Individual Single Nucleotide Polymorphisms Research Articles

Mendelian randomization analysis reveals an independent causal relationship between four gut microbes and acne vulgaris.

Numerous studies have suggested a correlation between gut microbiota and acne vulgaris; however, no specific causal link has been explored. To investigate the possible causal relationship between gut microbiota and acne vulgaris, this study employed a large-scale genome-wide association study (GWAS) summary statistic. Initially, a two-sample Mendelian randomization (MR) analysis was utilized to identify the specific gut microflora responsible for acne vulgaris. We used the Inverse Variance Weighted (IVW) method as the main MR analysis method. Additionally, we assessed heterogeneity and horizontal pleiotropy, while also examining the potential influence of individual single-nucleotide polymorphisms (SNPs) on the analysis results. In order to eliminate gut microbiota with reverse causal associations, we conducted reverse MR analysis. Multivariate Mendelian randomization analysis (MVMR) was then employed to verify the independence of the causal associations. Finally, we performed SNP annotation on the instrumental variables of independent gut microbiota and acne vulgaris to determine the genes where these genetic variations are located. We also explored the biological functions of these genes through enrichment analysis. The IVW method of forward MR identified nine gut microbes with a causal relationship with acne vulgaris (p < 0.05). The findings from the sensitivity analysis demonstrate the absence of heterogeneity or horizontal pleiotropy, and leave-one-out analysis indicates that the results are not driven by a single SNP. Additionally, the Reverse MR analysis excluded two reverse-correlated pathogenic gut microbes. And then, MVMR was used to analyze seven gut microbes, and it was found that Cyanobacterium and Family XIII were risk factors for acne vulgaris, while Ruminococcus1 and Ruminiclostridium5 were protective factors for acne vulgaris. After conducting biological annotation, we identified six genes (PLA2G4A, FADS2, TIMP17, ADAMTS9, ZC3H3, and CPSF4L) that may be associated with the pathogenic gut microbiota of acne vulgaris patients. The enrichment analysis results indicate that PLA2G4A/FADS2 is associated with fatty acid metabolism pathways. Our study found independent causal relationships between four gut microbes and acne vulgaris, and revealed a genetic association between acne vulgaris patients and gut microbiota. Consider preventing and treating acne vulgaris by interfering with the relative content of these four gut microbes.

Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection.

Effects of individual single-nucleotide polymorphism (SNP) markers and the size of "training" and "test" populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of "training" to "test" population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of "training" to "test" population size increased. However, the prediction accuracy of the six GS methods reduced to a range of -0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for "training" sets, but rrBLUP tended to outperform Bayesian methods in independent "test" sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.

Genetic susceptibility to low-level lead exposure in men: Insights from ALAD polymorphisms

The genetic susceptibility to low-level lead (Pb) exposure in general populations has been poorly investigated and is limited to the single nucleotide polymorphism (SNP) rs1800435 in the delta-aminolevulinic acid dehydratase gene (ALAD). This study explored associations between ten selected ALAD SNPs with Pb concentrations in blood (BPb) and urine (UPb) among 281 men aged 18–49 years from Slovenia, including 20 individuals residing in a Pb-contaminated area. The geometric mean (range) of BPb and UPb were 19.6 (3.86–84.7) μg/L and 0.69 (0.09–3.82) μg/L SG, respectively. The possible genetic influence was assessed by examining SNP haplotypes, individual SNPs, and the combination of two SNPs using multiple linear regression analyses. While no significant associations were found for haplotypes, the presence of variant alleles of rs1800435 and rs1805312 resulted in an 11% and 13% decrease in BPb, respectively, while the presence of variant allele of rs1139488 (homozygous only) exhibited significant 20% increase in BPb, respectively. Additionally, variant allele of rs1800435 resulted in lower UPb. Individual SNPs in the model explained only around 1 additional percentage point of BPb variability. In contrast, combination analyses identified six combinations of two SNPs, which significantly explained 3–22 additional percentage points of BPb variability, with the highest explanatory power observed for the rs1800435-rs1139488 and rs1139488-rs1805313 combinations. Moreover, excluding participants from the Pb-contaminated area indicated that exposure level influenced SNPs-Pb associations.Our results confirm the importance of the ALAD gene in Pb kinetics even at low exposure levels. Additionally, we demonstrated that identifying individuals with specific combinations of ALAD SNPs explained a larger part of Pb variability, suggesting that these combinations, pending confirmation in other populations and further evaluation through mechanistic studies, may serve as superior susceptibility biomarker in Pb exposure compared to individual SNPs.

