A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer

The role of the brain-bone axis in skeletal degenerative diseases and psychiatric disorders, A genome-wide pleiotropic analysis

INTRODUCTION

Skeletal degenerative diseases and psychiatric disorders often coexist clinically. However, the genetic correlations and underlying biological mechanisms between these two types of diseases remain unclear.

OBJECTIVES

To investigate the genetic correlations between skeletal degenerative diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways.

METHODS

This comprehensive genome-wide pleiotropic association study utilized summary statistics from publicly available genome-wide association data. Various statistical genetic correlation methods were employed, including LDSC, HDL, PLACO, Coloc, Hyprcoloc, and Mendelian randomization (MR) analysis, along with immune cell colocalization analysis. The study aimed to identify potential shared genetic factors among three skeletal degenerative diseases (osteoarthritis, intervertebral disc degeneration, and osteoporosis) and three psychiatric disorders (schizophrenia, anxiety disorder, and major depressive disorder).

RESULTS

Analyses using LDSC, HDL, and Bonferroni corrections revealed significant genetic correlations between intervertebral disc degeneration (IVDD) and anxiety disorder (ANX); fractures, IVDD, and arthritis with major depressive disorder (MDD); and arthritis with schizophrenia (SCZ). Significant genetic correlations were also observed between VDD and ANX, fractures, IVDD, hip osteoarthritis (HipOA), knee osteoarthritis (KneeOA) and MDD, and KneeOA and SCZ. Pleiotropy analysis using PLACO, MAGMA, and multitrait colocalization Hyprcoloc identified 65 pleiotropic loci, 27 shared causal loci, and 9 shared risk loci involving immune cells related to both psychiatric and bone-related diseases. Additionally, tissue-specific enrichment analysis showed that genes mapped to these loci were enriched in brain, cardiovascular, pancreatic, and other tissues. The IVW method demonstrated that MDD increased the risk of IVDD and KneeOA, while IVDD increased the risk of ANX and MDD. Conversely, SCZ was associated with a reduced risk of KneeOA. Multiple sensitivity analyses further supported a positive causal effect of IVDD on MDD.

CONCLUSION

These findings suggest significant genetic correlations between skeletal degenerative diseases and psychiatric disorders, highlighting multiple shared comorbid genes and key immune cell types. Importantly, the study supports the role of the brain-bone axis in the regulation of skeletal degenerative diseases and psychiatric disorders, which could provide valuable insights for potential therapeutic targets and interventions for these conditions.

Genetic association of attention-deficit/hyperactivity disorder with thirteen ocular disorders

PURPOSE

Attention deficit hyperactivity disorder (ADHD) is linked to elevated risk of various ocular disorders, though the genetic association and causality remain ambiguous.

METHODS

This study performed linkage disequilibrium score regression (LDSC) and pleiotropic analysis under composite null hypothesis (PLACO) to explore genetic associations, and bidirectional mendelian randomization (MR) to assess the causality between ADHD and thirteen ocular disorders.

RESULTS

LDSC showed ADHD genetically correlated with corneal ulcer, keratitis, blepharochalasis, lacrimal system disorders, senile cataract, retinal vascular occlusion, and age-related macular degeneration. MR revealed liability to ADHD increased the risk of corneal ulcer (OR = 1.18, FDR adjusted P = 0.01), keratitis (OR = 1.13, P = 0.007), blepharochalasis (OR = 1.23, P = 0.002), and lacrimal system disorders (OR = 1.09, P = 0.04), while decreasing the risk of primary open-angle glaucoma (OR = 0.83, P = 0.003), exfoliation glaucoma (OR = 0.71, P = 0.001), and normotensive glaucoma (OR = 0.79, P = 0.02). Conversely, the liability to strabismus was causally associated with an increased risk of ADHD (OR = 1.09, P = 0.03). The identification of pleiotropic loci using PLACO suggests that genetic factors may play a role in the association between ADHD and ocular diseases.

CONCLUSIONS

This study revealed genetic associations between ADHD and multiple ocular disorders, identifying causal effects of ADHD on an increased risk of corneal ulcer, keratitis, blepharochalasis, and lacrimal system disorders, while showing a protective effect against glaucoma. Conversely, genetic liability to strabismus was causally linked to a higher ADHD risk.

Shared genetic architecture of gastroesophageal reflux disease and age related phenotypes

Increasing age is a risk factor of gastroesophageal reflux disease. This study aims to uncover the shared genetic architecture of gastroesophageal reflux disease (GERD) and age-related phenotypes. Based on publicly available GWAS statistics, this genome-wide pleiotropic association research was performed with multiple genetic approaches sequentially to explore the pleiotropic associations from single-nucleotide polymorphism (SNP) and gene levels, to reveal the underlying shared genetic etiology between GERD and age-related phenotypes. This study featured shared genetic mechanisms between GERD and age-related phenotypes, including frailty index (FI), telomere length (TL), longevity, and parental lifespan (PL). Strong genetic association were observed. A set of pleiotropic loci and genes were identified by PLACO, FUMA, Bayesian colocalization and additional MAGMA analysis. Our research provided strong evidence of genetic correlation between GERD and several age-related phenotypes, especially frailty index (FI) and telomere length (TL), brought novel insight into the shared genetic architecture between GERD and aging.

Type-2 diabetes epigenetic biomarkers: present status and future directions for global and Indigenous health

Type-2 diabetes is a systemic condition with rising global prevalence, disproportionately affecting Indigenous communities worldwide. Recent advances in epigenomics methods, particularly in DNA methylation detection, have enabled the discovery of associations between epigenetic changes and Type-2 diabetes. In this review, we summarise DNA methylation profiling methods, and discuss how these technologies can facilitate the discovery of epigenomic biomarkers for Type-2 diabetes. We critically evaluate previous DNA methylation biomarker studies, particularly those using microarray platforms, and advocate for a shift towards sequencing-based approaches to improve genome-wide coverage. Furthermore, we emphasise the need for biomarker studies that include genetically diverse populations, especially Indigenous communities who are significantly impacted by Type-2 diabetes. We discuss research approaches and ethical considerations that can better facilitate Type-2 diabetes biomarker development to ensure that future genomics-based precision medicine efforts deliver equitable health outcomes. We propose that by addressing these gaps, future research can better capture the genetic and environmental complexities of Type-2 diabetes among populations at disproportionate levels of risk, ultimately leading to more effective diagnostic and therapeutic strategies.

Genetic approach uncovering the pathways between childhood maltreatment and suicide attempt

Childhood maltreatment significantly heightens the risk of suicide attempt, but the causal mechanisms and underlying pathways are not fully understood. Using genetic instruments for both childhood maltreatment (n = 185,414) and suicide attempt (cases = 29,782; controls = 519,961), we performed two-sample Mendelian randomization analyses. Our results show that higher level of childhood maltreatment is causally associated with an increased risk of suicide attempt (OR = 3.40; 95% CI, 2.34-4.96, P = 1.3e-10). We then conducted a two-step Mendelian randomization mediation analysis, identifying 11 out of 58 potential mediators between childhood maltreatment and suicide attempt. These mediators included neurobiological, psychopathological and behavioral factors. The psychopathological factors had the most significant impact, accounting for 10.4-50.2% the mediation. This study confirms the causal relationship between childhood maltreatment and suicide attempt, highlighting specific mediators-especially within the psychopathological dimension-that can guide targeted interventions to alleviate the adverse effects of childhood maltreatment and prevent suicide attempt.

RFX3 is essential for the generation of functional human pancreatic islets from stem cells

AIMS/HYPOTHESIS

The role of regulatory factor X 3 (RFX3) in human pancreatic islet development has not been explored. This study aims to investigate the function of RFX3 in human pancreatic islet development using human islet organoids derived from induced pluripotent stem cells (iPSCs), hypothesising that RFX3 regulates human islet cell differentiation.

METHODS

We generated RFX3 knockout (RFX3 KO) iPSC lines using CRISPR/Cas9 and differentiated them into pancreatic islet organoids. Various techniques were employed to assess gene expression, cell markers, apoptosis, proliferation and glucose-stimulated insulin secretion. Single-cell RNA-seq datasets from human embryonic stem cell-derived pancreatic islet differentiation were re-analysed to investigate RFX3 expression in specific cell populations at various developmental stages. Furthermore, bulk RNA-seq was conducted to further assess transcriptomic changes. RFX3 overexpression was implemented to reverse dysregulated gene expression.

RESULTS

RFX3 was found to be highly expressed in pancreatic endocrine cell populations within pancreatic progenitors (PPs), endocrine progenitors (EPs) and mature islet stages derived from iPSCs. Single-cell RNA-seq further confirmed RFX3 expression across different endocrine cell clusters during differentiation. The loss of RFX3 disrupted pancreatic endocrine gene regulation, reduced the number of hormone-secreting islet cells and impaired beta cell function and insulin secretion. Despite a significant reduction in the expression levels of pancreatic islet hormones, the pan-endocrine marker chromogranin A remained unchanged at both EP and islet stages, likely due to an increase in the abundance of enterochromaffin cells (ECs). This was supported by our findings of high EC marker expression levels in RFX3 KO EPs and islets. In addition, RFX3 loss led to smaller islet organoids, elevated thioredoxin-interacting protein levels and increased apoptosis in EPs and islets. Furthermore, RFX3 overexpression rescued the expression of dysregulated genes in RFX3 KO at the PP and EP stages.

CONCLUSIONS/INTERPRETATION

These findings underscore the crucial role of RFX3 in regulating human islet cell differentiation and its role in suppressing EC specification. These insights into RFX3 function have implications for understanding islet biology and potential diabetes susceptibility.

