Population-based Association Studies Research Articles

Genetically proxied liability to migraine and risk of intracranial aneurysm and subarachnoid hemorrhage.

Observational studies have reported inconsistent relationships between migraine and the risk of intracranial aneurysm and subarachnoid hemorrhage (SAH). To determine whether genetic liability to migraine is associated with risk of intracranial aneurysm and aneurysmal SAH. This study was designed as a two-sample Mendelian randomization (MR) analysis. Genetic associations with migraine were obtained from a large-scale meta-analysis of five population and clinic-based genome-wide association studies of migraine (102,084 cases and 771,257 controls of European ancestry). Genetic associations with intracranial aneurysm and SAH were obtained from a meta-analysis of 22 population-based genome-wide association studies (7495 cases and 71,934 controls of European ancestry). Findings were corroborated by sensitivity analyses and replicated in an independent sample of combined cases of intracranial aneurysm and SAH (3529 cases and 234,948 controls of Asian ancestry from the Biobank Japan and China Kadoorie Biobanks). In secondary analyses, we investigated the outcomes of extracranial aneurysm in the FinnGen and UK Biobank cohorts (up to 7466 combined cases of thoracic and abdominal aortic aneurysm). Genetic liability to migraine was associated with increased risk of the combined outcome of unruptured intracranial aneurysm and SAH (odds ratio [OR] of outcome per doubling in migraine liability 1.19, 95% confidence interval [CI] 1.06-1.35; p = 0.005). This finding was replicated in an independent sample (OR 1.15, 95% CI 1.02-1.30; p = 0.027), and there were similar associations across the component outcomes of unruptured intracranial aneurysm (OR 1.20, 95% CI 1.01-1.42; p = 0.035) and SAH (OR 1.18, 95% 1.04-1.33; p = 0.008). These findings were consistent in sensitivity analyses robust to violations of the MR assumptions. In reverse MR analyses, genetic liability to intracranial aneurysm was not associated with migraine (OR 1.03, 95% CI 0.99-1.07; p = 0.141). In a secondary analysis, there were similar associations of genetic liability to migraine with all forms of aortic aneurysm (OR for combined thoracic and aortic aneurysm 1.18, 95% CI 1.10-1.27; p = 8.49 × 10-6). Genetic liability to migraine was associated with increased risk of intracranial and extracranial aneurysms, supporting a causal relationship between liability to migraine and these traits. Further work is needed to identify the biological mechanisms and clinical relevance of these findings.

Bidirectional Mendelian randomization analysis of plasma lipidome and psychiatric disorders

BackgroundEvidence from observational studies and clinical experiments suggests a close association between plasma lipidome and psychiatric disorders. However, the causal relationship between plasma lipidome and psychiatric disorders remains insufficiently determined. Plasma lipidome are relatively easy to measure and regulate clinically, and they play a crucial role in neuronal signal transduction, making them a focus of interest as potential therapeutic targets for psychiatric disorders. MethodsIn this study, we utilized the latest Finnish population-based genome-wide association study (GWAS) data on 179 lipid species. We downloaded data on five psychiatric disorders from the IEU database, including schizophrenia, bipolar disorder, depression, autism from the PGC consortium, and anxiety disorder from the Neale lab. Using two-sample bidirectional Mendelian randomization (MR) analysis, we assessed the relationship between these 179 lipid species and the risk of the five psychiatric disorders. To validate the assumptions of Mendelian randomization, we conducted tests for horizontal pleiotropy and heterogeneity. ResultsAfter applying FDR correction to assess the relationship between 179 lipid species traits and the risk of five psychiatric disorders, our analysis provided evidence of a causal relationship specifically between genetic susceptibility in the plasma lipidome and bipolar disorder. This relationship notably involves eight phosphatidylcholines (PCs) and two sterols, with PCs displaying a dual and complex role in the disorder. Reverse Mendelian randomization (MR) analysis did not reveal a significant causal impact of psychiatric disorders on the plasma lipidome. LimitationsDespite using two-sample bidirectional Mendelian randomization analysis, the complex biological pathways and potential confounding factors may still affect the accuracy of the causal relationships. The impact of genetic variations on the lipidome and psychiatric disorders may involve multiple mechanisms, which cannot be fully elucidated in this study. ConclusionThis study identified a causal relationship between genetic susceptibility in plasma lipidome and bipolar disorder, indicating that plasma lipidome may influence the risk of psychiatric disorders and providing direction for exploring them as potential intervention targets. The findings not only deepen our understanding of the etiology of psychiatric disorders but also provide a critical theoretical foundation for future clinical interventions and prevention strategies, potentially contributing to the development of novel therapeutic approaches.

