Multi-marker Analysis Of GenoMic Annotation Research Articles

Genetic overlap between major depressive disorder and obstructive sleep apnea

ObjectiveObservational studies have frequently shown a co-occurrence of psychiatric disorders and Obstructive sleep apnea (OSA), with major depressive disorder (MDD) being a prevalent psychiatric disorder. This study aims to investigate the genetic overlap between MDD and OSA to explore their underlying pathological mechanisms.MethodsLeveraging the extensive and recent GWAS for OSA and MDD, we conducted genetic correlation analyses utilizing Linkage disequilibrium score regression (LDSC), re-evaluated their pleiotropic Single-nucleotide polymorphisms (SNP) with Cross-Phenotype Association (CPASSOC) and Colocalization (COLOC), investigated the overlap at the gene level using physical annotations and Multi-marker Analysis of GenoMic Annotation (MAGMA), and finally employed Mendelian randomization (MR) to assess potential causal relationships between the two disorders.ResultsUpon our investigation, we established that MDD and OSA exhibit high heritability (h2MDD=0.02, h2OSA=0.04) alongside a significant genetic correlation (rg=0.31, P= 1.42E-23). Utilizing CPASSOC, we identified 397 pleiotropic SNPs, associable with 45 loci, two of which share common genetic fragments with a pleiotropic role. Furthermore, the MAGMA study uncovered a total of 154 pleiotropic genes capable of influencing multiple brain regions. Lastly, leveraging MR analysis, we concluded that MDD heightens the risk of developing OSA (P=3. 10E-04, OR (95%CI):1.28(1.12~ 1.47)).ConclusionIn summary, our study identified PCLO as a common gene between OSA and MDD and provided evidence that MDD causally contributes to the development of OSA. These insights enhance our understanding of the shared mechanisms underlying the comorbidity of these conditions.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

Does PhenoAge acceleration impact adverse outcomes following major surgery (rehospitalization or death)? A genome-wide association study and mendelian randomisation

Abstract Background Biological aging reflects the state of an individual's cellular and molecular function, and is distinct from chronological age; which reflects the passage of time[1]. Validated measures of biological aging, such as Phenotypic aging (PhenoAge) estimated from nine routine clinical biomarkers, have been associated with increased risk of age-related diseases and mortality in cohort studies[2]. However, the potential causal relationship between them remains unclear. In this study, we aim to identify genetic variants associated with PhenoAge Acceleration (PhenoAgeAccl) and adverse outcome after surgery (rehospitalisation or death within 365-days] and assess the causal relationship between these two phenotypes using Mendelian Randomization framework. Objectives -Perform a genome-wide association study(GWAS) on adverse outcomes following major surgery. -Assess the potential causal relationship between PhenoAgeAccl and adverse outcomes following major surgery using Mendelian randomization analysis. Methods We conducted a GWAS via plink using participants from the UKBiobank (Application 77596) to generate summary statistics for 6509 participants who had undergone major surgery within 365 days of their baseline assessment date(3). For quality control, SNPs were excluded if they didn't meet the following criteria: (1) imputation information score <0.3 (2) minor allele frequency <1%, (3) Hardy–Weinberg equilibrium test p-value significant at the Bonferroni-corrected level. Summary statistics for PhenoAgeAccl was obtained from a previous GWAS of European descents[4]. Gene enrichment analysis was performed using Multi-marker Analysis of GenoMic Annotation (MAGMA) in Functional Mapping and Annotation (FUMA). Then, genetic instruments for PhenoAgeAccl and adverse outcomes following surgery were identified to conduct a summary-based Mendelian Randomisation [Assumptions are listed in Figure 1]. Results GWAS for adverse outcomes following major surgery identified 3 lead SNPs (rs1360618, rs11848297, rs7978) [Figure 2]. Gene-set analysis showed enrichment in the immune system, cell migration, fear response, and iron metabolism. For PhenoAge Acceleration, gene-set analysis showed enrichment in immune system, and cell signalling pathways. 17 SNPs were selected which were used for the Two-Sample MR. We found no causal relationship, as the beta coefficient for Inverse variance weighted was -0.308 [standard error - 0.075, p=0.68]. For every 5-year increase in PhenoAge compared to chronological age, the risk of emergency re-hospitalisation or death increased by 21% (HR 1.16 – 1.3, p < 0.001). Conclusion Gene-set analysis for adverse outcomes following major surgery showed enrichment in immune system, iron metabolism, and fear response which have all recently been associated with mortality post-surgery in cohort studies(4,5). No causal relationship between PhenoAgeAccel and mortality post-surgery was established.Mendelian Randomisation assumptionsManhattan plot adverse-outcomes post-sur