Periodontitis and osteoporosis: a two-sample Mendelian randomization analysis.

The incidences of periodontitis and osteoporosis are rising worldwide. Observational studies have shown that periodontitis is associated with increased risk of osteoporosis. We performed a Mendelian randomization (MR) study to genetically investigate the causality of periodontitis on osteoporosis. We explored the causal effect of periodontitis on osteoporosis by MR analysis. A total of 9 single nucleotide polymorphisms (SNP) were related to periodontitis. The primary approach in this MR analysis was the inverse variance-weighted (IVW) method. Simple median, weighted median, and penalized weighted median were used to analyze sensitivity. The fixed-effect IVW model and random-effect IVW model showed no significant causal effect of genetically predicted periodontitis on the risk of osteoporosis (OR=1.032; 95%CI: 0.923-1.153; P=0.574; OR=1.032; 95%CI: 0.920-1.158; P=0.588, respectively). Similar results were observed in simple mode (OR=1.031; 95%CI: 0.780-1.361, P=0.835), weighted mode (OR=1.120; 95%CI: 0.944-1.328, P=0.229), simple median (OR=1.003; 95%CI: 0.839-1.197, P=0.977), weighted median (OR=1.078; 95%CI: 0.921-1.262, P=0.346), penalized weight median (OR 1.078; 95%CI: 0.919-1.264, P=0.351), and MR-Egger method (OR=1.360; 95%CI: 0.998-1.853, P=0.092). There was no heterogeneity in the IVW and MR-Egger analyses (Q=7.454, P=0.489 and Q=3.901, P=0.791, respectively). MR-Egger regression revealed no evidence of a pleiotropic influence through genetic variants (intercept: -0.004; P=0.101). The leave-one-out sensitivity analysis indicated no driven influence of any individual SNP on the association between periodontitis and osteoporosis. The Mendelian randomization analysis did not show a significant detrimental effect of periodontitis on the risk of osteoporosis.

Association between circulating unsaturated fatty acid and preeclampsia: a two-sample Mendelian randomization study

Objective In recent years, several studies have reported an association between unsaturated fatty acids (UFAs) and the risk of developing preeclampsia; however, its exact causal effect is unclear. This study assessed the causal association between circulating UFAs and preeclampsia. Methods A two-sample Mendelian randomization (MR) study using publicly available genome-wide association study (GWAS) summary data for circulating UFA s (N = 114,999) and preeclampsia (N = 118,291) was performed. Single nucleotide polymorphisms (SNPs) significantly associated with exposure was selected as instrumental variables (IVs). The inverse variance weighted (IVW) test was used as the primary method for estimating causality in MR analysis, while MR pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression methods were used to assess horizontal pleiotropy. Cochran’s Q test was used to evaluate heterogeneity among SNPs, and leave-one-out sensitivity analysis was used to determine the effect of individual SNPs on the results of the MR analysis. Bonferroni correction was used as a correction for multiple corrections. Results Two-sample MR analysis suggested that the ratio of monounsaturated fatty acids (MUFAs) to total fatty acids (OR 1.150, 95% CI 1.006–1.315, p = 0.041), the ratio of polyunsaturated fatty acids (PUFAs) to total fatty acids (OR 0.805, 95% CI 0.658–0.986, p = 0.036) and the ratio of PUFAs to MUFAs (OR 0.807, 95% CI 0.694–0.938, p = 0.005) were causally associated with preeclampsia. After Bonferroni correction, the causal association between the ratio of polyunsaturated to MUFAs and preeclampsia remained statistically different. Conclusions This MR analysis provides evidence for a genetic causal association between circulating UFAs and preeclampsia.