Causal Relationship between Ischemic Stroke and Malignant Brain Tumors: A Two-Sample Mendelian Randomization Analysis

Object: Contemporary observational research has identified associations between cerebrovascular accidents, commonly referred to as strokes, and malignant cerebral neoplasms. However, these studies fall short of elucidating the presence of a direct causal relationship between the two conditions. In our investigation, we employ aggregated data derived from large-scale population-based genome-wide association studies (GWASs), to interrogate the potential causal linkages between these two pathologies. Method: This investigation leverages the two-sample Mendelian randomization (MR) methodology to elucidate the genetic underpinnings of the causal nexus between malignant cerebral neoplasms and ischemic cerebrovascular accidents. Rigorous quality control protocols were enacted to identify SNPs that exhibit a robust association with the exposure variable, namely malignant brain tumors. Analytical strategies, including MR-Egger regression, weighted median, inverse-variance weighted (IVW), simple mode, and weight mode methods, constituted the core of our MR analytical framework. Predominantly, the findings hinge upon the IVW method. To fortify the veracity of our conclusions, an array of sensitivity analyses such as MR-Egger regression, weighted median, MR pleiotropy, MR-PRESSO and radial MR, were deployed to meticulously evaluate heterogeneity and pleiotropy. Result: The findings derived from the IVW model reveal an absence of a significant causal nexus between malignant cerebral neoplasms and any categorization of cerebrovascular accidents, encompassing any strokes (AS) (OR 0.998, 95% CI 0.99-1.01, P=0.741), any ischemic strokes (AIS) (OR 0.999, 95% CI 0.99-1.01, P=0.999), large artery strokes (LAS) (OR 0.993, 95% CI 0.97-1.02, P=0.603), cardioembolic strokes (CES) (OR 1.012, 95% CI 0.99-1.03, P=0.275), and small vessel strokes (SVS) (OR 0.998, 95% CI 0.98-1.02, P=0.880) with respect to either an escalation or reduction in risk. Additionally, the analyses did not unveil any indications of heterogeneity or horizontal pleiotropy within the dataset. On a genome-wide scale, none of the variants associated with malignant brain tumors demonstrated relevance to the investigated outcomes. Conclusion: The outcomes of this MR investigation reveal the absence of a causal linkage between malignant cerebral neoplasms and ischemic cerebrovascular accidents. Nonetheless, the observed correlation between these two pathologies warrants attention, underscoring the necessity for heightened surveillance and care for individuals concurrently afflicted with both conditions.

Association between cardiovascular risk factors and dilated and hypertrophic cardiomyopathy: Mendelian randomization analysis