Cross-Tissue Regulatory Network Analyses Reveal Novel Susceptibility Genes and Potential Mechanisms for Endometriosis

Endometriosis (EMT) is a common gynecological disease with a strong genetic component, while its precise etiology remains elusive. This study aims to integrate transcriptome-wide association study (TWAS), Mendelian randomization (MR), and bioinformatics analyses to reveal novel putatively causal genes and potential mechanisms. We obtained summary-level data of the Genotype-Tissue Expression Project (GTEx), v8 expression quantitative loci (eQTL) data, and the genome-wide association study (GWAS) data of EMT and its subtypes from the R11 release results of the FinnGen consortium for analysis. GWAS data of modifiable risk factors were collected from IEU Open GWAS. Cross-tissue TWAS analyses were performed using the unified test for molecular signature (UTMOST), while functional summary-based imputation (FUSION) was employed for single-tissue TWAS analyses. Furthermore, we also conducted multi-marker analysis of genomic annotation (MAGMA) analyses to validate the significant associations. Subsequent Mendelian randomization (MR) and colocalization analysis elucidated the causal associations between the identified genes across various tissues and EMT. To further delve into mechanisms, two-sample network MR analyses were conducted. At last, bioinformatics analyses were employed to enhance our understanding of the functional implications and expression patterns of these identified genes. For EMT, 22 significant gene signals were identified by UTMOST, 615 by FUSION, and 354 by MAGMA. Ultimately, six genes, including CISD2, EFRB, GREB1, IMMT, SULT1E1, and UBE2D3, were identified as candidate susceptibility genes for EMT. Through similar procedures, we identified GREB1, IL1A, and SULT1E1 for EMT of the ovary, and we identified GREB1 for EMT of the pelvic peritoneum, EMT of rectovaginal septum and vagina, and deep EMT. In MR analyses, the expression of IMMT in 21 tissues, EFR3B in the adrenal gland, CISD2 in 17 tissues, and UBE2D3 in 7 tissues demonstrated causal relationships with EMT risk. In addition, CISD2, IMMT, and UBE2D3, across different tissues, exhibited strong colocalization with EMT (PPH4 > 0.7). Two-sample network MR analyses revealed that CISD2, EFR3B, and UBE2D3 could potentially regulate the levels of blood lipids and hip circumference so as to influence the risk of EMT. Furthermore, bioinformatics analyses confirmed our findings and delved into the biological functions of the identified genes. Our study unveiled seven novel candidate genes whose predicted expression was associated with the risk of EMT, providing new insights into the underlying genetic framework of EMT. These findings will facilitate a deeper comprehension of the tissue-specific transcriptional regulatory mechanisms associated with EMT, paving the way for optimizing the management and treatment of EMT.

Identification of Potential Drug Targets for Sleep Apnea Through Mendelian Randomization

Background: Sleep apnea is a significant health impediment, and it presently lacks efficacious therapeutic interventions. Thus, it is imperative to discover novel therapeutic targets that could guide clinical treatment strategies. Objective: This study aims to utilize an integrative analytic approach to unearth previously unappreciated protein-encoding genes implicated in sleep apnea susceptibility. Methods: Through the Multi-Marker Analysis of Genomic Annotation (MAGMA), we aligned Single-Nucleotide Polymorphism (SNP) summary statistics from Genome-Wide Association Studies (GWAS) of gene bodies to discern potential risk genes. Following the MAGMA results, we conducted a round of Transcriptome-Wide Association Studies (TWAS) and Proteome-Wide Association Studies (PWAS) to expedite the conversion of genetic associations into probable protein targets. Mendelian Randomization (MR) and co-localization analysis were employed to ascertain the causal linkage between the candidate target genes and sleep apnea. Finally, a mediation analysis was undertaken to explore the possible intermediary role of 150 inflammatory metabolites and 1,124 proteins. Results: The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetyl carnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway. result: The MAGNA analysis revealed 2,819 genes in association with sleep apnea. TWAS and PWAS analyses indicated that cis-regulation of nine particular genes could play a role in sleep apnea onset via blood protein level alterations. MR and co-localization analyses also suggested a causal relationship with sleep apnea for three genes (ACADVL, CCDC134, UPP1). Consistent associations were found between genetically predicted biomarkers and Albumin, HCC-1, N-acetylcarnosine, and RELT, pointing towards their potential mediating roles in sleep apnea's etiological pathway. Conclusion: Our findings indicate that ACADVL, CCDC134, and UPP1 genes are potentially significant targets for further functional investigation and therapeutic interventions for sleep apnea.

