Genetic correlations between COVID-19 and a variety of traits and diseases.
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ABSTRACT:
SUBMITTER: Chang X
PROVIDER: S-EPMC8081576 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
| S-EPMC4797329 | biostudies-literature
Project description:ObjectiveThe aim of the study was to comprehensively explore the genetic susceptibility correlations among diseases and traits from large-scale individual genotype data.Materials and methodsBased on a knowledge base of genetic variants significantly (P < 5 × 10 -8 ) linked with human phenotypes, genetic risk scores (GRSs) of diseases or traits were calculated for 2504 individuals with whole-genome sequencing data from the 1000 Genomes Project. Associations between diseases/traits were statistically evaluated by pairwise correlation analysis of GRSs. Overlaps between the genetic susceptibility correlations and disease comorbidity associations from hospital claims data in more than 30 million patients in United States were assessed.ResultsCorrelation analysis of GRSs revealed 823 significant correlations among 78 diseases and 89 traits (false discovery rate adjusted P -value or Q -value < 0.01). It is noticeable that GRSs were correlated in 464 associations (56.4%) even if they were combinations of distinct sets of risk variants without chromosomal linkage, suggesting the presence of genetic interactions beyond chromosome position. When 312 significant genetic susceptibility correlations between diseases were compared to nationwide disease comorbidity correlations obtained from data from 32 million Medicare claims in the United States, 108 overlaps (34.6%) were found that had both genetic susceptibility and epidemiologic comorbid correlations.ConclusionThe study suggests that common genetic background exists between diseases and traits with epidemiologic associations. The GRS correlation approach provides a rich source of candidate associations among diseases and traits from the genetic perspective, warranting further epidemiologic studies.
| S-EPMC7651975 | biostudies-literature
Project description:Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.
| S-EPMC5841449 | biostudies-literature
Project description:Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated. Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis. In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes PPP2R3A for BUN, VAMP8 for UACR, DOCK7 for creatinine, and HIBADH for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them. This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.
| S-EPMC11288879 | biostudies-literature
Project description:One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified therapeutics for cancer, lifestyle related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will help accelerate drug discovery and fill the gap between bench side and bedside.
| S-EPMC9549714 | biostudies-literature
Project description:Aims: This study was aimed to apply a Mendelian randomization design to explore the causal association between coronavirus disease 2019 (COVID-19) and three cardio-cerebrovascular diseases, including atrial fibrillation, ischemic stroke, and coronary artery disease. Methods: Two-sample Mendelian randomization was used to determine the following: 1) the causal effect of COVID-19 on atrial fibrillation (55,114 case participants vs 482,295 control participants), coronary artery disease (34,541 case participants vs 261,984 control participants), and ischemic stroke (34,217 case participants vs 40,611 control participants), which were obtained from the European Bioinformatics Institute, and 2) the causal effect of three cardio-cerebrovascular diseases on COVID-19. The single-nucleotide polymorphisms (SNPs) of COVID-19 were selected from the summary-level genome-wide association study data of COVID-19-hg genome-wide association study (GWAS) meta-analyses (round 5) based on the COVID-19 Host Genetics Initiative for participants with European ancestry. The random-effects inverse-variance weighted method was conducted for the main analyses, with a complementary analysis of the weighted median and Mendelian randomization (MR)-Egger approaches. Results: Genetically predicted hospitalized COVID-19 was suggestively associated with ischemic stroke, with an odds ratio (OR) of 1.049 [95% confidence interval (CI) 1.003-1.098; p = 0.037] in the COVID-19 Host Genetics Initiative GWAS. When excluding the UK Biobank (UKBB) data, our analysis revealed a similar odds ratio of 1.041 (95% CI 1.001-1.082; p = 0.044). Genetically predicted coronary artery disease was associated with critical COVID-19, with an OR of 0.860 (95% CI 0.760-0.973; p = 0.017) in the GWAS meta-analysis and an OR of 0.820 (95% CI 0.722-0.931; p = 0.002) when excluding the UKBB data, separately. Limited evidence of causal associations was observed between critical or hospitalized COVID-19 and other cardio-cerebrovascular diseases included in our study. Conclusion: Our findings provide suggestive evidence about the causal association between hospitalized COVID-19 and an increased risk of ischemic stroke. Besides, other factors potentially contribute to the risk of coronary artery disease in patients with COVID-19, but not genetics.