DATA AVAILABILITY

The RNA-seq datasets have been submitted to the Zenodo repository and can be accessed via the following links: DOI https://doi.org/10.5281/zenodo.13647651 (PPs); and DOI https://doi.org/10.5281/zenodo.13762055 (SC-islets).

Biologically targeted discovery-replication scan identifies G×G interaction in relation to risk of Barrett's esophagus and esophageal adenocarcinoma

Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett's esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (1) known/plausible links to BE/EAC pathogenesis (n = 493) or (2) prior evidence of biological interactions (n = 4,196). Approximately 75 × 106 SNP×SNP interactions were screened via hierarchical group lasso (glinternet) using BEACON GWAS data. The top ∼2,000 interactions retained in each scan were prioritized using p values from single logistic models. Identical scans were repeated among males only (78%), with two independent GWAS datasets used for replication. In overall and male-specific primary replications, 11 of 187 and 20 of 191 interactions satisfied p < 0.05, respectively. The strongest evidence for secondary replication was for rs17744726×rs3217992 among males, with consistent directionality across all cohorts (Pmeta = 2.19 × 10-8); rs3217992 "T" was associated with reduced risk only in individuals homozygous for rs17744726 "G." Rs3217992 maps to the CDKN2B 3' UTR and reportedly disrupts microRNA-mediated repression. Rs17744726 maps to an intronic enhancer region in BLK. Through in silico prioritization and experimental validation, we identified a nearby proxy variant (rs4841556) as a functional modulator of enhancer activity. Enhancer-gene mapping and eQTLs implicated BLK and FAM167A as targets. The first systematic G×G investigation in BE/EAC, this study uncovers differential risk associations for CDKN2B variation by BLK genotype, suggesting novel biological dependency between two risk loci encoding key mediators of tumor suppression and inflammation.

Integrated genetic analysis and single cell-RNA sequencing for brain image-derived phenotypes and Parkinson's disease

BACKGROUND

Previous studies have reported Parkinson's disease (PD) patients usually have changes in brain image-derived phenotypes (IDPs). However, the role of genetic factors in their association and biological mechanism remains unclear. We aimed to unveil genetic and biological links between brain IDPs and PD.

METHODS

Using genome-wide association study (GWAS) summary statistics and single-cell RNA sequencing (scRNA-seq) data, we performed a comprehensive analysis between 624 brain IDPs and PD. The genetic correlations and causality were examined by linkage disequilibrium score regression (LDSC), two-sample bidirectional Mendelian randomization (MR) and meta-analysis. Potential shared genes were identified using MAGMA and PLACO. Finally, pathway enrichment using FUMA and Metascape, and scRNA-seq analysis were performed to determine biological mechanisms and gene expression atlas across various cell types in brain tissue.

RESULTS

LDSC revealed that 50 brain IDPs were genetically correlated with PD (P < 0.05), in which 5 IDPs, exhibited putative causality on PD through MR (P < 0.05). For instance, we identified that the increased volume of the right thalamus (IVW: OR = 2.08, 95 % CI: 1.33 to 3.25, PFDR = 0.03) was positively correlated with the risk of PD, which was also supported by replicated MR (IVW: OR = 1.63, 95 % CI: 1.17-2.26, PFDR = 0.02) in FinnGen and meta-analysis (OR = 1.78, 95 % CI: 1.36-2.31, PFDR = 5.00 × 10-4). Additionally, we identified 56 unique pleiotropic genes, such as FAM13A, with notable enrichment in neuronal cells. Biological mechanism analysis revealed these genes were enriched in brain tissues and a variety of pathways such as negative regulation of neuron apoptotic processes.

CONCLUSION

We indicated the shared genetic architecture and biological mechanisms between brain IDPs and PD. These findings might provide insights on the therapeutic intervention and early prediction of PD at the brain imaging level.

Genetic correlation, pleiotropic loci and shared risk genes between major depressive disorder and gastrointestinal tract disorders

BACKGROUND

Major depressive disorder (MDD) is associated with gastrointestinal tract (GIT) disorders, while genetic correlation, pleiotropic loci and shared risk genes remain to be explored.

METHODS

Leveraging genome-wide association study statistics for MDD (n = 170,756), peptic ulcer disease (PUD; n = 16,666), gastroesophageal reflux disease (GORD; n = 54,854), PUD and/or GORD and/or medications (PGM; n = 90,175), irritable bowel syndrome (IBS; n = 28,518), and inflammatory bowel disease (IBD; n = 7045), we determined global and local genetic correlations, identified pleiotropic loci, performed gene-level evaluations, and inferred causal associations using bidirectional Mendelian randomization.

RESULTS

We found global correlation of MDD with PUD (rg = 0.444, P = 3.135 × 10-24), GORD (rg = 0.459, P = 2.568 × 10-65), PGM (rg = 0.498, P = 6.094 × 10-114), IBS (rg = 0.621, P = 2.483 × 10-63), and IBD (rg = 0.171, P = 1.824 × 10-5). We identified 12 locally correlated regions between MDD and GIT disorders except for IBD, and one shared region (chr11:111985737-113,103,996) for PGM, GORD, and IBS. We found one pleiotropic locus for PUD, 12 for GORD, 30 for PGM, eight for IBS, and seven for IBD, and five shared loci (rs138786869, rs2284189, rs3130063, rs35789010, rs7568369) for GORD and PGM. We respectively observed 14 and 20 overlapping genes for MDD-GORD and MDD-PGM. We showed genetic liabilities to GORD, PGM, and IBS causally increase MDD risk, while all reverse causalities are significant.

CONCLUSIONS

Our work identifies genetic architectures shared between MDD and GIT disorders, contributes genetic insights to understand depression in the context of gut-brain interactions, and provides potential targets to treat gastrointestinal symptoms in depressive patients.

Genetic Links between Gastrointestinal Disorders and Kidney Stone Disease: Insights from a Genome-Wide Cross-Trait Analysis

Key Points

Positive genetic links and shared genetic architecture exist between gastrointestinal disorders and kidney stone disease. Ion homeostasis and response to vitamin D bridge two types of disorders. Genetically predicted irritable bowel syndrome, gastroesophageal reflux, and Crohn's disease were associated with higher risk of kidney stone disease.

Background

Epidemiological associations between kidney stone disease (KSD) and gastrointestinal disorders have been reported, and intestinal homeostasis plays a critical role in stone formation. However, the underlying intrinsic link is not adequately understood. This study aims to investigate the genetic associations between these two types of diseases.

Methods

We obtained summary statistics from large-scale genome-wide association studies of KSD and gastrointestinal diseases, including gastroesophageal reflux disease, peptic ulcer disease, inflammatory bowel disease and its subtypes, irritable bowel syndrome, and diverticular disease (N =311,254–720,199). Their overall genetic correlations were first estimated. We then detected the shared genetic architecture, including pleiotropic single nucleotide polymorphisms, loci, genes, and biological processes, through cross-trait analyses. In addition, bidirectional Mendelian randomization analysis was performed to look for their causal relationships.

Results

We found significantly positive genetic correlations between KSD and all five gastrointestinal diseases. The cross-trait analysis identified 3184 potential pleiotropic single nucleotide polymorphisms, and 33 of which were pleiotropic loci shared by the two disorders. Gene-level analyses revealed eight pleiotropic causal genes, primarily enriched in biological pathways involving ion homeostasis and response to vitamin D. In the Mendelian randomization analysis, we detected causal effects from gastroesophageal reflux disease, irritable bowel syndrome, and Crohn's disease to KSD, while no reverse causality was observed.

Conclusions

Our study demonstrated the positive genetic links between KSD and gastrointestinal diseases and reported pleiotropic variants, loci, and genes, implicating potential biological mechanisms in the pathogenesis of stone disease. These findings further support the role of the gut-kidney axis and provide a genetic basis for the prevention, coregulation, and treatment of these diseases.

A genome-wide cross-trait analysis characterizes the shared genetic architecture between lung and gastrointestinal diseases

Lung and gastrointestinal diseases often occur together, leading to more adverse health outcomes than when a disease of one of these systems occurs alone. However, the potential genetic mechanisms underlying lung-gastrointestinal comorbidities remain unclear. Here, we leverage lung and gastrointestinal trait data from individuals of European, East Asian and African ancestries, to perform a large-scale genetic cross trait analysis, followed by functional annotation and Mendelian randomization analysis to explore the genetic mechanisms involved in the development of lung-gastrointestinal comorbidities. Notably, we find significant genetic correlations between 27 trait pairs among the European population. The highest correlation is between chronic bronchitis and peptic ulcer disease. At the variant level, we identify 42 candidate pleiotropic genetic variants (3 of them previously uncharacterized) in 14 trait pairs by integrating cross-trait meta-analysis, fine-mapping and colocalization analyses. We also find 66 candidate pleiotropic genes, most of which were enriched in immune or inflammatory response-related activities. Causal inference approaches result in 4 potential lung-gastrointestinal associations. Introducing the gut microbiota as a variable establishes a relationship between the genus Parasutterella, gastro-oesophageal reflux disease and asthma. In summary, our findings highlight the genetic relationship between lung and gastrointestinal diseases, providing insights into the genetic mechanisms underlying the development of lung gastrointestinal comorbidities.

Dissecting the causal association of periodontitis with biological aging and its underlying mechanisms: findings from Mendelian randomization and integrative genetic analysis

PURPOSE

Chronic low-grade inflammation is linked to the biology of aging; however, evidence supporting a causal relationship between periodontitis-a dysbiotic biofilm-initiated inflammatory disease-and accelerated aging remains limited. This study investigated the causality between periodontitis and biological aging and identified potentially shared genomic loci, genes, and pathways.

