Genome-wide Association Studies Associations Research Articles

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation.

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits around APOE region on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.

Cefadroxil targeting of SLC15A2/PEPT2 protects from colistin nephrotoxicity

Acute kidney injury (AKI) and chronic kidney disease (CKD) are considered interconnected syndromes, as AKI episodes may accelerate CKD progression and CKD increases the risk of AKI. Genome-wide association studies (GWAS) may identify novel actionable therapeutic targets. Human genome-wide association studies (GWAS) for AKI or CKD were combined with murine AKI transcriptomics datasets to identify 13 (ACACB, ACSM5, CNDP1, DPEP1, GATM, SLC6A12, AGXT2L1, SLC15A2, CTSS, ICAM1, ITGAX, ITGAM, PPM1J) potentially actionable therapeutic targets to modulate kidney disease severity across species and across the AKI-CKD spectrum. Among them, SLC15A2, encoding the cell membrane proton-coupled peptide transporter 2 (PEPT2), was prioritized for data mining and functional intervention studies in vitro and in vivo because of its known function to transport nephrotoxic drugs such as colistin and the possibility for targeting with small molecules already in clinical use, such as cefadroxil. Data mining disclosed that SLC15A2 was upregulated in the tubulointerstitium of human CKD, including diabetic nephropathy, and the upregulation was localized to proximal tubular cells. Colistin elicited cytotoxicity and a proinflammatory response in cultured human and murine proximal tubular cells that was decreased by concomitant exposure to cefadroxil. In proof-of-concept in vivo studies, cefadroxil protected from colistin nephrotoxicity in mice. The GWAS association of SLC15A2 with human kidney disease may be actionable and related to the modifiable transport of nephrotoxins causing repeated subclinical episodes of AKI and/or chronic nephrotoxicity.

Genome-Wide Association Study of Temporomandibular Disorder-Related Pain in Finnish Populations.

Temporomandibular disorders (TMD) are multifactorial musculoskeletal pain and dysfunctions in temporomandibular joints (TMJs) and masticatory muscles. Genetic factors play a role in TMD-related pain, but only a few genome-wide association studies (GWAS) have been conducted. The aim of this GWAS was to explore genetic factors associated with painful TMD in Finnish populations. Data from two epidemiological surveys, the Northern Finland Birth Cohort 1966 (NFBC1966) and the Health 2000 Survey in Finland, including altogether 468 cases and 6833 controls, were used. Case definition was based on pain on palpation of masticatory muscles and/or TMJs. GWASs of the whole data and stratified by sex were conducted from both cohorts using additive models, followed by meta-analysis of the two cohorts. Replications of the previously reported TMD risk loci (rs73460075, DMD; rs4794106, SGCA; rs73271865, SP4; rs60249166, RXP2; rs1531554, BAHCCI; rs5862730, OTUD4/SMAD1; rs10092633, SFRP1; rs34612513, SOX14/CLDN18; rs878962, TSPAN9) were also investigated. Four genome-wide significant loci were found in sex-stratified analysis of NFBC1966, including associations at three loci in males (rs1023114, PRIM2, p = 5 × 10-9; rs4244867, ALG10, p = 3 × 10-8; rs79841648, ADCYAP1, p = 4 × 10-9) and one locus in females (rs148476652, DNER, p = 4 × 10-9). However, the results could not be replicated in the Health 2000 Survey or in the meta-analysis of these two cohorts. The previous TMD GWAS associations did not replicate in our data either. Several TMD pain-associated variants were found in sex-stratified analysis of NFBC1966, suggesting the role of neuroendocrine stress responses and central nervous system. These findings need to be confirmed in future studies.

