Genetic correlation and Mendelian randomization analyses support causal relationships between dietary habits and age at menarche.

Dietary habits are essential in the mean age at menarche (AAM). However, the causal relationship between these factors remains unclear. Therefore, this study aimed to elucidate the genetic relationship between dietary habits and AAM. Genetic summary statistics for dietary habits were obtained from the UK Biobank. GWAS summary data for AAM was obtained from the ReproGen Consortium. Linkage disequilibrium score regression was used to test genetic correlations between dietary habits and AAM. The Mendelian randomization (MR) analyses used the inverse-variance weighted method. Genetic correlations with AAM were identified for 29 candi-date dietary habits, such as milk type (skimmed, semi-skimmed, full cream; coefficient = 0.2704, Pldsc = 1.13 × 10-14). MR evaluations revealed that 19 dietary habits were associated with AAM, including bread type (white vs. any other; OR 1.71, 95% CI 1.28-2.29, Pmr = 3.20 × 10-4), tablespoons of cooked vegetables (OR 0.437, 95% CI 0.29-0.67; Pmr = 1.30 × 10-4), and cups of coffee per day (OR 0.72, 95% CI 0.57-0.92, Pmr = 8.31 × 10-3). These results were observed to be stable under the sensitivity analysis. Our study provides potential insights into the genetic mechanisms underlying AAM and evidence that dietary habits are associated with AAM.

Genomic wide association study and selective sweep analysis identify genes associated with improved yield under drought in Turkish winter wheat germplasm.

A panel comprising of 84 Turkish winter wheat landraces (LR) and 73 modern varieties (MV) was analyzed with genome wide association study (GWAS) to identify genes/genomic regions associated with increased yield under favorable and drought conditions. In addition, selective sweep analysis was conducted to detect signatures of selection in the winter wheat genome driving the differentiation between LR and MV, to gather an understanding of genomic regions linked to adaptation and yield improvement. The panel was genotyped with 25 K wheat SNP array and phenotyped for agronomic traits for two growing seasons (2018 and 2019) in Konya, Turkey. Year 2018 was treated as drought environment due to very low precipitation prior to heading whereas year 2019 was considered as a favorable season. GWAS conducted with SNPs and haplotype blocks using mixed linear model identified 18 genomic regions in the vicinities of known genes i.e., TaERF3-3A, TaERF3-3B, DEP1-5A, FRIZZY PANICLE-2D, TaSnRK23-1A, TaAGL6-A, TaARF12-2A, TaARF12-2B, WAPO1, TaSPL16-7D, TaTGW6-A1, KAT-2B, TaOGT1, TaSPL21-6B, TaSBEIb, trs1/WFZP-A, TaCwi-A1-2A and TaPIN1-7A associated with grain yield (GY) and yield related traits. Haplotype-based GWAS identified five haplotype blocks (H1A-42, H2A-71, H4A-48, H7B-123 and H7B-124), with the favorable haplotypes showing a yield increase of > 700 kg/ha in the drought season. SNP-based GWAS, detected only one larger effect genomic region on chromosome 7B, in common with haplotype-based GWAS. On an average, the percentage variation (PV) explained by haplotypes was 8.0% higher than PV explained by SNPs for all the investigated traits. Selective sweep analysis detected 39 signatures of selection between LR and MV of which 15 were within proximity of known functional genes controlling flowering (PRR-A1, PPR-D1, TaHd1-6B), GY and GY components (TaSus2-2B, TaGS2-B1, AG1-1A/WAG1-1A, DUO-A1, DUO-B1, AG2-3A/WAG2-3A, TaLAX1, TaSnRK210-4A, FBP, TaLAX1, TaPIL1 and AP3-1-7A/WPA3-7A) and 10 regions underlying various transcription factors and regulatory genes. The study outcomes contribute to utilization of LR in breeding winter wheat.

Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study.

