Multi-marker Analysis Of GenoMic Annotation Research Articles

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.

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<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<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.

Evaluation of genetic variants in ferroptosis-related genes and house dust mite-induced allergic rhinitis risk.

Ferroptosis-related genes disrupt iron homeostasis and enhance lipid peroxidation to initiate respiratory system diseases. However, the association between genetic variants in the ferroptosis-related genes with house dust mite (HDM)-induced allergic rhinitis (AR) susceptibility remains unclear. A case-control study, involving 222 cases and 237 healthy controls from a Chinese population, was conducted to evaluate the relationship between single nucleotide polymorphisms (SNPs) in ferroptosis-related genes and HDM-induced AR risk. A gene-based analysis was performed by multi-marker analysis of genomic annotation (MAGMA) to identify candidate associated ferroptosis-related genes. A logistic regression model and joint analysis were used to assess the effect of SNPs on HDM-induced AR susceptibility. Two independent SNPs (rs2305128 in ENPP2 and rs1868088 in EPAS1) were significantly associated with HDM-induced AR risk (OR=1.82, 95% CI=1.19-2.79, P=5.98×10-3, PFDR=4.88×10-2; OR=2.14, 95% CI=1.23-3.72, P=6.95×10-3, PFDR=4.87×10-2, respectively). Moreover, combined analysis of these two SNPs revealed that an increased risk of HDM-induced AR was positively associated with an increasing number of risk genotypes (Ptrend= 8.48×10-5). The stratification analysis showed that the cumulative effect of two SNPs on HDM-induced AR risk was more pronounced among patients presenting more serious symptoms and harboring one or two risk genotypes. These findings suggest that the genetic variants in ferroptosis-related genes ENPP2 and EPAS1 may increase HDM-induced AR risk and serve as potential predictors of HDM-induced AR susceptibility.

Identification of MKNK1 and TOP3A as ovarian endometriosis risk-associated genes using integrative genomic analyses and functional experiments

The risk of endometriosis (EM), which is a common complex gynaecological disease, is related to genetic predisposition. However, it is unclear how genetic variants confer the risk of EM. Here, via Sherlock integrative analysis, we combined large-scale genome-wide association studies (GWAS) summary statistics on EM (N = 245,494) with a blood-based eQTL dataset (N = 1490) to identify EM risk-related genes. For validation, we leveraged two independent eQTL datasets (N = 769) for integration with the GWAS data. Thus, we prioritised 14 genes, including GIMAP4, TOP3A, and NMNAT3, which showed significant association with susceptibility to EM. We also utilised two independent methods, Multi-marker Analysis of GenoMic Annotation and S-PrediXcan, to further validate the EM risk-associated genes. Moreover, protein–protein interaction network analysis showed the 14 genes were functionally connected. Functional enrichment analyses further demonstrated that these genes were significantly enriched in metabolic and immune-related pathways. Differential gene expression analysis showed that in peripheral blood samples from patients with ovarian EM, TOP3A, MKNK1, SIPA1L2, and NUCB1 were significantly upregulated, while HOXB2, GIMAP5, and MGMT were significantly downregulated compared with their expression levels in samples from the controls. Immunohistochemistry further confirmed the increased expression levels of MKNK1 and TOP3A in the ectopic and eutopic endometrium compared to normal endometrium, while HOBX2 was downregulated in the endometrium of women with ovarian EM. Finally, in ex vivo functional experiments, MKNK1 knockdown inhibited ectopic endometrial stromal cells (EESCs) migration and invasion. TOP3A knockdown inhibited EESCs proliferation, migration, and invasion, while promoting their apoptosis. Convergent lines of evidence suggested that MKNK1 and TOP3A are novel EM risk-related genes.

Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis.

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10-6, PeMAGMA = 1.94 × 10-7 for whole blood), IL-6R (PFUSION = 8.63 × 10-7, PeMAGMA = 2.74 × 10-6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10-5, PeMAGMA = 1.08 × 10-6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10-7, PeMAGMA = 3.93 × 10-8 for Liver), LAT (PFUSION = 1.53 × 10-4, PeMAGMA = 4.62 × 10-7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10-5, PeMAGMA = 1.73 × 10-7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.