Association between type 1 diabetes mellitus and ankylosing spondylitis: a two-sample Mendelian randomization study.

The development of ankylosing spondylitis (AS) is closely related to autoimmune system dysfunction. Type 1 diabetes mellitus (T1DM) is an autoimmune disease that is a risk factor for many diseases. This study aimed to investigate the causal relationship between T1DM mellitus and AS genetically. A genome-wide association study (GWAS) of causal relationships between exposure (T1DM) and outcome (AS) was performed using summary data from the GWAS database. We conducted a two-sample Mendelian randomization (MR) study of these two diseases. Inverse variance weighting (IVW) was used as the primary analysis method, with MR Egger, weighted median, and weighted mode used as supplementary methods. Sensitivity analyses were performed using Cochran's Q test, MR-Egger intercept, MR-Pleiotropy RESidual Sum and outlier methods, leave-one-out analysis, and funnel plots. A total of 11 single nucleotide polymorphisms (SNPs)were identified for instrumental variables(IVs) for MR analysis.IVW found that T1DM was causally associated with AS ((IVW: OR = 1.0006 (95% CI 1.0001, 1.0011), p = 0.0057; MR-Egger: OR = 1.0003 (95% CI 0.9995, 1.0012), p = 0.4147; weighted median: OR = 1.0006 (95% CI 1.0003, 1.0008), p = 0.0001; weighted mode: OR = 1.0007 (95% CI 1.0005, 1.0009), p = 0.0001). No horizontal pleiotropy was found for the MR-Egger intercept, and leave -one-out analysis found that the results remained stable after the removal of individual SNPs. The results of the two-sample MR analysis supported a causal relationship between T1DM and AS risk.

Causal relationship between aspirin consumption and heart failure: a Mendelian randomization study.

This study aimed to investigate the causal association of aspirin consumption with the risk of heart failure. Our study included a total of 218208 individuals, with 23397 cases of heart failure. Genetic summary data on the association between single-nucleotide polymorphisms (SNPs) and aspirin consumption were obtained from a large-scale genome-wide association study involving 462933 individuals, of which 61702 people were taking aspirin. After the exclusion of critical confounding factors, we assessed the final and independent association between the aspirin consumption and the risk of heart failure using 3 two-sample Mendelian randomization (MR) methods-inverse variance weighted (IVW), weighted-median, and MR-Egger regression. Sensitivity analyses and directionality test were employed to further validate the stability of the results. After excluding the SNPs that exhibited associations with potential confounders and harmonizing the data, a total of 32 SNPs were finally selected for MR analysis from the initially identified 60 SNPs that displayed strong associations with the exposure. The results of the main method (IVW) showed a significant positive association between aspirin use and the occurrence of heart failure (OR [odds ratio]: 1.085; 95% CI [confidence interval]: 1.015-1.161; P=0.017), although other methods did not showed statistically significant results (MR-Egger, OR: 1.211, 95% CI: 0.842-1.21, P=0.896; weighted-median, OR: 1.087, 95% CI: 0.983-1.202, P=0.105). Heterogeneity test, the MR-Egger intercept, and the funnel plot did not reveal any evidence of heterogeneity (Cochran's Q statistic=29.263; P=0.556) or horizontal pleiotropy (intercept=0.007; P=0.319). The 'leave-one-out' analysis indicated that no individual SNP exerted a dominant influence on the main estimate. Directionality test confirmed the accuracy of the causal relationship between exposure and outcome direction in our data. Our results support a potential positive causal relationship between aspirin consumption and the occurrence of heart failure.

Kernel Bioassay Evaluation of Maize Ear Rot and Genome-Wide Association Analysis for Identifying Genetic Loci Associated with Resistance to Fusarium graminearum Infection.