Endometriosis is a prevalent and chronic inflammatory gynecologic disorder affecting approximately 6-10% of women globally, and has been associated with an increased risk of cancer. Nevertheless, previous studies have been hindered by methodological limitations that compromise the validity and robustness of their findings. In this study we conducted a comprehensive two-sample Mendelian randomization analysis to explore the genetically driven causal relationship between endometriosis and the risk of cancer. We conducted the analysis via the inverse variance weighted method, MR Egger method, and weighted median method utilizing publicly available genome-wide association study summary statistics. Furthermore, we implemented additional sensitivity analyses to assess the robustness and validity of the causal associations identified. We found strong evidence of a significant causal effect of endometriosis on a higher risk of ovarian cancer via inverse-variance weighted method (OR = 1.19, 95% CI 1.11-1.29, p < 0.0001), MR-Egger regression, and weighted median methodologies. Remarkably, our findings revealed a significant association between endometriosis and an increased risk of clear cell ovarian cancer (OR = 2.04, 95% CI 1.66-2.51, p < 0.0001) and endometrioid ovarian cancer (OR = 1.45, 95% CI 1.27-1.65, p < 0.0001). No association between endometriosis and other types of cancer was observed. We uncovered a causal relationship between endometriosis and an elevated risk of ovarian cancer, particularly clear cell ovarian cancer and endometrioid ovarian cancer. No significant associations between endometriosis and other types of cancer could be identified.

Trans-eQTL mapping in gene sets identifies network effects of genetic variants.

Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways.

Causality of genetically determined blood metabolites on irritable bowel syndrome: A Mendelian randomization study.

BACKGROUND: Irritable bowel syndrome (IBS) is one of the most common functional bowel disorders and dysmetabolism plays an important role in the pathogenesis of disease. Nevertheless, there remains a lack of information regarding the causal relationship between circulating metabolites and IBS. A two-sample Mendelian randomization (MR) analysis was conducted in order to evaluate the causal relationship between genetically proxied 486 blood metabolites and IBS. METHODS: A two-sample MR analysis was implemented to assess the causality of blood metabolites on IBS. The study utilized a genome-wide association study (GWAS) to examine 486 metabolites as the exposure variable while employing a GWAS study with 486,601 individuals of European descent as the outcome variable. The inverse-variance weighted (IVW) method was used to estimate the causal relationship of metabolites on IBS, while several methods were performed to eliminate the pleiotropy and heterogeneity. Another GWAS data was used for replication and meta-analysis. In addition, reverse MR and linkage disequilibrium score regression (LDSC) were employed for additional assessment. Multivariable MR analysis was conducted in order to evaluate the direct impact of metabolites on IBS. RESULTS: Three known and two unknown metabolites were identified as being associated with the development of IBS. Higher levels of butyryl carnitine (OR(95%CI):1.10(1.02-1.18),p = 0.009) and tetradecanedioate (OR(95%CI):1.13(1.04-1.23),p = 0.003)increased susceptibility of IBS and higher levels of stearate(18:0)(OR(95%CI):0.72(0.58-0.89),p = 0.003) decreased susceptibility of IBS. CONCLUSION: The metabolites implicated in the pathogenesis of IBS possess potential as biomarkers and hold promise for elucidating the underlying biological mechanisms of this condition.

Sjögren's syndrome and Parkinson's Disease: A bidirectional two-sample Mendelian randomization study.

BACKGROUND: Previous observational studies have reported an association between Sjögren's syndrome (SS) and an increased risk of Parkinson's Disease (PD). However, the causal relationship between these conditions remains unclear. The objective of this study was to investigate the causal impact of SS on the risk of developing PD, utilizing the Mendelian randomization (MR) approach. METHODS: We conducted a bidirectional MR analysis using publicly available genome-wide association studies (GWAS) data. The primary analysis utilized the inverse-variance weighted (IVW) method. Complementary methods, such as MR-Egger regression, weighted mode, weighted median, and MR-pleiotropy residual sum and outlier (MR-PRESSO), were utilized to identify and correct for the presence of horizontal pleiotropy. RESULTS: The IVW MR analysis revealed no significant association between SS and PD (IVW: OR = 1.00, 95% CI = 0.94-1.07, P = 0.95). Likewise, the reverse MR analysis did not identify any significant causal relationship between PD and SS (IVW: OR = 0.98, 95% CI = 0.85-1.12, P = 0.73). The results from MR-Egger regression, weighted median, and weighted mode approaches were consistent with the IVW method. Sensitivity analyses suggested that horizontal pleiotropy is unlikely to introduce bias to the causal estimates. CONCLUSION: This study does not provide evidence to support the assertion that SS has a conclusive impact on the risk of PD, which contradicts numerous existing observational reports. Further investigation is necessary to determine the possible mechanisms behind the associations observed in these observational studies.