| S-EPMC8966609 | biostudies-literature
Project description:BACKGROUND AND OBJECTIVES:Metabolic syndrome is a cluster of risk factors associated with CKD. By studying the genetic and environmental influences on how traits of metabolic syndrome correlate with CKD, the understanding of the etiological relationships can be improved. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:From the population-based TwinGene project within the Swedish Twin Registry, 4721 complete twin pairs (9442 European ancestry participants) were included in this cross-sectional twin study. Metabolic syndrome-related continuous traits were measured, and the binary components as well as the status of metabolic syndrome were defined according to the National Cholesterol Education Program-Adult Treatment Panel III. The eGFR was calculated by cystatin C-based equations from the CKD epidemiology collaboration group, and CKD was defined by eGFR<60 ml/min per 1.73 m2. Genetic and environmental contributions to the correlations between traits of metabolic syndrome and CKD were estimated by using twin-based bivariate structural equation models. RESULTS:The correlation between metabolic syndrome and eGFR-defined CKD was 0.16 (95% confidence interval [95% CI], 0.12 to 0.20), out of which 51% (95% CI, 12% to 90%) was explained by genes, whereas 15% (95% CI, 0% to 42%) and 34% (95% CI, 16% to 52%) was explained by the shared and nonshared environment, respectively. The genetic and environmental correlations between metabolic syndrome and CKD were 0.29 (95% CI, 0.07 to 0.51) and 0.27 (95% CI, 0.13 to 0.41), respectively. For the correlation between abdominal obesity and eGFR, 69% (95% CI, 10% to 100%) was explained by genes and 23% (95% CI, 5% to 41%) was explained by environment. The genetic correlation between abdominal obesity and eGFR was -0.30 (95% CI, -0.54 to -0.06), whereas the environmental correlation was -0.14 (95% CI, -0.22 to -0.06). CONCLUSIONS:Both genes and environment contribute to the correlation between metabolic syndrome and eGFR-defined CKD. The genetic contribution is particularly important to the correlation between abdominal obesity and eGFR.
| S-EPMC6832046 | biostudies-literature
Project description:The evolution of elaborate forms of parental care is an important topic in behavioral ecology, yet the factors shaping the evolution of complex suites of parental and offspring traits are poorly understood. Here, we use a multivariate quantitative genetic approach to study phenotypic and genetic correlations between parental and offspring traits in the burying beetle Nicrophorus vespilloides. To this end, we recorded 2 prenatal traits (clutch size and egg size), 2 postnatal parental behaviors (direct care directed toward larvae and indirect care directed toward resource maintenance), 1 offspring behavior (begging), and 2 measures of breeding success (larval dispersal mass and number of dispersing larvae). Females breeding on larger carcasses provided less direct care but produced larger larvae than females breeding on smaller carcasses. Furthermore, there were positive phenotypic correlations between clutch size, direct, and indirect care. Both egg size and direct care were positively correlated with dispersal mass, whereas clutch size was negatively correlated with dispersal mass. Clutch size and number of dispersed larvae showed genetic variance both in terms of differences between populations of origin and significant heritabilities. However, we found no evidence of genetic variance underlying other parental or offspring traits. Our results suggest that correlations between suites of parental traits are driven by variation in individual quality rather than trade-offs, that some parental traits promote offspring growth while others increase the number of offspring produced, and that parental and offspring traits might respond slowly to selection due to low levels of additive genetic variance.