Gibberella ear rot (GER) caused by Fusarium graminearum (teleomorph Gibberella zeae) is one of the most destructive diseases in maize, which severely reduces yield and contaminates several potential mycotoxins in the grain. However, few efforts had been devoted to dissecting the genetic basis of maize GER resistance. In the present study, a genome-wide association study (GWAS) was conducted in a maize association panel consisting of 303 diverse inbred lines. The phenotypes of GER severity were evaluated using kernel bioassay across multiple time points in the laboratory. Then, three models, including the fixed and random model circulating probability unification model (FarmCPU), general linear model (GLM), and mixed linear model (MLM), were conducted simultaneously in GWAS to identify single-nucleotide polymorphisms (SNPs) significantly associated with GER resistance. A total of four individual significant association SNPs with the phenotypic variation explained (PVE) ranging from 3.51 to 6.42% were obtained. Interestingly, the peak SNP (PUT-163a-71443302-3341) with the greatest PVE value, was co-localized in all models. Subsequently, 12 putative genes were captured from the peak SNP, and several of these genes were directly or indirectly involved in disease resistance. Overall, these findings contribute to understanding the complex plant-pathogen interactions in maize GER resistance. The regions and genes identified herein provide a list of candidate targets for further investigation, in addition to the kernel bioassay that can be used for evaluating and selecting elite germplasm resources with GER resistance in maize.

Feasibility for non-invasive prenatal fetal blood group and platelet genotyping by massively parallel sequencing: A single test system for multiple atypical red cell, platelet and quality control markers.

Non-invasive prenatal tests (NIPT) to predict fetal red cell or platelet antigen status for alloimmunised women are provided for select antigens. This study reports on massively parallel sequencing (MPS) using a red cell and platelet probe panel targeting multiple nucleotide variants, plus individual identification single nucleotide polymorphisms (IISNPs). Maternal blood samples were provided from 33 alloimmunised cases, including seven with two red cell antibodies. Cell-free and genomic DNA was sequenced using targeted MPS and bioinformatically analysed using low-frequency variant detection. The resulting maternal genomic DNA allele frequency was subtracted from the cell-free DNA counterpart. Outcomes were matched against validated phenotyping/genotyping methods, where available. A 2.5% subtractive allele frequency threshold was set after comparing MPS predictions for K, RhC/c, RhE/e and Fya /Fyb against expected outcomes. This threshold was used for subsequent predictions, including HPA-15a, Jka /Jkb , Kpa /Kpb and Lua . MPS outcomes were 97.2% concordant with validated methods; one RhC case was discordantly negative and lacked IISNPs. IISNPs were informative for 30/33 cases as controls. NIPT MPS is feasible for fetal blood group genotyping and covers multiple blood groups and control targets in a single test. Noting caution for the Rh system, this has the potential to provide a personalised service for alloimmunised women.

No causal effect of genetically determined circulating homocysteine levels on psoriasis in the European population: evidence from a Mendelian randomization study.

Although numerous studies demonstrated a link between plasma homocysteine (Hcy) levels and psoriasis, there still exists a certain level of controversy. Therefore, we conducted a Mendelian randomization study to investigate whether homocysteine plays a causative role in the development or exacerbation of psoriasis. A two-sample Mendelian randomization (MR) analysis was conducted. Summary-level data for psoriasis were acquired from the latest R9 release results from the FinnGen consortium (9,267 cases and 364,071 controls). Single nucleotide polymorphisms (SNPs) robustly linked with plasma Hcy levels at the genome-wide significance threshold (p < 5 × 10-8) (18 SNPs) were recognized from the genome-wide meta-analysis on total Hcy concentrations (n = 44,147 participants) in individuals of European ancestry. MR analyses were performed utilizing the random-effect inverse variance-weighted (IVW), weighted median, and MR-Egger regression methods to estimate the associations between the ultimately filtrated SNPs and psoriasis. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. MR analyses revealed no causal effects of plasma Hcy levels on psoriasis [IVW: odds ratio (OR) = 0.995 (0.863-1.146), p = 0.941; weighed median method: OR = 0.985 (0.834-1.164), p = 0.862; MR-Egger regression method: OR = 0.959 (0.704-1.305), p = 0.795]. The sensitivity analyses displayed no evidence of heterogeneity and directional pleiotropy, and the causal estimates of Hcy levels were not influenced by any individual SNP. Our study findings did not demonstrate a causal effect of genetically determined circulating Hcy levels on psoriasis.

