Significant Genetic Correlations Research Articles

Shared genetic architecture between periodontal disease and type 2 diabetes: a large scale genome-wide cross-trait analysis

PurposeTo investigate the relationship between abnormal glucose metabolism, type 2 diabetes (T2D), and periodontal disease (PER) independent of Body Mass Index (BMI), we employed a genome-wide cross-trait approach to clarify the association.MethodsOur study utilized the most extensive genome-wide association studies conducted for populations of European ancestry, including PER, T2D, fasting glucose, fasting insulin, 2-hour glucose after an oral glucose challenge, HOMA-β, HOMA-IR (unadjusted or adjusted for BMI) and HbA1c.ResultsWith this approach, we were able to identify pleiotropic loci, establish expression-trait associations, and quantify global and local genetic correlations. There was a significant positive global genetic correlation between T2D (rg = 0.261, p = 2.65 × 10−13), HbA1c (rg = 0.182, p = 4.14 × 10−6) and PER, as well as for T2D independent of BMI (rg = 0.158, p = 2.34 × 10−6). A significant local genetic correlation was also observed between PER and glycemic traits or T2D. We also identified 62 independent pleiotropic loci that impact both PER and glycemic traits, including T2D. Nine significant pathways were identified between the shared genes between T2D, glycemic traits and PER. Genetically liability of HOMA-βadjBMI was causally associated with the risk of PER.ConclusionOur research has revealed a genetic link between T2D, glycemic traits, and PER that is influenced by biological pleiotropy. Notably, some of these links are not related to BMI. Our research highlights an underlying link between patients with T2D and PER, regardless of their BMI.

Genetic architecture of the structural connectome

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Genetic correlations between liver fat content, metabolic health, and adiposity distribution in the Fels Longitudinal Study

Background and aimsHepatic steatosis is known to be heritable, but its genetic basis is mostly uncharacterized. Steatosis is associated with metabolic and adiposity features; recent studies hypothesize that shared genetic effects between these traits could account for some of the unexplained heritability. This study aimed to quantify these genetic associations in a family-based sample of non-Hispanic white adults. Methods and results704 participants (18–95 years, 55.8% female) from the Fels Longitudinal Study with an MRI assessment of liver fat were included. Quantitative genetic analyses estimated the age- and sex-adjusted heritability of individual traits and the genetic correlations within trait pairs. Mean liver fat was 5.95% (SE = 0.23) and steatosis (liver fat >5.56%) was present in 29.8% of participants. Heritability (h2± SE) of steatosis was 0.72 ± 0.17 (p = 6.80e−6). All other traits including liver enzymes, fasting glucose, HOMA-IR, visceral and subcutaneous adipose tissue (VAT, SAT), body mass index, body fat percent, waist circumference, lipids and blood pressure were also heritable. Significant genetic correlations were found between liver fat and all traits except aspartate aminotransferase (AST), and among most trait pairs. Highest genetic correlations were between liver fat and HOMA-IR (0.85 ± 0.08, p = 1.73e−8), fasting glucose and ALT (0.89 ± 0.26, p = 6.68e−5), and HOMA-IR with: waist circumference (0.81 ± 0.12, p = 3.76e−6), body fat percent (0.78 ± 0.12 p = 2.42e−5) and VAT (0.73 ± 0.07, p = 6.37e−8). ConclusionsCommon genes may exist between liver fat accumulation, metabolic features and adiposity phenotypes.

Comprehensive Sex-Stratified Genetic Analysis of 28 Blood Biomarkers and Depression Reveals a Significant Association between Depression and Low Levels of Total Protein in Females

