Scalable privacy-preserving data sharing methodology for genome-wide association studies

The field of stroke genetics continues to make substantive advances. Large-scale partnerships have led to meta-analyses in ischemic and hemorrhagic stroke, which are yielding reproducible genetic risk factors. Most ischemic stroke risk factors seem to be specific to type, for example, large-vessel stroke. Progress is being made in understanding the pathophysiology of single-gene stroke syndromes. Genetics is also being used to potentially advance pharmacotherapeutics. In the genome-wide association study (GWAS) era, several themes are emerging. First, heritability seems to vary by ischemic stroke subtype. Using complex trait analysis on a GWAS data set of >3000 individuals, Bevan et al1 found a 37.9% heritability for ischemic stroke overall, but the heritability varied widely by subtype from a high of 40.3% for large-vessel stroke to a low of 16.1% for small-vessel stroke.1 Second, genetic risk factors seem to be subtype-selective. Third, GWAS have yielded more reliable discoveries than candidate gene studies uninformed by GWAS. In the previously cited study by Bevan,1 no candidate gene previously reported as associated with ischemic stroke could be replicated using GWAS data, but 3 loci from related cardiovascular GWAS were significant even after rigorous correction for multiple comparisons. In the area of ischemic stroke genetics, a partnership between the International Stroke Genetics Consortium and the Wellcome Trust Case Control Consortium 2, which included GWAS data from 5859 cases and 6281 controls, replicated associations …

To Identify Associations with Rare Variants, Just WHaIT: Weighted Haplotype and Imputation-Based Tests

See related article, pages 825–832. With a swiftly moving, highly technical field, it helps to have a reliable guide. In this issue of Stroke, Lanktree et al1 provide a timely overview of genomic analysis applied to stroke.1 Readers find a clear and concise synopsis of single-nucleotide polymorphisms, copy number variations, listings of the strengths and limitations of genome-wide association studies (GWAS), and a distillation of the findings from GWAS performed in 6 cohorts (5 of which were ischemic stroke cohorts). The review also covers a topic not usually found in clinical reviews, namely techniques to visually display quantitative information. Excellence in statistical graphics should, among other things, avoid distorting what the data have to say, present many numbers in a small space, and make large data sets coherent.2 The Manhattan and quantile–quantile plots are excellent examples of statistical graphics that have become invaluable for interpreting GWAS results. Along with visualization comes interpretation of data in the context of GWAS. Clinical investigators are well aware of the problem of multiple testing from such settings as interim and subgroup analyses in clinical trials, which can lead to wildly spurious conclusions, such as concluding that aspirin only helped individuals of certain astrological signs in the second International Study of Infarct Survival.3 GWAS simply escalates the problem of multiple testing by orders of magnitude. Because single-nucleotide polymorphism-based GWAS test hundreds of thousands …

Metabolic disorders are a hallmark feature of cancer. However, the evidence for the causality of circulating metabolites to promote or prevent colorectal cancer (CRC) is still lacking. We performed a two-sample Mendelian randomization (MR) analysis to assess the causality from genetically proxied 486 blood metabolites to CRC. Genome-wide association study (GWAS) data for exposures were extracted from 7824 Europeans GWAS on metabolite levels. GWAS data for CRC from the GWAS catalog database GCST012879 were used for the preliminary analysis. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. Cochran Q test, MR-Egger intercept test, MR-PRESSO, Radial MR, and leave-one-out analysis were used for sensitivity analyses. For significant associations, additional independent CRC GWAS data GCST012880 were used for replication analysis and meta-analysis. For the final identification of metabolites, Steiger test, linkage disequilibrium score regression, and colocalization analysis were performed for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on CRC. The results of this study indicated significant associations between six metabolites pyruvate (odds ratio [OR]: 0.49, 95% confidence interval [CI]: 0.32-0.77, p = 0.002), 1,6-anhydroglucose (OR: 1.33, 95% CI: 1.11-1.59, p = 0.002), nonadecanoate (19:0) (OR: 0.40, 95% C I:0.4-0.68, p = 0.0008), 1-linoleoylglycerophosphoethanolamine (OR: 0.47, 95% CI: 0.30-0.75, p = 0.001), 2-hydroxystearate (OR: 0.39, 95% CI: 0.23-0.67, p = 0.0007), gamma-glutamylthreonine (OR: 2.14, 95% CI: 1.02-4.50, p = 0.040) and CRC. MVMR analysis revealed that genetically predicted pyruvate, 1-linoleoylglycerophosphoethanolamine and gamma-glutamylthreonine can directly influence CRC independently of other metabolites. The current work provides evidence to support the causality of the six circulating metabolites on CRC and a new perspective on the exploration of the biological mechanisms of CRC by combining genomics and metabolomics. These findings contribute to the screening, prevention and treatment of CRC.

