Association between BCL11B gene polymorphisms and age-related hearing loss in the elderly: A case-control study in Qingdao, China.

Age-related hearing loss is a complex disease caused by a combination of genetic and environmental factors, and a study have conducted animal experiments to explore the association between BCL11B heterozygosity and age-related hearing loss. The present study used established genetic models to examine the association between BCL11B gene polymorphisms and age-related hearing loss. A total of 410 older adults from two communities in Qingdao, China, participated in this study. The case group comprised individuals aged ≥ 60 years with age-related hearing loss, and the control group comprised individuals without age-related hearing loss from the same communities. The groups were matched 1:1 for age and sex. The individual characteristics of the participants were analyzed descriptively using the Mann-Whitney U test and the chi-square test. To explore the association between BCL11B gene polymorphisms and age-related hearing loss, conditional logistic regression was performed to construct genetic models for two single-nucleotide-polymorphisms (SNPs) of BCL11B, and haplotype analysis was conducted to construct their haplotype domains. Two SNP sites of the BCL11B gene, four genetic models of rs1152781 (additive, dominant, recessive, and codominant), and five genetic models of rs1152783 (additive, dominant, recessive, codominant, and over dominant) were significantly associated with age-related hearing loss in the models both unadjusted and adjusted for all covariates (P < 0.05). Additionally, a linkage disequilibrium between rs1152781 and rs1152783 was revealed through haplotype analysis. Our study revealed that BCL11B gene polymorphisms were significantly associated with age-related hearing loss.

Genetic effects and causal association analyses of 14 common conditions/diseases in multimorbidity patterns.

BACKGROUND: Multimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown. METHODS: Summary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases. RESULTS: LDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (P<0.05/91 = 5.49E-04). Significant pleiotropic SNPs were detected for 11 pairs of correlated conditions/diseases. The corresponding pleiotropic genes were differentially expressed in the brain, nerves, heart, and blood vessels and enriched in gluconeogenesis and drug metabolism, biotransformation, and neurons. Comprehensive causal analyses showed strong causality between hypertension, stroke, and high cholesterol, which drive the development of multiple diseases. CONCLUSIONS: This study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.

Functional classes of SNPs related to psychiatric disorders and behavioral traits contrast with those related to neurological disorders.

We investigated the functional classes of genomic regions containing SNPS contributing most to the SNP-heritability of important psychiatric and neurological disorders and behavioral traits, as determined from recent genome-wide association studies. We employed linkage-disequilibrium score regression with several brain-specific genomic annotations not previously utilized. The classes of genomic annotations conferring substantial SNP-heritability for the psychiatric disorders and behavioral traits differed systematically from the classes associated with neurological disorders, and both differed from the classes enriched for height, a biometric trait used here as a control outgroup. The SNPs implicated in these psychiatric disorders and behavioral traits were highly enriched in CTCF binding sites, in conserved regions likely to be enhancers, and in brain-specific promoters, regulatory sites likely to affect responses to experience. The SNPs relevant for neurological disorders were highly enriched in constitutive coding regions and splice regulatory sites.

Effect of serum metabolites on the risk of iridocyclitis: a bidirectional Mendelian randomization study.

Previous research has linked serum metabolite levels to iridocyclitis, yet their causal relationship remains unexplored. This study investigated this potential causality by analyzing pooled data from 7824 iridocyclitis patients in a Genome-Wide Association Study (GWAS) using Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC). Employing rigorous quality control and comprehensive statistical methods, including sensitivity analyses, we examined the influence of 486 serum metabolites on iridocyclitis. Our MR analysis identified 23 metabolites with significant causal effects on iridocyclitis, comprising 17 known and 6 unidentified metabolites. Further refinement using Cochran's Q test and MR-PRESSO indicated 16 metabolites significantly associated with iridocyclitis risk. LDSC highlighted the heritability of certain metabolites, underscoring genetic influences on their levels. Notably, tryptophan, proline, theobromine, and 7-methylxanthine emerged as risk factors, while 3,4-dihydroxybutyrate appeared protective. These findings enhance our understanding of the metabolic interactions in iridocyclitis, offering insights for diagnosis, unraveling pathophysiological mechanisms, and informing potential avenues for prevention and personalized treatment.

Unraveling the complex role of MAPT-containing H1 and H2 haplotypes in neurodegenerative diseases.

