Kidney stones is a common urological disease with a rising incidence in global. The association between kidney stones and urological cancers remains controversial. This study utilized the data from the 2007-2020 National Health and Nutrition Examination Survey (NHANES) and Mendelian randomization (MR) analysis to evaluate the association and potential causal relationship between kidney stones and renal cell carcinoma, bladder cancer, and prostate cancer.

Multivariate logistic regression was used to examine the association between kidney stones history and urological cancers, followed by stratified analyses. Subsequently, causal relationships were explored via the inverse variance weighted (IVW), weighted median, and MR-Egger methods. Sensitivity analyses were performed to ensure the robustness of the findings.

Data from 13,013 individuals (5,138 males) were analyzed. Kidney stones was significantly associated with an increased risk of renal cell carcinoma (OR = 1.92, 95% CI 1.90-1.95, P < 0.001), bladder cancer (OR = 2.749, 95% CI 2.71-2.78, P < 0.001), and prostate cancer (OR = 2.03, 95% CI 2.02-2.04, P < 0.001). However, MR analysis did not provide evidence for a genetic causal relationship between kidney stones and these cancers. Sensitivity analyses confirmed the stability and reliability of the MR results.

Kidney stones increased the risk of renal cell carcinoma, bladder cancer, and prostate cancer in the US population. MR analysis did not establish a genetic causal relationship between kidney stones and renal cell carcinoma, bladder cancer, and prostate cancer in the European population.

Previous studies have suggested that aging may influence reproductive functions of female. Nonetheless, the causal relationship between aging and endometriosis has yet to be completely understood.

This study aims to determine whether aging had a causal association with the incidence of endometriosis.

We conducted bidirectional MR analyses to evaluate the causal relationship between aging biomarkers, particularly leukocyte telomere length (LTL), and endometriosis risk. Instrumental variables for LTL were derived from the UK Biobank GWAS, while endometriosis-associated variants were obtained from the FinnGen GWAS dataset. Subgroup analyses were performed to investigate the association between LTL and endometriosis subtypes. Additionally, validation was performed using independent GWAS meta-analysis datasets.

Inverse variance-weighted (IVW) analysis revealed a significant association between longer LTL and an increased risk of endometriosis (OR-IVW = 1.276, 95% CI: 1.143 to 1.424, FDR-adjusted P = 7.00E-5), with consistent findings across multiple MR methods. Sensitivity analysis using an independent GWAS meta-analysis dataset did not confirm the LTL-endometriosis association (OR-IVW = 1.128, 95% CI: 0.140 to 9.115, P = 0.910). Bidirectional MR analysis found no causal relationship between endometriosis and LTL. Subgroup analyses indicated that longer LTL was significantly associated with endometriosis of the ovary (OR-IVW = 1.343, 95% CI: 1.143 to 1.577, P = 3.00E-4) and endometriosis of the rectovaginal septum and vagina (OR-IVW = 1.336, 95% CI: 1.064 to 1.676, P = 0.013), while no significant association was found with endometriosis of the pelvic peritoneum.

Our findings suggest that longer LTL may contribute to an increased risk of endometriosis, particularly in ovarian and rectovaginal subtypes. However, no causal effect of endometriosis on aging was observed. The lack of replication in independent datasets highlights the potential influence of population heterogeneity and dataset-specific factors, warranting further validation in diverse cohorts.

Inflammatory bowel diseases (IBDs) are relapsing, remitting inflammatory diseases of the intestinal tract. Familial aggregation and genome-wide association studies revealed susceptibility variants that point toward a combination of innate immune and adaptive immune dysregulation that in concert with environmental factors, such as our microbiome, can initiate and perpetuate inflammation. Innate immune perturbations include functional abnormalities in the intestinal barrier, endoplasmic reticulum stress, and abnormal recognition of microbes. Adaptive immune changes include dysregulation of cytokines, regulatory T cells, and leukocyte migration. IBD is linked with an abnormal wound-healing response leading to fibrosis. This article summarizes key pathogenic mechanisms in the pathogenesis of IBDs.

Androgenetic alopecia is a highly heritable trait. However, much of our understanding about the genetics of male-pattern baldness comes from individuals of European descent. Here, we examined a dataset comprising 2,136 men from Ghana, Nigeria, Senegal, and South Africa that were genotyped using the Men of African Descent and Carcinoma of the Prostate Array. We first tested how genetic predictions of baldness generalize from Europe to Africa and found that polygenic scores from European genome-wide association studies (GWASs) yielded area under the curve statistics that ranged from 0.513 to 0.546, indicating that genetic predictions of baldness generalized poorly from European to African populations. Subsequently, we conducted an African GWAS of androgenetic alopecia, focusing on self-reported baldness patterns at age 45. After correcting for age at recruitment, population structure, and study site, we identified 266 moderately significant associations, 51 of which were independent (p < 10-5, r2 < 0.2). Most baldness associations were autosomal, and the X chromosome does not seem to have a large impact on baldness in African men. Although Neanderthal alleles have previously been associated with skin and hair phenotypes, within the limits of statistical power, we did not find evidence that continental differences in the genetic architecture of baldness are due to Neanderthal introgression. While most loci that are associated with androgenetic alopecia do not have large integrative haplotype scores or fixation index statistics, multiple baldness-associated SNPs near the EDA2R and AR genes have large allele frequency differences between continents. Collectively, our findings illustrate how population genetic differences contribute to the limited portability of polygenic predictions across ancestries.

Previous studies have suggested that hypoxia-inducible factor 1-α (HIF-1α) exerted multiple effects on different central nervous system disorders. However, it is still uncertain whether plasma HIF-1α can be a causal indicator for the relevant diseases. This study aimed to test the causality relationship between plasma HIF-1α and neurological diseases, including cerebrovascular diseases, migraines, and neurodegenerative diseases with a Mendelian randomization (MR) method.

Single-nucleotide polymorphisms (SNPs) genetically representing plasma HIF-1α were screened as instrumental variables (IVs). Summary-level data for neurological disorder from genome-wide association studies (GWAS) were identified as outcomes. The causal effects between the IVs and outcomes were determined via the major analysis of inverse-variance-weighted (IVW) method. The reverse causal direction was also performed to investigate the possibility of reverse causation.