Introduction: Major depression (MD) is more common amongst women than men, and MD episodes have been associated with fluctuations in reproductive hormones amongst women. To investigate biological underpinnings of heterogeneity in MD, the associations between depression, stratified by sex and including perinatal depression (PND), and blood biomarkers, using UK Biobank (UKB) data, were evaluated, and extended to include the association of depression with biomarker polygenic scores (PGS), generated as proxy for each biomarker. Method: Using female (N = 39,761) and male (N = 38,821) UKB participants, lifetime MD and PND were tested for association with 28 blood biomarkers. A GWAS was conducted for each biomarker and genetic correlations with depression subgroups were estimated. Using independent data from the Australian Genetics of Depression Study, PGS were constructed for each biomarker, and tested for association with depression status (n [female cases/controls] = 9,006/6,442; n [male cases/controls] = 3,106/6,222). Regions of significant local genetic correlation between depression subgroups and biomarkers highlighted by the PGS analysis were identified. Results: Depression in females was significantly associated with levels of twelve biomarkers, including total protein (OR = 0.90, CI = [0.86, 0.94], p = 3.9 × 10−6) and vitamin D (OR = 0.94, CI = [0.90, 0.97], p = 2.6 × 10−4), and PND with five biomarker levels, also including total protein (OR = 0.88, CI = [0.81, 0.96], p = 4.7 × 10−3). Depression in males was significantly associated with levels of eleven biomarkers. In the independent Australian Genetics of Depression Study, PGS analysis found significant associations for female depression and PND with total protein (female depression: OR = 0.93, CI = [0.88, 0.98], p = 3.6 × 10−3; PND: OR = 0.91, CI = [0.86, 0.96], p = 1.1 × 10−3), as well as with vitamin D (female depression: OR = 0.93, CI = [0.89, 0.97], p = 2.0 × 10−3; PND: OR = 0.92, CI = [0.87, 0.97], p = 1.4 × 10−3). The male depression sample did not report any significant results, and the point estimate of total protein (OR = 0.98, CI = [0.92–1.04], p = 4.7 × 10−1) did not indicate any association. Local genetic correlation analysis highlighted significant genetic correlation between PND and total protein, located in 5q13.3 (rG = 0.68, CI = [0.33, 1.0], p = 3.6 × 10−4). Discussion and Conclusion: Multiple lines of evidence from genetic analysis highlight an association between total serum protein levels and depression in females. Further research involving prospective measurement of total protein and depressive symptoms is warranted.

Dissecting the genetic overlap between severe mental disorders and markers of cellular aging: Identification of pleiotropic genes and druggable targets.

Patients with severe mental disorders such as bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) show a substantial reduction in life expectancy, increased incidence of comorbid medical conditions commonly observed with advanced age and alterations of aging hallmarks. While severe mental disorders are heritable, the extent to which genetic predisposition might contribute to accelerated cellular aging is not known. We used bivariate causal mixture models to quantify the trait-specific and shared architecture of mental disorders and 2 aging hallmarks (leukocyte telomere length [LTL] and mitochondrial DNA copy number), and the conjunctional false discovery rate methodto detect shared genetic loci. We integrated gene expression data from brain regions from GTEx and used different tools to functionally annotate identified loci and investigate their druggability. Aging hallmarks showed low polygenicity compared with severe mental disorders. We observed a significant negative global genetic correlation between MDD and LTL (rg = -0.14, p = 6.5E-10), and no significant results for other severe mental disorders or for mtDNA-cn. However, conditional QQ plots and bivariate causal mixture models pointed to significant pleiotropy among all severe mental disorders and aging hallmarks. We identified genetic variants significantly shared between LTL and BD (n = 17), SCZ (n = 55) or MDD (n = 19), or mtDNA-cn and BD (n = 4), SCZ (n = 12) or MDD (n = 1), with mixed direction of effects. The exonic rs7909129 variant in the SORCS3 gene, encoding a member of the retromer complex involved in protein trafficking and intracellular/intercellular signaling, was associated with shorter LTL and increased predisposition to all severe mental disorders. Genetic variants underlying risk of SCZ or MDD and shorter LTL modulate expression of several druggable genes in different brain regions. Genistein, a phytoestrogen with anti-inflammatory and antioxidant effects, was an upstream regulator of 2 genes modulated by variants associated with risk of MDD and shorter LTL. While our results suggest that shared heritability might play a limited role in contributing to accelerated cellular aging in severe mental disorders, we identified shared genetic determinants and prioritized different druggable targets and compounds.