[Current status and prospects of genetic research on IgA nephropathy].

IgA nephropathy is the most common primary glomerulonephritis worldwide, and genetic factors may play an important role in its pathogenesis. Following candidate gene association analysis and genome-wide linkage study, genome-wide association studies (GWAS) have found multiple susceptibility genes related to the pathogenesis and clinical phenotype of IgA nephropathy. Meanwhile, structural variation and epigenetic changes are also closely related to IgA nephropathy. Genetic variants have been found to explain about 11% of its heritability. In the current era of genomic medicine, how to find more susceptible genes/loci, whole genome sequencing studies (WGS) provide clues to further understand the genetic variation of IgA nephropathy. How to find the cell type-specific susceptibility genes associated with IgA nephropathy, multi-omics studies will conduct comprehensive analysis via single-cell sequencing, expression quantitative trait locus (eQTL) and genomics to find the pathogenic genes and offer insights into the development of targeted drugs, which will be the trend and direction of future research.

[Current status and prospects of genetic research on membranous nephropathy].

Primary membranous nephropathy (PMN) is one of the most frequent pathological subtypes of nephrotic syndrome in adults. The use of genome-wide association study (GWAS) technology has propelled the transition from conventional medicine to precision medicine, offering a fresh perspective for comprehending the pathogenesis of PMN and individual variations in greater detail. Furthermore, GWAS will aid in clinical translation, laying a firm foundation for the precise diagnosis and treatment of PMN.

[Exploring the causality between intestinal flora and hyperplastic scars of human based on two-sample Mendelian randomization analysis].