METHODS

We conducted a 2-sample Mendelian randomization (MR) analysis to explore the causality of periodontitis on age acceleration measures (DNAm PhenoAge acceleration, GrimAge acceleration, Hannum age acceleration, and intrinsic epigenetic age acceleration) using a dataset from genome-wide association studies of European ancestry populations. Independent genetic variants associated with each trait were used as instrumental variables. The inverse variance-weighted (IVW) method served as the primary MR approach, supplemented by sensitivity testing. We also performed additional statistical genetic analyses to identify pleiotropic loci, shared functional genes, and potential biological pathways, integrating large-scale expression quantitative trait loci data from blood samples.

RESULTS

The MR analysis indicated a causal relationship between periodontitis and DNAm PhenoAge acceleration (IVW β=0.308; 95% confidence interval, 0.056-0.561; P=0.017), a finding corroborated by sensitivity analyses. There was a significant genetic overlap between periodontitis and age acceleration. Pleiotropic analysis revealed 24 shared SNPs associated with 242 genes, predominantly involved in immune functions and pathways related to cellular processes. Further integration analysis showed that 91 of these pleiotropic genes were causally linked to both conditions, with C6orf183 identified as a potential mediator.

CONCLUSIONS

This study presents compelling genetic evidence supporting a causal relationship between periodontitis and accelerated aging. Further research is required to validate these findings and investigate the underlying mechanisms.

Shared genetic architecture of type 2 diabetes with muscle mass and function and frailty reveals comorbidity etiology and pleiotropic druggable targets

BACKGROUND

Delineating the shared genetic architecture of type 2 diabetes with muscle mass and function and frailty is essential for unraveling the common etiology and developing holistic therapeutic strategies for these co-existing conditions.

METHODS

In this genome-wide pleiotropic association study, we performed multi-level pairwise trait pleiotropic analyses using genome-wide association study summary statistics from up to 461,026 European ancestry individuals to dissect the shared genetic factors and causal relationships of type 2 diabetes and seven glycemic traits with four muscle mass- and function-related phenotypes and the frailty index.

RESULTS

We first identified 27 pairs with significant genetic correlations through the linkage disequilibrium score regression and high-definition likelihood analysis. Then we determined 79 pleiotropic loci and 109 pleiotropic genes across linkage pairs via the pleiotropic analysis under the composite null hypothesis (PLACO), the colocalization, and the Multi-marker Analysis of GenoMic Annotation (MAGMA) analyses. We subsequently performed transcriptome-wide association study (TWAS) analyses using joint-tissue imputation, refined by gene-based integrative fine-mapping through a conditional TWAS approach, and identified 44 unique causal shared genes across 13 tissues in linkage pairs, including eight druggable genes (ABO, AOC1, FTO, GCKR, MTOR, POLK, PPARG, and APEH), with MTOR and PPARG categorized as clinically actionable. Two-sample Mendelian randomization analysis supported bidirectional causality between diabetes and frailty index and unidirectional causal effects of muscle phenotypes on glycemic profiles.

CONCLUSIONS

Our findings highlight the common genetic underpinnings between type 2 diabetes and muscle loss and frailty and inform drug targets with pleiotropic effects on both of these aging-related challenges.

Investigating the common genetic architecture and causality of metabolic disorders with neurodegenerative diseases

BACKGROUND

The co-occurrence of metabolic dysfunction and neurodegenerative diseases suggests a genetic link, yet the shared genetic architecture and causality remain unclear. We aimed to comprehensively characterise these genetic relationships.

METHODS

We investigated genetic correlations among four neurodegenerative diseases and seven metabolic dysfunctions, followed by bidirectional Mendelian randomisation (MR) to assess potential causal relationships. Pleiotropy analysis (PLACO) was used to detect the pleiotropic effects of genetic variants. Significant pleiotropic loci were refined and annotated using functional mapping and annotation (FUMA) and Bayesian colocalisation analysis. We further explored mapped genes with tissue-specific expression and gene set enrichment analyses.

RESULTS

We identified significant genetic correlations in nine out of 28 trait pairs. MR suggested causal relationships between specific trait pairs. Pleiotropy analysis revealed 25 931 significant single-nucleotide polymorphisms, with 246 pleiotropic loci identified via FUMA and 55 causal loci through Bayesian colocalisation. These loci are involved in neurotransmitter transport and immune response mechanisms, notably the missense variant rs41286192 in SLC18B1. The tissue-specific analysis highlighted the pancreas, left ventricle, amygdala, and liver as critical organs in disease progression. Drug target analysis linked 74 unique genes to existing therapeutic agents, while gene set enrichment identified 189 pathways related to lipid metabolism, cell differentiation and immune responses.

CONCLUSION

Our findings reveal a shared genetic basis, pleiotropic loci, and potential causal relationships between metabolic dysfunction and neurodegenerative diseases. These insights highlight the biological connections underlying their phenotypic association and offer implications for future research to reduce the risk of neurodegenerative diseases.

A study on the genetic comorbidity between autoimmune diseases and pulmonary hypertension: an observational study and POST-GWAS analysis

Background: The causal relationship between various prevalent autoimmune diseases (ADs) and pulmonary hypertension (PH) has yet to be fully understood, and the contribution of genetic factors to their coexistence remains largely unexplored. Methods: We utilized Post-GWAS and the Medical Information Mart for Intensive Care (MIMIC) database to investigate the relationship between autoimmune diseases and PH. Results: After a series of MR Analyses, only Type 1 diabetes Mellitus (T1DM) (OR = 1.06, 95% CI 0.99-1.13, P = 0.083; OR = 1.07, 95% CI 1.02-1.13, P = 0.005) and primary biliary cholangitis (PBC) (OR = 1.10, 95% CI 1.05-1.15, P = 8.22E-5; OR = 1.08, 95% CI 1.03-1.14, P = 0.002) emerged as significantly correlated with PH. Additionally, reverse MR indicated that PH could trigger the development of systemic lupus erythematosus (SLE) (OR=1.090, 95% CI = 1.014-1.171, P = 0.014). An observational study using real-world data found a clear association between rheumatoid arthritis and increased risk of PH after adjusting confounding various variables (OR = 1.39, 95% CI 1.11-1.75, P = 0.005). Furthermore, the genetic correlation results between the diseases: T1DM & PAH: P (LDSC) = 1.20e-11, P (GNOVA) = 3.36e-08; PBC & PAH: P(LDSC) = 9.40e-07, P (GNOVA) = 5.17e-05. Conclusions: Our study indicates a genetic correlation and shared risk genes between PH and autoimmune diseases, offering insights into the mechanisms underlying their co-occurrence and potential implications for future therapeutic strategies.

Genome-wide pleiotropy analysis of longitudinal blood pressure and harmonized cognitive performance measures

Background

Genome-wide association studies (GWAS) have identified over 1,000 blood pressure (BP) loci and over 80 loci for Alzheimer's disease (AD). Considering BP is an AD risk factor, identifying pleiotropy in BP and cognitive performance measures may indicate mechanistic links between BP and AD.

Methods

Genome-wide scans for pleiotropy in BP variables-systolic (SBP), diastolic (DBP), mean arterial (MAP), and pulse pressure (PP)-and co-calibrated scores for cognitive domains (executive function, language, and memory) were performed using generalized linear mixed models and 116,075 longitudinal measures from 25,726 participants of clinic-based and prospective cohorts. GWAS was conducted using PLACO to estimate each SNP's main effect and interaction with age, and their joint effect on pleiotropy. Effects of genome-wide significant (GWS) pleiotropic SNPs on cognition as direct or mediated through BP were evaluated using Mendelian randomization. Potential contribution of genes in top-ranked pleiotropic loci to cognitive resilience was assessed by comparing their expression in brain tissue from pathologically confirmed AD cases with and without clinical symptoms.

Results

Pleiotropy GWAS identified GWS associations with APOE and 11 novel loci. In the total sample, pleiotropy was identified for SBP and language with JPH2 ( P Joint =6.09×10 -9 ) and GATA3 ( P G×Age =1.42×10 -8 ), MAP and executive function with PAX2 ( P G×Age =4.22×10 -8 ), MAP and language with LOC105371656 ( P G×Age =1.75×10 -8 ), and DBP and language with SUFU ( P G =2.10×10 -8 ). In prospective cohorts, pleiotropy was found for SBP and language with RTN4 ( P G×Age =1.49×10 -8 ), DBP and executive function with ULK2 ( P Joint =2.85×10 -8 ), PP and memory with SORBS2 ( P G =2.33×10 -8 ), and DBP and memory with LOC100128993 ( P G×Age =2.81×10 -8 ). In clinic-based cohorts, pleiotropy was observed for PP and language with ADAMTS3 ( P G =2.37×10 -8 ) and SBP and memory with LINC02946 ( P G×Age =3.47×10 -8 ). Five GWS pleiotropic loci influence cognition directly, and genes at six pleiotropic loci were differentially expressed between pathologically confirmed AD cases with and without clinical symptoms.

Conclusion

Our results provide insight into the underlying mechanisms of high BP and AD. Ongoing efforts to harmonize BP and cognitive measures across several cohorts will improve the power of discovering, replicating, and generalizing novel associations with pleiotropic loci.

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

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.