The findings revealed that plasma HIF-1α was identified to be genetically associated with cardioembolic stroke (CES) (OR = 0.885; 95% confidence interval [CI] = 0.796-0.985, p = 0.026), migraine (OR = 0.941, 95% CI = 0.888-0.998, p = 0.041), and drug-induced migraine without aura (MOA) (OR = 0.586, 95% CI = 0.375-0.916, p = 0.019). There was no association identified in plasma HIF-1α with subarachnoid hemorrhage (SAH), other stroke and migraine subtype, and neurodegenerative disorders. The reverse-MR analysis revealed that the above-stated neurological diseases did not have a causal effect on plasma HIF-1α levels. Sensitivity and validation analyses support that the above results are stable.

Our research indicated that plasma HIF-1α may have a causal effect on the risk of CES, migraine and drug-induced MOA, providing new insights for those disease prevention and therapeutic approaches.

Since its outbreak, the novel coronavirus (COVID-19) has significantly impacted the pediatric population. Pulmonary surfactant dysfunction has been linked to other respiratory diseases in children and COVID-19 in adults, but its role in COVID-19 severity remains unclear. We hypothesized that elevated surfactant protein (SP) levels and single nucleotide polymorphisms (SNPs) of SP genes are associated with severe COVID-19 in children. We enrolled 325 COVID-19 positive children and categorized them as having mild or severe disease. Plasma SP-A, SP-B, and SP-D levels were measured. DNA was extracted and genotyped for SNPs in five SP genes, SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD. Quantile regression was used to compare SP levels between groups, and receiver operating curve analysis determined an optimal cutoff value of SP level for predicting severe COVID-19. Logistic regression evaluated the odds ratio (OR) for severe disease and associations between SNPs and COVID-19 severity. We found that increased plasma SP-A levels, but not SP-B or SP-D, were significantly associated with severe COVID-19. No significant correlation was observed between age and SP levels. A plasma SP-A level of 10 ng/mL was identified as the optimal cutoff for predicting severe COVID-19, with an OR of 5.9, indicating that children with SP-A levels above this threshold are nearly six times more likely to develop severe COVID-19 disease. In addition, the rs8192340 of SFTPC was associated with decreased risk of severe COVID-19 before, but not after, Bonferroni correction. These findings suggest that plasma SP-A may serve as a potential biomarker for severe COVID-19 in children.NEW & NOTEWORTHY Surfactant dysfunction is linked to other pulmonary diseases, but its role in pediatric coronavirus (COVID-19) is unclear. We found elevated plasma surfactant protein (SP)-A levels, but not SP-B or SP-D, significantly associated with severe COVID-19. A plasma SP-A threshold of 10 ng/mL predicted severe COVID-19. The rs8192340 of SFTPC was associated with decreased risk of severe COVID-19 before, but not after, Bonferroni correction. These findings suggest plasma SP-A may serve as a potential biomarker for pediatric COVID-19 severity.

Greenlandic Inuit and other indigenous populations are underrepresented in genetic research1,2, leading to inequity in healthcare opportunities. To address this, we performed analyses of sequenced or imputed genomes of 5,996 Greenlanders with extensive phenotypes. We quantified their historical population bottleneck and how it has shaped their genetic architecture to have fewer, but more common, variable sites. Consequently, we find twice as many high-impact genome-wide associations to metabolic traits in Greenland compared with Europe. We infer that the high-impact variants arose after the population split from Native Americans and thus are Arctic-specific, and show that some of them are common due to not only genetic drift but also selection. We also find that European-derived polygenic scores for metabolic traits are only half as accurate in Greenlanders as in Europeans, and that adding Arctic-specific variants improves the overall accuracy to the same level as in Europeans. Similarly, lack of representation in public genetic databases makes genetic clinical screening harder in Greenlandic Inuit, but inclusion of Greenlandic data remedies this by reducing the number of non-causal candidate variants by sixfold. Finally, we identify pronounced genetic fine structure that explains differences in prevalence of monogenic diseases in Greenland and, together with recent changes in mobility, leads to a predicted future reduction in risk for certain recessive diseases. These results illustrate how including data from Greenlanders can greatly reduce inequity in genomic-based healthcare.

Missing data problems are common in biological, high-dimensional data, where data can be partially or completely missing. Algorithms have been developed to reconstruct the missing values by means of imputation or expectation-maximization algorithms. For missing data problems, it has been suggested that the regression model of interest should be incorporated into the imputation procedure to reduce bias of the regression coefficients. We here consider a challenging missing data problem, where diplotypes of the KIR loci are to be reconstructed. These loci are difficult to genotype, resulting in ambiguous genotype calls. We extend a previously proposed expectation-maximization algorithm by incorporating a potentially high-dimensional regression model to model the outcome. Three strategies are evaluated: (1) only allelic predictors, (2) allelic predictors and forward-backward selection on haplotype predictors, and (3) penalized regression on a saturated model. In a simulation study, we compared these strategies with a baseline expectation-maximization algorithm without outcome model. For extreme choices of effect sizes and missingness levels, the outcome-based expectation-maximization algorithms outperformed the no-outcome expectation-maximization algorithm. However, in all other cases, the no-outcome expectation-maximization algorithm performed either superior or comparable to the three strategies, suggesting the outcome model can have a harmful effect. In a data analysis concerning death after allogeneic hematopoietic stem cell transplantation as a function of donor KIR genes, expectation-maximization algorithms with and without outcome showed very similar results. In conclusion, outcome based missing data models in the high-dimensional setting have to be used with care and are likely to lead to biased results.

Mendelian randomization (MR) is a statistical method that uses genetic variants as instrumental variables to estimate the causal effect of exposure on an outcome in the presence of unmeasured confounding. In this review, we argue that it is crucial to acknowledge the instrumental variable assumptions in MR analysis. We describe widely used MR methods, using an example from obesity-related metabolic disorders. We describe situations in which instrumental variable assumptions are violated and explain how to evaluate these violations and employ robust methods for accommodating such violations.