Unsupervised electrocardiogram feature extraction using deep learning empowers discovery of genetic and phenotypic determinants of cardiac electrophysiology

Abstract Background Conventional analysis of the electrocardiogram (ECG) is based on the extraction of human-defined, visually recognisable features. However, these are not optimised to capture all the information content in the ECG. The variational autoencoder (VAE), a form of unsupervised machine learning, can address this shortcoming by computationally extracting comprehensive and interpretable new ECG features, called latent factors. These latent factors provide a low-dimensional representation of the ECG that maximises capture of data content. This approach could expand our understanding of electrophysiology by identifying novel genetic and phenotypic traits associations with the ECG. Purpose This study aims to uncover genetic determinants of VAE latent factors, comparing them to determinants of conventional, human-derived ECG parameters to gain novel insights into cardiac electrophysiology and related diseases. Methods Over one million median beat ECGs derived from a United States (US) based secondary care centre were used to train a machine learning VAE model, with external validation in the UK Biobank (UKB). We performed common and rare variant association studies for VAE latent factors and conventional ECG traits on quality-controlled UKB data. Associated genetic variants were compared to loci for conventional ECG parameters available in the UKB and literature. Novel GWAS associations were validated in a withheld subset of the UKB cohort. Additionally, we compared the associations of the VAE latent factors and conventional ECG traits with phenotypic traits, disease codes and echocardiographic traits. Results Utilising median beat ECGs, the VAE model identified 20 features, with partial correlations to traditional ECG traits. A genome-wide association study (GWAS) identified 105 associated genomic regions, compared with 62 regions identified with conventional ECG traits on the same dataset. Most discovered loci have been previously associated with ECG features in larger GWAS, providing a positive control validation. We also discovered and validated five genomic regions not previously linked to the ECG (mapped to TRIOBP, EFEMP1, NEDD9, ATP10A and P2RX1). We found rare variant associations for seven genes (NEK6, IL17RA, NME7, MYBPC3, CCT8, ADAMTS6 and SCN5A). The latent factors uncovered associations with 158 phenotypic traits (2093 phenotypes tested) and 147 disease codes (2074 disease phecodes tested) which were not identified by ECG traits. Out of 38 echocardiographic traits, 35 showed higher correlations with the latent factors, while six exhibited significant associations exclusively with the latent factors. Conclusion Our study demonstrates that VAE-derived latent factors outperform conventional ECG parameters in uncovering novel genetic and phenotypic associations. Our findings underscore the opportunity for optimised data-driven feature extraction to enhance our understanding of cardiac electrophysiology.

Valid inference for machine learning-assisted genome-wide association studies.

Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS.

Genotyping-by-sequencing targets genic regions and improves resolution of genome-wide association studies in autotetraploid potato

Key messageDe novo genotyping in potato using methylation-sensitive GBS discovers SNPs largely confined to genic or gene-associated regions and displays enhanced effectiveness in estimating LD decay rates, population structure and detecting GWAS associations over ‘fixed’ SNP genotyping platform. Study also reports the genetic architectures including robust sequence-tagged marker–trait associations for sixteen important potato traits potentially carrying higher transferability across a wider range of germplasm.This study deploys recent advancements in polyploid analytical approaches to perform complex trait analyses in cultivated tetraploid potato. The study employs a ‘fixed’ SNP Infinium array platform and a ‘flexible and open’ genome complexity reduction-based sequencing method (GBS, genotyping-by-sequencing) to perform genome-wide association studies (GWAS) for several key potato traits including the assessment of population structure and linkage disequilibrium (LD) in the studied population. GBS SNPs discovered here were largely confined (~ 90%) to genic or gene-associated regions of the genome demonstrating the utility of using a methylation-sensitive restriction enzyme (PstI) for library construction. As compared to Infinium array SNPs, GBS SNPs displayed enhanced effectiveness in estimating LD decay rates and discriminating population subgroups. GWAS using a combined set of 30,363 SNPs identified 189 unique QTL marker–trait associations (QTL-MTAs) covering all studied traits. The majority of the QTL-MTAs were from GBS SNPs potentially illustrating the effectiveness of marker-dense de novo genotyping platforms in overcoming ascertainment bias and providing a more accurate correction for different levels of relatedness in GWAS models. GWAS also detected QTL ‘hotspots’ for several traits at previously known as well as newly identified genomic locations. Due to the current study exploiting genome-wide genotyping and de novo SNP discovery simultaneously on a large tetraploid panel representing a greater diversity of the cultivated potato gene pool, the reported sequence-tagged MTAs are likely to have higher transferability across a wider range of potato germplasm and increased utility for expediting genomics-assisted breeding for the several complex traits studied.

