Summary-level Genome-wide Association Studies Data Research Articles

The integrity of the corticospinal tract and corpus callosum, and the risk of ALS: univariable and multivariable Mendelian randomization

Studies suggest that amyotrophic lateral sclerosis (ALS) compromises the integrity of white matter fiber tracts, primarily affecting motor fibers. However, it remains uncertain whether the integrity of these fibers influences the risk of ALS. We performed bidirectional two-sample Mendelian randomization (MR) and multivariable MR analyses to evaluate the associative relationships between the integrity of fiber tracts [including the corticospinal tract (CST) and corpus callosum (CC)] and the risk of ALS. Genetic instrumental variables for specific fiber tracts were obtained from published genome-wide association studies (GWASs), including 33,292 European individuals from five diffusion magnetic resonance imaging (dMRI) datasets. Summary-level GWAS data for ALS were derived from 27,205 ALS patients and 110,881 controls. The MR results suggested that an increase in the first principal component (PC1) of fractional anisotropy (FA) in the genu of the CC (GCC) was correlated with an increased risk of ALS (PFDR = 0.001, odds ratio = 1.363, 95% confidence interval 1.178–1.577). Although other neuroimaging phenotypes [mean diffusivity in the CST, radial diffusivity (RD) in the CST, FA in the GCC, PC1 in the body of the CC (BCC), PC1 in the CST, and RD in the GCC] did not pass correction, they were also considered to have suggestive associations with the risk of ALS. No evidence revealed that ALS caused changes in the integrity of fiber tracts. In summary, the results of this study provide genetic support for the potential association between the integrity of specific fiber tracts and the risk of ALS. Greater fiber integrity in the GCC and BCC may be a risk factor for ALS, while greater fiber integrity in the CST may have a protective effect on ALS. This study provides insights into ALS development.

Physical activity, sedentary behavior, and the risk of functional gastrointestinal disorders: A two-sample Mendelian randomization study.

Functional dyspepsia (FD) and irritable bowel syndrome (IBS) are prevalent functional gastrointestinal disorders (FGIDs). In this study we aimed to explore the causal association between physical activity or sedentary behavior and the risk of FD and IBS. Mendelian randomization (MR) analysis was employed. Candidate genetic instruments for physical activity and sedentary behavior were retrieved from the latest published Genome-Wide Association Study (GWAS), which included up to 703 901 participants. Summary-level GWAS data for FD (8 875 cases and 320 387 controls) and IBS (9 323 cases and 301 931 controls) were obtained from the FinnGen study. The causal effects were mainly estimated by inverse variance weighted (IVW) method. Sensitivity analyses were implemented with Cochran's Q test, MR-Egger intercept test, leave-one-out analysis, and the funnel plot. No significant association of moderate-to-vigorous intensity physical activity (MVPA), leisure screen time (LST), sedentary behavior at work (SDW), and sedentary commuting (SDC) with the risk of FD was found. However,there was a suggestive correlation between MVPA and the decreased risk of FD (odds ratio [OR] 0.63, 95% confidence interval [CI] 0.39-0.99, P = 0.047). Genetically predicted MVPA decreased the risk of IBS (OR 0.58, 95% CI 0.40-0.84, P = 0.004), while increased LST was positively associated with IBS risk (OR 1.33, 95% CI 1.15-1.53, P < 0.001). No causal effects of SDW or SDC on IBS risk were observed. MVPA and LST are causally linked to the development of IBS, which will facilitate primary prevention of IBS.