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

BackgroundThe 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. MethodsWe 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). Mendelian randomization was employed to assess causal relationships. ResultsAmong 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. ConclusionsThis 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.

Predicting structure-targeted food bioactive compounds for middle-aged and elderly Asians with myocardial infarction: insights from genetic variations and bioinformatics-integrated deep learning analysis.

Myocardial infarction (MI) is a significant global health issue. Despite the advances in genome-wide association studies, a complete genetic and molecular understanding of MI is elusive and needs to be fully explored. This study aimed to elucidate the genetic framework of MI and explore the potential health benefits of natural compounds (NCs). The genetic architecture of MI was explored using data from the Korean Genome and Epidemiology Study. We pinpointed crucial protein-coding genes related to MI by multi-marker analysis of genomic annotation for gene-based analysis. The bioinformatics-integrated deep neural analysis of NCs (BioDeepNat), a novel disease discovery application, was utilized to assess the influence of NCs on MI-related target proteins and validated with molecular docking analysis. The BioDeepNat application revealed significant NCs on MI-related target proteins, such as E-resveratrol, epicatechin 3-gallate, and kaempferol. The E3 region of RNF213 protein with a point mutation (Arg4810Lys) had different binding energies with NCs, such as ursolic acid and olean-12-en-28-oic acid, compared to the wild type. However, ginsenosides, eleutheroside, oleanolic acid, and hederagenin showed similar binding energies to wild and mutated types of RNF213 protein. The predicted NCs were primarily sourced from foods such as common grapes and teas. Aromatic hydrocarbons are frequently observed as the prevalent functional groups with high binding affinity for NCs in a molecular docking analysis. In conclusion, the proteins encoded by these genes identified by gene-based analysis interacted with several NCs with health promotion found in day-to-day foods, particularly E-resveratrol and kaempferol. This understanding offers promising directions for precision nutrition strategies in MI.

Exploring the shared genetic basis of major depressive disorder and frailty

BackgroundMajor depressive disorder (MDD) and frailty impose substantial health and economic burdens. MDD is recognized as a significant risk factor for frailty, but the genetic associations between these conditions remain unclear. This study investigates the genetic correlation, shared pleiotropic loci, causal relationships, and comorbid genes between MDD and frailty. MethodsThe genetic correlation between MDD and frailty was assessed using linkage disequilibrium score regression (LDSC) based on data from genome-wide association studies (GWAS). A detailed analysis was performed to identify shared pleiotropic loci and causal relationships through cross-phenotype association tests and Mendelian randomization. Additionally, tissue enrichment analysis was conducted using stratified LDSC, gene-based associations with both conditions were assessed using Multimarker Analysis of Genomic Annotation (MAGMA), and pathway analysis of comorbid genes was performed using the g: GOSt tool. ResultsOur findings revealed a significant positive genetic correlation between MDD and frailty (rg = 0.65, P = 1.49E-219). We identified 57 shared risk SNPs between the two conditions, including 6 novel SNPs. Mendelian randomization analyses indicated robust causal effects of MDD on frailty and vice versa. Furthermore, we observed tissue-specific heritability enrichment in 9 brain tissues. By combining MAGMA and CPASSOC analyses, we identified 10 comorbid genes associated with both MDD and frailty, primarily involved in synapse formation, modulation, plasticity, and desaturase activity. ConclusionThis study provides strong evidence for a shared genetic basis between MDD and frailty. The identification of comorbid genes offers new insights into the mechanisms underlying the relationship between these conditions.