Existing research suggests an association between smoking and the incidence of recurrent aphthous stomatitis (RAS); however, the causal relationship remains ambiguous. We employed Mendelian randomization (MR) to clarify the potential causal association between smoking and the risk of developing RAS.

We utilized genome-wide association study (GWAS) sequencing data related to smoking from the Finnish database as instrumental variables (IVs) and GWAS data for RAS from the UK Biobank (UKB) as the outcome to perform a two-sample MR analysis. The selection of IVs was rigorously controlled according to the three principal assumptions of relevance, independence, and exclusivity. The primary analytical methods utilized were inverse variance weighting (IVW) and weighted median (WM), supplemented by MR-Egger, simple mode, and weighted mode techniques to infer causality between smoking and RAS. Sensitivity analyses were conducted using MR-PRESSO, Cochran's Q, and the MR-Egger intercept to ensure the robustness of the findings.

The findings from the IVW and WM analyses suggest a causal association between smoking and an elevated risk of RAS (IVW: OR=1.003; 95% CI: 1.0002-1.005, p=0.033; WM: OR=1.003; 95% CI: 1.00006-1.007, p=0.044). Compared to non-smokers, smokers have a 0.3% increase in the risk of RAS. Furthermore, the sensitivity analysis did not reveal any inconsistencies that would contradict the MR results.

Our findings provide preliminary evidence of a potential causal relationship between smoking and the risk of RAS, which may contribute to a deeper understanding of the underlying mechanisms. Further research is needed to confirm these results and explore their implications for clinical practice.

Circulating cytokines orchestrate immune reactions and are promising drug targets for immune-mediated and inflammatory diseases. Exploring the genetic architecture of circulating cytokine levels could yield key insights into causal mediators of human disease. Here, we performed genome-wide association studies (GWAS) for 40 circulating cytokines in meta-analyses of 74,783 individuals. We detected 359 significant associations between cytokine levels and variants in 169 independent loci, including 150 trans- and 19 cis-acting loci. Integration with transcriptomic data point to key regulatory mechanisms, such as the buffering function of the Atypical Chemokine Receptor 1 (ACKR1) acting as scavenger for multiple chemokines and the role of tumor necrosis factor receptor-associated factor 1 (TRAFD1) in modulating the cytokine storm triggered by TNF signaling. Applying Mendelian randomization (MR), we detected a network of complex cytokine interconnections with TNF-b, VEGF, and IL-1ra exhibiting pleiotropic downstream effects on multiple cytokines. Drug target cis-MR using 2 independent proteomics datasets paired with colocalization revealed G-CSF/CSF-3 and CXCL9/MIG as potential causal mediators of asthma and Crohn's disease, respectively, but also a potentially protective role of TNF-b in multiple sclerosis. Our results provide an overview of the genetic architecture of circulating cytokines and could guide the development of targeted immunotherapies.

Previous studies have shown that bone mineral density (BMD) has a certain impact on scoliosis. However, up to now, there is no clear evidence that there is a causal association between the two. The aim of this study is to investigate whether there is a causal association between BMD at different body positions and scoliosis by two-sample Mendelian randomization (MR).

Genetic variants (SNPS) strongly associated with BMD (total body BMD (TB-BMD), lumbar spine BMD (LS-BMD), femoral neck BMD (FN-BMD), heel BMD (HE-BMD), and forearm BMD (FA-BMD)) were extracted from GEFOS and genome-wide association analysis (GWAS) databases SNPs) were used as instrumental variables (IVs). Scoliosis was also selected from the Finnish database as the outcome. Inverse variance weighting (IVW) method was used as the main analysis method, and multiple sensitivity analysis was performed by combining weighted median, MR-Egger, MR Multi-effect residuals and outliers.

IVW results showed that TB-BMD (OR = 0.83, 95%CI: 0.66-1.55 P = 0.13), LS-BMD (OR = 0.72, 95%CI: 0.52-0.99, P = 0.04), FN-BMD (OR = 0.74, 95%CI: 0.50-1.09, P = 0.13), FA-BMD (OR = 0.95,95%CI: 0.70-1.28, P = 0.75), HE-BMD (OR = 0.91, 95%CI: 0.77-1.08, P = 0.29). Sensitivity analyses showed no evidence of pleiotropy or heterogeneity (p > 0.05) (MR-PRESSO and Cochrane). The results were further validated by leave-one-out test and MR-Egger intercept, which confirmed the robustness of the study results.

In conclusion, the present study demonstrates that the causal role of genetic prediction of scoliosis increases with decreasing lumbar BMD. There was no evidence that BMD at the remaining sites has a significant causal effect on scoliosis. Our results suggest that the lumbar spine BMD should be routinely measured in the population at high risk of scoliosis. If osteoporosis occurs, appropriate treatment should be given to reduce the incidence of scoliosis.

Not applicable.

Evolutionary models of quantitative traits often assume trade-offs between beneficial and detrimental traits, requiring modellers to specify a function linking trait values. The choice of trade-off function can be consequential; functions that assume diminishing returns (accelerating costs) typically lead to single equilibrium genotypes, while decelerating costs often lead to genetic polymorphisms. Despite their importance, our current theory has little to say on which trade-off functions are the most biologically plausible. To address this gap, we explored how the genetic determination of quantitative traits can lead to different trade-off functions, using resistance to infectious diseases as an example trait. We developed a model where alleles at separate loci pleiotropically increase resistance while decreasing fecundity. We then used this model to generate genotype landscapes and investigate how epistasis effects the trade-off function. Regardless of the strength of epistasis, our model consistently led to accelerating costs. We then incorporated our genotype model into an eco-evolutionary model of disease resistance. Unlike other models with accelerating costs, our approach often led to genetic polymorphisms. Our results suggest that accelerating costs are a strong null model for evolutionary trade-offs and that the eco-evolutionary conditions required for polymorphism may be more nuanced than previously thought.