Drug Target Mendelian Randomization Study of PCSK9 and HMG-CoA Reductase Inhibition and Atrial Fibrillation

Introduction: Atrial fibrillation (AF) is a prevalent cardiac arrhythmia with significant clinical implications. The potential influence of lipid-lowering therapies, specifically PCSK9 inhibitors (PCSK9i) and HMG-CoA reductase inhibitors (statins), on AF risk remains a topic of interest. This mendelian randomization (MR) study aimed to elucidate the causal relationship between genetically predicted inhibition of PCSK9 and HMG-CoA reductase and the risk of AF. Methods: Utilizing publicly available, summary-level genome-wide association study data, we employed single-nucleotide polymorphisms associated with lower LDL-C levels as instruments for gene-simulated inhibition of PCSK9 and HMG-CoA reductase. Multiple MR techniques were applied to estimate the causal effects, and sensitivity analyses were conducted to validate the results. Results: Genetically predicted inhibition of PCSK9 demonstrated a reduced risk of AF, with an odds ratio (OR) of 0.92 (95% CI: 0.85–0.99, p = 0.01) using the inverse variance-weighted (IVW) method. In contrast, the inhibition of HMG-CoA reductase did not exhibit a statistically significant association with AF risk (IVW: OR = 1.11, 95% CI: 1.00–1.22, p = 0.05). Conclusion: Our MR study suggests that genetically predicted inhibition of PCSK9, but not HMG-CoA reductase, is associated with a lower risk of AF. These findings provide evidence for a causal protective effect of PCSK9i on AF and support the use of PCSK9i for AF prevention in patients with dyslipidemia. Further studies are needed to elucidate the mechanisms underlying the differential effects of PCSK9i and statins on AF and to confirm the clinical implications of our findings.

Nine dietary habits and risk of colorectal cancer: a Mendelian randomization study

BackgroundEpidemiological studies have provided evidence that there is an association between diet and colorectal cancer. However, the causal relationship between dietary habits and colorectal cancer remains unknown.MethodsThe UK Biobank provided summary-level genome-wide association study data for nine dietary habits, including alcohol consumption (n = 549,703), instant coffee consumption (n = 250,308), fruit consumption (n = 210,947), meat consumption (n = 210,947), full cream milk consumption (n = 41,306), sweets consumption (n = 25,521), tea consumption (n = 501,494), vegetable consumption (n = 210,947), and yogurt/ice cream consumption (n = 210,947). Additionally, data on colorectal cancer were collected, consisting of 5,567 cases and 372,016 controls. The MR analysis employed inverse variance weighted, weighted median, MR-Egger regression, and MR multivariate residuals tests.ResultsIn the predominantly European population, a positive association was observed between vegetables (OR = 1.014, 95% CI = 1.000-1.029, p = 0.048) and an increased risk of colorectal cancer. The results for vegetable did not survive correction for multiple comparisons. However, no strong evidence was found for other dietary factors, such as alcohol (OR = 1.012, 95% CI = 0.974–1.051, p = 0.556), fruit (OR = 1.007, 95% CI = 0.986–1.029, p = 0.512), meat (OR = 1.000, 95% CI = 0.987–1.026, p = 0.968), full cream milk (OR = 1.019, 95% CI = 0.979–1.061, p = 0.357), sweets (OR = 0.998, 95% CI = 0.991–1.004, p = 0.524), and tea (OR = 1.002, 95% CI = 0.994–1.009, p = 0.672), with regards to colorectal cancer risk in the European population.ConclusionsOur study highlights the need for a more nuanced approach to dietary recommendations for CRC prevention, with greater emphasis adherence to the Mediterranean dietary pattern.

Novel therapeutic targets for primary open-angle glaucoma identified through multicenter proteome-wide mendelian randomization.

