SNPs. Competitive gene-set analysis using MAGMA linked concordant SNP loci with neurodevelopmental pathways (e.g., neurogenesis), whereas discordant loci were associated with mature neuronal synaptic functions. These distinctions were also observed in BrainSpan analysis of temporal enrichment patterns across developmental periods, with concordant loci containing more prenatally expressed genes than discordant loci. Dual loci were enriched for genes related to messenger RNA translation initiation, which represents a novel finding in the SCZ literature.

CONCLUSIONS

Pleiotropic analysis permits not only enhanced statistical power for locus discovery but also the ability to parse distinct biological processes associated with endophenotypes.

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.

Mapping Structural Neuroimaging Trajectories in Bipolar Disorder: Neurobiological and Clinical Implications

Neuroimaging is a powerful noninvasive method for studying brain alterations in bipolar disorder (BD). To date, most neuroimaging studies of BD have included smaller cross-sectional samples reporting case versus control comparisons, revealing small to moderate effect sizes. In this narrative review, we discuss the current state of structural neuroimaging studies using magnetic resonance imaging, which inform our understanding of altered brain trajectories in BD across the lifespan. Alternative methodologies such as those that model patient deviations from age-specific norms are discussed, which may help derive new markers of BD pathophysiology. We discuss evidence from neuroimaging genetics and transcriptomics studies, which attempt to bridge the gap between macroscale brain variations and underlying microscale neurodevelopmental mechanisms. We conclude with a look toward the future and how ambitious investments in longitudinal, deeply phenotyped, population-based cohorts can improve modeling of complex clinical factors and provide more clinically actionable brain markers for BD.

Multi-ancestry meta-analysis of genome-wide association studies discovers 67 new loci associated with chronic back pain

This multi-ancestry meta-analysis of genome-wide association studies (GWAS) investigated the genetic factors underlying chronic back pain (CBP) in a sample from the Million Veteran Program comprised of 553,601 Veterans of African (19.2%), European (72.6%), and Hispanic (8.2%) ancestry. The results revealed novel (N = 67) and known (N = 20) genome-wide significant loci associated with CBP, with 43 independent variants replicating in a non-overlapping contemporary meta-GWAS of the spinal pain dorsalgia phenotype. The most significant novel variant was rs12533005 (chr7:114416000, p = 1.61 × 10-20, OR = 0.96 (95% CI: 0.95-0.97), EA = C, EAF = 0.39), in an intron of the FOXP2 gene. In silico functional characterization revealed enrichment in brain and pituitary tissues. Mendelian randomization analysis of 62 variants for CBP-MVP revealed 48 with causal links to dorsalgia. Notably, four genes (INPP5B, DRD2, HTT, SLC30A6) associated with these variants are targets of existing drugs. Our findings more than double the number of previously reported genetic predictors across all spinal pain phenotypes.

Integrating HiTOP and RDoC Frameworks Part I: Genetic Architecture of Externalizing and Internalizing Psychopathology

Background

There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP).

Methods

We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in European-ancestry individuals (n = 16,400 to 1,074,629). Traits included clinical (e.g., major depressive disorder, alcohol use disorder) and subclinical measures (e.g., risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors.

Results

A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r = 0.37, SE = 0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE = 3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits.

Conclusions

The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.

Identification of risk variants and cross-disorder pleiotropy through multi-ancestry genome-wide analysis of alcohol use disorder

Alcohol use disorder (AUD) is highly heritable and burdensome worldwide. Genome-wide association studies (GWASs) can provide new evidence regarding the aetiology of AUD. We report a multi-ancestry GWAS focusing on a narrow AUD phenotype, using novel statistical tools in a total sample of 1,041,450 individuals [102,079 cases; European, 75,583; African, 20,689 (mostly African-American); Hispanic American, 3,449; East Asian, 2,254; South Asian, 104; descent]. Cross-ancestry functional analyses were performed with European and African samples. Thirty-seven genome-wide significant loci (105 variants) were identified, of which seven were novel for AUD and six for other alcohol phenotypes. Loci were mapped to genes, which show altered expression in brain regions relevant for AUD (striatum, hypothalamus, and prefrontal cortex) and encode potential drug targets (GABAergic, dopaminergic and serotonergic neurons). African-specific analysis yielded a unique pattern of immune-related gene sets. Polygenic overlap and positive genetic correlations showed extensive shared genetic architecture between AUD and both mental and general medical phenotypes, suggesting they are not only complications of alcohol use but also share genetic liability with AUD. Leveraging a cross-ancestry approach allowed identification of novel genetic loci for AUD and underscores the value of multi-ancestry genetic studies. These findings advance our understanding of AUD risk and clinically-relevant comorbidities.