Shared genetic etiology of vessel diseases: A genome-wide multi-traits association analysis

BackgroundThe comorbidity among vascular diseases has been widely reported, however, the contribution of shared genetic components remains ambiguous. MethodsBased on genome-wide association study summary statistics, we employed statistical genetics methodologies to explore the shared genetic basis of eight vascular diseases: coronary artery disease, abdominal aortic aneurysm, ischemic stroke, peripheral artery disease, thoracic aortic aneurysm, phlebitis, varicose veins, and venous thromboembolism. We assessed global and local genetic correlations among these disorders by linkage disequilibrium score regression, high-definition likelihood, and local analysis of variant association. Cross-trait analyses conducted with CPASSOC identified pleiotropic variants and loci. Further, biological pathways at the multi-omics level were explored using multimarker analysis of genomic annotation, transcriptome-wide and proteome-wide association studies. Causal associations among the vascular diseases were evaluated by mendelian randomization and latent causal variable to assess vertical pleiotropic effects. ResultsWe found significant global genetic associations in 18 pairs of vascular diseases. Additionally, we discovered 317 unique genomic regions where at least one pair of traits demonstrated significant correlation. Multi-trait association analysis identified 19,361 significant potential pleiotropic variants in 274 independent pleiotropic loci. Multi-trait colocalization analysis revealed 56 colocalized loci in specific disease sets. Gene-based analysis identified 700 potential pleiotropic genes, which were subsequently validated at both transcriptome and protein levels. Gene-set enrichment analysis supports the role of biological pathways such as vessel wall structure, coagulation and lipid transport in vascular disease. Additionally, 7 pairs of vascular diseases have a causal relationship. ConclusionsOur study indicates a shared genetic basis and the presence of common risk genes among vascular diseases. These findings offer novel insights into potential mechanisms underlying the association between vascular diseases, as well as provide guidance for interventions and treatments of multi-vascular conditions.

Dissecting the shared genetic architecture between anti-Müllerian hormone and age at menopause based on genome-wide association study

BackgroundWhile the phenotypic association between anti-Müllerian hormone (AMH) and age at menopause has been widely studied, the role of AMH in predicting the age at menopause is currently controversial, and the genetic architecture or causal relationships underlying these two traits is not well understood. AimWe aimed to explore the shared genetic architecture between AMH and age at menopause, to identify shared pleiotropic loci and genes, and to investigate causal association and potential causal mediators. Study DesignUsing summary statistics from publicly available genome-wide association studies on AMH (N=7,049) and age at menopause (N=201,323) in Europeans, we investigated the global genetic architecture between AMH and age at menopause through linkage disequilibrium score regression. We employed pleiotropic analysis under composite null hypothesis (PLACO), Functional Mapping and Annotation of Genetic Associations (FUMA), Multimarker analysis of GenoMic annotation (MAGMA), and colocalization analysis to identify loci and genes with pleiotropic effects. Tissue enrichment analysis based on GTEx data was conducted using the Linkage Disequilibrium Score for the specific expression of genes analysis (LDSC-SEG). Functional genes that were shared were additionally identified through summary data-based Mendelian randomization (SMR). The relationship between AMH and age at menopause was examined through two-sample Mendelian randomization (MR), and potential mediators were further explored using colocalization and metabolite-mediated analysis. ResultsA positive genetic association (correlation coefficient = 0.88, P = 1.33 × 10-5) was observed between AMH and age at menopause. By using PLACO and FUMA, 42 significant pleiotropic loci were identified that were associated with AMH and age at menopause, and ten of these (rs10734411, rs61913600, rs2277339, rs75770066, rs28416520, rs9796, rs11668344, rs403727, rs6011452, and rs62237617) had colocalized loci. Additionally, 245 significant pleiotropic genes were identified by MAGMA. Genetic associations between AMH and age at menopause were markedly concentrated in various tissues including whole blood, brain, heart, liver, muscle, pancreas, and kidneys. Further, SMR analysis revealed nine genes that may have a causative effect on both AMH and age at menopause. A potential causal effect of age at menopause on AMH was suggested by two-sample MR analysis, with very-low-density lipoprotein identified as a potential mediator. ConclusionsOur study revealed a shared genetic architecture between AMH and age at menopause, providing a basis for experimental investigations and individual therapies to enhance reproductive outcomes. Furthermore, our findings emphasized that relying solely on AMH is not sufficient for accurately predicting the age at menopause, and a combination of other factors needs to be considered. Exploring new therapeutics aimed at delaying at the onset of menopause holds promise, particularly when targeting shared genes based on their shared genetic architecture.