Impact of the inaccessible genome on genotype imputation and genome-wide association studies.

Genotype imputation is widely used in genome-wide association studies (GWAS). However, both the genotyping chips and imputation reference panels are dependent on next-generation sequencing (NGS). Due to the nature of NGS, some regions of the genome are inaccessible to sequencing. To date, there has been no complete evaluation of these regions and their impact on the identification of associations in GWAS remains unclear. In this study, we systematically assess the extent to which variants in inaccessible regions are underrepresented on genotyping chips and imputation reference panels, in GWAS results and in variant databases. We also determine the proportion of genes located in inaccessible regions and compare the results across variant masks defined by the 1000 Genomes Project and the TOPMed program. Overall, fewer variants were observed in inaccessible regions in all categories analyzed. Depending on the mask used and normalized for region size, only 4%-17% of the genotyped variants are located in inaccessible regions and 52 to 581 genes were almost completely inaccessible. From the Cooperative Health Research in South Tyrol (CHRIS) study, we present a case study of an association located in an inaccessible region that is driven by genotyped variants and cannot be reproduced by imputation in GRCh37. We conclude that genotyping, NGS, genotype imputation and downstream analyses such as GWAS and fine mapping are systematically biased in inaccessible regions, due to missed variants and spurious associations. To help researchers assess gene and variant accessibility, we provide an online application (https://gab.gm.eurac.edu).

PathGPS: discover shared genetic architecture using GWAS summary data.

The increasing availability and scale of biobanks and "omic" datasets bring new horizons for understanding biological mechanisms. PathGPS is an exploratory data analysis tool to discover genetic architectures using Genome Wide Association Studies (GWAS) summary data. PathGPS is based on a linear structural equation model where traits are regulated by both genetic and environmental pathways. PathGPS decouples the genetic and environmental components by contrasting the GWAS associations of "signal" genes with those of "noise" genes. From the estimated genetic component, PathGPS then extracts genetic pathways via principal component and factor analysis, leveraging the low-rank and sparse properties. In addition, we provide a bootstrap aggregating ("bagging") algorithm to improve stability under data perturbation and hyperparameter tuning. When applied to a metabolomics dataset and the UK Biobank, PathGPS confirms several known gene-trait clusters and suggests multiple new hypotheses for future investigations.

Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Cell type-specific network analysis in Diversity Outbred mice identifies genes potentially responsible for human bone mineral density GWAS associations.

Genome-wide association studies (GWASs) have identified many sources of genetic variation associated with bone mineral density (BMD), a clinical predictor of fracture risk and osteoporosis. Aside from the identification of causal genes, other difficult challenges to informing GWAS include characterizing the roles of predicted causal genes in disease and providing additional functional context, such as the cell type predictions or biological pathways in which causal genes operate. Leveraging single-cell transcriptomics (scRNA-seq) can assist in informing BMD GWAS by linking disease-associated variants to genes and providing a cell type context for which these causal genes drive disease. Here, we use large-scale scRNA-seq data from bone marrow-derived stromal cells cultured under osteogenic conditions (BMSC-OBs) from Diversity Outbred (DO) mice to generate cell type-specific networks and contextualize BMD GWAS-implicated genes. Using trajectories inferred from the scRNA-seq data, we identify networks enriched with genes that exhibit the most dynamic changes in expression across trajectories. We discover 21 network driver genes, which are likely to be causal for human BMD GWAS associations that colocalize with expression/splicing quantitative trait loci (eQTL/sQTL). These driver genes, including Fgfrl1 and Tpx2, along with their associated networks, are predicted to be novel regulators of BMD via their roles in the differentiation of mesenchymal lineage cells. In this work, we showcase the use of single-cell transcriptomics from mouse bone-relevant cells to inform human BMD GWAS and prioritize genetic targets with potential causal roles in the development of osteoporosis.