This study is the first to use genome-wide association study (GWAS) data to evaluate the multidimensional genetic architecture underlying nasopharyngeal cancer. Since analysis of data from GWAS confirms a close and consistent association between elevated risk for nasopharyngeal carcinoma (NPC) and major histocompatibility complex class 1 genes, our goal here was to explore lesser effects of gene-gene interactions. We conducted an exhaustive genome-wide analysis of GWAS data of NPC, revealing two-locus interactions occurring between single nucleotide polymorphisms (SNPs), and identified a number of suggestive interaction loci which were missed by traditional GWAS analyses. Although none of the interaction pairs we identified passed the genome-wide Bonferroni-adjusted threshold for significance, using independent GWAS data from the same population (Stage 2), we selected 66 SNP pairs in 39 clusters with P<0.01. We identified that in several chromosome regions, multiple suggestive interactions group to form a block-like signal, effectively reducing the rate of false discovery. The strongest cluster of interactions involved the CREB5 gene and a SNP rs1607979 on chromosome 17q22 (P = 9.86×10−11) which also show trans-expression quantitative loci (eQTL) association in Chinese population. We then detected a complicated cis-interaction pattern around the NPC-associated HLA-B locus, which is immediately adjacent to copy-number variations implicated in male susceptibility for NPC. While it remains to be seen exactly how and to what degree SNP-SNP interactions such as these affect susceptibility for nasopharyngeal cancer, future research on these questions holds great promise for increasing our understanding of this disease’s genetic etiology, and possibly also that of other gene-related cancers.

The link between salt intake and the risk of gastric cancer remains uncertain, and a causal relationship has not been established. Therefore, clarifying the causal effect of salt intake on the risk of gastric cancer using reliable causal inference methods is necessary. The aim of this study was to assess the causal association between salt intake and cancer by integrating summary-level genome-wide association study (GWAS) data. Two-sample Mendelian randomization (MR) analyses were performed using summary statistics from a GWAS. Inverse-variance weighted (IVW) regression, Mendelian randomization-Egger (MR-Egger) regression, and weighted median analyses were used to evaluate the causal relationship between salt intake and gastric cancer. Moreover, Mendelian Randomization Pleiotropy RESidual Sum of Squares and Outliers and MR-Egger analyses were used to evaluate the level of multipotency, and "leave-one-out" sensitivity analysis was assessed. The IVW method estimate indicated that salt intake was not correlated with gastric cancer incidence. The IVW (β = 0.1008, standard error [SE] = 0.1510, odds ratio [OR] = 1.1061, 95% confidence interval [CI], 0.82-1.48, P = .5042), MR-Egger regression (β = 0.059, SE = 0.5318, OR = 1.0612, 95% CI, 0.37-3.01, P = .9111), and weighted median (β = 0.2639, SE = 0.2298, OR = 1.3020, 95% CI, 0.82-2.04, P = .2508) analyses revealed no causal relationship between salt intake and gastric cancer risk (P > .05). In addition, the funnel plot and MR-Egger analysis (P = .6694 > .05) did not indicate horizontal pleiotropy or heterogeneity. GWAS data from public databases were used in this study, and the causal relationship between salt intake and gastric cancer risk was analyzed via a two-sample MR method. The results revealed no genetic causal relationship between salt intake and gastric cancer.