Backgroundand aims: Dilated cardiomyopathy is a major cause of heart failure, and hypertrophic cardiomyopathy is a common cause of sudden cardiac death in young adults. Epidemiological studies reporting the association between these cardiomyopathies and common cardiovascular risk factors, including smoking, alcohol, and obesity, are limited, and the published studies are mostly observational, making them vulnerable to bias. Methods and resultsWe performed a two-sample Mendelian randomization analysis to assess whether cardiovascular risk factors were causally associated with dilated and hypertrophic cardiomyopathies. Independent genetic variants associated with body mass index, smoking, and alcohol were selected as instrumental variables, with two sets of instrumental variables utilized for alcohol. Dilated cardiomyopathy data on 355,318 samples and hypertrophic cardiomyopathy data on 489,727 samples were obtained from a European population-based genome-wide association study (GWAS) meta-analysis. The large GWAS data sample size improved the statistical power. Our results showed significant associations between a genetic predisposition for smoking and the risk of dilated cardiomyopathy (odds ratio (OR) = 1.33; 95% confidence level (CI): 1.07–1.67; p = 0.012) and between a genetic predisposition for obesity and the risk of dilated cardiomyopathy (OR = 1.62; 95% CI, 1.30 – 2.02; p = 1.51 × 10-5). The results of the other associations were not significant. ConclusionsThis study suggests that smoking and obesity are causally associated with an increased risk of dilated cardiomyopathy.

Population-based germline breast cancer gene association studies and meta-analysis to inform wider mainstream testing

Germline genetic testing, previously restricted to familial and young-onset breast cancer, is now offered increasingly broadly to patients with 'population-type' breast cancer in mainstream oncology clinics, with wide variation in the genes included. Weighted meta-analysis was carried out for three population-based case-control studies (BRIDGES, CARRIERS and UK Biobank) comprising in total 101 397 women with breast cancer and 312 944 women without breast cancer, to quantify 37 putative breast cancer susceptibility genes (BCSGs) for the frequency of pathogenic variants (PVs) in unselected, 'population-type' breast cancer cases and their association with breast cancer and its subtypes. Meta-analysed odds ratios (ORs) and frequencies of PVs in 'population-type' breast cancer cases were generated for BRCA1 (OR 8.73, 95% confidence interval (CI) 7.47-10.20; 1 in 101), BRCA2 (OR 5.68, 95% CI 5.13-6.30; 1 in 68) and PALB2 (OR 4.30, 95% CI 3.68-5.03; 1 in 187). For both CHEK2 (OR 2.40, 95% CI 2.21-2.62; 1 in 73) and ATM (OR 2.16, 95% CI 1.93-2.41; 1 in 132) subgroup analysis showed a stronger association with oestrogen receptor-positive disease. The magnitude of association and frequency of PVs were low for RAD51C (OR 1.53, 95%CI 1.29-2.04; 1 in 913), RAD51D (OR 1.76, 95% CI 1.29-2.41; 1 in 1079) and BARD1 (OR 2.34, 95% CI 1.85-2.97; 1 in 672); frequencies and associations were higher when the analysis was restricted to triple-negative breast cancers. The PV frequency in 'population-type' breast cancer cases was very low for 'syndromic' BCSGs TP53 (1 in 1844), STK11 (1 in 11 525), CDH1 (1 in 2668), PTEN (1 in 3755) and NF1 (1 in 1470), with metrics of association also modest ranging from OR 3.62 (95% CI 1.98-6.61) for TP53 down to OR 1.60 (95% CI 0.48-5.30) for STK11. These metrics reflecting 'population-type' breast cancer will be informative in defining the appropriate gene set as we continue to expand to germline testing to an increasingly unselected group of breast cancer cases.

Iron Status, Thyroid Dysfunction, and Iron Deficiency Anemia: A Two-Sample Mendelian Randomization Study.

Given the clinical association between thyroid dysfunction and iron deficiency anemia (IDA), as well as their shared association with iron status, this study aimed to investigate the causal relationship between iron status and thyroid dysfunction, while also examining the risk of IDA in relation to thyroid dysfunction. A two-sample mendelian randomization (MR) study was conducted to identify the causal relationship of iron status on thyroid dysfunction, as well as thyroid dysfunction on IDA. Large-scale European population-based genome-wide association study databases were utilized (Genetics of Iron Status consortium, ThyroidOmics consortium, FinnGen consortium, and UK Biobank). Inverse variance-weighted (IVW) was used as the main analysis. In addition, we used weighted median and MR-Egger to enhance the robustness. Sensitivity analysis was conducted to evaluate the robustness of MR results. The IVW estimates did not reveal any significant causal relationship between serum iron status markers and thyroid dysfunction. However, a significant causal relationship was observed between hypothyroidism and IDA (odds ratio [OR] = 1.101, 95% confidence interval [CI] = 1.048-1.157, p < 0.001). Repeated analyses also demonstrated a similar trend (OR = 1.023, 95% CI = 1.011-1.035, p < 0.001). Sensitivity analysis supported that the MR estimates were robust. In our MR study, an upregulation of the hypothyroidism-associated gene was found to be significantly associated with an elevated risk of IDA in the European population. These findings may offer novel therapeutic insights for clinicians managing patients with hypothyroidism, IDA, or their comorbidities.