Upper gastrointestinal (UGI) tumors, notably gastric cancer (GC) and esophageal cancer (EC), are significant global health concerns due to their high morbidity and mortality rates. However, only a limited number of metabolites have been identified as biomarkers for these cancers. To explore the association between metabolites and UGI tumors, the present study conducted a comprehensive two‑sample Mendelian randomization (MR) analysis using publicly available genetic data. In the present study, the causal relationships were examined between 1,400 metabolites and UGI cancer using methods such as inverse variance weighting and weighted medians, along with sensitivity analyses for heterogeneity and pleiotropy. Functional experiments were conducted to validate the MR results. The analysis identified 57 metabolites associated with EC and 58 with GC. Key metabolites included fructosyllysine [EC: Odds ratio (OR)=1.450, 95% confidence interval (CI)=1.087‑1.934, P=0.011; GC: OR=1.728, 95% CI=1.202‑2.483, P=0.003], 2'‑deoxyuridine to cytidine ratio (EC: OR=1.464, 95% CI=1.111‑1.929, P=0.007; GC: OR=1.464, 95% CI=1.094‑1.957, P=0.010) and carnitine to protonylcarnitine (C3) ratio (EC: OR=0.655, 95% CI=0.499‑0.861, P=0.002; GC: OR=0.664, 95% CI=0.486‑0.906, P=0.010). Notably, fructosyllysine levels and the 2'‑deoxyuridine to cytidine ratio were identified as risk factors for both EC and GC, while the C3 ratio served as a protective factor. Functional experiments demonstrated that fructosyllysine and the 2'‑deoxyuridine to cytidine ratio promoted the proliferation of EC and GC cells, whereas carnitine inhibited their proliferation. In conclusion, the present findings provide insights into the causal factors and biomarkers associated with UGI tumors, which may be instrumental in guiding targeted dietary and pharmacological interventions, thereby contributing to the prevention and treatment of UGI cancer.

Large scale cancer genomic studies in patients have unveiled millions of non-coding variants. While a handful have been shown to drive cancer development, the vast majority have unknown function. This review describes the challenges of functionally annotating non-coding cancer variants and understanding how they contribute to cancer. We summarize recently developed high-throughput technologies to address these challenges. Finally, we outline future prospects for non-coding cancer genetics to help catalyze personalized cancer therapy.

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Men of African descent have the highest prostate cancer incidence and mortality rates, yet the genetic basis of prostate cancer in African men has been understudied. We used genomic data from 3,963 cases and 3,509 controls from Ghana, Nigeria, Senegal, South Africa and Uganda to infer ancestry-specific genetic architectures and fine-map disease associations. Fifteen independent associations at 8q24.21, 6q22.1 and 11q13.3 reached genome-wide significance, including four new associations. Intriguingly, multiple lead associations are private alleles, a pattern arising from recent mutations and the out-of-Africa bottleneck. These African-specific alleles contribute to haplotypes with odds ratios above 2.4. We found that the genetic architecture of prostate cancer differs across Africa, with effect size differences contributing more to this heterogeneity than allele frequency differences. Population genetic analyses reveal that African prostate cancer associations are largely governed by neutral evolution. Collectively, our findings emphasize the utility of conducting genetic studies that use diverse populations.

Genetic features are currently unknown in myelinated retinal nerve fibers (MRNF). For a 20-year-old asymptomatic female with unilateral MRNF, we performed whole genome sequencing (WGS) by standard workflow protocol to produce contiguous long-read sequences with Illumina DNA PCR-Free Prep. After tagmentation, libraries were sequenced on separate runs via NovaSeq 6000 platform at 2 x 150bp read length. Gene variants included rs2248799, rs2672589, rs7555070, rs247616_T and rs2043085_C all associated with an increased macular degeneration risk, and seven novel variants of uncertain significance. For optic disc enlargement, variants rs9988687_A, rs11079419_T, rs6787363 and rs10862708_A suggested an increased risk for this condition. In contrast, modeling revealed retinal detachment risk was reduced by variants identified at rs9651980_T, rs4373767_T, and rs7940691_T which were among five other previously unreported variants. WGS data placed proband at the 66th and 64th percentiles for disc anomaly and retinal detachment risk, respectively. Additionally, risk determined from 16 loci associated with age-related macular degeneration found the patient to be at the 18th percentile for this diagnosis (i.e., below average genetic predisposition). Fundoscopic findings showed mean RNFL thickness was lower with MRNF (77 OS vs. 96?m OD) and RNFL symmetry was impaired (43 %) but stable between 2020 and 2023. Rim area and cup volume were also substantially different (2.33 OS vs. 1.34mm2 OD, and 0.001 OS vs. 0.151mm3 OD, respectively). As the first known evaluation of MRNF via WGS, these data reveal a mixed picture with variants associated with different risks for potentially related ocular pathologies. In addition, we identify multiple new variants of unknown significance. Factors affecting gene expression in MRNF require further study. Key words: Whole genome sequencing, Retina, Myelination, Anatomy, Gene variants.

To explore the potential causal associations between coeliac disease(CD) and postpartum depression(PPD) by using two-sample Mendelian randomization(MR) analysis.

The IEU OPEN GWAS project was utilized to identify genetic loci strongly associated with CD as instrumental variables (IVs), and MR analysis was performed using inverse variance weighting(IVW), weighted median, weighted model, and MR-Egger. MR analyses were used to examine whether there was a link between CD and PPD, with an OR and 95% CI. Meanwhile, the relationship between CD and depression(DP) was analyzed using MR. The sensitivity analysis was conducted using MR-Egger intercept analysis, Cochran's Q test, and leave-one-out analysis.

From the GWAS online database, 13 single-nucleotide polymorphisms (SNPs) were chosen as IVs. The IVW results showed a relationship between PPD and a genetically predicted risk of developing CD (OR = 1.022, 95% CI: 1.001-1.044, P = 0.043). However, the presence of DP was not linked with CD (OR=0.991, 95% CI: 0.978-1.003, P=0.151). Potential horizontal pleiotropy was not discovered using MR-Egger intercept analysis (PPD: P=0.725; DP: P=0.785), and Cochran's Q test for heterogeneity revealed no significant heterogeneity (PPD: P=0.486; DP: P=0.909). A leave-one-out analysis found that individual SNPs had minimal effect on overall causal estimations.

MR research discovered a link between CD and PPD.

Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.