Approaches and progress in breeding drought-tolerant maize hybrids for tropical lowlands in west and central Africa.

Drought represents a significant production challenge to maize farmers in West and Central Africa, causing substantial economic losses. Breeders at the International Institute of Tropical Agriculture have therefore been developing drought-tolerant maize varieties to attain high grain yields in rainfed maize production zones. The present review provides a historical overview of the approaches used and progress made in developing drought-tolerant hybrids over the years. Breeders made a shift from a wide area testing approach, to the use of managed screening sites, to precisely control the intensity, and timing of drought stress for developing drought-tolerant maize varieties. These sites coupled with the use of molecular markers allowed choosing suitable donors with drought-adaptive alleles for integration into existing elite maize lines to generate new drought-tolerant inbred lines. These elite maize inbred lines have then been used to develop hybrids with enhanced tolerance to drought. Genetic gains estimates were made using performance data of drought-tolerant maize hybrids evaluated in regional trials for 11 years under managed drought stress, well-watered conditions, and across diverse rainfed environments. The results found significant linear annual yield gains of 32.72kg ha-1 under managed drought stress, 38.29kg ha-1 under well-watered conditions, and 66.57kg ha-1 across multiple rainfed field environments. Promising hybrids that deliver high grain yields were also identified for areas affected by drought and variable rainfed growing conditions. The significant genetic correlations found among the three growing conditions highlight the potential to exploit the available genetic resources and modern tools to further enhance tolerance to drought in hybrids.

Shared genetic architecture between hypothyroidism and rheumatoid arthritis: A large-scale cross-trait analysis

BackgroundIn recent years, mounting evidence has indicated a co-morbid relationship between hypothyroidism and rheumatoid arthritis (RA), however, the shared genetic factors underlying this association remain unclear. This study aims to investigate the common genetic architecture between hypothyroidism and RA. MethodsGenome-wide association study (GWAS) summary statistics from recently published studies were utilized to examine the genetic correlation, shared genetic loci, and potential causal relationship between hypothyroidism and RA. Statistical methods included linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), cross-trait meta-analyses, colocalization analysis, multi-marker analysis of genomic annotation (MAGMA), tissue-specific enrichment analysis (TSEA), functional enrichment analysis, and latent causal variable method (LCV). ResultsOur study demonstrated a significant genetic correlation between hypothyroidism and RA(LDSC:rg=0.3803,p=7.23e-11;HDL:rg=0.3849,p=1.02e-21). Through cross-trait meta-analysis, we identified 1035 loci, including 43 novel genetic loci. By integrating colocalization analysis and the MAGMA algorithm, we found a substantial number of genes, such as PTPN22, TYK2, and CTLA-4, shared between the two diseases, which showed significant enrichment across 14 tissues. These genes were primarily associated with the regulation of alpha-beta T cell proliferation, positive regulation of T cell activation, positive regulation of leukocyte cell-cell adhesion, T cell receptor signaling pathway, and JAK-STAT signaling pathway. However, our study did not reveal a significant causal association between the two diseases using the LCV approach. ConclusionBased on these findings, there is a significant genetic correlation between hypothyroidism and RA, suggesting a shared genetic basis for these conditions.

Investigating the shared genetic architecture between frailty and insomnia.