Objective: To investigate the causality between intestinal flora and hypertrophic scars (HS) of human. Methods: This study was a study based on two-sample Mendelian randomization (TSMR) analysis. The data on intestinal flora (n=18 473) and HS (n=208 248) of human were obtained from the genome-wide association study database. Genetically variable genes at five levels (phylum, class, order, family, and genus) of known intestinal flora, i.e., single nucleotide polymorphisms (SNPs), were extracted as instrumental variables for linkage disequilibrium (LD) analysis. Human genotype-phenotype association analysis was performed using PhenoScanner V2 database to exclude SNPs unrelated to HS in intestinal flora and analyze whether the selected SNPs were weak instrumental variables. The causal relationship between intestinal flora SNPs and HS was analyzed through four methods of TSMR analysis, namely inverse variance weighted (IVW), MR-Egger regression, weighted median, and weighted mode. Scatter plots of significant results from the four aforementioned analysis methods were plotted to analyze the correlation between intestinal flora SNPs and HS. Both IVW test and MR-Egger regression test were used to assess the heterogeneity of intestinal flora SNPs, MR-Egger regression test and MR-PRESSO outlier test were used to assess the horizontal multiplicity of intestinal flora SNPs, and leave-one-out sensitivity analysis was used to determine whether HS was caused by a single SNP in the intestinal flora. Reverse TSMR analyses were performed for HS SNPs and genus Intestinimonas or genus Ruminococcus2, respectively, to detect whether there was reverse causality between them. Results: A total of 196 known intestinal flora, belonging to 9 phyla, 16 classes, 20 orders, 32 families, and 119 genera, were obtained, and multiple SNPs were obtained from each flora as instrumental variables. LD analysis showed that the SNPs of the intestinal flora were consistent with the hypothesis that genetic variation was strongly associated with exposure factors, except for rs1000888, rs12566247, and rs994794. Human genotype-phenotype association analysis showed that none of the selected SNPs after LD analysis was excluded and there were no weak instrumental variables. IVW, MR-Egger regression, weighted median, and weighted mode of TSMR analysis showed that both genus Intestinimonas and genus Ruminococcus2 were causally associated with HS. Among them, forest plots of IVW and MR-Egger regression analyses also showed that 16 SNPs (the same SNPs number of this genus below) of genus Intestinimonas and 15 SNPs (the same SNPs number of this genus below) of genus Ruminococcus2 were protective factors for HS. Further, IVW analysis showed that genus Intestinimonas SNPs (with odds ratio of 0.62, 95% confidence interval of 0.41-0.93, P<0.05) and genus Ruminococcus2 SNPs (with odds ratio of 0.62, 95% confidence interval of 0.40-0.97, P<0.05) were negatively correlated with the risk of HS. Scatter plots showed that SNPs of genus Intestinimonas and genus Ruminococcus2 were protective factors of HS. Both IVW test and MR-Egger regression test showed that SNPs of genus Intestinimonas (with Q values of 5.73 and 5.76, respectively, P>0.05) and genus Ruminococcus2 (with Q values of 13.67 and 15.61, respectively, P>0.05) were not heterogeneous. MR-Egger regression test showed that the SNPs of genus Intestinimonas and genus Ruminococcus2 had no horizontal multiplicity (with intercepts of 0.01 and 0.06, respectively, P>0.05); MR-PRESSO outlier test showed that the SNPs of genus Intestinimonas and genus Ruminococcus2 had no horizontal multiplicity (P>0.05). Leave-one-out sensitivity analysis showed that no single intestinal flora SNP drove the occurrence of HS. Reverse TSMR analysis showed no reverse causality between HS SNPs and genus Intestinimonas or genus Ruminococcus2 (with odds ratios of 1.01 and 0.99, respectively, 95% confidence intervals of 0.97-1.06 and 0.96-1.04, respectively, P>0.05). Conclusions: There is a causal relationship between intestinal flora and HS of human, in which genus Intestinimonas and genus Ruminococcus2 have a certain effect on inhibiting HS.

Germline genetic regulation of the colorectal tumor immune microenvironment.

OBJECTIVE: To evaluate the contribution of germline genetics to regulating the briskness and diversity of T cell responses in CRC, we conducted a genome-wide association study to examine the associations between germline genetic variation and quantitative measures of T cell landscapes in 2,876 colorectal tumors from participants in the Molecular Epidemiology of Colorectal Cancer Study (MECC). METHODS: Germline DNA samples were genotyped and imputed using genome-wide arrays. Tumor DNA samples were extracted from paraffin blocks, and T cell receptor clonality and abundance were quantified by immunoSEQ (Adaptive Biotechnologies, Seattle, WA). Tumor infiltrating lymphocytes per high powered field (TILs/hpf) were scored by a gastrointestinal pathologist. Regression models were used to evaluate the associations between each variant and the three T-cell features, adjusting for sex, age, genotyping platform, and global ancestry. Three independent datasets were used for replication. RESULTS: We identified a SNP (rs4918567) near RBM20 associated with clonality at a genome-wide significant threshold of 5 × 10- 8, with a consistent direction of association in both discovery and replication datasets. Expression quantitative trait (eQTL) analyses and in silico functional annotation for these loci provided insights into potential functional roles, including a statistically significant eQTL between the T allele at rs4918567 and higher expression of ADRA2A (P = 0.012) in healthy colon mucosa. CONCLUSIONS: Our study suggests that germline genetic variation is associated with the quantity and diversity of adaptive immune responses in CRC. Further studies are warranted to replicate these findings in additional samples and to investigate functional genomic mechanisms.

xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data.

BACKGROUND: Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes. METHODS: In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored. RESULTS: Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes. CONCLUSIONS: The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

Identifying latent genetic interactions in genome-wide association studies using multiple traits.

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Polygenic risk score-based phenome-wide association for glaucoma and its impact on disease susceptibility in two large biobanks.