In recent years, Genome-Wide Association Studies (GWAS) has identified risk variants related to complex diseases, but most genetic variants have less impact on phenotypes. To solve the above problems, methods that can use variants with low genetic effects, such as genetic risk score (GRS), have been developed to predict disease risk.

As the GRS model with the most incredible prediction power for complex diseases has not been determined, our study used simulation data and prostate cancer data to explore the disease prediction power of three GRS models, including the simple count genetic risk score (SC-GRS), the direct logistic regression genetic risk score (DL-GRS), and the explained variance weighted GRS based on directed logistic regression (EVDL-GRS).

We used 26 SNPs to establish GRS models to predict the risk of biochemical recurrence (BCR) after radical prostatectomy. Combining clinical variables such as age at diagnosis, body mass index, prostate-specific antigen, Gleason score, pathologic T stage, and surgical margin and GRS models has better predictive power for BCR. The results of simulation data (statistical power = 0.707) and prostate cancer data (area under curve = 0.8462) show that DL-GRS has the best prediction performance. The rs455192 was the most relevant locus for BCR (p = 2.496 × 10-6) in our study.

Evolutionary models of quantitative traits often assume trade-offs between beneficial and detrimental traits, requiring modelers to specify a function linking costs to benefits. The choice of trade-off function is often consequential; functions that assume diminishing returns (accelerating costs) typically lead to single equilibrium genotypes, while decelerating costs often lead to evolutionary branching. Despite their importance, we still lack a strong theoretical foundation to base the choice of trade-off function. To address this gap, we explore how trade-off functions can emerge from the genetic architecture of a quantitative trait. We developed a multi-locus model of disease resistance, assuming each locus had random antagonistic pleiotropic effects on resistance and fecundity. We used this model to generate genotype landscapes and explored how additive versus epistatic genetic architectures influenced the shape of the trade-off function. Regardless of epistasis, our model consistently led to accelerating costs. We then used our genotype landscapes to build an evolutionary model of disease resistance. Unlike other models with accelerating costs, our approach often led to genetic polymorphisms at equilibrium. Our results suggest that accelerating costs are a strong null model for evolutionary trade-offs and that the eco-evolutionary conditions required for polymorphism may be more nuanced than previously believed.

This study aimed to investigate the association between n-3 PUFA and lung function. First, a cross-sectional study was conducted based on the National Health and Nutrition Examination Survey (NHANES) 2007-2012 data. n-3 PUFA intake was obtained from 24-h dietary recalls. A multivariable linear regression model was used to assess the observational associations of n-3 PUFA intake with lung function. Subsequently, a two-sample Mendelian randomisation (MR) was performed to estimate the potential causal effect of n-3 PUFA on lung function. Genetic instrumental variables were extracted from published genome-wide association studies. Summary statistics about n-3 PUFA was from UK Biobank. Inverse variance weighted was the primary analysis approach. The observational study did not demonstrate a significant association between n-3 PUFA intake and most lung function measures; however, a notable exception was observed with significant findings in the highest quartile for forced vital capacity (FVC) and % predicted FVC. The MR results also showed no causal effect of circulating n-3 PUFA concentration on lung function (forced expiratory volume in one second (FEV1), β = 0·01301, se = 0·01932, P = 0·5006; FVC, β = -0·001894, se = 0·01704, P = 0·9115; FEV1:FVC, β = 0·03118, se = 0·01743, P = 0·07359). These findings indicate the need for further investigation into the impact of higher n-3 PUFA consumption on lung health.

The role of metabolism in the variation of age at menarche (AAM) and age at natural menopause (ANM) in the female population is not entirely known. We aimed to investigate the causal role of circulating metabolites in AAM and ANM using Mendelian randomization (MR).

We combined MR with genetic colocalization to investigate potential causal associations between 658 metabolites and AAM and between 684 metabolites and ANM. We extracted genetic instruments for our exposures from four genome-wide association studies (GWAS) on circulating metabolites and queried the effects of these variants on the outcomes in two large GWAS from the ReproGen consortium. Additionally, we assessed the mediating role of the body mass index (BMI) in these associations, identified metabolic pathways implicated in AAM and ANM, and sought validation for selected metabolites in the Avon Longitudinal Study of Parents and Children (ALSPAC).

Our analysis identified 10 candidate metabolites for AAM, but none of them colocalized with AAM. For ANM, 76 metabolites were prioritized (FDR-adjusted MR P-value ≤ 0.05), with 17 colocalizing, primarily in the glycerophosphocholines class, including the omega-3 fatty acid and phosphatidylcholine (PC) categories. Pathway analyses and validation in ALSPAC mothers also highlighted the role of omega and polyunsaturated fatty acids levels in delaying age at menopause.

Our study suggests that metabolites from the glycerophosphocholine and fatty acid families play a causal role in the timing of both menarche and menopause. This underscores the significance of specific metabolic pathways in the biology of female reproductive longevity.

Observational studies have previously shown a potential link between psycho-emotional disorders, such as mood swings, highly strung, anxious feelings, and gastroesophageal reflux disease (GERD). However, the credibility of these associations could be influenced by various confounding factors. Consequently, our study sought to employ a Mendelian randomization (MR) approach to elucidate a potential causal relationship between psycho-emotional disorders and GERD.

Information on independent genetic variants linked to mood swings, highly strung, and anxious feelings was gathered from European populations participating in the IEU Open GWAS research. The FinnGen Consortium provided the genome-wide association study (GWAS) summary statistics for GERD. Our analysis employed the inverse variance weighted (IVW) method under the random effects model as the main analytical method. To further bolster our findings, we employed the weighted median and MR Egger methods. In addition, we conducted a series of sensitivity analyses.

Our study supports the existence of a causal relationship between psycho-emotional disorders and GERD. Mood swings, highly strung, and anxious feelings adversely affected GERD risk (mood swings: OR 2.21, 95% CI 1.19-5.59, p = 3.09 × 10-2; highly strung: OR 5.63, 95% CI 1.77-17.94, p = 3.42 × 10-3; anxious feelings: OR 2.48, 95% CI 1.08-4.33, p = 2.89 × 10-2).

This Mendelian randomization study provides robust support for the notion that mood swings, highly strung and anxious feelings, are associated with an increased risk of developing GERD.