Annotating genetic variants to target genes using H-MAGMA.

An outstanding goal in modern genomics is to systematically predict the functional outcome of noncoding variation associated with complex traits. To address this, we developed Hi-C-coupled multi-marker analysis of genomic annotation (H-MAGMA), which builds on traditional MAGMA-a gene-based analysis tool that assigns variants to their target genes-by incorporating 3D chromatin configuration in assigning variants to their putative target genes. Applying this approach, we identified key biological pathways implicated in a wide range of brain disorders and showed its utility in complementing other functional genomic resources such as expression quantitative trait loci-based variant annotation. Here, we provide a detailed protocol for generating the H-MAGMA variant-gene annotation file by using chromatin interaction data from the adult human brain. In addition, we provide an example of how H-MAGMA is run by using genome-wide association study summary statistics of Parkinson's disease. Lastly, we generated variant-gene annotation files for 28 tissues and cell types, with the hope of contributing a resource for researchers studying a broad set of complex genetic disorders. H-MAGMA can be performed in <2 h for any cell type in which Hi-C data are available.

Identifying pleiotropic genes for major psychiatric disorders with GWAS summary statistics using multivariate adaptive association tests

BackgroundGenome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests. MethodsSummary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes. ResultsWe confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. These genes were functionally enriched in neuron differentiation and synaptic transmission. LimitationsThe effect direction of pleiotropic genes couldn't be distinguished due to without individual-level data. ConclusionWe identified pleiotropic genes using multivariate adaptive association tests and explored their biological function. The findings may provide novel insight into the development and implementation of prevention and treatment as well as targeted drug discovery in practice.

Novel insight into the aetiology of rheumatoid arthritis gained by a cross-tissue transcriptome-wide association study

BackgroundAlthough genome-wide association studies (GWASs) have identified more than 100 loci associated with rheumatoid arthritis (RA) susceptibility, the causal genes and biological mechanisms remain largely unknown.MethodsA cross-tissue transcriptome-wide association study...

ChromMAGMA: regulatory element-centric interrogation of risk variants.

Candidate causal risk variants from genome-wide association studies reside almost exclusively in noncoding regions of the genome and innovative approaches are necessary to understand their biological function. Multi-marker analysis of genomic annotation (MAGMA) is a widely used program that nominates candidate risk genes by mapping single-nucleotide polymorphism summary statistics from genome-wide association studies to gene bodies. We augmented MAGMA to create chromatin-MAGMA (chromMAGMA), a method to nominate candidate risk genes based on the presence of risk variants within noncoding regulatory elements (REs). We applied chromMAGMA to a genetic susceptibility dataset for epithelial ovarian cancer (EOC), a rare gynecologic malignancy characterized by high mortality. This identified 155 unique candidate EOC risk genes across five EOC histotypes; 83% (105/127) of high-grade serous ovarian cancer risk genes had not previously been implicated in this EOC histotype. Risk genes nominated by chromMAGMA converged on mRNA splicing and transcriptional dysregulation pathways. chromMAGMA is a pipeline that nominates candidate risk genes through a gene regulation-focused approach and helps interpret the biological mechanism of noncoding risk variants for complex diseases.

Polygenic Resilience Modulates the Penetrance of Parkinson Disease Genetic Risk Factors.