A cross-tissue transcriptome-wide association study reveals novel susceptibility genes for migraine

BackgroundMigraine is a common neurological disorder with a strong genetic component. Despite the identification of over 100 loci associated with migraine susceptibility through genome-wide association studies (GWAS), the underlying causative genes and biological mechanisms remain predominantly elusive.MethodsThe FinnGen R10 dataset, consisting of 333,711 subjects (20,908 cases and 312,803 controls), was utilized in conjunction with the Genotype-Tissue Expression Project (GTEx) v8 EQTls files to conduct cross-tissue transcriptome association studies (TWAS). Functional Summary-based Imputation (FUSION) was employed to validate these findings in single tissues. Additionally, candidate susceptibility genes were screened using Gene Analysis combined with Multi-marker Analysis of Genomic Annotation (MAGMA). Subsequent Mendelian randomization (MR) and colocalization analyses were conducted. Furthermore, GeneMANIA analysis was employed to enhance our understanding of the functional implications of these susceptibility genes.ResultsWe identified a total of 19 susceptibility genes associated with migraine in the cross-tissue TWAS analysis. Two novel susceptibility genes, REV1 and SREBF2, were validated through both single tissue TWAS and MAGMA analysis. Mendelian randomization and colocalization analyses further confirmed these findings. REV1 may reduce the migraine risk by regulating DNA damage repair, while SREBF2 may increase the risk of migraine by regulating cholesterol metabolism.ConclusionOur study identified two novel genes whose predicted expression was associated with the risk of migraine, providing new insights into the genetic framework of migraine.

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.

Shared genetic architecture between hypothyroidism and rheumatoid arthritis: A large-scale cross-trait analysis

BackgroundIn recent years, mounting evidence has indicated a co-morbid relationship between hypothyroidism and rheumatoid arthritis (RA), however, the shared genetic factors underlying this association remain unclear. This study aims to investigate the common genetic architecture between hypothyroidism and RA. MethodsGenome-wide association study (GWAS) summary statistics from recently published studies were utilized to examine the genetic correlation, shared genetic loci, and potential causal relationship between hypothyroidism and RA. Statistical methods included linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), cross-trait meta-analyses, colocalization analysis, multi-marker analysis of genomic annotation (MAGMA), tissue-specific enrichment analysis (TSEA), functional enrichment analysis, and latent causal variable method (LCV). ResultsOur study demonstrated a significant genetic correlation between hypothyroidism and RA(LDSC:rg=0.3803,p=7.23e-11;HDL:rg=0.3849,p=1.02e-21). Through cross-trait meta-analysis, we identified 1035 loci, including 43 novel genetic loci. By integrating colocalization analysis and the MAGMA algorithm, we found a substantial number of genes, such as PTPN22, TYK2, and CTLA-4, shared between the two diseases, which showed significant enrichment across 14 tissues. These genes were primarily associated with the regulation of alpha-beta T cell proliferation, positive regulation of T cell activation, positive regulation of leukocyte cell-cell adhesion, T cell receptor signaling pathway, and JAK-STAT signaling pathway. However, our study did not reveal a significant causal association between the two diseases using the LCV approach. ConclusionBased on these findings, there is a significant genetic correlation between hypothyroidism and RA, suggesting a shared genetic basis for these conditions.

Gaining new insights into the etiology of ulcerative colitis through a cross-tissue transcriptome-wide association study.

Genome-wide association studies (GWASs) have identified 38 loci associated with ulcerative colitis (UC) susceptibility, but the risk genes and their biological mechanisms remained to be comprehensively elucidated. Multi-marker analysis of genomic annotation (MAGMA) software was used to annotate genes on GWAS summary statistics of UC from FinnGen database. Genetic analysis was performed to identify risk genes. Cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signatures (UTMOST) was performed to compare GWAS summary statistics with gene expression matrix (from Genotype-Tissue Expression Project) for data integration. Subsequently, we used FUSION software to select key genes from the individual tissues. Additionally, conditional and joint analysis was conducted to improve our understanding on UC. Fine-mapping of causal gene sets (FOCUS) software was employed to accurately locate risk genes. The results of the four genetic analyses (MAGMA, UTMOST, FUSION and FOCUS) were combined to obtain a set of UC risk genes. Finally, Mendelian randomization (MR) analysis and Bayesian colocalization analysis were conducted to determine the causal relationship between the risk genes and UC. To test the robustness of our findings, the same approaches were taken to verify the GWAS data of UC on IEU. Multiple correction tests screened PIM3 as a risk gene for UC. The results of Bayesian colocalization analysis showed that the posterior probability of hypothesis 4 was 0.997 and 0.954 in the validation dataset. MR was conducted using the inverse variance weighting method and two single nucleotide polymorphisms (SNPs, rs28645887 and rs62231924) were included in the analysis (p < 0.001, 95%CI: 1.45-1.89). In the validation dataset, MR result was p < 0.001, 95%CI: 1.19-1.72, indicating a clear causal relationship between PIM3 and UC. Our study validated PIM3 as a key risk gene for UC and its expression level may be related to the risk of UC, providing a novel reference for further improving the current understanding on the genetic structure of UC.