Causal association between air pollution and frailty: a Mendelian randomization study.

Frailty is a significant problem for older persons since it is linked to a number of unfavorable consequences. According to observational researches, air pollution may raise the risk of frailty. We investigated the causal association between frailty and air pollution (including PM2.5, PM2.5-10, PM10, nitrogen dioxide, and nitrogen oxides) using Mendelian randomization approach. We conducted MR analysis using extensive publically accessible GWAS (genome-wide association studies) summary data. The inverse variance weighted (IVW) method was employed as the primary analysis method. The weighted median model, MR-Egger, simple model, and weighted model approaches were chosen for quality control. The Cochran's Q test was utilized to evaluate heterogeneity. Pleiotropy is found using the MR-Egger regression test. The MR-PRESSO method was used to recognize outliers. The leave-one-out strategy was used to conduct the sensitivity analysis. MR results suggested that PM2.5 was statistically significantly associated with frailty [odds ratio (OR) = 1.33; 95%confidence interval (CI) = 1.12-1.58, p = 0.001] in IVW method. We observed no statistical association between PM2.5-10(OR = 1.00, 95% CI = 0.79-1.28, p = 0.979), PM10(OR = 0.91, 95% CI = 0.75-1.11, p = 0.364), nitrogen dioxide (OR = 0.98, 95% CI = 0.85-1.12, p = 0.730), nitrogen oxides (OR = 1.15, 95% CI = 0.98-1.36, p = 0.086) and frailty. There was no pleiotropy in the results. The sensitivity analysis based on the leave-one-out method showed that the individual single nucleotide polymorphisms (SNPs) did not affect the robustness of the results. The current MR investigation shows a causal association between PM2.5 and frailty. Frailty's detrimental progression may be slowed down with the help of air pollution prevention and control.

Common autoimmune diseases and urticaria: the causal relationship from a bidirectional two-sample mendelian randomization study.

The immune response assumes a pivotal role in the underlying mechanisms of urticaria pathogenesis. The present study delves into an investigation of the genetic causal connections between urticaria and prevalent autoimmune afflictions, notably rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), ulcerative colitis (UC), and Crohn's disease (CD). A bidirectional two-sample Mendelian randomization (MR) analysis was conducted to investigate the causal relationships involving four autoimmune diseases and urticaria. The genome-wide association study (GWAS) summary data of four autoimmune disease were sourced from the IEU OpenGWAS database. The GWAS summary data for urticaria were derived from the Finnish consortium dataset. The principal analytical approach employed in this study was the random-effects inverse variance weighted (IVW) method. Subsequently, a series of sensitivity analyses were performed, encompassing assessments of heterogeneity, horizontal pleiotropy, outliers, "Leave-one-out" analyses, and tests for adherence to the assumption of normal distribution. The random-effects IVW analysis indicate a positive genetic causal association between RA and urticaria (P < 0.001, OR 95% CI = 1.091 [1.051-1.133]). Conversely, SLE, UC, and CD do not exhibit a significant genetic causal relationship with urticaria. The reverse MR analysis reveals a positive genetic causal linkage between urticaria and SLE (P = 0.026, OR 95% CI = 1.289 [1.031-1.612]). However, the analysis demonstrates no substantial genetic causal relationship between urticaria and RA, UC, or CD. Importantly, the genetic causal assessment absence of heterogeneity, horizontal pleiotropy, and outliers. Furthermore, it remains unaffected by any individual single nucleotide polymorphism (SNP), demonstrating adherence to a normal distribution. This investigation establishing RA as a predisposing factor for urticaria. Moreover, urticaria as a plausible risk determinant for SLE. Heightened vigilance is recommended among RA patients to monitor the manifestation of urticaria within clinical settings. Similarly, individuals afflicted by urticaria should duly acknowledge the prospective susceptibility to SLE.

Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis.

Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.

Genetic factors influencing milk and fat yields in tropically adapted dairy cattle: insights from quantitative trait loci analysis and gene associations.

The objective of this study was to identify genes associated with 305-day milk yield (MY) and fat yield (FY) that also influence the adaptability of the Thai multibreed dairy cattle population to tropical conditions. A total of 75,776 imputed and actual single nucleotide polymorphisms (SNPs) from 2,661 animals were used to identify genomic regions associated with MY and FY using the single-step genomic best linear unbiased predictions. Fixed effects included herd-yearseason, breed regression, heterosis regression and calving age regression effects. Random effects were animal additive genetic and residual. Individual SNPs with a p-value smaller than 0.05 were selected for gene mapping, function analysis, and quantitative trait loci (QTL) annotation analysis. A substantial number of QTLs associated with MY (9,334) and FY (8,977) were identified by integrating SNP genotypes and QTL annotations. Notably, we discovered 17 annotated QTLs within the health and exterior QTL classes, corresponding to nine unique genes. Among these genes, Rho GTPase activating protein 15 (ARHGAP15) and catenin alpha 2 (CTNNA2) have previously been linked to physiological traits associated with tropical adaptation in various cattle breeds. Interestingly, these two genes also showed signs of positive selection, indicating their potential role in conferring tolerance to trypanosomiasis, a prevalent tropical disease. Our findings provide valuable insights into the genetic basis of MY and FY in the Thai multibreed dairy cattle population, shedding light on the underlying mechanisms of tropical adaptation. The identified genes represent promising targets for future breeding strategies aimed at improving milk and fat production while ensuring resilience to tropical challenges. This study significantly contributes to our understanding of the genetic factors influencing milk production and adaptability in dairy cattle, facilitating the development of sustainable genetic selection strategies and breeding programs in tropical environments.

Causal relationship between atrial fibrillation and stroke risk: a Mendelian randomization

ObjectivesThis study aimed to investigate the causal relationship between Atrial Fibrillation (AF) and the risk of Stroke using a Mendelian randomization (MR) approach. MethodsA two-sample MR analysis was conducted using publicly available genome-wide association study (GWAS) summary statistics data. In this analysis, genetic variants associated with AF were used as instrumental variables to estimate the causal effect. The inverse-variance weighted (IVW) method, weighted median estimator, and MR-Egger regression were employed for estimation. Additionally, sensitivity analysis was performed using the leave-one-out method. ResultsThe analysis included 87 single nucleotide polymorphisms (SNPs) associated with AF. The results from the IVW method indicated a positive association between genetic predisposition to AF and the risk of stroke (OR 1.002, 95 % CI 1.001-1.003, P < 0.001). The weighted median and MR-Egger methods showed consistent results (weighted median: OR 1.001, 95 % CI 1.000-1.002, P = 0.034; MR-Egger: OR 1.001, 95 % CI 1.000-1.003, P = 0.086). Sensitivity analysis demonstrated that no individual SNP significantly influenced the causal inference. ConclusionsThis study provides evidence of a causal relationship between AF and an elevated risk of stroke. These findings emphasize the significance of managing AF in order to prevent and treat strokes. Additional research is required to better understand the underlying mechanisms of this causal association.

Machine learning to predict ceftriaxone resistance using single nucleotide polymorphisms within a global database of Neisseria gonorrhoeae genomes.

Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is an urgent global health issue. Machine learning (ML) is a powerful tool that can aid in identifying mutations and predicting their impact on AMR. The study aimed to use ML models to predict ceftriaxone susceptibility and decreased susceptibility (S/DS). A public database of N. gonorrhoeae genomes with minimum inhibitory concentration (MIC) data was used to evaluate seven ML models using 97 single nucleotide polymorphisms (SNPs) known to be associated with ceftriaxone resistance. Ceftriaxone MICs ≤ 0.064 mg/L were classified as susceptible, and ceftriaxoneMICs > 0.064 mg/L were classified as DS. The contributions of individual SNPs to predict S/DS were calculated using SHapley Additive exPlanation (SHAP) values. An ML model was retrained using different combinations of SNPs with the highest SHAP values. The performance of ML models was assessed using different metrics including area under the curve (AUC) and balanced accuracy (bAcc). The ML analyses included 9,540 N. gonorrhoeae genomes; 368 (0.04%) were classified as DS. Of the models evaluated, the model trained with a random forest classifier had the highest performance (AUC 0.965; bAcc 0.926). A model retrained the top five SNPs, according to SHAP values, demonstrated a similar performance (AUC 0.916; bAcc 0.879) as the model with 97 SNPs. An ML approach using mutations in N. gonorrhoeae can be used to predict S/DS to ceftriaxone. The results highlight a practical application of ML to identify mutations most associated with S/DS to ceftriaxone, which can aid in the development of assays to predict AMR. IMPORTANCE Antimicrobial resistance in Neisseria gonorrhoeae is an urgent global health issue. The objectives of the study were to use a global collection of 12,936 N. gonorrhoeae genomes from the PathogenWatch database to evaluate different machine learning models to predict ceftriaxone susceptibility/decreased susceptibility using 97 mutations known to be associated with ceftriaxone resistance. We found the random forest classifier model had the highest performance. The analysis also reported the relative contributions of different mutations within the ML model predictions, allowing for the identification of the mutations with the highest importance for ceftriaxone resistance. A machine learning model retrained with the top five mutations performed similarly to the model using all 97 mutations. These results could aid in the development of molecular tests to detect resistance to ceftriaxone in N. gonorrhoeae. Moreover, this approach could be applied to building and evaluating machine learning models for predicting antimicrobial resistance in other pathogens.

Genotypes of the UCP1 gene polymorphisms and cardiometabolic diseases: A multifactorial study of association with disease probability

Cardiometabolic diseases (CMDs) are complex disorders with a heterogenous phenotype, which are caused by multiple factors including genetic factors. Single nucleotide polymorphisms (SNPs) rs45539933 (p.Ala64Thr), rs10011540 (c.-112A>C), rs3811791 (c.-1766A>G), and rs1800592 (c.-3826A>G) in the UCP1 gene have been analyzed for association with CMDs in many studies providing controversial results. However, previous studies only considered individual UCP1 SNPs and did not evaluate them in an integrated manner, which is a more powerful approach to uncover genetic component of complex diseases. This study aimed to investigate associations between UCP1 genotype combinations and CMDs or CMD risk factors in the context of non-genetic factors. We performed multiple logistic regression analysis and proposed new methodology of testing different combinations of SNP genotypes. We found that probability of CMDs increased in presence of the three-SNP combination of genotypes with minor alleles of c.-3826A>G and p.Ala64Thr and wild allele of c.-112A>C, with increasing age, body mass index (BMI), body fat percentage (BF%) and may differ between sexes and between countries. The combination of genotypes with c.-3826A>G minor allele and wild homozygotes of c.-112A>C and p.Ala64Thr was associated with increased probability of diabetes. While combination of genotypes with minor alleles of all three SNPs reduced the CMD probability. The present results suggest that age, BMI, sex, and UCP1 three-SNP combinations of genotypes significantly contribute to CMD probability. Varying of c.-112A>C alleles in the genotype combination with minor alleles of c.-3826A>G and p.Ala64Thr markedly changes CMD probability.

Evaluation of Density-Based Spatial Clustering for Identifying Genomic Loci Associated with Ischemic Stroke in Genome-Wide Data.