Poor statistical power in population-based association study of gene interaction

BackgroundStatistical epistasis, or “gene–gene interaction” in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven’t found enough clues of statistical epistasis so far.MethodsWe developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene–gene interaction was calculated and then compared in different hypothesis scenarios.ResultsOur results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene–gene interaction became a very difficult task. Since the criterion of significance was given to be p-value ≤ 5.0 × 10–8, the same as that of many genome-wide association studies, there is little chance to identify the gene–gene interaction in all kind of circumstances.ConclusionsThe lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

Genetics of IgA nephrology: risks, mechanisms, and therapeutic targets.

IgA nephropathy (IgAN) is a genetically complex multifactorial trait. Over the past decade, population-based genome-wide association studies (GWAS) have identified more than 30 IgAN risk loci, providing novel perspectives on both the epidemiology of the disease and its underlying molecular mechanisms. In addition, the association between IgAN and galactose-deficient IgA1 (Gd-IgA1) presented another avenue for genetic exploration due to the heritability of the elevated serum Gd-IgA1 levels. These endeavors also yielded and enabled refinement of polygenic risk scores, which may help identify specific groups of individuals at significantly increased risks, leading to stratifications of medical treatments. In this review, we aim to explore the existing evidence for genetic causation in IgAN. We summarize the state of genetic research in IgAN and how it has led to the reformulation of the new pathogenesis model and novel therapeutic targets.

Risk relationship between inflammatory bowel disease and urolithiasis: A two-sample Mendelian randomization study.

The causal genetic relationship between common parenteral manifestations of inflammatory bowel disease (IBD) and urolithiasis remains unclear because their timing is difficult to determine. This study investigated the causal genetic association between IBD and urolithiasis using Mendelian randomization (MR) based on data from large population-based genome-wide association studies (GWASs). A two-sample MR analysis was performed to assess the potential relationship between IBD and urolithiasis. Specific single nucleotide polymorphism data were obtained from GWASs, including IBD (n = 59957) and its main subtypes, Crohn's disease (CD) (n = 40266) and ulcerative colitis (UC) (n = 45975). Summarized data on urolithiasis (n = 218792) were obtained from different GWAS studies. A random-effects model was analyzed using inverse-variance weighting, MR-Egger, and weighted medians. Genetic predisposition to IBD and the risk of urolithiasis were significantly associated [odds ratio (OR), 1.04 (95% confidence interval [CI], 1.00-.08), P = 0.01]. Consistently, the weighted median method yielded similar results [OR, 1.06 (95% CI, 1.00-1.12), P = 0.02]. The MR-Egger method also demonstrated comparable findings [OR, 1.02 (95% CI, 0.96-1.08), P = 0.45]. Both funnel plots and MR-Egger intercepts indicated no directional pleiotropic effects between IBD and urolithiasis. CD was strongly associated with it in its subtype analysis [OR, 1.04 (95% CI, 1.01-1.07), P = 0.01], and UC was also causally associated with urolithiasis, although the association was not significant [OR, 0.99 (95% CI, 0.95-1.03), P = 0.71]. A unidirectional positive causal correlation was identified between IBD and urolithiasis, with varying degrees of association observed among the different subtypes of IBD. Recognizing the increased incidence of urolithiasis in patients with IBD is crucial in clinical practice. Early detection and surveillance of IBD, improved patient awareness, adoption of preventive strategies, and promotion of collaborative efforts among healthcare providers regarding treatment methodologies are vital for improving patient outcomes.