The epidemiological association between frailty and insomnia is well established, yet the presence of a common genetic etiology is still uncertain. Further exploration is needed to ascertain the causal relationship between frailty and insomnia. Utilizing data obtained from genome-wide association studies (GWAS) summaries, we utilized the linkage disequilibrium score regression (LDSC) to determine the genetic correlation existing between frailty and insomnia. The determination of causality was achieved through the application of two-sample Mendelian randomization. We investigated the enrichment of single nucleotide polymorphism (SNP) at various tissue types utilizing stratified LD score regression (S-LDSC) and multimarker analysis of genome annotation (MAGMA). Common risk SNPs were identified using Multi-Trait Analysis of GWAS (MTAG) and Cross-Phenotype Association (CPASSOC). We further investigated the expression profiles of risk genes in tissues using Summary-data-based Mendelian randomization(SMR) based on pooled data, to explore potential functional genes. Our findings indicated a significant genetic correlation between frailty and insomnia, highlighting SNPs sharing risk (rs34290943, rs10865954), with a pronounced correlation in the localized genomic region 3p21.31. Partitioned genetic analysis revealed 24 functional elements significantly associated with both frailty and insomnia. Furthermore, mendelian randomization revealed a causal connection between frailty and insomnia. The genetic correlation between frailty and insomnia showed enrichment in 11 brain regions (S-LDSC) and 9 brain regions (MAGMA), where four functional genes (RMB6, MST1R, RF123, and FAM212A) were identified. This study suggests the existence of a genetic correlation and common risk genes between frailty and insomnia, contributing to a deeper comprehension of their pathogenesis and assists in identifying potential therapeutic targets.

Evaluation of bidirectional causal association between cardiovascular diseases and pneumonia: A Mendelian randomization study.

Observational evidence suggests a bidirectional relationship between cardiovascular diseases (CVDs) and pneumonia. However, the causality between CVDs and pneumonia remains undetermined. Thus, we aimed to investigate the bidirectional causality between CVDs and pneumonia using Mendelian randomization (MR) analysis. Global genetic correlation analysis and bidirectional two-sample MR analysis were performed to infer the genetic correlation and causality between CVDs and pneumonia by using genome-wide association study (GWAS) summary data from GWAS meta-analysis study, FinnGen or UK Biobank consortium. Post-hoc power calculation was conducted to assess the power for detecting the causality. The linkage disequilibrium score regression analysis suggested a positive significant genetic correlation between CVDs and pneumonia. In the MR analysis, only genetically predicted ischemic stroke was causally associated with any pneumonia (odds ratio [OR]: 1.119, 95% confidence interval [CI]: 1.031-1.393), bacterial pneumonia (OR: 1.251, 95% CI: 1.032-1.516), and pneumococcal pneumonia (OR: 1.308, 95% CI: 1.093-1.565), but the causality was attenuated to non-significance after adjusting for deep venous thrombosis. However, the causal effects of pneumonia on CVDs were not detected. Post-hoc power calculations supported strong power (more than 80%) to detect the causality. Ischemic stroke is causally associated with an increased risk of pneumonia, but there is no evidence for the causal effect of pneumonia on CVDs. Our findings have important implications as they provide further support for the thrombosis risk screening as a strategy to reduce the incidence of pneumonia in patients with ischemic stroke.

Genetic correlation and causal associations between circulating C-reactive protein levels and lung cancer risk.

We aimed to characterize genetic correlations and causal associations between circulating C-reactive protein (CRP) levels and the risk of lung cancer (LC). Leveraging summary statistics from genome-wide association studies of circulating CRP levels among 575,531 individuals of European ancestry, and LC risk among 29,266 cases and 56,450 controls, we investigated genetic associations of circulating CRP levels with the risk of overall lung cancer and its histological subtypes, by using linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analyses. Significant positive genetic correlations between circulating CRP levels and the risk of LC and its histological subtypes were identified from LDSC regression, with correlation coefficients ranging from 0.12 to 0.26, and all false discovery adjusted p < 0.05. Univariable MR demonstrated a nominal association between CRP levels and an increased risk of lung squamous cell carcinoma (SCC) (inverse variance-weighted OR = 1.15, 95% CI 1.01-1.30). However, this association disappeared when multivariable MR included cigarettes per day and/or body mass index. By using our recently developed constrained maximum likelihood-based MR method, we identified significant associations of CRP levels with the risk of overall LC (OR 1.06, 95% CI 1.03-1.09), SCC (OR 1.06, 95% CI 1.02-1.09), and small cell lung cancer (SCLC, OR 1.09, 95% CI 1.03-1.15). Moreover, most univariable and multivariable MR analyses also revealed consistent CRP-SCLC associations. There may be a genetic and causal association between circulating CRP levels and the risk of SCLC, which is in line with previous population-based observational studies.