Illuminating Shared Genetic Associations Between Oesophageal Carcinoma and Pulmonary Carcinoma Risk.

Background: Lung cancer and oesophageal cancer are prevalent malignancies with rising incidence and mortality worldwide. While some environmental and behavioural risk factors for these cancers are established, the contribution of genetic factors to their pathogenesis remains incompletely defined. This study aimed to interrogate the intricate genetic relationship between lung cancer and oesophageal cancer and their potential comorbidity. Methods: We utilised linkage disequilibrium score regression (LDSC) to analyse the genetic correlation between oesophageal carcinoma and lung carcinoma. We then employed several approaches, including pleiotropic analysis under the composite null hypothesis (PLACO), multi-marker analysis of genomic annotation (MAGMA), cis-expression quantitative trait loci (eQTL) analysis, and a pan-cancer assessment to identify pleiotropic loci and genes. Finally, we performed bidirectional Mendelian randomisation (MR) to evaluate the causal relationship between these malignancies. Results: LDSC revealed a significant genetic correlation between oesophageal carcinoma and lung carcinoma. Further analysis identified shared gene loci including PGBD1, ZNF323, and WNK1 using PLACO. MAGMA identified enriched pathways and 9 pleiotropic genes including HIST1H1B, HIST1H4L, and HIST1H2BL. eQTL analysis integrating oesophageal, lung, and blood tissues revealed 26 shared genes including TERT, NKAPL, RAD52, BTN3A2, GABBR1, CLPTM1L, and TRIM27. A pan-cancer exploration of the identified genes was undertaken. MR analysis showed no evidence for a bidirectional causal relationship between oesophageal carcinoma and lung carcinoma. Conclusions: This study provides salient insights into the intricate genetic links between lung carcinoma and oesophageal carcinoma. Utilising multiple approaches for genetic correlation, locus and gene analysis, and causal assessment, we identify shared genetic susceptibilities and regulatory mechanisms. These findings reveal new leads and targets to further elucidate the genetic basis of lung and oesophageal carcinoma, aiding development of preventive and therapeutic strategies.

Genetics of vegetarianism: A genome-wide association study.

A substantial body of evidence points to the heritability of dietary preferences. While vegetarianism has been practiced for millennia in various societies, its practitioners remain a small minority of people worldwide, and the role of genetics in choosing a vegetarian diet is not well understood. Dietary choices involve an interplay between the physiologic effects of dietary items, their metabolism, and taste perception, all of which are strongly influenced by genetics. In this study, we used a genome-wide association study (GWAS) to identify loci associated with strict vegetarianism in UK Biobank participants. Comparing 5,324 strict vegetarians to 329,455 controls, we identified one SNP on chromosome 18 that is associated with vegetarianism at the genome-wide significant level (rs72884519, β = -0.11, P = 4.997 x 10-8), and an additional 201 suggestively significant variants. Four genes are associated with rs72884519: TMEM241, RIOK3, NPC1, and RMC1. Using the Functional Mapping and Annotation (FUMA) platform and the Multi-marker Analysis of GenoMic Annotation (MAGMA) tool, we identified 34 genes with a possible role in vegetarianism, 3 of which are GWAS-significant based on gene-level analysis: RIOK3, RMC1, and NPC1. Several of the genes associated with vegetarianism, including TMEM241, NPC1, and RMC1, have important functions in lipid metabolism and brain function, raising the possibility that differences in lipid metabolism and their effects on the brain may underlie the ability to subsist on a vegetarian diet. These results support a role for genetics in choosing a vegetarian diet and open the door to future studies aimed at further elucidating the physiologic pathways involved in vegetarianism.