Systematic dissection of pleiotropic loci and critical regulons in excitatory neurons and microglia relevant to neuropsychiatric and ocular diseases

Advancements in single-cell multimodal techniques have greatly enhanced our understanding of disease-relevant loci identified through genome-wide association studies (GWASs). To investigate the biological connections between the eye and brain, we integrated bulk and single-cell multiomic profiles with GWAS summary statistics for eight neuropsychiatric and five ocular diseases. Our analysis uncovered five latent factors explaining 61.7% of the genetic variance across these 13 diseases, revealing diverse correlational patterns among them. We identified 45 pleiotropic loci with 91 candidate genes that contribute to disease risk. By integrating GWAS and single-cell profiles, we implicated excitatory neurons and microglia as key contributors in the eye-brain connections. Polygenic enrichment analysis further identified 15 pleiotropic regulons in excitatory neurons and 16 in microglia that were linked to comorbid conditions. Functionally, excitatory neuron-specific regulons were involved in axon guidance and synaptic activity, while microglia-specific regulons were associated with immune response and cell activation. In sum, these findings underscore the genetic link between psychiatric disorders and ocular diseases.

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.

Mendelian randomization and genetic pleiotropy analysis for the connection between inflammatory bowel disease and Alzheimer's disease

BACKGROUND

The gut-microbiome-brain axis (GMBA) implies the connection between inflammatory bowel disease (IBD) and Alzheimer's disease (AD). We aimed to comprehensively explore the relation between IBD (and its subtypes) and AD, early-onset AD (EOAD) and late-onset AD (LOAD) from a genetic pleiotropy perspective.

METHODS

Relying on summary statistics (N = 472,868 for AD, 185,204 for EOAD, 191,061 for LOAD, 59,957 for IBD, 45,975 for CD, and 40,266 for UC), we first performed Mendelian Randomization to examine the causal association between IBD and AD by leveraging vertical pleiotropy. Then, we estimated global and local genetic correlations, followed by cross-trait association analysis to identify SNPs and genes with horizontal pleiotropy. Particularly, we utilized multi-trait colocalization analysis to assess the role of microbes in the common genetic etiology underlying the two types of diseases. Finally, we conducted functional enrichment analysis for pleiotropic genes.

RESULTS

We discovered suggestively causal relations between IBD (and its subtypes) and EOAD (ORIBD = 1.06 [1.01-1.11], ORCD = 1.05 [1.01-1.10], ORUC = 1.08 [1.01-1.15]) as well as between UC and LOAD (OR = 1.04 [1.01-1.08]), and discovered 44 local regions showing suggestively significant genetic correlations between IBD (and its subtypes) and AD (and EODA and LOAD). We further detected substantial genetic overlap, as characterized by 182  AD-associated, 3 EOAD-associated and 51 LOAD-associated pleiotropic SNPs as well as 291 pleiotropic genes. Pleiotropic genes more likely enriched in the GMBA-relevant tissues such as brain, intestine and esophagus. Moreover, we identified three microorganisms related to these disease pairs, including the Catenibacterium, Clostridia, and Prevotella species.

CONCLUSION

The suggestively causal associations and shared genetic basis between IBD and its subtypes with AD, EOAD and LOAD may commonly drive their co-occurrence, and gut microbes might partly explain the shared genetic etiology. Further studies are warranted to elaborate the possibly biological mechanisms underlying the two types of diseases.

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.