Previous observational studies have reported an increased risk of venous thromboembolism (VTE) among individuals with migraine. This study aimed to investigate the causal effect of migraine on the development of VTE, as well as explore the genetic correlation between them.

We conducted a two-sample Mendelian randomization (MR) study using publicly available summary statistics from large-scale genome-wide association studies for migraine and VTE. Linkage disequilibrium score regression analysis was performed to estimate the genetic correlation between migraine and VTE.

There were several shared risk variants (p-value < 5 × 10-8) between migraine and VTE. Linkage disequilibrium score regression analysis found a significant positive genetic correlation between migraine and VTE. The genetic correlations based on two migraine datasets were 0.208 (se = 0.031, p-value = 2.91 × 10-11) and 0.264 (se = 0.040, p-value = 4.82 × 10-11), respectively. Although main MR analysis showed that migraine was associated with an increased risk of VTE (odds ratio = 1.069, 95% confidence interval = 1.022-1.118, p-value = 0.004), the association attenuated to non-significance when using several other MR methods and using another set of genetic instruments. In addition, evidence of heterogeneity was found. Reverse MR analysis showed VTE was associated with increased risk of migraine with aura (odds ratio = 1.137, 95% confidence interval = 1.062-1.218, p-value = 2.47 × 10-4) with no evidence of pleiotropy and heterogeneity.

We showed suggestive evidence indicating an association between migraine and increased risk of VTE. Additionally, we found robust evidence suggesting that VTE is associated with an increased risk of migraine. The positive genetic correlation indicates that migraine and VTE has shared genetic basis. Further investigations will be necessary to address potential sex-specific effects in the analysis.

Inflammatory responses are associated with the development of vascular dementia (VaD). Circulating cytokines modulate the inflammatory response and are important for the immune system. To further elucidate the role of the immune system in VaD, we used Mendelian randomization (MR) to comprehensively and bi-directionally assess the role of circulating cytokines in VaD. Using state-of-the-art genome-wide association studies, we primarily assessed whether different genetic levels of 41 circulating cytokines affect the risk of developing VaD and, in turn, whether the genetic risk of VaD affects these circulating cytokines. We used inverse variance weighting (IVW) and several other MR methods to assess the bidirectional causality between circulating cytokines and VaD, and performed sensitivity analyses. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was inversely associated with VaD risk [odds ratio (OR): 0.74, 95 % confidence interval (CI): 0.60-0.92, P = 0.007, 0.007]. VaD was associated with seven circulating cytokines: macrophage inflammatory protein 1b (MIP-1 beta) [OR: 1.05, 95 % CI: 1.01-1.08, P = 0.009], Interleukin-12p70 (IL-12) [OR: 1.04, 95 % CI: 1.00-1.08, P = 0.047], Interleukin-17 (IL-17) [OR: 1.04, 95 % CI: 1.00-1.07, P = 0.038], Interleukin-7 (IL-7) [OR: 1.07, 95 % CI: 1.02-1.12, P = 0.009], Interferon gamma (IFN-γ) [OR: 1.03, 95 % CI: 1.00-1.07, P = 0.046], Granulocyte-colony stimulating factor (GCSF) [OR: 1.06, 95 % CI: 1.02-1.09, P = 0.001], Fibroblast growth factor (FGF) [P = 0.001], and Fibroblast growth factor (FGF) [P = 0.001]. Fibroblast growth factor basic (FGF-Basic) [OR: 1.04, 95 % CI: 1.01-1.08, P = 0.02] were positively correlated. Circulating cytokines are associated with VaD, and further studies are needed to determine whether they are effective targets for intervention to prevent or treat VaD.

Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear.

A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association.

In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively.

Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development.

Telomere length is closely linked to the aging phenotype, where cellular aging results in the production of a cascade of cell factors and the senescence-associated secretory phenotype (SASP), leading to an inflammatory response. The presence of inflammation plays a crucial role in the formation of intracranial aneurysms. Nevertheless, the relationship between telomere length and intracranial aneurysms remains unclear. This study aims to explore the causal connection between telomere length and intracranial aneurysms through the utilization of Mendelian randomization (MR) analysis.

Data on telomere length were obtained from the genome-wide association studies conducted on the UK Biobank, comprising a total of 472,174 participants. Data on intracranial aneurysms were obtained from the summary dataset of the Global Genome-wide Association Study (GWAS) conducted by the International Stroke Genetics Consortium. The dataset consisted of 7,495 cases and 71,934 controls, all of European descent. Initially, the linkage disequilibrium score was used to investigate the connection between telomere length and intracranial aneurysms. Subsequently, a bidirectional MR was conducted using two-sample analysis to assess whether there is a causal connection between telomere length and intracranial aneurysm risk. The results were analyzed utilizing five MR methods, with the inverse variance weighted method serving as the main methodology. In addition, we did various analyses to evaluate the presence of heterogeneity, pleiotropy, and sensitivity in the study results. A reverse MR analysis was conducted to investigate potential reverse causal links.

In the forward MR analysis, it was observed that both the inverse variance-weighted and weighted median analyses implied a potential causal relationship between longer telomere length and a decreased incidence of intracranial aneurysms (IVW: OR = 0.66, 95% CI: 0.47-0.92, p = 1.49 × 10-2). There was no heterogeneity or horizontal pleiotropy. The findings were verified to be robust through the utilization of leave-one-out analysis. The use of reverse MR analysis did not establish a potential causal link between the occurrence of intracranial aneurysms and telomere length.

There may exist a potential correlation between longer telomere length and a decreased likelihood of intracranial aneurysms within the European population. The present study offers novel insights into the correlation between telomere length and intracranial aneurysms. Additional research is required to clarify the underlying mechanisms and validate our discoveries in diverse populations.