Oesophageal cancer is the fourth leading cause of cancer death in China where essentially all cases are histologically oesophageal squamous cell carcinoma (ESCC). Agnostic pathway-based analyses of genome-wide association study (GWAS) data combined with tissue-specific expression quantitative trait loci (eQTL) analysis and publicly available functional data can identify biological pathways and/or genes enriched with functionally-relevant disease-associated variants. We used the adaptive multilocus joint test to analyse 1827 pathways containing 6060 genes using GWAS data from 1942 ESCC cases and 2111 controls with Chinese ancestry. We examined the function of risk alleles using in silico and eQTL analyses in oesophageal tissues. Associations with ESCC risk were observed for 36 pathways predominantly involved in apoptosis, cell cycle regulation and DNA repair and containing known GWAS-associated genes. After excluding genes with previous GWAS signals, candidate pathways (and genes) for ESCC risk included taste transduction (KEGG_hsa04742; TAS2R13, TAS2R42, TAS2R14, TAS2R46,TAS2R50), long-patch base excision repair (Reactome_pid; POLD2) and the metabolics pathway (KEGG_hsa01100; MTAP, GAPDH, DCTD, POLD2, AMDHD1). We identified and validated CASP8 rs13016963 and IDH2 rs11630814 as eQTLs, and CASP8 rs3769823 and IDH2 rs4561444 as the potential functional variants in high-linkage disequilibrium with these single nucleotide polymorphisms (SNPs), respectively. Further, IDH2 mRNA levels were down-regulated in ESCC (tumour:normal-fold change = 0.69, P = .75E-14). Agnostic pathway-based analyses and integration of multiple types of functional data provide new evidence for the contribution of genes in taste transduction and metabolism to ESCC susceptibility, and for the functionality of both established and new ESCC risk-related SNPs.

Systemic lupus erythematosus (SLE) is characterized as an autoimmune disorder with unclear etiology. \n\nTo identify the genetic effect of SLE, a genome wide association study (GWAS) and its further replication were conducted on SLE patients in Asian populations and ethnically matched controls. Before this study, most of the confirmed association loci were identified by GWAS studies in European populations. Apart from the established associations, we identified a SLE susceptible single nucleotide polymorphisms (SNP) located in ETS1 (rs1128334, P=2.3E-11, OR=1.29) in four different cohorts. This locus is probably an Asian-specific susceptibility locus since no Caucasian study has reported further validation in the last years. A new susceptibility variant in UHRF1BP1 (rs13205210, P=4.4E-09, OR=1.49) independent from the previously confirmed SNPs in Caucasian study was also confirmed to be associated with SLE in the Hong Kong Chinese population.\n\n\n\nMeta-analysis was performed by introducing another Chinese Han GWAS data set from Anhui province, China. Three loci, TET3-DGUOK, CD80, DRAM1, were confirmed to be associated with SLE. Two loci with suggestive signals in Hong Kong GWAS and further replication were also confirmed by the meta-analysis: PTTG1-MiRNA146a, YDJC.\n\n\n\nIn order to identify the genetic effect for females who have predominant chance to suffer from SLE, X chromosome specific meta-analysis based on the Hong Kong and Anhui GWAS data and further replication study were performed by considering the difference between females and males. A signal in PRPS2, and three independent signals in the Xq28 were confirmed with the replication in three different cohorts by considering both females and males.\n\n\n\nGene-gene interactions were also investigated genome-widely in a hypothesis free manner based on the meta-analysis results. The further validation processes were preceeded based on each independent GWAS data set. Four pairswise interacting loci were found and cross validated by three methods including logistic regression and Multifactor Dimensionality Reduction (MDR) and information gain theory based on the Anhui GWAS data set. Further studies are still needed to better explain the real features of genetic epistasis and the potential biological roles.\n\n\n\nBy incorporating two GWAS from the same population, the population difference is efficiently avoided. Together with the putative gene-gene interactions, this study presents a comprehensive analysis based on the GWAS data conducted on SLE. It may shed new light on the disease mechanisms of SLE.

Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) at multiple loci that are significantly associated with coronary artery disease (CAD) risk. In this study, we sought to determine and compare the predictive capabilities of 9p21.3 alone and a panel of SNPs identified and replicated through GWAS for CAD. We used the Ottawa Heart Genomics Study (OHGS) (3323 cases, 2319 control subjects) and the Wellcome Trust Case Control Consortium (WTCCC) (1926 cases, 2938 control subjects) data sets. We compared the ability of allele counting, logistic regression, and support vector machines. Two sets of SNPs, 9p21.3 alone and a set of 12 SNPs identified by GWAS and through a model-fitting procedure, were considered. Performance was assessed by measuring area under the curve (AUC) for OHGS using 10-fold cross-validation and WTCCC as a replication set. AUC for logistic regression using OHGS increased significantly from 0.555 to 0.608 (P=3.59×10⁻¹⁴) for 9p21.3 versus the 12 SNPs, respectively. This difference remained when traditional risk factors were considered in a subgroup of OHGS (1388 cases, 2038 control subjects), with AUC increasing from 0.804 to 0.809 (P=0.037). The added predictive value over and above the traditional risk factors was not significant for 9p21.3 (AUC 0.801 versus 0.804, P=0.097) but was for the 12 SNPs (AUC 0.801 versus 0.809, P=0.0073). Performance was similar between OHGS and WTCCC. Logistic regression outperformed both support vector machines and allele counting. Using the collective of 12 SNPs confers significantly greater predictive capabilities for CAD than 9p21.3, whether traditional risks are or are not considered. More accurate models probably will evolve as additional CAD-associated SNPs are identified.

Despite the development of genomic research on common cardiovascular diseases, genetic variants and/or loci shown to be disease associated have been seldom applied to risk prediction in a clinical setting, although they could provide us with novel mechanistic insights into the pathophysiology and potential therapeutic targets. Here, we discuss the significance and limitations of the up-to-date genomic studies on common diseases and propose what kind of genomic research should be prioritized in view of the realization of precision medicine. Many disease-associated loci have been identified using a genome-wide association study (GWAS) in a large-scale case–control design (more than several thousand cases and controls). A GWAS is an analytical method for the identification of disease-associated tag (lead) SNPs, which are indicators of disease-associated loci (linkage disequilibrium blocks) with a genome-wide significance ( P <5×10−8). Consequently, further analysis is required to identify the specific variants functioning as the most associated and/or risk variants1 within each disease-associated locus. Consistent with the genetic cause of monogenic diseases, novel biology on the risk genes and their regulatory pathways can be learned through detailed genetic and functional analysis after GWAS. Also, GWAS data on disease-associated pathways might be used in choosing drugs for treatment.2 We have experienced several successful stories. Single-nucleotide polymorphisms (SNPs) in PCSK9 , HMGCR , and NPC1L 1 were shown to be associated with hypercholesterolemia in GWAS on blood lipids.3 Although their effects shown in that GWAS were relatively weak (+2–3 mg/dL change in plasma cholesterol values), potent compounds targeting these gene-encoded proteins have been established as lipid-lowering agents (anti-PCSK9 antibody, statin, and ezetimibe, respectively). In this way, GWAS data can provide the scientific community with information about novel biology, mechanical pathways, and potential pharmaceutical drugs. Because current GWASs are usually performed with a case–control design, the results are …

Numerous studies have suggested an association between gut microbiota and polycystic ovarian syndrome (PCOS). However, the causal relationship between these two factors remains unclear. A review of observational studies was conducted to compare changes in gut microbiota between PCOS patients and controls. The analysis focused on four levels of classification, namely, phylum, family, genus, and species/genus subgroups. To further investigate the causal relationship, Mendelian randomization (MR) was employed using genome-wide association study (GWAS) data on gut microbiota from the MiBioGen consortium, as well as GWAS data from a large meta-analysis of PCOS. Additionally, a reverse MR was performed, and the results were verified through sensitivity analyses. The present review included 18 observational studies that met the inclusion and exclusion criteria. The abundance of 64 gut microbiota taxa significantly differed between PCOS patients and controls. Using the MR method, eight bacteria were identified as causally associated with PCOS. The protective effects of the genus Sellimonas on PCOS remained significant after applying Bonferroni correction. No significant heterogeneity or horizontal pleiotropy was found in the instrumental variables (IVs). Reverse MR analyses did not reveal a significant causal effect of PCOS on gut microbiota. The differences in gut microbiota between PCOS patients and controls vary across observational studies. However, MR analyses identified specific gut microbiota taxa that are causally related to PCOS. Future studies should investigate the gut microbiota that showed significant results in the MR analyses, as well as the underlying mechanisms of this causal relationship and its potential clinical significance.

BackgroundGestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. The oral microbiota, influenced by genetic factors, may play a role in GDM development, but the causal association remains unclear.MethodsWe employed a two-sample Mendelian randomization (MR) approach using Genome-Wide Association Study (GWAS) data on GDM from FINN cohort data (ID: finngen_R10_GEST_DIABETES) and GWAS data on the Oral microbiota from the Danish ADDITION-PRO cohort. We screened SNPs significantly associated with Oral microbiota abundance as instrumental variables (IVs) and assessed their association with GDM risk. The study primarily used an inverse variance weighting (IVW) approach and further applied MR-Egger regression, weighted median, and weighted mode methods for robustness testing. Sensitivity analyses were conducted to evaluate the impact of heterogeneity and pleiotropy, including MR-Egger, MR-PRESSO, Cochran’s Q, and leave-one-out methods.ResultsWe identified 267 IVs associated with Oral microbiota abundance. IVW analysis revealed a positive causal association between Genus Schaalia and GDM risk (OR = 1.03, 95% CI: 1.01–1.06, P = 0.02) and a negative association between Genus Haemophilus and GDM risk (OR = 0.96, 95% CI: 0.93–1.00, P = 0.034). Sensitivity analyses confirmed the robustness of these two results, showing no evidence of heterogeneity or pleiotropy.ConclusionOur study provides evidence for a causal association between Genus Schaalia and Haemophilus and GDM risk. This highlights the potential role of the Oral microbiota in GDM pathogenesis and suggests potential targets for GDM prevention and treatment.

To analyse the genome-wide association study (GWAS) data of patients with type 1 diabetes mellitus (T1D) in order to develop a risk score for the genetic effects on T1D risk and age at diagnosis in the Taiwanese population. We selected 610 patients with T1D and 2511 healthy individuals from an electronic medical record database of more than 300 000 individuals with genetic information, analysed their GWAS data, and developed a polygenic risk score (PRS). The PRS, based on 149 selected single-nucleotide polymorphisms, could effectively predict T1D risk. A PRS increase was associated with increased T1D risk (odds ratio [OR] 2.09, 95% confidence interval [CI] 1.72-2.55). Moreover, a 1-unit increase in standardized T1D PRS decreased the age at diagnosis by 0.74 years. Combined PRS and human leukocyte antigen (HLA) DQA1*03:02-DQA1*05:01 genotypes could accurately predict T1D risk. In multivariable models, HLA variants and PRS were independent risk factors for T1D risk (OR 3.76 [95% CI 1.54-9.16] and 1.71 [95% CI 1.37-2.13] for HLA DQA1*03:02-DQA1*05:01 and PRS, respectively). In a limited study population of those aged ≤18 years, PRS remained significantly associated with T1D risk. The association between T1D PRS and age at diagnosis was more obvious among males and patients aged ≤18 years. Polygenic risk score and HLA variations enable personalized risk estimates, enhance newborn screening efficiency for ketoacidosis prevention, and addresses the gap in data on T1D prediction in isolated Asian populations.

Preeclampsia (PE) is a global pregnancy concern, characterized by hypertension with an unclear etiology. This study employs Mendelian randomization (MR) and single-cell RNA sequencing (scRNA-seq) to clarify its genetic and molecular roots, offering insights into diagnosis and treatment avenues. We integrated PE-specific genome-wide association study (GWAS) data, expression and protein quantitative trait loci (eQTL and pQTL) data, and single-cell data from peripheral blood mononuclear cells (PBMCs). We identified highly variable genes using single-cell information and employed MR to determine potential causality. We also combined pQTL and GWAS data, discerned genes positively associated with PE through scRNA-seq, and leveraged the Enrichr platform to unearth drug-gene interactions. Our scRNA-seq pinpointed notable cell type distribution variances, especially in T helper cells (Th cells), between PE and control groups. We unveiled 591 highly variable genes and 6 directly PE-associated genes. Although MR revealed correlations with PE risk, pQTL analysis was inconclusive due to data constraints. Using DSigDB, 93 potential therapeutic agents, like Retinoic acid targeting core genes (IFITM3, NINJ1, COTL1, CD69, and YWHAZ), emerged as prospective multi-target treatments. Utilizing MR and scRNA-seq, this study underscores significant cellular disparities, particularly in Th cells, and identifies crucial genes related to PE. Despite some limitations, these genes have been revealed in PE's underlying mechanism. Potential therapeutic agents, such as Retinoic acid, suggest promising treatment pathways.

The genetic support for the causal relationship between 91 circulating inflammatory proteins and pneumothorax: A two-sample mendelian randomization study.