This study aimed to identify novel therapeutic targets for primary open-angle glaucoma (POAG). The summary-data-based Mendelian randomization (SMR) method was used to evaluate the genetic association between plasma proteins and POAG. Two sets of plasma protein quantitative trait loci (pQTLs) data considered exposures were obtained from the Icelandic Decoding Genetics Study and UK Biobank Pharma Proteomics Project. The summary-level genome-wide association studies data for POAG were extracted from the latest Round 10 release of the FinnGen consortium (8,530 cases and 391,275 controls) and the UK Biobank (4,737 cases and 458,196 controls). Colocalization analysis was used to screen out pQTLs that share the same variant with POAG as drug targets identified. The two-sample Mendelian randomization, reverse causality testing and phenotype scanning were performed to further validate the main findings. Protein-protein interaction, pathway enrichment analysis and druggability assessment were conducted to determine whether the identified plasma proteins have potential as drug targets. After systematic analysis, this study identified eight circulating proteins as potential therapeutic targets for POAG. Three causal proteins with strong evidence of colocalization, ROBO1 (OR = 1.38, p = 1.48 × 10-4, PPH4 = 0.865), FOXO3 (OR = 0.35, p = 4.34 × 10-3, PPH4 = 0.796), ITIH3 (OR = 0.89, p = 2.76 × 10-4, PPH4 = 0.767), were considered tier one targets. Five proteins with medium support evidence of colocalization, NCR1 (OR = 1.25, p = 4.18 × 10-4, PPH4 = 0.682), NID1 (OR = 1.38, p = 1.54 × 10-3, PPH4 = 0.664), TIMP3 (OR = 0.91, p = 4.01 × 10-5, PPH4 = 0.659), SERPINF1 (OR = 0.81, p = 2.77 × 10-4, PPH4 = 0.59), OXT (OR = 1.17, p = 9.51 × 10-4, PPH4 = 0.526), were classified as tier two targets. Additional sensitivity analyses further validated the robustness and directionality of these findings. According to druggability assessment, Pimagedine, Resveratrol, Syringaresinol and Clozapine may potentially be important in the development of new anti-glaucoma agents. Our integrated study identified eight potential associated proteins for POAG. These proteins play important roles in neuroprotection, extracellular matrix regulation and oxidative stress. Therefore, they have promising potential as therapeutic targets to combat POAG.

Causal Relationships Between Circulating Inflammatory Proteins and Obstructive Sleep Apnea: A Bidirectional Mendelian Randomization Study.

Clinical studies have demonstrated the intricate association between the onset and progression of obstructive sleep apnea (OSA) and the activation of the inflammatory cascade reaction. This study delves into investigating the causal links between 91 circulating inflammatory proteins (CIPs) and OSA through the application of Mendelian randomization (MR) techniques. Utilizing genetic data on OSA sourced from the Finnish Biobank (FinnGen) Genome-wide Association Studies (GWAS) of the European population, alongside summary-level GWAS data of CIPs from 14,824 European participants, we conducted a bidirectional MR study. This study suggests that several factors may be associated with the risk of OSA. IL-17C (odds ratio (OR) = 1.090, p = 0.0311), CCL25 (OR = 1.079, p = 0.0493), FGF-5 (OR = 1.090, p = 0.0003), CD5 (OR = 1.055, p = 0.0477), and TNFSF14 (OR = 1.092, p = 0.0008) may positively correlate with OSA risk. Conversely, IL-20RA (OR = 0.877, p = 0.0107), CCL19 (OR = 0.933, p = 0.0237), MIP-1 alpha (OR = 0.906, p = 0.0042), Flt3L (OR = 0.941, p = 0.0019), CST5 (OR = 0.957, p = 0.0320), OPG (OR = 0.850, p = 0.0001), and TRAIL (OR = 0.956, p = 0.0063) may reduce the risk of OSA. Additionally, elevated levels of IL-10RA (OR = 1.153, p = 0.0478) were observed as a consequence of OSA. Conversely, OSA may potentially lead to decreased levels of CCL28 (OR = 0.875, p = 0.0317), DNER (OR = 0.874, p = 0.0324), FGF-21 (OR = 0.846, p = 0.0344), and CSF-1 (OR = 0.842, p = 0.0396). Through this bidirectional MR study, we have identified 12 upstream regulatory proteins and 5 downstream effect proteins that are linked to OSA. These findings hold promise in providing potential therapeutic targets for the inflammatory mechanisms underlying OSA.