Associations between artificial sweetener intake from cereals, coffee, and tea and the risk of type 2 diabetes mellitus: A genetic correlation, mediation, and mendelian randomization analysis.

Previous studies have emphasized the association between the intake of artificial sweeteners (AS) and type 2 diabetes mellitus (T2DM), but the causative relationship remains ambiguous. This study employed univariate Mendelian randomization (MR) analysis to assess the causal link between AS intake from various sources and T2DM. Linkage disequilibrium score (LDSC) regression was used to evaluate the correlation between phenotypes. Multivariate and mediation MR were applied to investigate confounding factors and mediating effects. Data on AS intake from different sources (N = 64,949) were sourced from the UK Biobank, while T2DM data were derived from the DIAbetes Genetics Replication And Meta-analysis.The primary method adopted was inverse variance weighted (IVW), complemented by three validation techniques. Additionally, a series of sensitivity analyses were performed to evaluate pleiotropy and heterogeneity. LDSC analysis unveiled a significant genetic correlation between AS intake from different sources and T2DM (rg range: -0.006 to 0.15, all P < 0.05). After correction by the false discovery rate (FDR), the primary IVW method indicated that AS intake in coffee was a risk factor for T2DM (OR = 1.265, 95% CI: 1.035-1.545, P = 0.021, PFDR = 0.042). Further multivariable and mediation MR analyses pinpointed high density lipoprotein-cholesterol (HDL-C) as mediating a portion of this causal relationship. In reverse MR analysis, significant evidence suggested a positive correlation between T2DM and AS intake in coffee (β = 0.013, 95% CI: 0.004-0.022, P = 0.004, PFDR = 0.012), cereal (β = 0.007, 95% CI: 0.002-0.012, P = 0.004, PFDR = 0.012), and tea (β = 0.009, 95% CI: 0.001-0.017, P = 0.036, PFDR = 0.049). No other causal associations were identified (P > 0.05, PFDR > 0.05). The MR analysis has established a causal relationship between AS intake in coffee and T2DM. The mediation by HDL-C emphasizes potential metabolic pathways underpinning these relationships.

Mendelian Randomization Highlights Gut Microbiota of Short-chain Fatty Acids' Producer as Protective Factor of Cerebrovascular Disease.

Recent research advancements have indicated a potential association between gut microbiota and cerebrovascular diseases, although the precise causative pathways and the directionality of this association remain to be fully elucidated. This study utilized a bidirectional two-sample Mendelian Randomization (MR) methodology to explore the causal impact of gut microbiota compositions on the risk of cerebrovascular disease. Genome-wide Association Study (GWAS) data pertaining to gut microbiota were obtained from the MiBioGen consortium. For Ischemic Stroke (IS), Transient Ischemic Attack (TIA), Vascular Dementia (VD), and Subarachnoid Hemorrhage (SAH), GWAS summary data were sourced from the FinnGen consortium, the IEU Open GWAS project, and the GWAS catalog, respectively. Our MR analyses identified that specific bacterial strains, notably those involved in the production of Short-chain Fatty Acids (SCFAs), including Barnesiella, Ruminococcus torques group, and Coprobacter, serve as protective factors against IS, TIA, and SAH. Linkage Disequilibrium Score Regression (LDSC) analysis corroborated a significant genetic correlation between these gut microbiota strains and various forms of cerebrovascular disease. In contrast, reverse MR analysis failed to establish a bidirectional causal relationship between genetically inferred gut microbiota profiles and these cerebrovascular conditions. This investigation has pinpointed particular strains of gut microbiota that play protective or detrimental roles in cerebrovascular disease pathogenesis. These findings offer valuable insights that could be pivotal for the clinical management, prevention, and treatment of cerebrovascular diseases.