BACKGROUND: Glaucoma is a leading cause of worldwide irreversible blindness. Considerable uncertainty remains regarding the association between a variety of phenotypes and the genetic risk of glaucoma, as well as the impact they exert on the glaucoma development. METHODS: We investigated the associations of genetic liability for primary open angle glaucoma (POAG) with a wide range of potential risk factors and to assess its impact on the risk of incident glaucoma. The phenome-wide association study (PheWAS) approach was applied to determine the association of POAG polygenic risk score (PRS) with a wide range of phenotypes in 377, 852 participants from the UK Biobank study and 43,623 participants from the Penn Medicine Biobank study, all of European ancestry. Participants were stratified into four risk tiers: low, intermediate, high, and very high-risk. Cox proportional hazard models assessed the relationship of POAG PRS and ocular factors with new glaucoma events. RESULTS: In both discovery and replication set in the PheWAS, a higher genetic predisposition to POAG was specifically correlated with ocular disease phenotypes. The POAG PRS exhibited correlations with low corneal hysteresis, refractive error, and ocular hypertension, demonstrating a strong association with the onset of glaucoma. Individuals carrying a high genetic burden exhibited a 9.20-fold, 11.88-fold, and 28.85-fold increase in glaucoma incidence when associated with low corneal hysteresis, high myopia, and elevated intraocular pressure, respectively. CONCLUSION: Genetic susceptibility to POAG primarily influences ocular conditions, with limited systemic associations. Notably, the baseline polygenic risk for POAG robustly associates with new glaucoma events, revealing a large combined effect of genetic and ocular risk factors on glaucoma incidents.

The impact of lipidome on breast cancer: a Mendelian randomization study.

OBJECTIVE: This study aims to investigate the association between specific lipidomes and the risk of breast cancer (BC) using the Two-Sample Mendelian Randomization (TSMR) approach and Bayesian Model Averaging Mendelian Randomization (BMA-MR) method. METHOD: The study analyzed data from large-scale GWAS datasets of 179 lipidomes to assess the relationship between lipidomes and BC risk across different molecular subtypes. TSMR was employed to explore causal relationships, while the BMA-MR method was carried out to validate the results. The study assessed heterogeneity and horizontal pleiotropy through Cochran's Q, MR-Egger intercept tests, and MR-PRESSO. Moreover, a leave-one-out sensitivity analysis was performed to evaluate the impact of individual single nucleotide polymorphisms on the MR study. RESULTS: By examining 179 lipidome traits as exposures and BC as the outcome, the study revealed significant causal effects of glycerophospholipids, sphingolipids, and glycerolipids on BC risk. Specifically, for estrogen receptor-positive BC (ER+ BC), phosphatidylcholine (P < 0.05) and phosphatidylinositol (OR: 0.916-0.966, P < 0.05) within glycerophospholipids play significant roles, along with the importance of glycerolipids (diacylglycerol (OR = 0.923, P < 0.001) and triacylglycerol, OR: 0.894-0.960, P < 0.05)). However, the study did not observe a noteworthy impact of sphingolipids on ER+BC. In the case of estrogen receptor-negative BC (ER- BC), not only glycerophospholipids, sphingolipids (OR = 1.085, P = 0.008), and glycerolipids (OR = 0.909, P = 0.002) exerted an influence, but the protective effect of sterols (OR: 1.034-1.056, P < 0.05) was also discovered. The prominence of glycerolipids was minimal in ER-BC. Phosphatidylethanolamine (OR: 1.091-1.119, P < 0.05) was an important causal effect in ER-BC. CONCLUSIONS: The findings reveal that phosphatidylinositol and triglycerides levels decreased the risk of BC, indicating a potential protective role of these lipid molecules. Moreover, the study elucidates BC's intricate lipid metabolic pathways, highlighting diverse lipidome structural variations that may have varying effects in different molecular subtypes.

GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction.

BACKGROUND: The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS). RESULTS: Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability. CONCLUSIONS: Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Asparagine reduces the risk of schizophrenia: a bidirectional two-sample mendelian randomization study of aspartate, asparagine and schizophrenia.