Identification of candidate genes and chemicals associated with osteoarthritis by transcriptome-wide association study and chemical-gene interaction analysis

BackgroundOsteoarthritis (OA) is a common degenerative joint disease and causes chronic pain and disability to the elderly. Several risk factors are involved, such as aging, obesity, genetic susceptibility, and environmental factors. We conducted a transcriptome-wide association study (TWAS) and chemical-related gene set enrichment analysis (CGSEA) to investigate the susceptibility genes and environmental factors.MethodsTWAS analysis was conducted to identify the susceptibility genes by integrating the summary-level genome-wide association study data of knee OA (KOA) and hip OA (HOA) with the precomputed expression weights from the Genotype-Tissue Expression Project (Version 8). The FUSION software was used for both single-tissue and cross-tissue TWAS, which were combined using an aggregate Cauchy association test. The biological function and pathways of the TWAS genes were explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases, and the human cartilage mRNA expression profiles were utilized to validate the TWAS genes. CGSEA analysis was performed to scan the OA-associated chemicals by integrating the TWAS results with the chemical-related gene sets.ResultsThere were 44 and 93 unique TWAS genes identified in 7 and 11 chromosomes for KOA and HOA, respectively, fourteen and four of which showed significantly differential expression in the mRNA profiles, such as CRHR1, LTBP1, WWP2, LMX1B, and PTHLH. OA-related pathways were found in the KEGG and GO analysis, such as TGF-beta signaling pathway, MAPK signaling pathway, hyaluronan metabolic process, and chondrocyte differentiation. Forty-five OA-associated chemicals were identified, including quercetin, bisphenol A, and cadmium chloride.ConclusionsSeveral candidate OA-associated genes and chemicals were identified through TWAS and CGSEA analysis, which expanded our understanding of the relationship between genes, chemicals, and their impact on OA.

Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n= 20,833-766,345) and xQTL data from seven omics layers (n= 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.

Mendelian randomization study reveals a causal relationship between serum iron status and coronary heart disease and related cardiovascular diseases.

Growing observational studies have shown that abnormal systemic iron status is associated with Coronary heart disease (CHD). However, these results from observational studies was not entirely consistent.It remains unclear whether this relationship represents causality.It is necessary to explore the causal relationship between iron status and CHD and related cardiovascular diseases (CVD). We aimed to investigate the potential casual relationship between serum iron status and CHD and related CVD using a two-sample Mendelian randomization (MR) approach. Genetic statistics for single nucleotide polymorphisms (SNPs) between four iron status parameters were identified in a large-scale genome-wide association study (GWAS) conducted by the Iron Status Genetics organization. Three independent single nucleotide polymorphisms (SNPs) (rs1800562, rs1799945, and rs855791) aligned with four iron status biomarkers were used as instrumental variables. CHD and related CVD genetic statistics We used publicly available summary-level GWAS data. Five different MR methods random effects inverse variance weighting (IVW), MR Egger, weighted median, weighted mode, and Wald ratio were used to explore the causal relationship between serum iron status and CHD and related CVD. In the MR analysis, we found that the causal effect of serum iron (OR = 0.995, 95% CI = 0.992-0.998, p = 0.002) was negatively associated with the odds of coronary atherosclerosis (AS). Transferrin saturation (TS) (OR = 0.885, 95% CI = 0.797-0.982, p = 0.02) was negatively associated with the odds of Myocardial infarction (MI). This MR analysis provides evidence for a causal relationship between whole-body iron status and CHD development. Our study suggests that a high iron status may be associated with a reduced risk of developing CHD.

Genetically proxied gut microbiota, gut metabolites with risk of epilepsy and the subtypes: A bi-directional Mendelian randomization study.