| S-EPMC5255903 | biostudies-literature
Project description:We carried out a bidirectional Mendelian randomization (MR) including cases of eczema (N = 218,792), asthma (N = 462,933), and allergic rhinitis (N = 112,583). COVID-19 susceptibility (N = 1,683,768), COVID-19 hospitalization (N = 1,887,658), and COVID-19 severe respiratory symptom (N = 1,388,342) were sampled from GWAS database. The MR analysis was primarily based on inverse variance weighted (IVW), supplemented by several other algorithms. In the bidirectional MR analysis, eczema was negatively associated with COVID-19 susceptibility (odds ratio (OR) IVW = 0.92; p = 0.031) and COVID-19 hospitalization (ORIVW = 0.81, p = 0.010); asthma was negatively associated with COVID-19 susceptibility (ORIVW = 0.65, p = 0.005) and COVID-19 severe respiratory symptom (ORIVW = 0.20, p = 0.001). No significant association was found between allergic rhinitis and COVID-19 susceptibility (ORIVW = 0.80, p = 0.174), COVID-19 hospitalization (ORIVW = 0.71, p = 0.207), or COVID-19 severe respiratory symptom (ORIVW = 0.56; p = 0.167). The reverse MR analysis showed no potential reverse causal association. Our findings provided new evidence that allergic diseases might be associated with different risks of COVID-19 susceptibility, hospitalization, and severe respiratory symptom.
| S-EPMC10200717 | biostudies-literature
Project description:ImportanceLate-onset Alzheimer disease (AD), the most common form of dementia, places a large burden on families and society. Although epidemiological and clinical evidence suggests a relationship between inflammation and AD, their relationship is not well understood and could have implications for treatment and prevention strategies.ObjectiveTo determine whether a subset of genes involved with increased risk of inflammation are also associated with increased risk for AD.Design, setting, and participantsIn a genetic epidemiology study conducted in July 2015, we systematically investigated genetic overlap between AD (International Genomics of Alzheimer's Project stage 1) and Crohn disease, ulcerative colitis, rheumatoid arthritis, type 1 diabetes, celiac disease, and psoriasis using summary data from genome-wide association studies at multiple academic clinical research centers. P values and odds ratios from genome-wide association studies of more than 100 000 individuals were from previous comparisons of patients vs respective control cohorts. Diagnosis for each disorder was previously established for the parent study using consensus criteria.Main outcomes and measuresThe primary outcome was the pleiotropic (conjunction) false discovery rate P value. Follow-up for candidate variants included neuritic plaque and neurofibrillary tangle pathology; longitudinal Alzheimer's Disease Assessment Scale cognitive subscale scores as a measure of cognitive dysfunction (Alzheimer's Disease Neuroimaging Initiative); and gene expression in AD vs control brains (Gene Expression Omnibus data).ResultsEight single-nucleotide polymorphisms (false discovery rate P < .05) were associated with both AD and immune-mediated diseases. Of these, rs2516049 (closest gene HLA-DRB5; conjunction false discovery rate P = .04 for AD and psoriasis, 5.37 × 10-5 for AD, and 6.03 × 10-15 for psoriasis) and rs12570088 (closest gene IPMK; conjunction false discovery rate P = .009 for AD and Crohn disease, P = 5.73 × 10-6 for AD, and 6.57 × 10-5 for Crohn disease) demonstrated the same direction of allelic effect between AD and the immune-mediated diseases. Both rs2516049 and rs12570088 were significantly associated with neurofibrillary tangle pathology (P = .01352 and .03151, respectively); rs2516049 additionally correlated with longitudinal decline on Alzheimer's Disease Assessment Scale cognitive subscale scores (β [SE], 0.405 [0.190]; P = .03). Regarding gene expression, HLA-DRA and IPMK transcript expression was significantly altered in AD brains compared with control brains (HLA-DRA: β [SE], 0.155 [0.024]; P = 1.97 × 10-10; IPMK: β [SE], -0.096 [0.013]; P = 7.57 × 10-13).Conclusions and relevanceOur findings demonstrate genetic overlap between AD and immune-mediated diseases and suggest that immune system processes influence AD pathogenesis and progression.
| S-EPMC4905783 | biostudies-literature