BACKGROUND: Despite ongoing research, the underlying causes of schizophrenia remain unclear. Aspartate and asparagine, essential amino acids, have been linked to schizophrenia in recent studies, but their causal relationship is still unclear. This study used a bidirectional two-sample Mendelian randomization (MR) method to explore the causal relationship between aspartate and asparagine with schizophrenia. METHODS: This study employed summary data from genome-wide association studies (GWAS) conducted on European populations to examine the correlation between aspartate and asparagine with schizophrenia. In order to investigate the causal effects of aspartate and asparagine on schizophrenia, this study conducted a two-sample bidirectional MR analysis using genetic factors as instrumental variables. RESULTS: No causal relationship was found between aspartate and schizophrenia, with an odds ratio (OR) of 1.221 (95%CI: 0.483-3.088, P-value = 0.674). Reverse MR analysis also indicated that no causal effects were found between schizophrenia and aspartate, with an OR of 0.999 (95%CI: 0.987-1.010, P-value = 0.841). There is a negative causal relationship between asparagine and schizophrenia, with an OR of 0.485 (95%CI: 0.262-0.900, P-value = 0.020). Reverse MR analysis indicates that there is no causal effect between schizophrenia and asparagine, with an OR of 1.005(95%CI: 0.999-1.011, P-value = 0.132). CONCLUSION: This study suggests that there may be a potential risk reduction for schizophrenia with increased levels of asparagine, while also indicating the absence of a causal link between elevated or diminished levels of asparagine in individuals diagnosed with schizophrenia. There is no potential causal relationship between aspartate and schizophrenia, whether prospective or reverse MR. However, it is important to note that these associations necessitate additional research for further validation.

Mendelian randomization study supports positive bidirectional causal relationships between genetically predicted insomnia symptom and liability to benign prostatic hyperplasia.

BACKGROUND: Sleep quality may be related to benign prostatic hyperplasia (BPH), however causal associations have not been established. This study aimed to evaluate causal relationships between six sleep traits ([i] day time napping, [ii] daytime sleepiness, [iii] insomnia, [iv] long sleep duration, [v] short sleep duration, and [vi] sleep duration per hour) and BPH through a bidirectional Mendelian randomization (MR) study. METHODS: Genome-wide association summary statistics of sleep traits and BPH were downloaded from public databases. Inverse variance weighting (IVW) was used as the main approach for causal inference. For causal estimates identified by IVW, various sensitivity analyses were performed to assess the reliability of the results: (i) four additional MR methods to complement IVW; (ii) Cochran's Q test to assess heterogeneity; (iii) MR-Egger intercept test and MR-PRESSO global test to assess horizontal pleiotropy; and (iv) leave-one-out method to assess stability. RESULTS: Forward MR analyses indicated that genetically predicted insomnia symptom significantly increased BPH risk (OR = 1.267, 95% CI: 1.003-1.601, P = 0.048), while reverse MR analyses identified that genetically predicted liability to BPH significantly increased the incidence of insomnia (OR = 1.026, 95% CI: 1.000-1.052, P = 0.048). In a replicate MR analysis based on summary statistics including exclusively male participants, the finding of increased risk of BPH due to genetically predicted insomnia symptom was further validated (OR = 1.488, 95% CI: 1.096-2.022, P = 0.011). No further causal links were identified. In addition, sensitivity tests demonstrated the reliability of the MR results. CONCLUSION: This study identified that a higher prevalence of genetically predicted insomnia symptoms may significantly increase the risk of BPH, while genetically predicted liability to BPH may in turn increase the incidence of insomnia symptom. Therefore, improving sleep quality and reducing the risk of insomnia could be a crucial approach for the prevention of BPH.

The Causal Relationship between Angina Pectoris and Gout Based on Two Sample Mendelian Randomization.