An increasing number of observational studies have revealed an association among the gut microbiota, gut metabolites, and epilepsy. However, this association is easily influenced by confounders such as diet, and the causality of this association remains obscure. Aiming to explore the causal relationship and ascertain specific gut microbe taxa for epilepsy, we conducted a bi-directional Mendelian randomization (MR) study based on the genome-wide association study (GWAS) data of epilepsy from the International League Against Epilepsy, with the gut microbiota GWAS results from MiBioGen, and summary-level GWAS data of gut microbiota-dependent metabolites trimethylamine N-oxide and its predecessors. Nine phyla, 15 classes, 19 orders, 30 families, and 96 genera were analyzed. A suggestive association of host-genetic-driven increase in family Veillonellaceae with a higher risk of childhood absence epilepsy (odds ratio [OR]: 1.033, confidential interval [CI]: 1.015-1.051, P IVW = 0.0003), class Melainabacteria with a lower risk of generalized epilepsy with tonic-clonic seizures (OR = 0.986, CI = 0.979-0.994, P IVW = 0.0002), class Betaproteobacteria (OR = 0.958, CI = 0.937-0.979, P IVW = 0.0001), and order Burkholderiales (OR = 0.960, CI = 0.937-0.984, P IVW = 0.0010) with a lower risk of juvenile myoclonic epilepsy were identified after multiple-testing correction. Our sensitivity analysis revealed no evidence of pleiotropy, reverse causality, weak instrument bias, or heterogeneity. This is the first MR analysis to explore the potential causal relationship among the gut microbiota, metabolites, and epilepsy. Four gut microbiota features (two class levels, one order level, and one family level) were identified as potential interventional targets for patients with childhood absence epilepsy, generalized epilepsy with tonic-clonic seizures, and juvenile myoclonic epilepsy. Previous associations in numerous observational studies may had been interfered by confounders. More rigorous studies were needed to ascertain the relationship among the gut microbiota, metabolites, and epilepsy.

Associations of alcohol and coffee with colorectal cancer risk in East Asian populations: a Mendelian randomization study.

Previous observational studies have shown that alcohol and coffee were associated with colorectal cancer (CRC) risk, but the causal relationships have not been adequately explored. This study aimed to assess the potential causal associations of alcohol and coffee with CRC risk using Mendelian randomization (MR) analyses in an East Asian population. Publicly available summary-level genome-wide association studies data on ever/never alcohol drinker (n = 165,084), alcohol consumption (n = 58,610), coffee consumption (n = 152,634), and CRC (7062 cases and 195,745 controls) were obtained from the BioBank Japan (BBJ). Single-nucleotide polymorphisms (SNPs) that were significantly related to the exposures were identified as instrumental variables. Five, two, and six SNPs were used forever/never alcohol drinkers, alcohol consumption, and coffee consumption, respectively. The inverse variance weighted method was used as the main MR method to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs) of CRC risk per one-unit change in exposures. Genetically predicted ever/never alcohol drinkers (OR: 1.08; 95% CI 1.06, 1.11; P < 0.001) and alcohol consumption (OR: 1.39; 95% CI 1.21, 1.60; P < 0.001) were positively associated with CRC risk. Conversely, genetically predicted coffee consumption was inversely related to CRC risk, with an OR (95% CI) of 0.80 (0.64, 0.99) (P = 0.037). Genetically predicted alcohol use and consumption were risk factors for CRC while genetically predicted coffee consumption was a protective factor. Our findings highlight the effectiveness of keeping healthy dietary habits to prevent CRC. Further studies with more valid SNPs and CRC cases are needed. Validation of our findings is also recommended.

A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease.

SummaryExisting methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are limited to using non-overlapped categorical annotations or limited by the computation burden of modeling genome-wide variants. To overcome these limitations, we propose a scalable Bayesian functional GWAS method to account for multivariate quantitative functional annotations (BFGWAS_QUANT), accompanied by a scalable computation algorithm enabling joint modeling of genome-wide variants. Simulation studies validated the performance of BFGWAS_QUANT for accurately quantifying annotation enrichment and improving GWAS power. Applying BFGWAS_QUANT to study five Alzheimer disease (AD)-related phenotypes using individual-level GWAS data (n = ∼1,000), we found that histone modification annotations have higher enrichment than expression quantitative trait locus (eQTL) annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that cis-eQTLs in microglia had higher enrichment than eQTLs of bulk brain frontal cortex tissue for all considered phenotypes. A similar enrichment pattern was also identified using the International Genomics of Alzheimer’s Project (IGAP) summary-level GWAS data of AD (n = ∼54,000). The strongest known APOE E4 risk allele was identified for all five phenotypes, and the APOE locus was validated using the IGAP data. BFGWAS_QUANT fine-mapped 32 significant variants from 1,073 genome-wide significant variants in the IGAP data. We also demonstrated that the polygenic risk scores (PRSs) using effect size estimates by BFGWAS_QUANT had a similar prediction accuracy as other methods assuming a sparse causal model. Overall, BFGWAS_QUANT is a useful GWAS tool for quantifying annotation enrichment and prioritizing potential causal variants.