From single nucleotide variations to genes: identifying the genetic links between sleep and psychiatric disorders

STUDY OBJECTIVES

Sleep disorders and psychiatric disorders frequently coexist and interact, yet the shared genetic basis linking these two domains remains poorly understood.

METHODS

We investigated the genetic correlation and overlap between seven sleep/circadian traits and three psychiatric disorders at the level of genome-wide association studies (GWAS), utilizing LDSC, HDL, and GPA. To identify potential polygenic single nucleotide variations (SNVs) within each trait pair, we used PLACO, while gene-level analyses were performed using MAGMA and POPS. Furthermore, the functions and biological mechanisms, enriched phenotypes, tissues, cellular features, and pathways were thoroughly investigated using FUMA, deTS, and enrichment analyses at the biological pathway level.

RESULTS

Our study revealed extensive genetic associations and overlaps in all 21 trait pairs. We identified 18 494 SNVs and 543 independent genomic risk loci, with 113 confirmed as causative through colocalization analysis. These loci collectively spanned 196 unique chromosomal regions. We pinpointed 43 distinct pleiotropic genes exhibiting significant enrichment in behavioral/physiological phenotypes, nervous system phenotypes, and brain tissue. Aberrations in synaptic structure and function, neurogenesis and development, as well as immune responses, particularly involving the MAPK pathway, emerged as potential underpinnings of the biology of sleep/circadian traits and psychiatric disorders.

CONCLUSIONS

We identified shared loci and specific sets of genes between sleep/circadian traits and psychiatric disorders, shedding light on the genetic etiology. These discoveries hold promise as potential targets for novel drug interventions, providing valuable insights for the development of therapeutic strategies for these disorders.

Possible linking and treatment between Parkinson's disease and inflammatory bowel disease: a study of Mendelian randomization based on gut-brain axis

BACKGROUND

Mounting evidence suggests that Parkinson's disease (PD) and inflammatory bowel disease (IBD) are closely associated and becoming global health burdens. However, the causal relationships and common pathogeneses between them are uncertain. Furthermore, they are uncurable. Thus, we aimed to identify the causal relationships and novel therapeutic targets shared between them based on their common pathophysiological mechanisms in gut-brain-axis (GBA).

METHODS

A meta-analysis on bidirectional Mendelian randomization (MR) utilizing various datasets was performed to estimate their causal relationship. Then, pleiotropic analysis under the composite null hypothesis (PLACO) with functional mapping combined with annotation of genetic associations (FUMA) analysis were conducted to identify pleiotropic genes. Next, blood, brain and intestine expression quantitative trait locus (eQTL) were taken to perform drug-target MR finding common causal genes in two diseases. Colocalization analysis ensured the eQTLs of corresponding gene colocalized with disease. Enrichment analysis and protein‒protein interaction (PPI) network were done to explore common pathogenesis pathways. Genes passed all analysis were regarded as drug targets.

RESULTS

Our MR meta-analysis revealed the bidirectional causal relationship between diseases, with combined ORs for PD on IBD, CD, UC (1.050 [95% CI 1.014-1.086], 1.044 [95% CI 0.995-1.095], 1.063 [95% CI 1.016-1.120]); for IBD, CD, UC on PD (1.003 [95% CI 0.973-1.034], 1.035 [95% CI 1.004-1.067], 1.008 [95% CI 0.977-1.040]). Overall, 277, 216 and 201 genes were identified as pleiotropic genes between PD and IBD, CD, UC. Total of 733 genes were classified as tier 3 (found in only one tissue) druggable targets, 57 as tier 2 (found in two tissues, 51 protein-coding genes) and 9 as tier 3 (found in three tissues). Among 60 protein-coding druggable targets over tier 2, 18 overlapped with pleiotropic genes and enriched in mitochondria, antigen presentation, processing and immune cell regulation pathways. Three druggable genes (LRRK2, RAB29 and HLA-DQA2) passed colocalization analysis. LRRK2 and RAB29 were reported to be pleiotropic genes, and RAB29 and HLA-DQA2 were reported for the first time as potential drug targets.

CONCLUSIONS

This study established a reliable causal relationship, possible shared drug targets and common pathogenesis pathways of two diseases, which had important implications for intervention and treatment of two diseases simultaneously.

Exploring the genetic landscape of the brain-heart axis: A comprehensive analysis of pleiotropic effects between heart disease and psychiatric disorders

BACKGROUND

The genetic links between heart disease and psychiatric disorders are complex and not well understood. This study uses genome-wide association studies (GWAS) and advanced multilevel analyses to explore these connections.

METHODS

We analyzed GWAS data from seven psychiatric disorders and five types of heart disease. Genetic correlations and overlaps were examined using linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), and Genetic analysis incorporating Pleiotropy and Annotation (GPA). Pleiotropic single-nucleotide variations (SNVs) were identified with pleiotropic analysis under the composite null hypothesis (PLACO) and annotated via Functional mapping and annotation of genetic associations (FUMA). Potential pleiotropic genes were identified using Multi-marker Analysis of GenoMic Annotation (MAGMA) and Summary data-based Mendelian Randomization (SMR).

RESULTS

Among 35 trait pairs, 32 showed significant genetic correlations or overlaps. PLACO identified 15,077 SNVs, with 287 recognized as pleiotropic loci and 20 colocalization sites. MAGMA and SMR revealed 75 potential pleiotropic genes involved in diverse pathways, including cancer, neurodevelopment, and cellular organization. Mouse Genome Informatics (MGI) queries provided evidence linking multiple genes to heart or psychiatric disorders.

CONCLUSIONS

This analysis reveals loci and genes with pleiotropic effects between heart disease and psychiatric disorders, highlighting shared biological pathways. These findings illuminate the genetic mechanisms underlying the brain-heart axis and suggest shared biological foundations for these conditions, offering potential targets for future prevention and treatment strategies.

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.

Establishing a robust triangulation framework to explore the relationship between hearing loss and Parkinson's disease

The relationship between hearing loss (HL) and Parkinson's disease (PD) remains unclear. Using individual-level and summary-level data from the UK Biobank and the largest genome-wide association studies, we examined this link through observational, Mendelian randomization and genetic pleiotropy analyses. Among 158,229 participants, PD risk rose with HL severity especially in elder and males, and hearing aids significantly reduced PD risk in males. Although our results did not support a causal association, genetic correlation analysis suggested a localized genetic overlap (17q21.31). We identified 1545 SNPs and 63 genes with pleiotropic effects on HL and PD, including 79 novel SNPs across 6 loci, with 3 showing strong co-localization. These loci were enriched in key tissues like brain, heart, liver and pancreas, linked to the dihydrolipoyl dehydrogenase complex pathway, and targeted by drugs such as Warfarin and Phenprocoumon. Overall, this study reveals the risk association, genetic basis, and pleiotropic loci connecting HL and PD.

Interactions of genes with alcohol consumption affect insulin sensitivity and beta cell function

AIMS/HYPOTHESIS

Alcohol consumption has complex effects on diabetes and metabolic disease, but there is widespread heterogeneity within populations and the specific reasons are unclear. Genetic factors may play a role and warrant exploration. The aim of this study was to elucidate genetic variants modulating the impact of alcohol consumption on insulin sensitivity and pancreatic beta cell function within populations presenting normal glucose tolerance (NGT).

METHODS

We recruited 4194 volunteers in Nanjing, 854 in Jurong and an additional 5833 in Nanjing for Discovery cohorts 1 and 2 and a Validation cohort, respectively. We performed an OGTT on all participants, establishing a stringent NGT group, and then assessed insulin sensitivity and beta cell function. Alcohol consumption was categorised as abstinent, light-to-moderate (<210 g per week) or heavy (≥210 g per week). After excluding ineligible individuals, an exploratory genome-wide association study identified potential variants interacting with alcohol consumption in 1862 NGT individuals. These findings were validated in an additional cohort of 2169 NGT individuals. Cox proportional hazard regression was further employed to evaluate the effect of the interaction between the potential variants and alcohol consumption on the risk of type 2 diabetes within the UK Biobank cohort.

RESULTS

A significant correlation was observed between drinking levels and insulin sensitivity, accompanied by a consequent inverse relationship with insulin resistance and beta cell insulin secretion after adjusting for confounding factors in NGT individuals. However, no significant associations were noted in the disposition indexes. The interaction of variant rs56221195 with alcohol intake exhibited a pronounced effect on the liver insulin resistance index (LIRI) in the discovery set, corroborated in the validation set (combined p=1.32 × 10-11). Alcohol consumption did not significantly affect LIRI in rs56221195 wild-type (TT) carriers, but a strong negative association emerged in heterozygous (TA) and homozygous (AA) individuals. The rs56221195 variant also significantly interacts with alcohol consumption, influencing the total insulin secretion index INSR120 (the ratio of the AUC of insulin to glucose from 0 to 120 min) (p=2.06 × 10-9) but not disposition index. In the UK Biobank, we found a significant interaction between rs56221195 and alcohol consumption, which was linked to the risk of type 2 diabetes (HR 0.897, p=0.008).

CONCLUSIONS/INTERPRETATION

Our findings reveal the effects of the interaction of alcohol and rs56221195 on hepatic insulin sensitivity in NGT individuals. It is imperative to weigh potential benefits and detriments thoughtfully when considering alcohol consumption across diverse genetic backgrounds.

Association between 25(OH) vitamin D and schizophrenia: shared genetic correlation, pleiotropy, and causality

Background

This study delves into the complex interplay between genetics, 25-hydroxyvitamin D (25OHD), and schizophrenia (SCZ). It leverages extensive sample data derived from Genome-Wide Association Studies (GWAS) to uncover genetic correlations.