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

COVID-19 pandemic represented a worldwide major challenge in different areas, and efforts undertaken by the scientific community led to the understanding of some of the genetic determinants that influence the different COVID-19 outcomes. In this paper, we review the studies about the role of human genetics in COVID-19 severity and how Brazilian studies also contributed to those findings. Rare variants in genes related to Inborn Errors of Immunity (IEI) in the type I interferons pathway, and its phenocopies, have been described as being causative of severe outcomes. IEI and its phenocopies are present in Brazil, not only in COVID-19 patients, but also in autoimmune conditions and severe reactions to yellow fever vaccine. In addition, studies focusing on common variants and GWAS studies encompassing worldwide patients have found several loci associated with COVID-19 severity. A GWAS study including only Brazilian COVID-19 patients identified a new locus 1q32.1 associated with COVID-19 severity. Thus, more comprehensive studies considering the Brazilian genomic diversity should be performed, since they can help to reveal not only what are the genetic determinants that contribute to the different outcomes for COVID-19 in the Brazilian population, but in the understanding of human genetics in different health conditions.

Observational studies have suggested blood cell counts may act as predictors of cancer. It is not known whether these hematologic traits are causally associated with lung cancer.

Two-sample bidirectional univariable Mendelian randomization (MR) and multivariable MR (MVMR) were performed to investigate the causal association between hematologic traits and the overall risk of lung cancer and three histologic subtypes [lung adenocarcinoma, squamous cell lung cancer, and small cell lung cancer (SCLC)]. The instrumental variables of 23 hematologic traits were strictly selected from large-scale genome-wide association studies. Inverse-variance weighted method and five extra methods were used to obtain robust causal estimates.

We found evidence that genetically influenced higher hematocrit [OR, 0.845; 95% confidence interval (CI), 0.783-0.913; P = 1.68 × 10-5] and hemoglobin concentration (OR, 0.868; 95% CI, 0.804-0.938; P = 3.20 × 10-4) and reticulocyte count (OR, 0.923; 95% CI, 0.872-0.976; P = 5.19 × 10-3) decreased lung carcinoma risk, especially in ever smokers. MVMR further identified hematocrit independently of smoking as an independent predictor. Subgroup analysis showed that a higher plateletcrit level increased the risk of small cell lung carcinoma (OR, 1.288; 95% CI, 1.126-1.474; P = 2.25 × 10-4).

Genetically driven higher levels of reticulocyte count and hematocrit decreased lung cancer risk. Higher plateletcrit had an adverse effect on SCLC. Hematologic traits may act as low-cost factors for lung cancer risk stratification.

Further studies are required to elucidate the potential mechanisms underlying the dysregulation of homeostasis related to hematologic traits, such as subclinical inflammation.

Age at menarche (AAM) and age at natural menopause (ANM) are highly heritable traits and have been linked to various health outcomes. We aimed to identify circulating proteins associated with altered ANM and AAM using an unbiased two-sample Mendelian randomization (MR) and colocalization approach. By testing causal effects of 1,271 proteins on AAM, we identified 22 proteins causally associated with AAM in MR, among which 13 proteins (GCKR, FOXO3, SEMA3G, PATE4, AZGP1, NEGR1, LHB, DLK1, ANXA2, YWHAB, DNAJB12, RMDN1 and HPGDS) colocalized. Among 1,349 proteins tested for causal association with ANM using MR, we identified 19 causal proteins among which 7 proteins (CPNE1, TYMP, DNER, ADAMTS13, LCT, ARL and PLXNA1) colocalized. Follow-up pathway and gene enrichment analyses demonstrated links between AAM-related proteins and obesity and diabetes, and between AAM and ANM-related proteins and various types of cancer. In conclusion, we identified proteomic signatures of reproductive ageing in women, highlighting biological processes at both ends of the reproductive lifespan.

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

Observational studies have shown that changes in circulating cytokine/growth factor levels occur throughout the initiation and progression of ankylosing spondylitis (AS), yet whether they are etiologic or downstream effects remains unclear. In this study, we performed a summarized-level bidirectional Mendelian randomization (MR) analysis to shed light on the causal relationship between the two.

Genetic instrumental-variables (IVs) associated with circulating cytokine/growth factor levels were derived from a genome-wide association study (GWAS) of 8,293 European individuals, whereas summary data for the AS were obtained from a FinnGen GWAS of 166,144 participants. We used the inverse-variance-weighted (IVW) method as the main analysis for causal inference. Furthermore, several sensitivity analyses (MR-Egger, weighted median, MR-PRESSO and Cochran's Q test) were utilized to examine the robustness of the results. Finally, reverse MR analysis was performed to assess reverse causality between AS and circulating cytokine/growth factor levels.

After Bonferroni correction, circulating levels of Cutaneous T-cell attracting (CTACK) and Monocyte specific chemokine 3 (MCP-3) were positively associated with a higher risk of AS (odds ratio [OR]: 1.224, 95% confidence interval [95% Cl]: 1.022 ~ 1.468, P = 0.028; OR: 1.250, 95% Cl: 1.016 ~ 1.539, P = 0.035). In addition, elevated circulating levels of Basic fibroblast growth factor (FGF-basic), Granulocyte colony-stimulating factor (G-CSF) and MCP-3 was considered a consequence of AS disease (β = 0.023, P = 0.017; β = 0.017, P = 0.025; β = 0.053, P = 0.025). The results of the sensitivity analysis were generally consistent.

The present study supplies genetic evidence for the relationship between circulating cytokine levels and AS. Targeted interventions of specific cytokines may help to reduce the risk of AS initiation and progression.

Adolescent idiopathic scoliosis (AIS) is the most common form of pediatric musculoskeletal disorder. Observational studies have pointed to several risk factors for AIS, but almost no evidence exists to support their causal association with AIS. Here, we applied Mendelian randomization (MR), known to limit bias from confounding and reverse causation, to investigate causal associations between body composition and puberty-related exposures and AIS risk in Europeans and Asians. For our two-sample MR studies, we used single nucleotide polymorphisms (SNPs) associated with body mass index (BMI), waist-hip ratio, lean mass, childhood obesity, bone mineral density (BMD), 25-hydroxyvitamin D (25OHD), age at menarche, and pubertal growth in large European genome-wide association studies (GWAS), and with adult osteoporosis risk and age of menarche in Biobank Japan. We extracted estimates of the aforementioned SNPs on AIS risk from the European or Asian subsets of the largest multiancestry AIS GWAS (N = 7956 cases/88,459 controls). The results of our inverse variance-weighted (IVW) MR estimates suggest no causal association between the aforementioned risk factors and risk of AIS. Pleiotropy-sensitive MR methods yielded similar results. However, restricting our analysis to European females with AIS, we observed a causal association between estimated BMD and the risk of AIS (IVW odds ratio for AIS = 0.1, 95% confidence interval 0.01 to 0.7, p = 0.02 per SD increase in estimated BMD), but this association was no longer significant after adjusting for BMI, body fat mass, and 25OHD and remained significant after adjusting for age at menarche in multivariable MR. In conclusion, we demonstrated a protective causal effect of BMD on AIS risk in females of European ancestry, but this effect was modified by BMI, body fat mass, and 25OHD levels. Future MR studies using larger AIS GWAS are needed to investigate small effects of the aforementioned exposures on AIS. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