Identifying the Involvement of Gut Microbiota in Retinal Vein Occlusion by Mendelian Randomization and Genetic Correlation Analysis

Purpose

Previous researches have suggested an important association between gut microbiota (GM) and vascular pathologies such as atherosclerosis. This study aimed to explore the association between 196 GM taxa and retinal vein occlusion (RVO).

Methods

This study used Mendelian randomization (MR), linkage disequilibrium score regression (LDSC), and polygenic overlap analysis. Genome-wide association study (GWAS) data associated with 196 GM taxa was obtained from the MiBioGen consortium, involving a large number of European-ancestry participants. GWAS data of RVO was obtained from the FinnGen consortium and another study that also involved European-ancestry participants. Inverse-variance weighted was used as the primary approach for MR estimation. Moreover, LDSC and polygenic overlap analyses were performed to evaluate the genetic correlation between GM taxa and RVO.

Results

The MR results identified the association of six GM taxa, including class Bacilli, order Lactobacillales, family Streptococcaceae, genus Clostridium innocuum group, genus Family XIII AD3011 group, and genus Subdoligranulum with the development of RVO. In addition, the polygenic overlap analysis supported the genetic association between GM and RVO.

Conclusions

Our findings confirmed the association between six GM taxa and the development of RVO, thereby highlighting the effects of GM on retinal vascular health.

Translational Relevance

The results may provide the rationale for developing GM-based strategies for preventing the onset of RVO.

A Novel Statistical Method for Unmasking Sex-Specific Genomics Signatures in Complex Traits

Genotype-phenotype association studies have advanced our understanding of complex traits but often overlook sex-specific genetic signals. The growing awareness of sex-specific influences on human traits and diseases necessitates tailored statistical methodologies to dissect these genetic intricacies. Rare genetic variants play a significant role in disease development, often exhibiting stronger per-allele effects than common variants. In sex-dimorphic analysis, traits are viewed as having two sex-specific subsets rather than being uniformly defined. Existing methods for gene-based analysis of rare variants across multiple traits can identify shared genetic signals but cannot reveal the specific subsets from which significant signals originate. This means that when a significant signal is detected, it remains unclear whether it arises from the male samples, female samples, or both. To address this limitation, we propose SubsetRV, a new methodology capable of identifying genes associated with specific traits or diseases in males, females, or both. SubsetRV can also be applied to broader applications in multiple traits analysis. Simulation studies have demonstrated SubsetRV's reliability, and real data analysis on bipolar disorder and schizophrenia has revealed potential sex-specific genetic signals. SubsetRV offers a valuable tool for identifying sex-specific genetic candidates, aiding in understanding disease mechanisms. An R package for SubsetRV is available on GitHub. It can be accessed directly through this link: https://github.com/Mansouri-S/SubsetRV.

Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

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.

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.

Pleiotropic effects on Sarcopenia subphenotypes point to potential molecular markers for the disease

Sarcopenia is a progressive age-related muscle disease characterized by low muscle strength, quantity and quality, and low physical performance. The clinical overlap between these subphenotypes (reduction in muscle strength, quantity and quality, and physical performance) was evidenced, but the genetic overlap is still poorly investigated. Herein, we investigated whether there is a genetic overlap amongst sarcopenia subphenotypes in the search for more effective molecular markers for this disease. For that, a Bioinformatics approach was used to identify and characterize pleiotropic effects at the genome, loci and gene levels using Genome-wide association study results. As a result, a high genetic correlation was identified between gait speed and muscle strength (rG=0.5358, p=3.39 × 10-8). Using a Pleiotropy-informed conditional and conjunctional false discovery rate method we identified two pleiotropic loci for muscle strength and gait speed, one of them was nearby the gene PHACTR1. Moreover, 11 pleiotropic loci and 25 genes were identified for muscle mass and muscle strength. Lastly, using a gene-based GWAS approach three candidate genes were identified in the overlap of the three Sarcopenia subphenotypes: FTO, RPS10 and CALCR. The current study provides evidence of genetic overlap and pleiotropy among sarcopenia subphenotypes and highlights novel candidate genes and molecular markers associated with the risk of sarcopenia.

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.