Methods

Employing Linkage Disequilibrium Score Regression (LDSC) and S-LDSC, this study investigates genetic connections between 25OHD and SCZ. It examines Single Nucleotide Polymorphism (SNP) heritability in specific tissues and incorporates diverse immune cell datasets for genetic enrichment analysis. Local genetic correlations were analyzed using HESS software, and pleiotropy analysis identified shared genetic loci in brain tissues. Hyprcoloc analysis was used to explore shared genetic factors between 25OHD, immune cells, and SCZ, complemented by a bidirectional Mendelian Randomization (MR) to probe potential causal links.

Results

We identified a significant negative genetic correlation between 25OHD levels and SCZ. PLACO analysis revealed 35 pleiotropic loci with strong enrichment in brain regions, particularly the cerebellum, frontal cortex, and hippocampus. Eight loci (1p34.2, 2p23.3, 3p21.1, 5q31.2, 12q23.2, 14q32.33, 16p13.3, and 16q24.3) exhibited strong colocalization, highlighting potential drug targets. Gene and tissue enrichment analyses emphasized neurological and immune-related mechanisms, including hyaluronan metabolism. Bidirectional MR analysis supported a causal effect of SCZ on 25OHD levels.

Conclusion

Our study identifies NEK4 as a potential therapeutic target and highlights the involvement of hyaluronan metabolism in the genetic association between 25OHD and SCZ. These findings provide valuable insights into shared genetic pathways, immune-related connections, and causal interactions in the context of SCZ.

Deletion of RFX6 impairs iPSC-derived islet organoid development and survival, with no impact on PDX1+/NKX6.1+ progenitors

AIMS/HYPOTHESIS

Homozygous mutations in RFX6 lead to neonatal diabetes accompanied by a hypoplastic pancreas, whereas heterozygous mutations cause MODY. Recent studies have also shown RFX6 variants to be linked with type 2 diabetes. Despite RFX6's known function in islet development, its specific role in diabetes pathogenesis remains unclear. Here, we aimed to understand the mechanisms underlying the impairment of pancreatic islet development and subsequent hypoplasia due to loss-of-function mutations in RFX6.

METHODS

We examined regulatory factor X6 (RFX6) expression during human embryonic stem cell (hESC) differentiation into pancreatic islets and re-analysed a single-cell RNA-seq dataset to identify RFX6-specific cell populations during islet development. Furthermore, induced pluripotent stem cell (iPSC) lines lacking RFX6 were generated using CRISPR/Cas9. Various approaches were then employed to explore the consequences of RFX6 loss across different developmental stages. Subsequently, we evaluated transcriptional changes resulting from RFX6 loss through RNA-seq of pancreatic progenitors (PPs) and endocrine progenitors (EPs).

RESULTS

RFX6 expression was detected in PDX1+ cells in the hESC-derived posterior foregut (PF). However, in the PPs, RFX6 did not co-localise with pancreatic and duodenal homeobox 1 (PDX1) or NK homeobox 1 (NKX6.1) but instead co-localised with neurogenin 3, NK2 homeobox 2 and islet hormones in the EPs and islets. Single-cell analysis revealed high RFX6 expression levels in endocrine clusters across various hESC-derived pancreatic differentiation stages. Upon differentiating iPSCs lacking RFX6 into pancreatic islets, a significant decrease in PDX1 expression at the PF stage was observed, although this did not affect PPs co-expressing PDX1 and NKX6.1. RNA-seq analysis showed the downregulation of essential genes involved in pancreatic endocrine differentiation, insulin secretion and ion transport due to RFX6 deficiency. Furthermore, RFX6 deficiency resulted in the formation of smaller islet organoids due to increased cellular apoptosis, linked to reduced catalase expression, implying a protective role for RFX6. Overexpression of RFX6 reversed defective phenotypes in RFX6-knockout PPs, EPs and islets.

CONCLUSIONS/INTERPRETATION

These findings suggest that pancreatic hypoplasia and reduced islet cell formation associated with RFX6 mutations are not due to alterations in PDX1+/NKX6.1+ PPs but instead result from cellular apoptosis and downregulation of pancreatic endocrine genes.

DATA AVAILABILITY

RNA-seq datasets have been deposited in the Zenodo repository with accession link (DOI: https://doi.org/10.5281/zenodo.10656891 ).

Association between 25(OH) vitamin D and multiple sclerosis: cohort, shared genetics, and Causality

BACKGROUND

Multiple Sclerosis (MS), an autoimmune disorder causing demyelination and neurological damage, has been linked to 25-hydroxyvitamin D (25OHD) levels, suggesting its role in immune response and MS onset. This study used GWAS datasets to investigate genetic associations between 25OHD and MS.

METHODS

We utilized a large-scale prospective cohort to evaluate serum 25OHD levels and MS risk. Linkage Disequilibrium Score Regression (LDSC) assessed genetic correlations between 25OHD levels and MS. Cross-trait genome-wide pleiotropy analysis revealed shared genetic loci. MAGMA analysis identified pleiotropic genes, enriched tissues, and gene sets. Stratified LDSC estimated tissue-specific and cell-specific heritability enrichment, and multi-trait co-localization analysis identified shared immune cell subsets. Bidirectional Mendelian Randomization (MR) assessed the causal association between 25OHD and MS risk.

RESULTS

The observational study found a nonlinear relationship between 25OHD levels and MS risk, with the lowest quartile showing significant risk elevation. Our findings revealed shared genetic structure between 25OHD levels and MS, suggesting a common biological pathway involving immune function and CNS integrity. We found 24 independent loci shared between 25OHD levels and MS risk, enriched in brain tissues and involved in pathways like LDL, HDL, and TG metabolism. Four loci (6p24.3, 6p22.2, 12q14.1, and 19p13.2) had strong co-localization evidence, with mapped genes as potential drug targets. Bidirectional MR analysis supported a causal effect of 25OHD levels on MS risk, suggesting 25OHD supplementation could modulate MS risk.

CONCLUSION

This study reveals the complex relationship between 25OHD levels and MS, indicating that higher levels are not always advantageous and recommending moderation in supplementation. We identified SMARCA4 as a potential therapeutic target and detailed key pathways influencing this interaction.

Unraveling the heart-brain axis: shared genetic mechanisms in cardiovascular diseases and Schizophrenia

The comorbidity between cardiovascular diseases (CVD) and schizophrenia (SCZ) has attracted widespread attention from researchers, with shared genetic causes potentially providing important insights into their association. This study conducted a comprehensive analysis of genetic data from 17 types of CVD and SCZ using genome-wide multi-trait association studies (GWAS), employing statistical methods such as LDSC, MTAG, LAVA, and bidirectional Mendelian randomization to explore global and local genetic correlations and identify pleiotropic single nucleotide variants (SNVs). The analysis revealed a significant genetic correlation between CVD and SCZ, identifying 842 potential pleiotropic single nucleotide variants (SNVs) and multiple associated biological pathways. Notably, genes such as TRIM27, CENPM, and MYH7B played critical roles in the shared genetic variations of both types of diseases. This study reveals the complex genetic relationship between CVD and SCZ, highlighting potential shared biological mechanisms involving immune responses, metabolic factors, and neurodevelopmental processes, thereby providing new directions for future interventions and treatments.

The shared genetic landscape of polycystic ovary syndrome and breast cancer: convergence on ER + breast cancer but not ER- breast cancer

BACKGROUND

The clinically high comorbidity between polycystic ovary syndrome (PCOS) and breast cancer (BC) has been extensively reported. However, limited knowledge exists regarding their shared genetic basis and underlying mechanisms.

METHOD

Leveraging summary statistics from the largest genome-wide association studies (GWASs) to date, we conducted a comprehensive genome-wide cross-trait analysis of PCOS and BC. A variety of genetic statistical methods were employed to uncover potential shared genetic causes.

RESULTS

Our analysis revealed genetic overlap between the three trait pairs. After partitioning the genome into 2,495 independent regions, we identified two loci, chr8: 75,011,700-76,295,483 and chr17: 6,305,079-7,264,458, with significant localized genetic correlations. Pleiotropic analysis under a composite null hypothesis identified 1,183 significant pleiotropic single nucleotide polymorphisms (SNPs) across three trait pairs. FUMA mapped 26 pleiotropic loci, with regions 16q12.2 and 6q25.1 duplicated across all three trait pairs, while COLOC detected three loci with colocalization evidence. Gene-based analysis identified 23 unique candidate pleiotropic genes, including the FTO shared by all trait pairs, as well as SER1, RALB, and others in two trait pairs. Pathway enrichment analysis further highlighted key biological pathways, primarily involving the significant biological pathways were the metabolism of regulation of autophagy, regulation of cellular catabolic process, and positive regulation of catabolic process. Latent Heritable Confounder Mendelian randomization (LHC-MR) supported a positive causal relationship between PCOS and both BCALL and ERPBC but not with ERNBC.

CONCLUSION

In conclusion, our genome-wide cross-trait analysis identified a shared genetic basis between PCOS and BC, specific identical genetic mechanisms and causality between PCOS and various BC subtypes, which could better explains the genetics of the co-morbidity of PCOS and ERPBC rather than PCOS and ERNBC. These findings provide new insights into the biological mechanisms underlying the co-morbidity of these two complex diseases, which have important implications for clinical disease intervention, treatment, and improved prognosis.