Using a two-sample mendelian randomization analysis to explore the relationship between physical activity and Alzheimer’s disease

Evidence from previous epidemiological studies on the effect of physical activity on the risk of Alzheimer’s disease (AD) is conflicting. We performed a two-sample Mendelian randomization analysis to verify whether physical activity is causally associated with AD. This study used two-sample Mendelian randomization (MR) analysis to estimate the association between physical activity (including overall activity, sedentary behavior, walking, and moderate-intensity activity) and AD. Genetic instruments for physical activity were obtained from published genome-wide association studies (GWAS) including 91,105 individuals from UK Biobank. Summary-level GWAS data were extracted from the International Genomics of Alzheimer’s Project IGAP (21,982 patients with AD and 41,944 controls). Inverse Variance Weighted (IVW) was used to estimate the effect of physical activity on AD. Sensitivity analyses including weighted median, MR-Egger, MR-PRESSO, and leave-one-out analysis were used to estimate pleiotropy and heterogeneity. Mendelian randomization evidences suggested a protective relationship between walking and AD (odds ratio (OR) = 0.30, 95% confidence interval (CI), 0.13–0.68, P = 0.0039). Genetically predicted overall activity, sedentary behavior, and moderate-intensity activity were not associated with AD. In summary, this study provided evidence that genetically predicted walking might associate with a reduced risk of AD. Further research into the causal association between physical activity and AD could help to explore the real relationship and provide more measures to reduce AD risk.

Associations between Genetically Proxied Inhibition of Lipid-Lowering Drug Targets and Serum Micronutrients among Individuals of European Descent: A Mendelian Randomization Study

BackgroundLimited and inconclusive data exist concerning the associations between lipid-lowering drugs and serum micronutrient concentrations. MethodsWe conducted Mendelian randomization (MR) analyses to explore the associations between lipid-lowering drug targets and serum micronutrients. Single-nucleotide polymorphisms in genes encoding molecular targets of LDL cholesterol-lowering therapies were selected as instrumental variables for 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR; target of statins), proprotein convertase subtilisin/kexin type 9 (PCSK9; target of PCSK9 inhibitors), and Niemann-Pick C1-Like 1 (NPC1L1; target of ezetimibe). Exposure data were extracted from a published genome-wide association study (GWAS) of lipids in 188,577 European individuals, with outcome data obtained from the Integrative Epidemiology Unit (IEU) GWAS database (https://gwas.mrcieu.ac.uk). Overall, age and sex information were not calculable from the summary-level GWAS data. MR analyses were performed using the inverse-variance weighted method and MR sensitivity analysis methods. ResultsWe found genetically proxied inhibition of HMGCR to lower iron (effect, –0.16; 95% CI: –0.27, –0.06; P value = 0.003), zinc (effect, –0.83; 95% CI: –1.36, –0.31; P value = 0.002), magnesium (effect, –0.17; 95% CI: –0.27, –0.06; P value = 0.003), potassium (effect, –0.17; 95% CI: –0.27, –0.06; P value = 0.002), genetically proxied inhibition of NPC1L1 to increase calcium (effect, 0.28; 95% CI: 0.10, 0.46; P value = 0.003), retinol (effect, 0.25; 95% CI: 0.07, 0.44; P value = 0.01), and genetically proxied inhibition of PCSK9 to increase vitamin D (effect, 0.10; 95% CI: 0.07, 0.12; P value = 1.8 × 10–19). These associations were robust in MR sensitivity analyses. However, the associations between genetically proxied inhibition of HMGCR and NPC1L1 and the micronutrients were not consistent in multiple comparisons. ConclusionsOur results provide evidence that statin use may lower serum concentrations of iron, zinc, magnesium, and potassium, PCSK9 inhibitors may increase serum vitamin D, and ezetimibe may increase serum calcium and retinol concentrations.