Effects of nonalcoholic fatty liver disease on sarcopenia: evidence from genetic methods

With the aging of the population, sarcopenia has become more common. Studies have shown a broad association between liver disease and sarcopenia. However, this link remains unclear. Our study explored the link between NAFLD and sarcopenia and predicting the pathogenesis. To begin, we investigated the causal relationship and genetic correlation between them using MR and LDSC. Second, each GWAS was annotated by MAGMA. The annotated genes were analyzed for pleiotropy using the PLACO approach. Finally, functional analysis was conducted on the identified pleiotropic genes. We observed a significant genetic correlation between NAFLD and sarcopenia. Subsequently, we conducted gene-level pleiotropy analysis using PLACO and identified a total of 153 genes with pleiotropic effects. Functional analysis revealed enrichment of these genes in various tissues, including pancreas, liver, heart, blood, brain, and muscle, with involvement in cellular regulation, intracellular function, and antigen response. Moreover, our MR analysis provided evidence of a causal relationship between NAFLD and sarcopenia. Our study has discovered the genetic and causal relationships between NAFLD and sarcopenia, providing further insights into their pathophysiological mechanisms. The identification of pleiotropic genes also offers potential targets for future drug therapies aimed at controlling or treating NAFLD and sarcopenia.

Relationship between autism and brain cortex surface area: genetic correlation and a two-sample Mendelian randomization study

BackgroundAlterations in surface area (SA) in specific regions of the cortex have been reported in many individuals with autism spectrum disorder (ASD), however, the genetic background between ASD and SA is still unclear. This study estimated the genetic correlation and causal effect of ASD and cortical SA.MethodsSummarized data of genome-wide association studies (GWAS) were separately downloaded from the Psychiatric Genomics Consortium (18,381 cases of ASD, and 27,969 controls) and the Enhancing Neuroimaging Genetics through Meta-Analysis Consortium (33,992 participants of Europeans). We used Linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics (HESS) to calculate the heritability of each trait. As for the genetic correlation between ASD and SA, LDSC was used for global correlation and HESS was used to examine the local genetic covariance further. We used three Mendelian randomization (MR) methods, Inverse-variance weighted, MR-Egger, and weighted median to estimate the causal relationship.ResultsLDSC observed a nominal significant genetic correlation (rg = 0.1229, P-value = 0.0346) between ASD and SA of the rostral anterior cingulate gyrus whereas analysis through HESS did not reveal any significant loci having genetic covariance. Based on MR results, statistically meaningful estimations were found in the following areas, postcentral cortex (β (SE) = 21.82 (7.84) mm, 95% CI: 6.46 to 37.19 mm, PIVW = 5.38 × 10− 3, PFDR = 3.09 × 10− 2), posterior cingulate gyrus (β (SE) = 6.23 (2.69) mm, 95% CI: 0.96 to 11.49 mm, PIVW = 2.05 × 10− 2, PFDR = 4.26 × 10− 2), supramarginal gyrus (β (SE) = 19.25 (8.43) mm, 95% CI: 29.29 to 35.77 mm, PIVW = 2.24 × 10− 2, PFDR = 4.31 × 10− 2).ConclusionOur results provided genetic evidence to support the opinion that individuals with ASD tend to develop differences in cortical SA of special areas. The findings contributed to understanding the genetic relationship between ASD and cortical SA.

Mendelian randomization analyses of known and suspected risk factors and biomarkers for myasthenia gravis overall and by subtypes

BackgroundMyasthenia gravis (MG) is an autoimmune disease that affects neuromuscular junction. The literature suggests the involvement of circulating cytokines (CK), gut microbiota (GM), and serum metabolites (SM) with MG. However, this research is limited to observational trials, and comprehensive causal relationship studies have not been conducted. Based on published datasets, this investigation employed Mendelian Randomization (MR) to analyze the known and suspected risk factors and biomarkers causal association of MG and its subtypes.MethodsThis research used two-sample MR and linkage disequilibrium score (LDSC) regression of multiple datasets to aggregate datasets acquired from the genome-wide association studies (GWAS) to assess the association of MG with 41-CK, 221-GM, and 486-SM. For sensitivity analysis and to validate the robustness of the acquired data, six methods were utilized, including MR-Egger regression, inverse variance weighting (IVW), weighted median, and MR-PRESSO.ResultsThe MR method identified 20 factors significantly associated with MG, including 2 CKs, 6 GMs, and 9 SMs. Further analysis of the factors related to the two MG subtypes, early-onset MG (EOMG) and late-onset MG (LOMG), showed that EOMG had a high overlap with MG in the intestinal flora, while LOMG had a greater similarity in CKs and SMs. Furthermore, LDSC regression analysis indicated that Peptococcaceae, oxidized biliverdin, and Kynurenine had significant genetic correlations with general MG, whereas EOMG was highly correlated with Intestinibacter, while LOMG had significant genetic associations with Kynurenine and Glucose.ConclusionThis research furnishes evidence for the potential causal associations of various risk factors with MG and indicates a heterogeneous relationship between CKs, GMs, and SMs with MG subtypes.