The causal association between circulating β-carotene concentrations and cardiovascular disease (CVD) remains controversial. We conducted a Mendelian randomization study to explore the effects of β-carotene on various cardiovascular diseases, including myocardial infarction, atrial fibrillation, heart failure, and stroke. Three single nucleotide polymorphisms (SNPs) associated with the β-carotene levels were obtained by searching published data and used as instrumental variables. Genetic association estimates for 4 CVDs (including myocardial infarction, atrial fibrillation, heart failure, and stroke) in the primary analysis, blood pressure and serum lipids (high-density lipoprotein [HDL] cholesterol, LDL cholesterol, and triglycerides) in the secondary analysis were obtained from large-scale genome-wide association studies (GWASs). We applied inverse variance-weighted as the primary analysis method, and 3 others were used to verify as sensitivity analysis. Genetically predicted circulating β-carotene levels (natural log-transformed, µg/L) were positively associated with myocardial infarction (odds ratio [OR] 1.10, 95% confidence interval [CI] 1.02-1.18, P = .011) after Bonferroni correction. No evidence supported the causal effect of β-carotene on atrial fibrillation (OR 1.02, 95% CI 0.96-1.09, P = .464), heart failure (OR 1.07, 95% CI 0.97-1.19, P = .187), stroke (OR 1.03, 95% CI 0.93-1.15, P = .540), blood pressure (P > .372) and serum lipids (P > .239). Sensitivity analysis produced consistent results. This study provides evidence for a causal relationship between circulating β-carotene and myocardial infarction. These findings have important implications for understanding the role of β-carotene in CVD and may inform dietary recommendations and intervention strategies for preventing myocardial infarction.

Atrial cardiomyopathy is a condition that causes electrical and contractile dysfunction of the atria, often along with structural and functional changes. Atrial cardiomyopathy most commonly occurs in conjunction with ventricular dysfunction, in which case it is difficult to discern the atrial features that are secondary to ventricular dysfunction from those that arise as a result of primary atrial abnormalities. Isolated atrial cardiomyopathy (atrial-selective cardiomyopathy [ASCM], with minimal or no ventricular function disturbance) is relatively uncommon and has most frequently been reported in association with deleterious rare genetic variants. The genes involved can affect proteins responsible for various biological functions, not necessarily limited to the heart but also involving extracardiac tissues. Atrial enlargement and atrial fibrillation are common complications of ASCM and are often the predominant clinical features. Despite progress in identifying disease-causing rare variants, an overarching understanding and approach to the molecular pathogenesis, phenotypic spectrum, and treatment of genetic ASCM is still lacking. In this review, we aim to analyze the literature relevant to genetic ASCM to understand the key features of this rather rare condition, as well as to identify distinct characteristics of ASCM and its arrhythmic complications that are related to specific genotypes. We outline the insights that have been gained using basic research models of genetic ASCM in vitro and in vivo and correlate these with patient outcomes. Finally, we provide suggestions for the future investigation of patients with genetic ASCM and improvements to basic scientific models and systems. Overall, a better understanding of the genetic underpinnings of ASCM will not only provide a better understanding of this condition but also promises to clarify our appreciation of the more commonly occurring forms of atrial cardiomyopathy associated with ventricular dysfunction.

Besides human leukocyte antigen (HLA-DRB1) locus, more than 100 loci across the genome have been identified and linked with the onset, expression and/or progression of rheumatoid arthritis (RA). However, there are still grey areas in our understanding of the key genetic contributors of the disease, particularly in familial cases.

In the present study, we have performed the whole exome sequencing (WES) of RA patients from two consanguineous families of Pakistan in a quest to identify novel, high-impact, RA-susceptibility genetic variants.

Through stepwise filtering, around 17,000 variants (common in the affected members) were recognized, out of which 2651 were predicted to be deleterious. Of these, 196 had direct relevance to RA. When selected for homozygous recessive mode of inheritance, two novel pathogenic variants (c.1324T>C, p.Cys442→Arg442; c.2036T>C, p.Ile679→Thr679) in the TLR1 gene displayed the role of compound heterozygosity in modulating the phenotypic expression and penetrance of RA. The structural and functional consequences of the TLR1 missense single nucleotide mutations (Cys442→Arg442; Ile679→Thr679) were evaluated through molecular dynamic simulation (MDS) studies. Analysis showed domain's rigidification, conferring stability to mutant TLR1-TIR/TIRAP-TIR complex with concomitant increase in molecular interactions with pro-inflammatory cytokines, compared to the wild-type conformation. Gene co-expression network analysis highlighted interlinked partnering genes along with interleukin-6 production of TLR1 (corrected p-value 2.98e-4) and acetylcholine receptor activity of CHRNG (corrected p-value 6.12e-2) as highly enriched associated functions.

The results, validated through case-control study subjects, suggested that the variants identified through WES and confirmed through Sanger sequencing and MDS are the novel disease variants and are likely to confer RA-susceptibility, independently and/or in a family-specific context. Key Points • Exploration of population based/ethno-specific big data is imperative to identify novel causal variants of RA. • Two new deleterious missense mutations in mutational hotspot exon 4 of TLR1 gene have been identified in Pakistani RA patients. • MD simulation data provides evidence for domain's rigidification, conferring stability to mutant TLR1-TIR/TIRAP-TIR complex, with concomitant increase in production of pro-inflammatory cytokines, thus adding to the onset/erosive outcome of RA.