Shared genetic architecture between COVID-19 and irritable bowel syndrome: a large-scale genome-wide cross-trait analysis

BACKGROUND

It has been reported that COVID-19 patients have an increased risk of developing IBS; however, the underlying genetic mechanisms of these associations remain largely unknown. The aim of this study was to investigate potential shared SNPs, genes, proteins, and biological pathways between COVID-19 and IBS by assessing pairwise genetic correlations and cross-trait genetic analysis.

MATERIALS AND METHODS

We assessed the genetic correlation between three COVID-19 phenotypes and IBS using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) methods. Two different sources of IBS data were combined using METAL, and the Multi-trait analysis of GWAS (MTAG) method was applied for multi-trait analysis to enhance statistical robustness and discover new genetic associations. Independent risk loci were examined using genome-wide complex trait analysis (GCTA)-conditional and joint analysis (COJO), multi-marker analysis of genomic annotation (MAGMA), and functional mapping and annotation (FUMA), integrating various QTL information and methods to further identify risk genes and proteins. Gene set variation analysis (GSVA) was employed to compute pleiotropic gene scores, and combined with immune infiltration algorithms, IBS patients were categorized into high and low immune infiltration groups.

RESULTS

We found a positive genetic correlation between COVID-19 infection, COVID-19 hospitalization, and IBS. Subsequent multi-trait analysis identified nine significantly associated genomic loci. Among these, eight genetic variants were closely related to the comorbidity of IBS and COVID-19. The study also highlighted four genes and 231 proteins associated with the susceptibility to IBS identified through various analytical strategies and a stratification approach for IBS risk populations.

CONCLUSIONS

Our study reveals a shared genetic architecture between these two diseases, providing new insights into potential biological mechanisms and laying the groundwork for more effective interventions.

The shared genetic etiology of antisocial behavior and psychiatric disorders: Insights from pleiotropy and causality analysis

BACKGROUND

Antisocial behavior (ASB) infringes on the rights of others and significantly disrupts social order. Studies have shown that ASB is phenotypically associated with various psychiatric disorders. However, these studies often neglected the importance of genetic foundations.

METHODS

This study utilized genome-wide association studies and pleiotropy analysis to explore the genetic correlation between ASB and psychiatric disorders. Linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) methods were employed to assess genetic correlations, and the PLACO method was used for pleiotropy analysis. Functional annotation and biological pathway analysis of identified pleiotropic genes were performed using enrichment analysis. Furthermore, Mendelian randomization (MR) analysis was conducted to validate these causal relationships.

RESULTS

LDSC and HDL analysis showed that significant positive genetic correlations were between ASB and attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), major depressive disorder (MDD), and post-traumatic stress disorder (PTSD). Multiple potential pleiotropic genetic loci were identified, particularly the FOXP2 and MDFIC genes located at the 7q31.1 locus. Enrichment analysis showed that these pleiotropic genes are highly expressed in several brain regions (such as the hypothalamus, cerebellar hemisphere, cortex, and amygdala) and immune-related cells. MR analysis further confirmed the causal effects ADHD, SCZ, and MDD on ASB risk.

CONCLUSION

This study reveals significant genetic correlations and potential causal mechanisms between ASB and various psychiatric disorders. The MR analysis confirmed the causal effects of psychiatric disorders on ASB. These findings deepen our understanding of the genetic architecture of psychiatric disorders and ASB.

Genetic architectures of the human hippocampus and those involved in neuropsychiatric traits

BACKGROUND

The hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysis of hippocampal substructures and their genetic correlations across a wide range of neuropsychiatric traits remains underexplored. Given the hippocampus's high heritability, considering hippocampal and subfield volumes (HASV) as endophenotypes for neuropsychiatric conditions is essential.

METHODS

We analyzed MRI-derived volumetric data of hippocampal and subfield structures from 41,525 UK Biobank participants. Genome-wide association studies (GWAS) on 24 HASV traits were conducted, followed by genetic correlation, overlap, and Mendelian randomization (MR) analyses with 10 common neuropsychiatric traits. Polygenic risk scores (PRS) based on HASV traits were also evaluated for predicting these traits.

RESULTS

Our analysis identified 352 independent genetic variants surpassing a significance threshold of 2.1 × 10-9 within the 24 HASV traits, located across 93 chromosomal regions. Notably, the regions 12q14.3, 17q21.31, 12q24.22, 6q21, 9q33.1, 6q25.1, and 2q24.2 were found to influence multiple HASVs. Gene set analysis revealed enrichment of neural differentiation and signaling pathways, as well as protein binding and degradation. Of 240 HASV-neuropsychiatric trait pairs, 75 demonstrated significant genetic correlations (P < 0.05/240), revealing 433 pleiotropic loci. Particularly, genes like ACBD4, ARHGAP27, KANSL1, MAPT, ARL17A, and ARL17B were involved in over 50 HASV-neuropsychiatric pairs. Leveraging Mendelian randomization analysis, we further confirmed that atrophy in the left hippocampus, right hippocampus, right hippocampal body, and right CA1-3 region were associated with an increased risk of developing Parkinson's disease (PD). Furthermore, PRS for all four HASVs were significantly linked to a higher risk of Parkinson's disease (PD), with the highest hazard ratio (HR) of 1.30 (95% CI 1.18-1.43, P = 6.15 × 10⁻⁸) for right hippocampal volume.

CONCLUSIONS

These findings highlight the extensive distribution of pleiotropic genetic determinants between HASVs and neuropsychiatric traits. Moreover, they suggest a significant potential for effectively managing and intervening in these diseases during their early stages.

Revealing genetic causality between blood-based biomarkers and major depression in east Asian ancestry

Introduction

Major Depression (MD) is a common mental disorder. In East Asian ancestry, the association, causality, and shared genetic basis between blood-based biomarkers and MD remain unclear.

Methods

We investigated the relationships between blood-based biomarkers and MD through a cross-sectional study and Mendelian randomization (MR) analysis. Cross-trait analysis and enrichment analyses were used to highlight the shared genetic determinants and biological pathways. We conducted summary data-based MR to identify shared genes, which were then validated using a transcriptome dataset from drug-naïve patients with MD.

Results

In the cross-sectional study, C-Reactive Protein showed the significantly positive correlation with depressive symptoms, while hematocrit, hemoglobin, and uric acid exhibited significantly negative correlations. In MR analysis, basophil count (BASO) and low-density lipoprotein cholesterol (LDLc) had a significant causal effect on MD. The enrichment analysis indicated a significant role of inflammatory cytokines and oxidative stress. The shared genes MFN2, FAM55C, GCC2, and SCAPER were validated, with MFN2 identified as a pleiotropic gene involved in MD, BASO, and LDLc.

Discussion

This study highlighted that BASO and LDLc have a causal effect on MD in East Asian ancestry. The pathological mechanisms of MD are related not only to inflammatory cytokines and oxidative stress but also to down regulation of MFN2 expression and mitochondrial dysfunction.

Shared genetic risk between major orofacial cleft phenotypes in an African population

Nonsyndromic orofacial clefts (NSOFCs) represent a large proportion (70%-80%) of all OFCs. They can be broadly categorized into nonsyndromic cleft lip with or without cleft palate (NSCL/P) and nonsyndromic cleft palate only (NSCPO). Although NSCL/P and NSCPO are considered etiologically distinct, recent evidence suggests the presence of shared genetic risks. Thus, we investigated the genetic overlap between NSCL/P and NSCPO using African genome-wide association study (GWAS) data on NSOFCs. These data consist of 814 NSCL/P, 205 NSCPO cases, and 2159 unrelated controls. We generated common single-nucleotide variants (SNVs) association summary statistics separately for each phenotype (NSCL/P and NSCPO) under an additive genetic model. Subsequently, we employed the pleiotropic analysis under the composite null (PLACO) method to test for genetic overlap. Our analysis identified two loci with genome-wide significance (rs181737795 [p = 2.58E-08] and rs2221169 [p = 4.5E-08]) and one locus with marginal significance (rs187523265 [p = 5.22E-08]). Using mouse transcriptomics data and information from genetic phenotype databases, we identified MDN1, MAP3k7, KMT2A, ARCN1, and VADC2 as top candidate genes for the associated SNVs. These findings enhance our understanding of genetic variants associated with NSOFCs and identify potential candidate genes for further exploration.

Characterizing Common and Rare Variations in Nontraditional Glycemic Biomarkers Using Multivariate Approaches on Multiancestry ARIC Study

Genetic studies of nontraditional glycemic biomarkers, glycated albumin and fructosamine, can shed light on unknown aspects of type 2 diabetes genetics and biology. We performed a multiphenotype genome-wide association study of glycated albumin and fructosamine from 7,395 White and 2,016 Black participants in the Atherosclerosis Risk in Communities (ARIC) study on common variants from genotyped/imputed data. We discovered two genome-wide significant loci, one mapping to a known type 2 diabetes gene (ARAP1/STARD10) and another mapping to a novel region (UGT1A complex of genes), using multiomics gene-mapping strategies in diabetes-relevant tissues. We identified additional loci that were ancestry- and sex-specific (e.g., PRKCA in African ancestry, FCGRT in European ancestry, TEX29 in males). Further, we implemented multiphenotype gene-burden tests on whole-exome sequence data from 6,590 White and 2,309 Black ARIC participants. Ten variant sets annotated to genes across different variant aggregation strategies were exome-wide significant only in multiancestry analysis, of which CD1D, EGFL7/AGPAT2, and MIR126 had notable enrichment of rare predicted loss of function variants in African ancestry despite smaller sample sizes. Overall, 8 of 14 discovered loci and genes were implicated to influence these biomarkers via glycemic pathways, and most of them were not previously implicated in studies of type 2 diabetes. This study illustrates improved locus discovery and potential effector gene discovery by leveraging joint patterns of related biomarkers across the entire allele frequency spectrum in multiancestry analysis. Future investigation of the loci and genes potentially acting through glycemic pathways may help us better understand the risk of developing type 2 diabetes.