Purpose: Two-sample Mendelian randomization (MR) was conducted to assess the causal relationship between angina pectoris and gout. Material and Methods. Based on genome-wide association studies, single nucleotide polymorphisms (SNPs) that were closely associated with gout were selected from the UK Biobank-Neale Lab (ukb-a-107) as genetic instrumental variables. Considering that gout is characterized by elevated blood uric acid levels, SNPs related to blood uric acid levels were screened from BioBank Japan (bbj-a-57) as auxiliary gene instrumental variables. SNPs closely associated with angina pectoris onset were screened from the FINN dataset (finn-b-I9_ANGINA) as outcome variables. Two-sample MR was conducted, with inverse variance weighting (IVW) of the random effects model as the primary result, along with the weighted median method (WME) and the MR-Egger regression method. To further confirm the causal relationship between angina and gout incidence, a meta-analysis was conducted on the IVW results of the ukb-a-107 and bbj-a-57. Results: The odds ratios and 95% confidence intervals of the IVW, WME, and MR-Egger results of ukb-a-107 were (OR = 33.72; 95% CI: 2.07∼550.38), (OR = 57.94; 95% CI: 2.75∼1219.82), and (OR = 96.38; 95% CI: 0.6∼15556.93), respectively. The P values of IVW and WME were 0.014 and 0.014 (both <0.05), respectively, indicating that the development of angina pectoris was significantly associated with the incidence of gout. The odds ratios and 95% confidence intervals of the IVW, WME, and MR-Egger about bbj-a-57 were (OR = 1.20; 95% CI: 1.07∼1.34), (OR = 1.19; 95% CI: 1.02∼1.38), and (OR = 1.30; 95% CI; 1.06∼1.60), respectively. The P values of IVW, WME and MR-Egger were 0.001, 0.027 and 0.017 (all <0.05), respectively, indicating a significant correlation between angina and blood uric acid levels. Scatter plots of ukb-a-107 and bbj-a-57 showed that the causal association estimates of the IVW, MR-Egger, and weighted median methods were similar and that the MR results were accurate. Funnel plots and the MR-Egger intercept of ukb-a-107 and bbj-a-57 showed the absence of horizontal pleiotropy. The leave-out sensitivity analysis results of ukb-a-107 and bbj-a-57 are stable. The meta-analysis of IVW results for ukb-a-107 and bbj-a-57 showed (OR = 1.20; 95% CI: 1.07-1.34, P=0.02), confirming that gout characterized by high blood uric acid levels significantly increases the risk of angina attacks. Conclusions: This MR study found a clear causal relationship between angina pectoris and gout, which increases the risk of angina pectoris.

Dissecting the shared genetic landscape of anxiety, depression, and schizophrenia.

BACKGROUND: Numerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies. METHODS: We utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein-protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets. RESULTS: We found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data. CONCLUSIONS: Our study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.

Multidimensional data analysis revealed thyroiditis-associated TCF19 SNP rs2073724 as a highly ranked protective variant in thyroid cancer.

BACKGROUND: Thyroid cancer represents the most prevalent malignant endocrine tumour, with rising incidence worldwide and high mortality rates among patients exhibiting dedifferentiation and metastasis. Effective biomarkers and therapeutic interventions are warranted in aggressive thyroid malignancies. The transcription factor 19 (TCF19) gene has been implicated in conferring a malignant phenotype in cancers. However, its contribution to thyroid neoplasms remains unclear. RESULTS: In this study, we performed genome-wide and phenome-wide association studies to identify a potential causal relationship between TCF19 and thyroid cancer. Our analyses revealed significant associations between TCF19 and various autoimmune diseases and human cancers, including cervical cancer and autoimmune thyroiditis, with a particularly robust signal for the deleterious missense variation rs2073724 that is associated with thyroid function, hypothyroidism, and autoimmunity. Furthermore, functional assays and transcriptional profiling in thyroid cancer cells demonstrated that TCF19 regulates important biological processes, especially inflammatory and immune responses. We demonstrated that TCF19 could promote the progression of thyroid cancer in vitro and in vivo and the C>T variant of rs2073724 disrupted TCF19 protein binding to target gene promoters and their expression, thus reversing the effect of TCF19 protein. CONCLUSIONS: Taken together, these findings implicate TCF19 as a promising therapeutic target in aggressive thyroid malignancies and designate rs2073724 as a causal biomarker warranting further investigation in thyroid cancer.