Disentangling genetic effects on transcriptional and post-transcriptional gene regulation through integrating exon and intron expression QTLs

Expression quantitative trait loci (eQTL) studies typically consider exon expression of genes and discard intronic RNA sequencing reads despite their information on RNA metabolism. Here, we quantify genetic effects on exon and intron levels of genes and their ratio in lymphoblastoid cell lines, revealing thousands of cis-QTLs of each type. While genetic effects are often shared between cis-QTL types, 7814 (47%) are not detected as top cis-QTLs at exon levels. We show that exon levels preferentially capture genetic effects on transcriptional regulation, while exon-intron-ratios better detect those on co- and post-transcriptional processes. Considering all cis-QTL types substantially increases (by 71%) the number of colocalizing variants identified by genome-wide association studies (GWAS). It further allows dissecting the potential gene regulatory processes underlying GWAS associations, suggesting comparable contributions by transcriptional (50%) and co- and post-transcriptional regulation (46%) to complex traits. Overall, integrating intronic RNA sequencing reads in eQTL studies expands our understanding of genetic effects on gene regulatory processes.

Investigating the shared genetic architecture between frailty and insomnia.

The epidemiological association between frailty and insomnia is well established, yet the presence of a common genetic etiology is still uncertain. Further exploration is needed to ascertain the causal relationship between frailty and insomnia. Utilizing data obtained from genome-wide association studies (GWAS) summaries, we utilized the linkage disequilibrium score regression (LDSC) to determine the genetic correlation existing between frailty and insomnia. The determination of causality was achieved through the application of two-sample Mendelian randomization. We investigated the enrichment of single nucleotide polymorphism (SNP) at various tissue types utilizing stratified LD score regression (S-LDSC) and multimarker analysis of genome annotation (MAGMA). Common risk SNPs were identified using Multi-Trait Analysis of GWAS (MTAG) and Cross-Phenotype Association (CPASSOC). We further investigated the expression profiles of risk genes in tissues using Summary-data-based Mendelian randomization(SMR) based on pooled data, to explore potential functional genes. Our findings indicated a significant genetic correlation between frailty and insomnia, highlighting SNPs sharing risk (rs34290943, rs10865954), with a pronounced correlation in the localized genomic region 3p21.31. Partitioned genetic analysis revealed 24 functional elements significantly associated with both frailty and insomnia. Furthermore, mendelian randomization revealed a causal connection between frailty and insomnia. The genetic correlation between frailty and insomnia showed enrichment in 11 brain regions (S-LDSC) and 9 brain regions (MAGMA), where four functional genes (RMB6, MST1R, RF123, and FAM212A) were identified. This study suggests the existence of a genetic correlation and common risk genes between frailty and insomnia, contributing to a deeper comprehension of their pathogenesis and assists in identifying potential therapeutic targets.

A transcriptome-wide association study identified susceptibility genes for hepatocellular carcinoma in East Asia