Genetic Predispositions Between COVID-19 and Three Cardio-Cerebrovascular Diseases.

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.

Assessing the role of cortisol in cancer: a wide-ranged Mendelian randomisation study.

Cortisol's immunosuppressive, obesogenic, and hyperglycaemic effects suggest that it may play a role in cancer development. However, whether cortisol increases cancer risk is not known. We investigated the potential causal association between plasma cortisol and risk of overall and common site-specific cancers using Mendelian randomisation. Three genetic variants associated with morning plasma cortisol levels at the genome-wide significance level (P < 5 × 10-8) in the Cortisol Network consortium were used as genetic instruments. Summary-level genome-wide association study data for the cancer outcomes were obtained from large-scale cancer consortia, the UK Biobank, and the FinnGen consortium. Two-sample Mendelian randomisation analyses were performed using the fixed-effects inverse-variance weighted method. Estimates across data sources were combined using meta-analysis. A standard deviation increase in genetically predicted plasma cortisol was associated with increased risk of endometrial cancer (odds ratio 1.50, 95% confidence interval 1.13-1.99; P = 0.005). There was no significant association between genetically predicted plasma cortisol and risk of other common site-specific cancers, including breast, ovarian, prostate, colorectal, lung, or malignant skin cancer, or overall cancer. These results indicate that elevated plasma cortisol levels may increase the risk of endometrial cancer but not other cancers. The mechanism by which this occurs remains to be investigated.

Plasma Cortisol and Risk of Atrial Fibrillation: A Mendelian Randomization Study.

ContextAtrial fibrillation (AF), cardiac arrhythmias, and related risk factors are common in patients with Cushing’s syndrome, or clinical chronic hypercortisolism. While hypercortisolism may be associated with AF, this association has not yet been ascertained causally.ObjectiveTo determine whether plasma cortisol is causally associated with AF using a 2-sample Mendelian randomization (MR) design.MethodsThree genetic variants in the SERPINA1/SERPINA6 locus and functionally associated with plasma cortisol were identified in the CORtisol NETwork consortium (12 597 participants). Summary-level genome-wide association study (GWAS) data for the associations between the cortisol-associated variants and AF were obtained from a GWAS meta-analysis of 6 studies (60 620 AF cases and 970 216 noncases) and the FinnGen consortium (17 325 AF cases and 97 214 noncases). The fixed-effects inverse-variance weighted approach accounting for genetic correlations between variants was used for analysis. Multivariable MR analyses were conducted to assess potential mediating effects of systolic blood pressure (SBP) and waist circumference (WC). Summary-level GWAS data for SBP and WC were obtained respectively from the International Consortium of Blood Pressure (757 601 participants) and the Genetic Investigation of ANthropometric Traits consortium (232 101 participants).ResultsOne standard deviation increase in genetically predicted plasma cortisol was associated with greater risk of AF (odds ratio [OR] 1.20, 95% CI 1.06-1.35). The association attenuated when adjusting for genetically predicted SBP and WC (OR 0.99, 95% CI 0.72-1.38).ConclusionEvidence derived from the MR study suggests a positive association between plasma cortisol and risk of AF, likely mediated through SBP and WC.

Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure.

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy.

A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area. In this study, we propose a novel statistical approach, LEP, which provides a novel way of modeling the connection between the individual-level data and summary-level data. LEP integrates both types of data by LEveraging Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction. The algorithm for parameter estimation is developed to handle genome-wide-scale data. Through comprehensive simulation studies, we demonstrated the advantages of LEP over the existing methods. We further applied LEP to perform integrative analysis of Crohn's disease from WTCCC and summary statistics from GWAS of some other diseases, such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.39% (±0.58%) to 68.33% (±0.32%) using about 195 000 variants. The LEP software is available at https://github.com/daviddaigithub/LEP. Supplementary data are available at Bioinformatics online.

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.

MotivationLD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.ResultsIn this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.Availability and ImplementationThe web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/Supplementary information Supplementary data are available at Bioinformatics online.