Haplotype-based association study of TCF7L2 gene variants with the development of diabetic retinopathy in an Iranian population

ABSTRACT Background Diabetic retinopathy (DR) is recognized as one of the most prevalent complications of diabetes and a major cause of morbidity. Transcription factor 7-like 2 (TCF7L2), a pivotal component in the Wnt-signaling pathway, plays a significant role in β-cell development, blood-glucose homeostasis, cell survival, cell migration, and cell proliferation. Thus, this study aimed to assess the association between TCF7L2 variants (rs7903146, rs11196205, and rs12255372) with DR in a population-based association study. Materials and Methods DNA was extracted from whole blood of all subjects by salting-out procedure. Total 524 T2DM patients including 234 T2DM individuals without DR and 290 T2DM individuals with DR were genotyped by TaqMan assay technology. Clinical characteristics of subjects were conducted to evaluate the plausible association between TCF7L2 variants and DR with univariate linear regression analysis. Results Demographic analysis between case and control groups revealed significant differences in FBS, HbA1c, lipidemia, heart disease, and family history of T2DM (p < 0.05). No significant difference was observed in either genotypes distribution or allele frequency (p > 0.05) between T2DM individuals with and without DR in any models of inheritance. Genotype-phenotype association showed no significant association. Result of analysis indicated that HbAlc with adjusted OR of 1.8 (p < 0.0001) and first-degree relatives of family history with adjusted OR of 3.04 (p < 0.0001) were significantly associated with DR. Finally, haplotype analysis showed no noticeable association. Conclusion In conclusion, there was no significant genetic association between rs7903146, rs11196205, and rs12255372 with DR among T2DM Iranians; however, these variants may play unknown roles in other populations.

Investigation of Genetic Alterations Associated With Interval Breast Cancer

Breast cancers (BCs) diagnosed between 2 screening examinations are called interval cancers (ICs), and they have worse clinicopathological characteristics and poorer prognosis than screen-detected cancers (SDCs). However, the association of rare germline genetic variants with IC have not been studied. To evaluate whether rare germline deleterious protein-truncating variants (PTVs) can be applied to discriminate between IC and SDC while considering mammographic density. This population-based genetic association study was based on women aged 40 to 76 years who were attending mammographic screening in Sweden. All women with a diagnosis of BC between January 2001 and January 2016 were included, together with age-matched controls. Patients with BC were followed up for survival until 2021. Statistical analysis was performed from September 2021 to December 2022. Germline PTVs in 34 BC susceptibility genes as analyzed by targeted sequencing. Odds ratios (ORs) were used to compare IC with SDC using logistic regression. Hazard ratios were used to investigate BC-specific survival using Cox regression. All 4121 patients with BC (IC, n = 1229; SDC, n = 2892) were female, with a mean (SD) age of 55.5 (7.1) years. There were 5631 age-matched controls. The PTVs of the ATM, BRCA1, BRCA2, CHEK2, and PALB2 genes were more common in patients with IC compared with SDC (OR, 1.48; 95% CI, 1.06-2.05). This association was primarily influenced by BRCA1/2 and PALB2 variants. A family history of BC together with PTVs of any of these genes synergistically increased the probability of receiving a diagnosis of IC rather than SDC (OR, 3.95; 95% CI, 1.97-7.92). Furthermore, 10-year BC-specific survival revealed that if a patient received a diagnosis of an IC, carriers of PTVs in any of these 5 genes had significantly worse survival compared with patients not carrying any of them (hazard ratio, 2.04; 95% CI, 1.06-3.92). All of these associations were further pronounced in a subset of patients with IC who had a low mammographic density at prior screening examination. The results of this study may be helpful in future optimizations of screening programs that aim to lower mortality as well as the clinical treatment of patients with BC.

Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease

Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Furthermore, the networks identify 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. These findings indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.