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and is prevalent in East Asia. Although genome-wide association studies (GWASs) of HCC have identified 23 risk regions, the susceptibility genes underlying these associations largely remain unclear. To identify novel candidate genes for HCC, we conducted liver single-tissue and cross-tissue transcriptome-wide association studies (TWASs) in two populations of East Asia. Methods GWAS summary statistics of 2,514 subjects (1,161 HCC cases and 1,353 controls) from the Chinese Qidong cohort and 161,323 subjects (2,122 HCC cases and 159,201 controls) from the BioBank Japan project were used to conduct TWAS analysis. The single-tissue and cross-tissue TWAS approaches were both used to detect the association between susceptible genes and the risk of HCC. TWAS identified genes were further annotated by Metascape, UALCAN, GEPIA2, and DepMap. Results We identified 22 novel genes at 16 independent loci significantly associated with HCC risk after Bonferroni correction. Of these, 13 genes were located in novel regions. Besides, we found 83 genes overlapped in the Chinese and Japanese cohorts with P < 0.05, of which, three genes (NUAK2, HLA-DQA1, and ATP6V1G2) were discerned by both single-tissue and cross-tissue TWAS approaches. Among the genes identified through TWAS, a significant proportion of them exhibit a credible role in HCC biology, such as FAM96B, HSPA5, POLRMT, MPHOSPH10, and RABL2A. HLA-DQA1, NUAK2, and HSPA5 associated with the process of carcinogenesis in HCC as previously reported. Conclusions Our findings highlight the value of leveraging the gene expression data to identify new candidate genes beyond the GWAS associations and could further provide a genetic insight for the biology of HCC.

FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts.

Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.

Assessment of multi-trait analysis of GWAS (MTAG) for discovery of novel genetic variants and mechanistic insight in common cardiovascular diseases

Abstract Background Variability in cardiovascular conditions is partly explained by common genomic variation identifiable by genome-wide association studies (GWAS). Genetic substrate may overlap in certain entities, this being quantified by genetic correlation. We aimed to leverage multi-trait analysis of GWAS (MTAG), (1) which boosts statistical power by joint analysis of genetically correlated traits, to improve genetic loci discovery in 3 common cardiovascular diseases: atrial fibrillation (AF), coronary artery disease (CAD), and heart failure (HF). Methods and Results Cardiovascular phenotypes with available GWAS summary statistics most correlated with the disease of interest were analysed jointly by MTAG (workflow depicted in Figure 1). These included body mass index and HF for AF (2); HF, LDL-cholesterol, and systolic blood pressure for CAD (3); and AF, CAD, and dilated cardiomyopathy for HF (4). MTAG increased statistical power for all 3 disease GWAS and allowed the identification of 19 new genomic loci for AF, 89 for CAD and 52 for HF (at a GWAS significance level P<5x10-8) (Figure 2). New loci were defined as not previously identified in the single trait original GWAS. Consistently, 19/19, 85/89 and 48/52 of new loci reached nominal significance (P<0.05) in the original studies; and 37%, 44% and 36% of new loci have been associated with AF, CAD and HF in GWAS and phenome wide association studies (PheWAS) in independent cohorts. MTAG summary statistics annotation and functional analysis integrated the distance to the canonical transcriptional start site, chromatin interaction experiments, quantitative molecular phenotype trait experiments and in silico functional prediction through OpenTargets Genetics. MAGMA gene-set functional analysis of the new MTAG results showed 87, 27 and 24 significant associated pathways to AF, CAD and HF respectively. Among the top associations, cardiac depolarization, repolarization, and muscle contraction in AF; macromolecular complex remodeling, triglyceride, chylomicron and LDL metabolism in CAD; and muscle structure, tissue development and RNA transcription initiation in HF. Among novel relevant genes in AF, SRR on chromosome 16 is involved in central nervous system development through regulation of NMDA receptors. This locus has been previously associated with QRS in another GWAS and SRR is related to reduction in heart rate variability and AF enhancement in rats. In CAD, RSPO3 and JAG1 genes, on chromosomes 6 and 20 respectively, have a relevant role in angiogenesis, where RSPO3 is a crucial regulator of coronary artery formation in the developing heart. Conclusions MTAG allows the discovery of new trait-specific loci involved in the pathogenesis of AF, CAD, and HF congruent with the mechanisms of each disease and consistent with independent external cohort PheWAS and GWAS. Thus, MTAG is an interesting tool to unravel relevant mechanisms and potential therapeutic targets in common cardiovascular diseases.