A ~ 1 Mb inversion polymorphism exists within the 17q21.31 locus of the human genome as direct (H1) and inverted (H2) haplotype clades. This inversion region demonstrates high linkage disequilibrium, but the frequency of each haplotype differs across ancestries. While the H1 haplotype exists in all populations and shows a normal pattern of genetic variability and recombination, the H2 haplotype is enriched in European ancestry populations, is less frequent in African ancestry populations, and nearly absent in East Asian ancestry populations. H1 is a known risk factor for several neurodegenerative diseases, and has been associated with many other traits, suggesting its importance in cellular phenotypes of the brain and entire body. Conversely, H2 is protective for these diseases, but is associated with predisposition to recurrent microdeletion syndromes and neurodevelopmental disorders such as autism. Many single nucleotide variants and copy number variants define H1/H2 haplotypes and sub-haplotypes, but identifying the causal variant(s) for specific diseases and phenotypes is complex due to the extended linkage equilibrium. In this review, we assess the current knowledge of this inversion region regarding genomic structure, gene expression, cellular phenotypes, and disease association. We discuss recent discoveries and challenges, evaluate gaps in knowledge, and highlight the importance of understanding the effect of the 17q21.31 haplotypes to promote advances in precision medicine and drug discovery for several diseases.

A powerful approach to identify replicable variants in genome-wide association studies.

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure. We show that such a strategy may produce either overly liberal or overly conservative results in practice. We develop an efficient method, ReAD, to detect replicable SNPs associated with the phenotype from two GWASs accounting for the LD structure. The local dependence structure of SNPs across two heterogeneous studies is captured by a four-state hidden Markov model (HMM) built on two sequences of p values. By incorporating information from adjacent locations via the HMM, our approach provides more accurate SNP significance rankings. ReAD is scalable, platform independent, and more powerful than existing replicability analysis methods with effective false discovery rate control. Through analysis of datasets from two asthma GWASs and two ulcerative colitis GWASs, we show that ReAD can identify replicable genetic loci that existing methods might otherwise miss.

Quantitative genomics-enabled selection for simultaneous improvement of lint yield and seed traits in cotton (Gossypium hirsutum L.).

KEY MESSAGE: A Bayesian linkage disequilibrium-based multiple-locus mixed model identified QTLs for fibre, seed and oil traits and predicted breeding worthiness of test lines, enabling their simultaneous improvement in cotton. Improving cotton seed and oil yields has become increasingly important while continuing to breed for higher lint yield. In this study, a novel Bayesian linkage disequilibrium-based multiple-locus mixed model was developed for QTL identification and genomic prediction (GP). A multi-parent population consisting of 256 recombinant inbred lines, derived from four elite cultivars with distinct combinations of traits, was used in the analysis of QTLs for lint percentage, seed index, lint index and seed oil content and their interrelations. All four traits were moderately heritable and correlated but with no large influence of genotype × environment interactions across multiple seasons. Seven to ten major QTLs were identified for each trait with many being adjacent or overlapping for different trait pairs. A fivefold cross-validation of the model indicated prediction accuracies of 0.46-0.62. GP results based on any two-season phenotypes were strongly correlated with phenotypic means of a pooled analysis of three-season experiments (r = 0.83-0.92). When used for selection of improvement in lint, seed and oil yields, GP captured 40-100% of individuals with comparable lint yields of those selected based on the three-season phenotypic results. Thus, this quantitative genomics-enabled approach can not only decipher the genomic variation underlying lint, seed and seed oil traits and their interrelations, but can provide predictions for their simultaneous improvement. We discuss future breeding strategies in cotton that will enhance the entire value of the crop, not just its fibre.

A genome-wide genetic association study reveals SNPs significantly associated with environmental variables and specific leaf area in European beech.