ARTICLE HIGHLIGHTS

Genetic interactions and pleiotropy in metabolic diseases: Insights from a comprehensive GWAS analysis

This study offers insights into the genetic and biological connections between nine common metabolic diseases using data from genome-wide association studies. Our goal is to unravel the genetic interactions and biological pathways of these complex diseases, enhancing our understanding of their genetic architecture. We employed a range of advanced analytical techniques to explore the genetic correlations and shared genetic variants of these diseases. These methods include Linked Disequilibrium Score Regression, High-Definition Likelihood (HDL), genetic analysis combining multiplicity and annotation (GPA), two-sample Mendelian randomization analyses, analysis under the multiplicity-complex null hypothesis (PLACO), and Functional mapping and annotation of genetic associations (FUMA). Additionally, Bayesian co-localization analyses were used to examine associations of specific loci across traits. Our study discovered significant genomic correlations and shared loci, indicating complex genetic interactions among these metabolic diseases. We found several shared single nucleotide variants and risk loci, notably highlighting the role of the immune system and endocrine pathways in these diseases. Particularly, rs2476601 and its associated gene PTPN22 appear to play a crucial role in the connection between type 2 diabetes mellitus, hypothyroidism/mucous oedema and hypoglycaemia. These findings enhance our understanding of the genetic underpinnings of these diseases and open new potential avenues for targeted therapeutic and preventive strategies. The results underscore the importance of considering pleiotropic effects in deciphering the genetic architecture of complex diseases, especially metabolic ones.

Mitochondrial related variants associated with cardiovascular traits

Introduction

Cardiovascular disease (CVD) is responsible for over 30% of mortality worldwide. CVD arises from the complex influence of molecular, clinical, social, and environmental factors. Despite the growing number of autosomal genetic variants contributing to CVD, the cause of most CVDs is still unclear. Mitochondria are crucial in the pathophysiology, development and progression of CVDs; the impact of mitochondrial DNA (mtDNA) variants and mitochondrial haplogroups in the context of CVD has recently been highlighted.

Aims

We investigated the role of genetic variants in both mtDNA and nuclear-encoded mitochondrial genes (NEMG) in CVD, including coronary artery disease (CAD), hypertension, and serum lipids in the UK Biobank, with sub-group analysis for diabetes.

Methods

We investigated 371,542 variants in 2,527 NEMG, along with 192 variants in 32 mitochondrial genes in 381,994 participants of the UK Biobank, stratifying by presence of diabetes.

Results

Mitochondrial variants showed associations with CVD, hypertension, and serum lipids. Mitochondrial haplogroup J was associated with CAD and serum lipids, whereas mitochondrial haplogroups T and U were associated with CVD. Among NEMG, variants within Nitric Oxide Synthase 3 (NOS3) showed associations with CVD, CAD, hypertension, as well as diastolic and systolic blood pressure. We also identified Translocase Of Outer Mitochondrial Membrane 40 (TOMM40) variants associated with CAD; Solute carrier family 22 member 2 (SLC22A2) variants associated with CAD and CVD; and HLA-DQA1 variants associated with hypertension. Variants within these three genes were also associated with serum lipids.

Conclusion

Our study demonstrates the relevance of mitochondrial related variants in the context of CVD. We have linked mitochondrial haplogroup U to CVD, confirmed association of mitochondrial haplogroups J and T with CVD and proposed new markers of hypertension and serum lipids in the context of diabetes. We have also evidenced connections between the etiological pathways underlying CVDs, blood pressure and serum lipids, placing NOS3, SLC22A2, TOMM40 and HLA-DQA1 genes as common nexuses.

Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants

Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value < 2.2 × 10 - 16 ). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.

RFX6 haploinsufficiency predisposes to diabetes through impaired beta cell function

AIMS/HYPOTHESIS

Regulatory factor X 6 (RFX6) is crucial for pancreatic endocrine development and differentiation. The RFX6 variant p.His293LeufsTer7 is significantly enriched in the Finnish population, with almost 1:250 individuals as a carrier. Importantly, the FinnGen study indicates a high predisposition for heterozygous carriers to develop type 2 and gestational diabetes. However, the precise mechanism of this predisposition remains unknown.

METHODS

To understand the role of this variant in beta cell development and function, we used CRISPR technology to generate allelic series of pluripotent stem cells. We created two isogenic stem cell models: a human embryonic stem cell model; and a patient-derived stem cell model. Both were differentiated into pancreatic islet lineages (stem-cell-derived islets, SC-islets), followed by implantation in immunocompromised NOD-SCID-Gamma mice.

RESULTS

Stem cell models of the homozygous variant RFX6-/- predictably failed to generate insulin-secreting pancreatic beta cells, mirroring the phenotype observed in Mitchell-Riley syndrome. Notably, at the pancreatic endocrine stage, there was an upregulation of precursor markers NEUROG3 and SOX9, accompanied by increased apoptosis. Intriguingly, heterozygous RFX6+/- SC-islets exhibited RFX6 haploinsufficiency (54.2% reduction in protein expression), associated with reduced beta cell maturation markers, altered calcium signalling and impaired insulin secretion (62% and 54% reduction in basal and high glucose conditions, respectively). However, RFX6 haploinsufficiency did not have an impact on beta cell number or insulin content. The reduced insulin secretion persisted after in vivo implantation in mice, aligning with the increased risk of variant carriers to develop diabetes.

CONCLUSIONS/INTERPRETATION

Our allelic series isogenic SC-islet models represent a powerful tool to elucidate specific aetiologies of diabetes in humans, enabling the sensitive detection of aberrations in both beta cell development and function. We highlight the critical role of RFX6 in augmenting and maintaining the pancreatic progenitor pool, with an endocrine roadblock and increased cell death upon its loss. We demonstrate that RFX6 haploinsufficiency does not affect beta cell number or insulin content but does impair function, predisposing heterozygous carriers of loss-of-function variants to diabetes.

DATA AVAILABILITY

Ultra-deep bulk RNA-seq data for pancreatic differentiation stages 3, 5 and 7 of H1 RFX6 genotypes are deposited in the Gene Expression Omnibus database with accession code GSE234289. Original western blot images are deposited at Mendeley ( https://data.mendeley.com/datasets/g75drr3mgw/2 ).

Shared genetic correlations between kidney diseases and sepsis

Background

Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated.

Methods

Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis.

Results

In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes PPP2R3A for BUN, VAMP8 for UACR, DOCK7 for creatinine, and HIBADH for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them.

Conclusions

This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.

Shared and Unique Genetic Links between Neuroticism and Gastrointestinal Tract Diseases

Objective

Association between neuroticism and gastrointestinal tract (GIT) diseases may not be attributable to the genetic overlaps between neuroticism and psychiatric disorders. We aim to explore the genetic links and mechanisms of neuroticism and GIT diseases.

Materials and Methods

We obtained European genome-wide association data of neuroticism (n = 390,278) or subclusters (depressed, n = 357,957; worry, n = 348,219) and six GIT diseases: gastroesophageal reflux disease (GERD, n = 456,327), inflammatory bowel disease (IBD, n = 456,327), peptic ulcer disease (PUD, n = 456,327), irritable bowel syndrome (IBS, n = 486,601), Crohn's disease (CD, n = 20,883), and ulcerative colitis (UC, n = 21,895). We performed genetic correlation analysis (high-definition likelihood method and cross-trait linkage disequilibrium score regression), pairwise pleiotropic analysis, single nucleic acid polymorphism annotation, Bayesian colocalization, gene-level analysis, transcriptome-wide association analysis, and gene set enrichment analysis.

Results

Neuroticism and its subclusters are associated with most GIT diseases (15 of 18 trait-pairs). GERD and PUD were highly correlated with depressed affect. We identified pleiotropic loci 11q23.2 (mapped gene: NCAM1/DRD2) and 18q12.2 (mapped gene: CELF4) in neuroticism and IBS/GERD, supporting the genetic overlap between neuroticism and depression. We found that 16q12.1 (mapped gene: NKD1/ZNF423/NOD2) and 2q37.1 (mapped gene: ATG16L1/SP140) are only highlighted in depressed/neuroticism CD, revealing pleiotropic loci with dissimilarities between neuroticism and different GIT diseases. MR analysis suggested that genetic liability to neuroticism is associated with increased risks of IBS, PUD, and GERD.

Conclusion

Our findings document the genetic links between neuroticism and six GIT diseases, highlighting the genetic overlaps and heterogeneity between neuroticism and psychiatric disorders in the context of gastrointestinal disorders. Both the shared and unique pleiotropic loci identified between neuroticism and different GIT diseases could facilitate mechanistic understandings and may stimulate further translational implications.

Shared genetic links between hypothyroidism and psychiatric disorders: evidence from a comprehensive genetic analysis

Background

Epidemiologic studies have suggested co-morbidity between hypothyroidism and psychiatric disorders. However, the shared genetic etiology and causal relationship between them remain currently unclear.

Methods

We assessed the genetic correlations between hypothyroidism and psychiatric disorders [anxiety disorders (ANX), schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP)] using summary association statistics from genome-wide association studies (GWAS). Two disease-associated pleiotropic risk loci and genes were identified, and pathway enrichment, tissue enrichment, and other analyses were performed to determine their specific functions. Furthermore, we explored the causal relationship between them through Mendelian randomization (MR) analysis.