Dissecting shared genetic architecture between obesity and multiple sclerosis.

Observational studies have associated obesity with an increased risk of multiple sclerosis (MS). However, the role of genetic factors in their comorbidity remains largely unknown. Our study aimed to investigate the shared genetic architecture underlying obesity and MS. By leveraging data from genome-wide association studies, we investigated the genetic correlation of body mass index (BMI) and MS by linkage disequilibrium score regression and genetic covariance analyser. The casualty was identified by bidirectional Mendelian randomisation. Linkage disequilibrium score regression in specifically expressed genes and multimarker analysis of GenoMic annotation was utilised to explore single-nucleotide polymorphism (SNP) enrichment at the tissue and cell-type levels. Shared risk SNPs were derived using cross-trait meta-analyses and Heritability Estimation from Summary Statistics. We explored the potential functional genes using summary-data-based Mendelian randomization (SMR). The expression profiles of the risk gene in tissues were further examined. We found a significantly positive genetic correlation between BMI and MS, and the causal association of BMI with MS was supported (β=0.22, P=8.03E-05). Cross-trait analysis yielded 39 shared risk SNPs, and the risk gene GGNBP2 was consistently identified in SMR. We observed tissue-specific level SNP heritability enrichment for BMI mainly in brain tissues for MS in immune-related tissues, and cell-type-specific level SNP heritability enrichment in 12 different immune cell types in brain, spleen, lung, and whole blood. The expressions of GGNBP2 were significantly altered in the tissues of patients with obesity or MS compared to those of control subjects. Our study indicates the genetic correlation and shared risk genes between obesity and MS. These findings provide insights into the potential mechanisms behind their comorbidity and the future development of therapeutics. This work was funded by the National Natural Science Foundation of China (82171698, 82170561, 81300279, and 81741067), the Program for High-level Foreign Expert Introduction of China (G2022030047L), the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province (2021B1515020003), Natural Science Foundation of Guangdong Province (2022A1515012081), the Foreign Distinguished Teacher Program of Guangdong Science and Technology Department (KD0120220129), the Climbing Programme of Introduced Talents and High-level Hospital Construction Project of Guangdong Provincial People's Hospital (DFJH201803, KJ012019099, KJ012021143, and KY012021183), and in part by VA Clinical Merit and ASGE clinical research funds (FWL).

Transcriptomic profiling of brain amyloidosis and Alzheimer’s disease phenotypes identifies novel gene targets from peripheral blood gene expression data

AbstractBackgroundIn an effort towards pre‐symptomatic risk assessment, precision medicine and biomarker development in AD, we evaluated the efficacy of characterizing and clustering peripheral blood transcriptomic data with respect to brain amyloidosis.Method356 ADNI participants with blood gene expression, Florbetapir SUVR, neurodegeneration measures and CSF measures (CN = 120, EMCI = 130, LMCI = 72 and AD = 34) were identified (Table‐1). Differential expressed genes from ∼50,000 transcripts (p&lt;0.001) were identified. Pairwise euclidean distance between genes was used to construct a gene‐gene distance matrix followed by K‐means clustering (Figure‐1). Gene enrichment analysis was performed on each cluster to identify biological processes associated with the cluster. “Eigengene”; a data vector representing the whole cluster was obtained for each cluster using Principal Component Analyses. The top driver genes were identified from each cluster. We used the tool MAGMA (Multi‐marker Analysis of GenoMic Annotation) to analyze variants in the driver genes. Voxel‐wise multiple regression in SPM12 with age and sex as covariates was used to visualize the pattern of association of driver gene expression with brain amyloidosis. The association of the transcripts identified through our analyses to amyloid SUVR, and disease diagnosis were validated in the external ImaGene(n = 160) dataset.ResultWe identified five clusters with distinct biological processes highly relevant in AD. All five clusters were significantly associated with Florbetapir SUVR and CSF Abeta measures (p&lt;0.05) (Figure‐2). 28 driver genes were identified. Six driver genes (BAIAP3, E2F2, PSMF1, SMOX, UBE20 and RNF11) had negative association with amyloidosis in the lateral temporal, lateral parietal and temporo‐ and parieto‐occipital areas, in addition to regions of the frontal lobe (Figure‐3). One of the driver genes, KANK2, and overall, 29 of the differentially expressed genes had SNPs significantly associated with AD and MCI phenotype (Table‐2). The top driver genes showed similar correlation to amyloid SUVR in the ImaGene dataset (Figure‐4B). A logistic regression model with 28 driver genes, and age and sex as covariates predicted amnestic MCI diagnosis in the external dataset with an AUC of 0.82(Figure‐4A).ConclusionUsing a data driven approach we identified novel gene targets from peripheral blood which directly correlate to amyloid‐related pathogenesis and AD phenotype