Unraveling shared susceptibility loci and Mendelian genetic associations linking educational attainment with multiple neuropsychiatric disorders.

Empirical studies have demonstrated that educational attainment (EA) is associated with neuropsychiatric disorders (NPDs), suggesting a shared etiological basis between them. However, little is known about the shared genetic mechanisms and causality behind such associations. This study explored the shared genetic basis and causal relationships between EA and NPDs using the high-definition likelihood (HDL) method, cross phenotype association study (CPASSOC), transcriptome-wide association study (TWAS), and bidirectional Mendelian randomization (MR) with summary-level data for EA (N = 293,723) and NPDs (N range = 9,725 to 455,258). Significant genetic correlations between EA and 12 NPDs (rg range - 0.49 to 0.35; all p < 3.85 × 10-3) were observed. CPASSOC identified 37 independent loci shared between EA and NPDs, one of which was novel (rs71351952, mapped gene: ARFGEF2). Functional analyses and TWAS found shared genes were enriched in brain tissue, especially in the cerebellum and highlighted the regulatory role of neuronal signaling, purine nucleotide metabolic process, and cAMP-mediated signaling pathways. CPASSOC and TWAS supported the role of three regions of 6q16.1, 3p21.31, and 17q21.31 might account for the shared causes between EA and NPDs. MR confirmed higher genetically predicted EA lower the risk of ADHD (ORIVW: 0.50; 95% CI: 0.39 to 0.63) and genetically predicted ADHD decreased the risk of EA (Causal effect: -2.8 months; 95% CI: -3.9 to -1.8). These findings provided evidence of shared genetics and causation between EA and NPDs, advanced our understanding of EA, and implicated potential biological pathways that might underlie both EA and NPDs.

Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups.

Phenome-wide association studies (PheWASs) have been conducted on Asian populations, including Koreans, but many were based on chip or exome genotyping data. Such studies have limitations regarding whole genome-wide association analysis, making it crucial to have genome-to-phenome association information with the largest possible whole genome and matched phenome data to conduct further population-genome studies and develop health care services based on population genomics. Here, we present 4,157 whole genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest genomic resource of the Korean Genome Project. It encompasses most of the variants with allele frequency >0.001 in Koreans, indicating that it sufficiently covered most of the common and rare genetic variants with commonly measured phenotypes for Koreans. Korea4K provides 45,537,252 variants, and half of them were not present in Korea1K (1,094 samples). We also identified 1,356 new genotype-phenotype associations that were not found by the Korea1K dataset. Phenomics analyses further revealed 24 significant genetic correlations, 14 pleiotropic associations, and 127 causal relationships based on Mendelian randomization among 37 traits. In addition, the Korea4K imputation reference panel, the largest Korean variants reference to date, showed a superior imputation performance to Korea1K across all allele frequency categories. Collectively, Korea4K provides not only the largest Korean genome data but also corresponding health check-up parameters and novel genome-phenome associations. The large-scale pathological whole genome-wide omics data will become a powerful set for genome-phenome level association studies to discover causal markers for the prediction and diagnosis of health conditions in future studies.

Estimation of genetic parameters for visceral diseases of fattening Japanese Black cattle in Shimane Prefecture, Japan.