Results

We found significant genetic correlations between hypothyroidism with ANX, SCZ, and MDD, both in the Linkage disequilibrium score regression (LDSC) approach and the high-definition likelihood (HDL) approach. Meanwhile, the strongest correlation was observed between hypothyroidism and MDD (LDSC: rg=0.264, P=7.35×10-12; HDL: rg=0.304, P=4.14×10-17). We also determined a significant genetic correlation between MDD with free thyroxine (FT4) and thyroid-stimulating hormone (TSH) levels. A total of 30 pleiotropic risk loci were identified between hypothyroidism and psychiatric disorders, of which the 15q14 locus was identified in both ANX and SCZ (P values are 6.59×10-11 and 2.10×10-12, respectively) and the 6p22.1 locus was identified in both MDD and SCZ (P values are 1.05×10-8 and 5.75×10-14, respectively). Sixteen pleiotropic risk loci were identified between MDD and indicators of thyroid function, of which, four loci associated with MDD (1p32.3, 6p22.1, 10q21.1, 11q13.4) were identified in both FT4 normal level and Hypothyroidism. Further, 79 pleiotropic genes were identified using Magma gene analysis (P<0.05/18776 = 2.66×10-6). Tissue-specific enrichment analysis revealed that these genes were highly enriched into six brain-related tissues. The pathway analysis mainly involved nucleosome assembly and lipoprotein particles. Finally, our two-sample MR analysis showed a significant causal effect of MDD on the increased risk of hypothyroidism, and BIP may reduce TSH normal levels.

Conclusions

Our findings not only provided evidence of a shared genetic etiology between hypothyroidism and psychiatric disorders, but also provided insights into the causal relationships and biological mechanisms that underlie their relationship. These findings contribute to a better understanding of the pleiotropy between hypothyroidism and psychiatric disorders, while having important implications for intervention and treatment goals for these disorders.

Genome-wide association analysis reveals potential genetic correlation and causality between circulating inflammatory proteins and amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS), a fatal neurodegenerative disease, continues to elude complete comprehension of its pathological underpinnings. Recent focus on inflammation in ALS pathogenesis prompts this investigation into the genetic correlation and potential causal relationships between circulating inflammatory proteins and ALS.

METHODS

Genome-wide association study (GWAS) data encompassing 91 circulating inflammatory protein measures from 14,824 individuals of European ancestry, alongside records from 27,205 ALS cases and 110,881 controls, were employed. Assessment of genetic correlation and overlap utilized LD score regression (LDSC), high-definition likelihood (HDL), and genetic analysis integrating pleiotropy and annotation (GPA) methodologies. Identification of shared genetic loci involved pleiotropy analysis, functional mapping and annotation (FUMA), and co-localization analysis. Finally, Mendelian randomization was applied to probe causal relationships between inflammatory proteins and ALS.

RESULTS

Our investigation revealed significant genetic correlation and overlap between ALS and various inflammatory proteins, including C-C motif chemokine 28, Interleukin-18, C-X-C motif chemokine 1, and Leukemia inhibitory factor receptor (LIFR). Pleiotropy analysis uncovered shared variations at specific genetic loci, some of which bore potential harm. Mendelian randomization analysis suggested that alterations in specific inflammatory protein levels, notably LIFR, could impact ALS risk.

CONCLUSIONS

Our findings uncover a genetic correlation between certain circulating inflammatory proteins and ALS, suggesting their possible causal involvement in ALS pathogenesis. Moreover, the identification of LIFR as a crucial protein may yield new insights into ALS pathomechanisms and offer a promising avenue for therapeutic interventions. These discoveries provide novel perspectives for advancing the comprehension of ALS pathophysiology and exploring potential therapeutic avenues.

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.

Muscle-origin creatinine-cystatin C ratio is an osteoporosis marker in individuals with normal renal function: evidence from observational and Mendelian randomization analysis

Background

Creatinine-cystatin C ratio (CCR) has been demonstrated as an objective marker of sarcopenia in clinical conditions but has not been evaluated as an osteoporosis marker in individuals with normal renal function.

Methods

We selected 271,831 participants with normal renal function from UK Biobank cohort. Multivariable linear/logistic regression and Cox proportional hazards model were used to investigate the phenotypic relationship between CCR and osteoporosis in total subjects and gender-stratified subjects. Based on the genome-wide association study (GWAS) data, linkage disequilibrium regression (LDSC) and Mendelian randomization (MR) analysis were performed to reveal the shared genetic correlations and infer the causal effects, respectively.

Results

Amongst total subjects and gender-stratified subjects, serum CCR was positively associated with eBMD after adjusting for potential risk factors (all P<0.05). The multivariable logistic regression model showed that the decrease in CCR was associated with a higher risk of osteoporosis/fracture in all models (all P<0.05). In the multivariable Cox regression analysis with adjustment for potential confounders, reduced CCR is associated with the incidence of osteoporosis and fracture in both total subjects and gender-stratified subjects (all P<0.05). A significant non-linear dose-response was observed between CCR and osteoporosis/fracture risk (P non-linearity < 0.05). LDSC found no significant shared genetic effects by them, but PLACO identified 42 pleiotropic SNPs shared by CCR and fracture (P<5×10-8). MR analyses indicated the causal effect from CCR to osteoporosis/fracture.

Conclusions

Reduced CCR predicted increased risks of osteoporosis/fracture, and significant causal effects support their associations. These findings indicated that the muscle-origin serum CCR was a potential biomarker to assess the risks of osteoporosis and fracture.

Genetic effects and causal association analyses of 14 common conditions/diseases in multimorbidity patterns

BACKGROUND

Multimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown.

METHODS

Summary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases.

RESULTS

LDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (P<0.05/91 = 5.49E-04). Significant pleiotropic SNPs were detected for 11 pairs of correlated conditions/diseases. The corresponding pleiotropic genes were differentially expressed in the brain, nerves, heart, and blood vessels and enriched in gluconeogenesis and drug metabolism, biotransformation, and neurons. Comprehensive causal analyses showed strong causality between hypertension, stroke, and high cholesterol, which drive the development of multiple diseases.

CONCLUSIONS

This study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.

Exploring the interconnected between type 2 diabetes mellitus and nonalcoholic fatty liver disease: Genetic correlation and Mendelian randomization analysis

Epidemiological and clinical studies have indicated a higher risk of nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM), implying a potentially shared genetic etiology, which is still less explored. Genetic links between T2DM and NAFLD were assessed using linkage disequilibrium score regression and pleiotropic analysis under composite null hypothesis. European GWAS data have identified shared genes, whereas SNP-level pleiotropic analysis under composite null hypothesis has explored pleiotropic loci. generalized gene-set analysis of GWAS data determines pleiotropic pathways and tissue enrichment using eQTL mapping to identify associated genes. Mendelian randomization analysis was used to investigate the causal relationship between NAFLD and T2DM. Linkage disequilibrium score regression analysis revealed a strong genetic correlation between T2DM and NAFLD, and identified 24 pleiotropic loci. These single-nucleotide polymorphisms are primarily involved in biosynthetic regulation, RNA biosynthesis, and pancreatic development. generalized gene-set analysis of GWAS data analysis revealed significant enrichment in multiple brain tissues. Gene mapping using these 3 methods led to the identification of numerous pleiotropic genes, with differences observed in liver and kidney tissues. These genes were mainly enriched in pancreas, brain, and liver tissues. The Mendelian randomization method indicated a significantly positive unidirectional causal relationship between T2DM and NAFLD. Our study identified a shared genetic structure between NAFLD and T2DM, providing new insights into the genetic pathogenesis and mechanisms of NAFLD and T2DM comorbidities.

Exploring the Common Genetic Underpinnings of Chronic Pulmonary Disease and Esophageal Carcinoma Susceptibility

Background: Pulmonary diseases and esophageal cancer are highly prevalent conditions with rising incidence worldwide. Prior evidence supports shared environmental and behavioral factors, but less is known regarding potential genetic links underlying this comorbidity. This study aimed to elucidate the complex genetic relationship between chronic lung diseases and esophageal cancer risk. Methods: Linkage disequilibrium score regression assessed the genetic correlation between esophageal cancer and asthma, COPD, and idiopathic pulmonary fibrosis leveraging extensive GWAS datasets. Pleiotropic analysis, gene-set enrichment, eQTL mapping, and mendelian randomization causality analyses were then conducted to identify specific shared genetic variants, enriched pathways, causal relationships and gene regulatory mechanisms connecting lung disease and cancer susceptibility. Results: Significant genetic correlations were observed between esophageal cancer and both COPD and asthma, but not idiopathic pulmonary fibrosis. Further analyses identified 13 pleiotropic loci and 6 shared genes including CHRNA4, ERBB3, and SMAD3, as well as pathways related to immune function. eQTL integration highlighted 53 genes like SOCS1, FGF2, and CHRNA5 with tissue-specific regulatory effects on disease risk. Bidirectional relationships were noted, whereby genetic predisposition to asthma and COPD increased esophageal cancer risk, while cancer liability reciprocally raised pulmonary fibrosis risk. Conclusions: These genomic analyses provide initial evidence that shared genetic factors may underpin the comorbidity between lung conditions and esophageal malignancy. The genes and pathways identified offer insights into biological mechanisms linking both diseases, aiding future screening, prevention and therapeutic efforts to mitigate this growing comorbidity burden.

Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach

Background

Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present.

Methods

The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene.

Results

We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis.

Conclusions

In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

Shared genetic architecture and causal relationship between liver and heart disease

This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.