Investigating the shared genetic architecture of post-traumatic stress disorder and gastrointestinal tract disorders: a genome-wide cross-trait analysis.

Observational studies suggest a correlation between post-traumatic stress disorder (PTSD) and gastrointestinal tract (GIT) disorders. However, the genetic overlap, causal relationships, and underlining mechanisms between PTSD and GIT disorders were absent. We obtained genome-wide association study statistics for PTSD (23 212 cases, 151 447 controls), peptic ulcer disease (PUD; 16 666 cases, 439 661 controls), gastroesophageal reflux disease (GORD; 54 854 cases, 401 473 controls), PUD and/or GORD and/or medications (PGM; 90 175 cases, 366 152 controls), irritable bowel syndrome (IBS; 28 518 cases, 426 803 controls), and inflammatory bowel disease (IBD; 7045 cases, 449 282 controls). We quantified genetic correlations, identified pleiotropic loci, and performed multi-marker analysis of genomic annotation, fast gene-based association analysis, transcriptome-wide association study analysis, and bidirectional Mendelian randomization analysis. PTSD globally correlates with PUD (rg = 0.526, p = 9.355 × 10-7), GORD (rg = 0.398, p = 5.223 × 10-9), PGM (rg = 0.524, p = 1.251 × 10-15), and IBS (rg = 0.419, p = 8.825 × 10-6). Cross-trait meta-analyses identify seven genome-wide significant loci between PTSD and PGM (rs13107325, rs1632855, rs1800628, rs2188100, rs3129953, rs6973700, and rs73154693); three between PTSD and GORD (rs13107325, rs1632855, and rs3132450); one between PTSD and IBS/IBD (rs4937872 and rs114969413, respectively). Proximal pleiotropic genes are mainly enriched in immune response regulatory pathways, and in brain, digestive, and immune systems. Gene-level analyses identify five candidates: ABT1, BTN3A2, HIST1H3J, ZKSCAN4, and ZKSCAN8. We found significant causal effects of GORD, PGM, IBS, and IBD on PTSD. We observed no reverse causality of PTSD with GIT disorders, except for GORD. PTSD and GIT disorders share common genetic architectures. Our work offers insights into the biological mechanisms, and provides genetic basis for translational research studies.

Genome-Wide Gene-Set Analysis Identifies Molecular Mechanisms Associated with ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal late-onset motor neuron disease characterized by the loss of the upper and lower motor neurons. Our understanding of the molecular basis of ALS pathology remains elusive, complicating the development of efficient treatment. Gene-set analyses of genome-wide data have offered insight into the biological processes and pathways of complex diseases and can suggest new hypotheses regarding causal mechanisms. Our aim in this study was to identify and explore biological pathways and other gene sets having genomic association to ALS. Two cohorts of genomic data from the dbGaP repository were combined: (a) the largest available ALS individual-level genotype dataset (N = 12,319), and (b) a similarly sized control cohort (N = 13,210). Following comprehensive quality control pipelines, imputation and meta-analysis, we assembled a large European descent ALS-control cohort of 9244 ALS cases and 12,795 healthy controls represented by genetic variants of 19,242 genes. Multi-marker analysis of genomic annotation (MAGMA) gene-set analysis was applied to an extensive collection of 31,454 gene sets from the molecular signatures database (MSigDB). Statistically significant associations were observed for gene sets related to immune response, apoptosis, lipid metabolism, neuron differentiation, muscle cell function, synaptic plasticity and development. We also report novel interactions between gene sets, suggestive of mechanistic overlaps. A manual meta-categorization and enrichment mapping approach is used to explore the overlap of gene membership between significant gene sets, revealing a number of shared mechanisms.