In this study, the genetic parameters of major visceral diseases were estimated using the postmortem inspection records of 9057 fattening Japanese Black cattle in Shimane Prefecture, Japan, and the genetic correlation between visceral diseases and carcass traits was analyzed. There were six visceral diseases with a prevalence of 5% or higher, namely, pleurisy, pneumonia, bovine abdominal fat necrosis (BFN), rumenitis, hemorrhagic hepatitis, and perihepatitis. Variance components were estimated using the Gibbs sampling method, and the heritability of the visceral disease ranged from 0.07 to 0.49 for perihepatitis and BFN, respectively. Significant negative genetic correlations were identified between pleurisy and rib thickness (-0.32), BFN and carcass weight (-0.29), and BFN and rib eye area (-0.22). No significant genetic correlation was observed among the visceral diseases. The least squares analysis of variance suggested that some visceral diseases decrease the value of carcass traits. In particular, carcass weight and rib eye area in individuals with BFN were 11.7kg and 1.87 cm2 lower than those of healthy cattle, respectively. Thus, it was inferred that genetic factors were involved in the visceral diseases of fattening Japanese Black cattle in Shimane Prefecture.

Exploring the Common Genetic Underpinnings of Chronic Pulmonary Disease and Esophageal Carcinoma Susceptibility.

Background: Pulmonary diseases and esophageal cancer are highly prevalent conditions with rising incidence worldwide. Prior evidence supports shared environmental and behavioral factors, but less is known regarding potential genetic links underlying this comorbidity. This study aimed to elucidate the complex genetic relationship between chronic lung diseases and esophageal cancer risk. Methods: Linkage disequilibrium score regression assessed the genetic correlation between esophageal cancer and asthma, COPD, and idiopathic pulmonary fibrosis leveraging extensive GWAS datasets. Pleiotropic analysis, gene-set enrichment, eQTL mapping, and mendelian randomization causality analyses were then conducted to identify specific shared genetic variants, enriched pathways, causal relationships and gene regulatory mechanisms connecting lung disease and cancer susceptibility. Results: Significant genetic correlations were observed between esophageal cancer and both COPD and asthma, but not idiopathic pulmonary fibrosis. Further analyses identified 13 pleiotropic loci and 6 shared genes including CHRNA4, ERBB3, and SMAD3, as well as pathways related to immune function. eQTL integration highlighted 53 genes like SOCS1, FGF2, and CHRNA5 with tissue-specific regulatory effects on disease risk. Bidirectional relationships were noted, whereby genetic predisposition to asthma and COPD increased esophageal cancer risk, while cancer liability reciprocally raised pulmonary fibrosis risk. Conclusions: These genomic analyses provide initial evidence that shared genetic factors may underpin the comorbidity between lung conditions and esophageal malignancy. The genes and pathways identified offer insights into biological mechanisms linking both diseases, aiding future screening, prevention and therapeutic efforts to mitigate this growing comorbidity burden.

Investigating the shared genetic architecture between breast and ovarian cancers.

High heritability and strong correlation have been observed in breast and ovarian cancers. However, their shared genetic architecture remained unclear. Linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) were applied to estimate heritability and genetic correlations. Bivariate causal mixture model (MiXeR) was used to qualify the polygenic overlap. Then, stratified-LDSC (S-LDSC) was used to identify tissue and cell type specificity. Meanwhile, the adaptive association test called MTaSPUsSet was performed to identify potential pleiotropic genes. The Single Nucleotide Polymorphisms (SNP) heritability was 13% for breast cancer and 5% for ovarian cancer. There was a significant genetic correlation between breast and ovarian cancers (rg=0.21). Breast and ovarian cancers exhibited polygenic overlap, sharing 0.4 K out 2.8 K of causal variants. Tissue and cell type specificity displayed significant enrichment in female breast mammary, uterus, kidney tissues, and adipose cell. Moreover, the 74 potential pleiotropic genes were identified between breast and ovarian cancers, which were related to the regulation of cell cycle and cell death. We quantified the shared genetic architecture between breast and ovarian cancers and shed light on the biological basis of the co-morbidity. Ultimately, these findings facilitated the understanding of disease etiology.