The genetic architecture of ischemic stroke (IS), which is one of the leading causes of death worldwide, is complex and underexplored. The traditional approach for associative gene mapping is genome-wide association studies (GWASs), testing individual single-nucleotide polymorphisms (SNPs) across the genomes of case and control groups. The purpose of this research is to develop an alternative approach in which groups of SNPs are examined rather than individual ones. We proposed, validated and applied to real data a new workflow consisting of three key stages: grouping SNPs in clusters, inferring the haplotypes in the clusters and testing haplotypes for the association with phenotype. To group SNPs, we applied the clustering algorithms DBSCAN and HDBSCAN to linkage disequilibrium (LD) matrices, representing pairwise r2 values between all genotyped SNPs. These clustering algorithms have never before been applied to genotype data as part of the workflow of associative studies. In total, 883,908 SNPs and insertion/deletion polymorphisms from people of European ancestry (4929 cases and 652 controls) were processed. The subsequent testing for frequencies of haplotypes restored in the clusters of SNPs revealed dozens of genes associated with IS and suggested the complex role that protocadherin molecules play in IS. The developed workflow was validated with the use of a simulated dataset of similar ancestry and the same sample sizes. The results of classic GWASs are also provided and discussed. The considered clustering algorithms can be applied to genotypic data to identify the genomic loci associated with different qualitative traits, using the workflow presented in this research.

Identifying potential genetic epistasis implicated in Alzheimer’s disease via detection of SNP-SNP interaction on quantitative trait CSF Aβ42

Although genome-wide association studies have identified multiple Alzheimer's disease (AD)-associated loci by selecting the main effects of individual single-nucleotide polymorphisms (SNPs), the interpretation of genetic variance in AD is limited. Based on the linear regression method, we performed genome-wide SNP-SNP interaction on cerebrospinal fluid Aβ42 to identify potential genetic epistasis implicated in AD, with age, gender, and diagnosis as covariates. A GPU-based method was used to address the computational challenges posed by the analysis of epistasis. We found 368 SNP pairs to be statistically significant, and highly significant SNP-SNP interactions were identified between the marginal main effects of SNP pairs, which explained a relatively high variance at the Aβ42 level. Our results replicated 100 previously reported AD-related genes and 5 gene-gene interaction pairs of the protein-protein interaction network. Our bioinformatics analyses provided preliminary evidence that the 5-overlapping gene-gene interaction pairs play critical roles in inducing synaptic loss and dysfunction, thereby leading to memory decline and cognitive impairment in AD-affected brains.

The genomic response to urbanization in the damselfly Ischnura elegans.

The complex and rapid environmental changes brought about by urbanization pose significant challenges to organisms. The multifaceted effects of urbanization often make it difficult to define and pinpoint the very nature of adaptive urban phenotypes. In such situations, scanning genomes for regions differentiated between urban and non-urban populations may be an attractive approach. Here, we investigated the genomic signatures of adaptation to urbanization in the damselfly Ischnura elegans sampled from 31 rural and urban localities in three geographic regions: southern and northern Poland, and southern Sweden. Genome-wide variation was assessed using more than 370,000 single nucleotide polymorphisms (SNPs) genotyped by ddRADseq. Associations between SNPs and the level of urbanization were tested using two genetic environment association methods: Latent Factors Mixed Models and BayPass. While we found numerous candidate SNPs and a highly significant overlap between candidates identified by the two methods within the geographic regions, there was a distinctive lack of repeatability between the geographic regions both at the level of individual SNPs and of genomic regions. However, we found "synapse organization" at the top of the functional categories enriched among the genes located in the proximity of the candidate urbanization SNPs. Interestingly, the overall significance of "synapse organization" was built up by the accretion of different genes associated with candidate SNPs in different geographic regions. This finding is consistent with the highly polygenic nature of adaptation, where the response may be achieved through a subtle adjustment of allele frequencies in different genes that contribute to adaptive phenotypes. Taken together, our results point to a polygenic adaptive response in the nervous system, specifically implicating genes involved in synapse organization, which mirrors the findings from several genomic and behavioral studies of adaptation to urbanization in other taxa.