Abstract 18642: GWAS to Function Highlights the Role of REST in Endothelial Biology and Atherosclerosis

Introduction: Despite the emergence of new lipid-lowering therapies, coronary artery disease (CAD) remains the leading cause of death worldwide. Genome-wide association studies (GWAS) for CAD have discovered multiple novel loci that contribute to CAD pathogenesis. However, limitations in the interpretability of those GWAS associations have hindered the clinical translation of those findings. Hypothesis: In this study, we sought to evaluate the mechanisms of the association between chromosomal locus 4q12 and CAD, replicated in multiple independent GWAS for CAD. Methods: We utilized a combination of computational methods and in vitro assays to achieve our objectives. Results: Meta-analysis of public expression quantitative trait locus (eQTL) data and multi-trait colocalization across atherosclerosis-relevant cell types revealed that the CAD 4q12 locus regulates the expression of REST, a transcriptional repressor, in human aortic endothelial cells (HAEC) and prioritized 2 enhancer variants as candidate causal variants for the GWAS association. CRISPR interference confirmed that the rs6853156 variant regulates REST expression in inducible human endothelial cells. Pathway enrichment analysis in HAEC after REST silencing showed that epithelial to mesenchymal transition was the most upregulated pathway suggesting a potential mechanism via which silencing REST contributes to atherosclerosis. Endothelial to mesenchymal transition (endMT) was confirmed in HAEC after REST silencing at the RNA and protein level by rt-qPCR, western blot, and immunofluorescence. Concordantly, REST silencing increased endothelial permeability and matrix metalloproteinase activity in endothelial cell supernatants in vitro. L1CAM, a known endMT activator was only expressed in HAEC upon REST silencing. Chromatin immunoprecipitation sequencing supports direct binding of REST to the L1CAM promoter. Simultaneous silencing of L1CAM and REST in HAEC inhibited the observed upregulation of mesenchymal genes induced by REST silencing. Conclusions: In summary, our data reveal a novel role of REST as a repressor that functions as a constitutive inhibitor of endMT in human arterial endothelial cells.

Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes.

Depression is a common comorbidity of type 2 diabetes. We assessed the causal relationships and shared genetics between them. We applied two-sample, bidirectional Mendelian randomization (MR) to assess causality between type 2 diabetes and depression. We investigated potential mediation using two-step MR. To identify shared genetics, we performed 1) genome-wide association studies (GWAS) separately and 2) multiphenotype GWAS (MP-GWAS) of type 2 diabetes (19,344 case subjects, 463,641 control subjects) and depression using major depressive disorder (MDD) (5,262 case subjects, 86,275 control subjects) and self-reported depressive symptoms (n = 153,079) in the UK Biobank. We analyzed expression quantitative trait locus (eQTL) data from public databases to identify target genes in relevant tissues. MR demonstrated a significant causal effect of depression on type 2 diabetes (odds ratio 1.26 [95% CI 1.11-1.44], P = 5.46 × 10-4) but not in the reverse direction. Mediation analysis indicated that 36.5% (12.4-57.6%, P = 0.0499) of the effect from depression on type 2 diabetes was mediated by BMI. GWAS of type 2 diabetes and depressive symptoms did not identify shared loci. MP-GWAS identified seven shared loci mapped to TCF7L2, CDKAL1, IGF2BP2, SPRY2, CCND2-AS1, IRS1, CDKN2B-AS1. MDD has not brought any significant association in either GWAS or MP-GWAS. Most MP-GWAS loci had an eQTL, including single nucleotide polymorphisms implicating the cell cycle gene CCND2 in pancreatic islets and brain and the insulin signaling gene IRS1 in adipose tissue, suggesting a multitissue and pleiotropic underlying mechanism. Our results highlight the importance to prevent type 2 diabetes at the onset of depressive symptoms and the need to maintain a healthy weight in the context of its effect on depression and type 2 diabetes comorbidity.

