Search Results (9)

Background: Rheumatoid arthritis (RA) is an autoimmune disease that remains incurable. An increasing number of proteomic genome-wide association studies (GWASs) are emerging, offering immense potential for identifying novel therapeutic targets for diseases. This study aims to identify potential therapeutic targets for RA based on human plasma proteome. Methods: Protein quantitative trait loci were extracted and integrated from eight large-scale proteomic GWASs. Proteome-wide Mendelian randomization (Pro-MR) was performed to prioritize proteins causally associated with RA. Further validation of the reliability and stratification of prioritized proteins was performed using MR meta-analysis, colocalization, and transcriptome-wide summary-data-based MR. Subsequently, prioritized proteins were characterized through protein–protein interaction and enrichment analyses, pleiotropy assessment, genetically engineered mouse models, cell-type-specific expression analysis, and druggability evaluation. Phenotypic expansion analyses were also conducted to explore the effects of the prioritized proteins on phenotypes such as endocrine disorders, cardiovascular diseases, and other immune-related diseases. Results: Pro-MR prioritized 32 unique proteins associated with RA risk. After validation, prioritized proteins were stratified into four reliability tiers. Prioritized proteins showed interactions with established RA drug targets and were enriched in an immune-related functional profile. Four trans-associated proteins exhibited vertical or horizontal pleiotropy with specific genes or proteins. Genetically engineered mouse models for 18 prioritized protein-coding genes displayed abnormal immune phenotypes. Single-cell RNA sequencing data were used to validate the enriched expression of several prioritized proteins in specific synovial cell types. Nine prioritized proteins were identified as targets of existing drugs in clinical trials or were already approved. Further phenome-wide MR and mediation analyses revealed the effects and potential mediating roles of some prioritized proteins on other phenotypes. Conclusions: This study identified 32 plasma proteins as potential therapeutic targets for RA, expanding the prospects for drug discovery and deepening insights into RA pathogenesis. Full article

Selenoprotein S (SELENOS), one of the carrier proteins of dietary selenium (Se), is a key regulator of inflammation, oxidative stress, and endoplasmic reticulum (ER) stress, all of which are implicated in the pathogenesis of stroke. However, the causality between SELENOS and stroke risk remains poorly understood. This study aimed to explore the association between genetically determined plasma SELENOS levels and the risk of all-cause stroke, ischemic stroke, and intracerebral hemorrhage (ICH) using a two-sample Mendelian randomization (MR) approach. We analyzed data from three large-scale Genome-Wide Association Study (GWAS) meta-analyses of individuals of European descent. The fixed-effect inverse-variance weighted (IVW) model analysis revealed that genetically elevated SELENOS levels were associated with an increased risk of all-cause stroke, ischemic stroke, and ICH. Sensitivity analyses showed no evidence of pleiotropy or heterogeneity, and leave-one-out analyses confirmed the robustness of our results. Here, we show that elevated plasma SELENOS levels are causally linked to increased stroke risk. Although the effect sizes were modest, these findings suggest SELENOS may play a role in stroke pathogenesis, emphasizing the need for further mechanistic and functional studies. Finally, our findings shed light on the importance of tailored Se intake management in the context of stroke prevention. Full article

The genetic basis of schizophrenia (SZ) remains elusive despite its characterization as a highly heritable disorder. This incomplete understanding has led to stagnation in therapeutics and treatment, leaving many suffering with insufficient relief from symptoms. However, recent large-cohort genome- and exome-wide association studies have provided insights into the underlying genetic machinery. The scale of these studies allows for the identification of ultra-rare mutations that confer substantial disease risk, guiding clinicians and researchers toward general classes of genes that are central to SZ etiology. One such large-scale collaboration effort by the Schizophrenia Exome Sequencing Meta-Analysis consortium identified ten, high-risk, ultra-rare, protein-truncating variants, providing the clearest picture to date of the dysfunctional gene products that substantially increase risk for SZ. While genetic studies of SZ provide valuable information regarding “what” genes are linked with the disorder, it is an open question as to “when” during brain development these genetic mutations impose deleterious effects. To shed light on this unresolved aspect of SZ etiology, we queried the BrainSpan developmental mRNA expression database for these ten high-risk genes and discovered three general expression trajectories throughout pre- and postnatal brain development. The elusiveness of SZ etiology, we infer, is not only borne out of the genetic heterogeneity across clinical cases, but also in our incomplete understanding of how genetic mutations perturb neurodevelopment during multiple critical periods. We contextualize this notion within the National Institute of Mental Health’s Research Domain Criteria framework and emphasize the utility of considering both genetic variables and developmental context in future studies. Full article

Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs. Full article

High blood pressure (HBP) has been implicated as a major risk factor for cardiovascular diseases in several populations, including individuals of African ancestry. Despite the elevated burden of HBP-induced cardiovascular diseases in Africa and other populations of African descent, limited genetic studies have been carried out to explore the genetic mechanism driving this phenomenon. We performed genome-wide association univariate and multivariate analyses of both systolic (SBP) and diastolic blood pressure (DBP) traits in 80,950 individuals of African ancestry. We used summary statistics data from six independent cohorts, including the African Partnership for Chronic Disease Research (APCDR), the UK Biobank, and the Million Veteran Program (MVP). FUMA was used to annotate, prioritize, visualize, and interpret our findings to gain a better understanding of the molecular mechanism(s) underlying the genetics of BP traits. Finally, we undertook a Bayesian fine-mapping analysis to identify potential causal variants. Our meta-analysis identified 10 independent variants associated with SBP and 9 with DBP traits. Whilst our multivariate GWAS method identified 21 independent signals, 18 of these SNPs have been previously identified. SBP was linked to gene sets involved in biological processes such as synapse assembly and cell–cell adhesion via plasma membrane adhesion. Of the 19 independent SNPs identified in the BP meta-analysis, only 11 variants had posterior probability (PP) of > 50%, including one novel variant: rs562545 (MOBP, PP = 77%). To facilitate further research and fine-mapping of high-risk loci/variants in highly susceptible groups for cardiovascular disease and other related traits, large-scale genomic datasets are needed. Our findings highlight the importance of including ancestrally diverse populations in large GWASs and the need for diversity in genetic research. Full article

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10⁻⁸) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk. Full article

The Arabian Peninsula, located at the nexus of Africa, Europe, and Asia, was implicated in early human migration. The Arab population is characterized by consanguinity and endogamy leading to inbreeding. Global genome-wide association (GWA) studies on metabolic traits under-represent the Arab population. Replicability of GWA-identified association signals in the Arab population has not been satisfactorily explored. It is important to assess how well GWA-identified findings generalize if their clinical interpretations are to benefit the target population. Our recent study from Kuwait, which performed genome-wide imputation and meta-analysis, observed 304 (from 151 genes) of the 4746 GWA-identified metabolic risk variants replicable in the Arab population. A recent large GWA study from Qatar found replication of 30 GWA-identified lipid risk variants. These complementing studies from the Peninsula increase the confidence in generalizing metabolic risk loci to the Arab population. However, both the studies reported a low extent of transferability. In this review, we examine the observed low transferability in the context of differences in environment, genetic correlations (allele frequencies, linkage disequilibrium, effect sizes, and heritability), and phenotype variance. We emphasize the need for large-scale GWA studies on deeply phenotyped cohorts of at least 20,000 Arab individuals. The review further presents GWA-identified metabolic risk variants generalizable to the Arab population. Full article

Nonalcoholic fatty liver disease (NAFLD) generally co-occurs with metabolic disorders, but it is unclear which genes have a pleiotripic effect on NAFLD and metabolic traits. We performed a large-scale cross-trait association analysis to identify the overlapping genes between NAFLD and nine metabolic traits. Among all the metabolic traits, we found that obesity and type II diabetes are associated with NAFLD. Then, a multitrait association analysis among NAFLD, obesity and type II diabetes was conducted to improve the overall statistical power. We identified 792 significant variants by a cross-trait meta-analysis involving 100 pleiotripic genes. Moreover, we detected another two common genes by a genome-wide gene test. The results from the pathway enrichment analysis show that the 102 shared risk genes are enriched in cancer, diabetes, insulin secretion, and other related pathways. This study can help us understand the molecular mechanisms underlying comorbid NAFLD and metabolic disorders. Full article

Previous genome-wide association studies did not show a consistent association between the BOLL gene (rs700651, 2q33.1) and intracranial aneurysm (IA) susceptibility. We aimed to perform an updated meta-analysis for the potential IA-susceptibility locus in large-scale multi-ethnic populations. We conducted a systematic review of studies identified by an electronic search from January 1990 to March 2019. The overall estimates of the “G” allele of rs700651, indicating IA susceptibility, were calculated under the fixed- and random-effect models using the inverse-variance method. Subsequent in silico function and cis-expression quantitative trait loci (cis-eQTL) analyses were performed to evaluate biological functions and genotype-specific expressions in human tissues. We included 4513 IA patients and 13,506 controls from five studies with seven independent populations: three European-ancestry, three Japanese, and one Korean population. The overall result showed a genome-wide significance threshold between rs700651 and IA susceptibility after controlling for study heterogeneity (OR = 1.213, 95% CI: 1.135–1.296). Subsequent cis-eQTL analysis showed significant genome-wide expressions in three human tissues, i.e., testis (p = 8.04 × 10⁻¹⁵ for ANKRD44), tibial nerves (p = 3.18 × 10⁻¹⁰ for SF3B1), and thyroid glands (p = 4.61 × 10⁻⁹ for SF3B1). The rs700651 common variant of the 2q33.1 region may be involved in genetic mechanisms that increase the risk of IA and may play crucial roles in regulatory functions. Full article