A computational study on structural and functional consequences of nsSNPs in human dopa decarboxylase

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson’s disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches. Communicated by Ramaswamy H. Sarma

An individualized Bayesian method for estimating genomic variants of hypertension

BackgroundGenomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual’s genome that provide a strong explanation of the phenotype observed in this individual.ResultsWe applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve.ConclusionsThe results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.

A Population-Based Outcome-Wide Association Study of the Comorbidities and Sequelae Following COVID-19 Infection

BackgroundImmense attention has been given to the outcome of COVID-19 infection. However, comprehensive studies based on large populational cohort with long-term follow-up are still lacking. This study aimed to investigate the risk of various short-term comorbidities (within one month) and long-term sequelae (above one month) after COVID-19 infection.MethodsIn this large prospective cohort study with 14 months follow-up information based on UK biobank, we included 16,776 COVID-19-positive participants and 58,281 COVID-19-negative participants matched for comparison. The risk of each comorbidity and sequela was evaluated by multivariable logistic regression analysis and presented as hazard ratio (HR) and 95% confidence interval (95% CI).ResultsCOVID-19-positive individuals had a higher risk of 47 types of comorbidities within one month following COVID-19 infection, especially those who were older, male, overweight/obese, ever-smoked, with more pre-existing comorbidities and hospitalized. About 70.37% of COVID-19 patients with comorbidities had more than one co-occurring comorbidities. Additionally, only 6 high-risk sequelae were observed after one month of COVID-19 infection, and the incidence was relatively low (< 1%).ConclusionIn addition to long-term sequelae following COVID-19 infection, plenty of comorbidities were observed, especially in patients with older age, male gender, overweight/obese, more pre-existing comorbidities and severe COVID-19, indicating that more attention should be given to these susceptible persons within this period.

Population stratification correction using Bayesian shrinkage priors for genetic association studies.

Population stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity. To address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs. Simulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses. The automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.

Genetic Ancestry Estimates within Dutch Family Units and Across Genotyping Arrays: Insights from Empirical Analysis Using Two Estimation Methods.

Accurate inference of genetic ancestry is crucial for population-based association studies, accounting for population heterogeneity and structure. This study analyzes genome-wide SNP data from the Netherlands Twin Register to compare genetic ancestry estimates. The focus is on the comparison of ancestry estimates between family members and individuals genotyped on multiple arrays (Affymetrix 6.0, Affymetrix Axiom, and Illumina GSA). Two conventional methods, principal component analysis and ADMIXTURE, were implemented to estimate ancestry, each serving its specific purpose, rather than for direct comparison. The results reveal that as the degree of genetic relatedness decreases, the Euclidean distances of genetic ancestry estimates between family members significantly increase (empirical p < 0.001), regardless of the estimation method and genotyping array. Ancestry estimates among individuals genotyped on multiple arrays also show statistically significant differences (empirical p < 0.001). Additionally, this study investigates the relationship between the ancestry estimates of non-identical twin offspring with ancestrally diverse parents and those with ancestrally similar parents. The results indicate a statistically significant weak correlation between the variation in ancestry estimates among offspring and differences in ancestry estimates among parents (Spearman's rho: 0.07, p = 0.005). This study highlights the utility of current methods in inferring genetic ancestry, emphasizing the importance of reference population composition in determining ancestry estimates.

Causal effect of vitamin D on myasthenia gravis: a two-sample Mendelian randomization study.