ObjectiveThe aim of the current study is to understand why some individuals avoid developing Parkinson disease (PD) despite being at relatively high genetic risk, using the largest datasets of individual‐level genetic data available.MethodsWe calculated polygenic risk score to identify controls and matched PD cases with the highest burden of genetic risk for PD in the discovery cohort (International Parkinson's Disease Genomics Consortium, 7,204 PD cases and 9,412 controls) and validation cohorts (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease, 8,968 cases and 7,598 controls; UK Biobank, 2,639 PD cases and 14,301 controls; Accelerating Medicines Partnership–Parkinson's Disease Initiative, 2,248 cases and 2,817 controls). A genome‐wide association study meta‐analysis was performed on these individuals to understand genetic variation associated with resistance to disease. We further constructed a polygenic resilience score, and performed multimarker analysis of genomic annotation (MAGMA) gene‐based analyses and functional enrichment analyses.ResultsA higher polygenic resilience score was associated with a lower risk for PD (β = −0.054, standard error [SE] = 0.022, p = 0.013). Although no single locus reached genome‐wide significance, MAGMA gene‐based analyses nominated TBCA as a putative gene. Furthermore, we estimated the narrow‐sense heritability associated with resilience to PD (h 2 = 0.081, SE = 0.035, p = 0.0003). Subsequent functional enrichment analysis highlighted histone methylation as a potential pathway harboring resilience alleles that could mitigate the effects of PD risk loci.InterpretationThe present study represents a novel and comprehensive assessment of heritable genetic variation contributing to PD resistance. We show that a genetic resilience score can modify the penetrance of PD genetic risk factors and therefore protect individuals carrying a high‐risk genetic burden from developing PD. ANN NEUROL 2022;92:270–278

A genome-wide association study on frequent exacerbation of asthma depending on smoking status

PurposeExacerbation of asthma is affected by genetic and environmental factors, but little is known about genetic differences according to smoking status. We evaluated genetic factors associated with asthma exacerbations in smokers and non-smokers, and identified the underlying mechanisms via a genome-wide association study (GWAS) and gene-level analyses according to smoking status. MethodsA GWAS on the annual frequency of asthma exacerbations was performed in 420 non-smoking and 188 smoking patients with asthma. Gene-wise associations were analyzed by Multi-marker Analysis of GenoMic Annotation (MAGMA); Gene Ontology analysis was also performed. ResultsIn the non-smoker group, 189 genes showed significant associations with the annual frequency of exacerbations (permutated P < 0.001). The top 10 genes were F5, KLRC1, TAFA2, AIRE, IER3IP1, CHMP2A, IL31RA, ZNF497, DNMT3L, and MYT1L (permutated P = 1.0 × 10−4 – 1.7 × 10−4). In smoking asthmatics, 140 genes—including KANK1, ZMYND12, ZNF34, ANXA11, VAV2, CCDC150, CCDC30, CATSPER3, ARMH2, and MPRIP (permutated P = 9.23 × 10−5 – 5.50 × 10−4)—were associated with asthma exacerbations. Genes participating in the innate immune response in non-smokers and the regulation of cell fate (including apoptosis) in smokers were the major causal genes of asthma exacerbation (FDR q < 0.05). ConclusionsOur findings not only suggest novel genetic candidates for predicting asthma exacerbations, but also that asthma treatment strategies should take into account smoking behavior.

Potential Pleiotropic Genes and Shared Biological Pathways in Epilepsy and Depression Based on GWAS Summary Statistics.

Current epidemiological and experimental studies have indicated the overlapping genetic foundation of epilepsy and depression. However, the detailed pleiotropic genetic etiology and neurobiological pathways have not been well understood, and there are many variants with underestimated effect on the comorbidity of the two diseases. Utilizing genome-wide association study (GWAS) summary statistics of epilepsy (15,212 cases and 29,677 controls) and depression (170,756 cases and 329,443 controls) from large consortia, we assessed the integrated gene-based association with both diseases by Multimarker Analysis of Genomic Annotation (MAGMA) and Fisher's meta-analysis. On the one hand, shared genes with significantly altered transcripts in Gene Expression Omnibus (GEO) data sets were considered as possible pleiotropic genes. On the other hand, the pathway enrichment analysis was conducted based on the gene lists with nominal significance in the gene-based association test of each disease. We identified a total of two pleiotropic genes (CD3G and SLCO3A1) with gene expression analysis validated and interpreted twenty-five common biological process supported with literature mining. This study indicates the potentially shared genes associated with both epilepsy and depression based on gene expression, meta-data analysis, and pathway enrichment strategy along with traditional GWAS and provides insights into the possible intersecting pathways that were not previously reported.