Association of Risk Variants in the CFH Gene With Elevated Levels of Coagulation and Complement Factors in Idiopathic Multifocal Choroiditis

Idiopathic multifocal choroiditis (MFC) is poorly understood, thereby hindering optimal treatment and monitoring of patients. To identify the genes and pathways associated with idiopathic MFC. This was a case-control genome-wide association study (GWAS) and protein study of blood plasma samples conducted from March 2006 to February 2022. This was a multicenter study involving 6 Dutch universities. Participants were grouped into 2 cohorts: cohort 1 consisted of Dutch patients with idiopathic MFC and controls, and cohort 2 consisted of patients with MFC and controls. Plasma samples from patients with idiopathic MFC who had not received treatment were subjected to targeted proteomics. Idiopathic MFC was diagnosed according to the Standardization of Uveitis Nomenclature (SUN) Working Group guidelines for punctate inner choroidopathy and multifocal choroiditis with panuveitis. Data were analyzed from July 2021 to October 2022. Genetic variants associated with idiopathic MFC and risk variants associated with plasma protein concentrations in patients. This study included a total of 4437 participants in cohort 1 (170 [3.8%] Dutch patients with idiopathic MFC and 4267 [96.2%] controls; mean [SD] age, 55 [18] years; 2443 female [55%]) and 1344 participants in cohort 2 (52 [3.9%] patients with MFC and 1292 [96.1%] controls; 737 male [55%]). The primary GWAS association mapped to the CFH gene with genome-wide significance (lead variant the A allele of rs7535263; odds ratio [OR], 0.52; 95% CI, 0.41-0.64; P = 9.3 × 10-9). There was no genome-wide significant association with classical human leukocyte antigen (HLA) alleles (lead classical allele, HLA-A*31:01; P = .002). The association with rs7535263 showed consistent direction of effect in an independent cohort of 52 cases and 1292 control samples (combined meta-analysis OR, 0.58; 95% CI, 0.38-0.77; P = 3.0 × 10-8). In proteomic analysis of 87 patients, the risk allele G of rs7535263 in the CFH gene was strongly associated with increased plasma concentrations of factor H-related (FHR) proteins (eg, FHR-2, likelihood ratio test, adjusted P = 1.1 × 10-3) and proteins involved in platelet activation and the complement cascade. Results suggest that CFH gene variants increase systemic concentrations of key factors of the complement and coagulation cascades, thereby conferring susceptibility to idiopathic MFC. These findings suggest that the complement and coagulation pathways may be key targets for the treatment of idiopathic MFC.

Topr: an R package for viewing and annotating genetic association results

BackgroundThe successful identification of genetic loci for complex traits in genome-wide association studies (GWAS) has resulted in thousands of GWAS summary statistics becoming publicly available for hundreds of complex traits from multiple cohorts and studies. Visualisation is an important aid for interpreting, comparing, validating, and obtaining an overview of large amounts of data. However, the current software is limited in its ability and flexibility to annotate and simultaneously display multiple GWAS results which is useful when interpreting and comparing association results. Therefore, I created the topr R package to facilitate visualisation, annotation, and comparisons of single or multiple GWAS results. It contains functions tailored for viewing and analysing GWAS results.Resultstopr provides a fast and elegant visual display of association results, along with the annotation of association peaks with their nearest gene. Association results from multiple analyses can be viewed simultaneously over the entire genome or in a more detailed regional view along with gene information. Users can perform the essential steps of visually exploring and annotating association results and generating elegant publication-ready plots.Conclusionstopr is developed as a package for the R statistical computing environment, released under the GNU General Public License, and is freely available on the Comprehensive R Archive Network (http://cran.r-project.org/package=topr). The source code is available at GitHub (https://github.com/totajuliusd/topr). topr provides several advantages and advances over the current alternatives, particularly in its gene annotation functionality and customisable display of single- or multiple-association results. With topr, I provide a flexible tool with multiple features to aid in the analysis and evaluation of GWAS association results.

The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle

BackgroundA detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle.ResultsWe identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits.ConclusionsThe extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.