Observational studies suggest that vitamin D supplementation may be effective in preventing myasthenia gravis (MG). However, the causal relationship between circulating vitamin D levels and MG remains unclear. This study aimed to examine the genetic causality of circulating vitamin D and MG using data from large population-based genome-wide association studies (GWAS). SNPs (single nucleotide polymorphisms) strongly associated with exposure were selected. Two-sample Mendelian Randomization (MR) was performed with inverse variance weighting (IVW), MR-Egger (Mendelian randomization-Egger), weight median and MR-PRESSO (Mendelian randomization pleiotropy residual sum and outlier) methods. Heterogeneity was tested via IVW and MR-Egger. Pleiotropy was tested using MR-Egger intercept test and MR-PRESSO method. MR-PRESSO was also used to detect outliers. Leave-one-out analysis was used to identify SNPs with potential effect. Reverse MR analysis was also performed. In IVW, circulating vitamin D levels had no causal effect on MG [OR = 0.91 (0.67-1.22), p = 0.532] and MG had no causal effect on circulating vitamin D [OR = 1.01 (099-1.02), p = 0.663]. No heterogeneity or pleiotropy was observed (p > 0.05). Other MR methods also agreed with IVW results. This study provides the causal relationship between genetically predicted circulating vitamin D levels and MG and provides new insights into the genetics of MG.

Association of Polymorphisms in ZFHX1B and PAX6 With Anisometropia in Chinese Children: The Hong Kong Children Eye Genetics Study.

To identify gene variants associated with anisometropia development in children. This is a population-based, cross-sectional, and longitudinal genetic association study involving 1057 children aged 6 to 10 years with both baseline and 3-year follow-up data. Six single nucleotide polymorphisms (SNPs), ZC3H11B rs4373767, ZFHX1B rs13382811, KCNQ5 rs7744813, SNTB1 rs7839488, PAX6 rs644242, and GJD2 rs524952 were analyzed in all children. Anisometropia was defined by an interocular difference in SE of ≥1 diopter (D) (Aniso-SE) and an interocular difference in axial length (AL) of ≥0.3 mm (Aniso-AL), respectively. Genetic associations of individual SNPs and joint SNP effects were analyzed. ZFHX1B rs13382811 was associated nominally with Aniso-AL (odds ratio [OR], 1.66; P = 0.003) at baseline. At 3 years, rs13382811 was significantly associated with Aniso-AL (OR, 1.49; P = 0.001) and became nominally associated with Aniso-SE (OR, 1.40; P = 0.01). In addition, PAX6 rs644242 was significantly associated with Aniso-AL at 3 years (OR, 1.45; P = 0.002). At the 3-year follow-up, PAX6 rs644242 was associated significantly with Aniso-AL development (OR, 1.61; P = 0.0003) and nominally with Aniso-SE development (P = 0.03) in children who were not anisometropic at baseline, whereas ZFHX1B rs13382811 was associated nominally with Aniso-AL development (P = 0.02). An additive SNP analysis indicated children carrying the risk allele T of ZFHX1B rs13382811 and allele A of PAX6 rs644242 might have a 4.33- and 6.90-fold of increased risk of Aniso-SE and Aniso-AL development by 3 years, respectively. This study identified two susceptible gene variants, ZFHX1B rs13382811 and PAX6 rs644242, for anisometropia development in Hong Kong Chinese children, implicating their role in imbalanced refractive change and axial elongation between both eyes.

Isogenic human trophectoderm cells demonstrate the role of NDUFA4 and associated variants in ZIKV infection

Population-based genome-wide association studies (GWAS) normally require a large sample size, which can be labor intensive and costly. Recently, we reported a human induced pluripotent stem cell (hiPSC) array-based GWAS method, identifying NDUFA4 as a host factor for Zika virus (ZIKV) infection. In this study, we extended our analysis to trophectoderm cells, which constitute one of the major routes of mother-to-fetus transmission of ZIKV during pregnancy. We differentiated hiPSCs from various donors into trophectoderm cells. We then infected cells carrying loss of function mutations in NDUFA4, harboring risk versus non-risk alleles of SNPs (rs917172 and rs12386620) or having deletions in the NDUFA4 cis-regulatory region with ZIKV. We found that loss/reduction of NDUFA4 suppressed ZIKV infection in trophectoderm cells. This study validated our published hiPSC array-based system as a useful platform for GWAS and confirmed the role of NDUFA4 as a susceptibility locus for ZIKV in disease-relevant trophectoderm cells.