European beech is negatively affected by climate change and a further growth decline is predicted for large parts of its distribution range. Despite the importance of this species, little is known about its genetic adaptation and especially the genetic basis of its physiological traits. Here, we used genotyping by sequencing to identify SNPs in 43 German European beech populations growing under different environmental conditions. In total, 28 of these populations were located along a precipitation and temperature gradient in northern Germany, and single tree-based hydraulic and morphological traits were available. We obtained a set of 13,493 high-quality SNPs that were used for environmental and SNP-trait association analysis. In total, 22 SNPs were identified that were significantly associated with environmental variables or specific leaf area (SLA). Several SNPs were located in genes related to stress response. The majority of the significant SNPs were located in non-coding (intergenic and intronic) regions. These may be in linkage disequilibrium with the causative coding or regulatory regions. Our study gives insights into the genetic basis of abiotic adaptation in European beech, and provides genetic resources that can be used in future studies on this species. Besides clear patterns of local adaptation to environmental conditions of the investigated populations, the analyzed morphological and hydraulic traits explained most of the explainable genetic variation. Thus, they could successfully be altered in tree breeding programs, which may help to increase the adaptation of European beech to changing environmental conditions in the future.

Dissecting the shared genetic architecture between endometriosis and polycystic ovary syndrome.

Background: Previous study suggested evidence for coexistence and similarities between endometriosis and polycystic ovary syndrome (PCOS), but it is unclear regarding the shared genetic architecture and causality underlying the phenotypic similarities observed for endometriosis and PCOS. Methods: By leveraging summary statistics from public genome-wide association studies regarding endometriosis (European-based: N=470,866) and PCOS (European-based: N=210,870), we explored the genetic correlation that shared between endometriosis and PCOS using linkage disequilibrium score regression. Shared risk SNPs were derived using PLACO (Pleiotropic analysis under composite null hypothesis) and FUMA (Functional Mapping and Annotation of Genetic Associations). The potential causal association between endometriosis and PCOS was investigated using two-sample Mendelian randomization (MR). Linkage disequilibrium score for the specific expression of genes analysis (LDSC-SEG) were performed for tissue enrichment analysis. The expression profiles of the risk gene in tissues were further examined. Results: A positive genetic association was observed between endometriosis and PCOS. 12 significant pleiotropic loci shared between endometriosis and PCOS were identified. Genetic associations between endometriosis and PCOS were particularly enriched in uterus, endometrium and fallopian tube. Two-sample MR analysis further indicated a potential causative effect of endometriosis on PCOS, and vice versa. Microarray and RNA-seq verified the expressions of SYNE1 and DNM3 were significantly altered in the endometrium of patients with endometriosis or PCOS compared to those of control subjects. Conclusion: Our study indicates the genetic correlation and shared risk genes between PCOS and endometriosis. These findings provide insights into the potential mechanisms behind their comorbidity and the future development of therapeutics.

Genome-wide association analysis explores the genetic loci of amino acid content in duck's breast muscle.

BACKGROUND: Amino acids are the basic components of protein and an important index to evaluate meat quality. With the rapid development of genomics, candidate regions and genes affecting amino acid content in livestock and poultry have been gradually revealed. Hence, genome-wide association study (GWAS) can be used to screen candidate loci associated with amino acid content in duck meat. RESULT: In the current study, the content of 16 amino acids was detected in 358 duck breast muscles. The proportion of Glu to the total amino acid content was relatively high, and the proportion was 0.14. However, the proportion of Met content was relatively low, at just 0.03. By comparative analysis, significant differences were found between males and females in 3 amino acids, including Ser, Met, and Phe. In addition, 12 SNPs were significantly correlated with Pro content by GWAS analysis, and these SNPs were annotated by 7 protein-coding genes; 8 significant SNPs were associated with Tyr content, and these SNPs were annotated by 6 protein-coding genes. At the same time, linkage disequilibrium (LD) analysis was performed on these regions with significant signals. The results showed that three SNPs in the 55-56 Mbp region of chromosome 3 were highly correlated with the leader SNP (chr3:55526954) that affected Pro content (r2 > 0.6). Similarly, LD analysis showed that there were three SNPs in the 21.2-21.6 Mbp region of chromosome 13, which were highly correlated with leader SNP (chr13:21421661) (r2 > 0.6). Moreover, Through functional enrichment analysis of all candidate genes. The results of GO enrichment analysis showed that several significant GO items were associated with amino acid transport function, including amino acid transmembrane transport and glutamine transport. The results further indicate that these candidate genes are closely associated with amino acid transport. Among them, key candidate genes include SLC38A1. For KEGG enrichment analysis, CACNA2D3 and CACNA1D genes were covered by significant pathways. CONCLUSION: In this study, GWAS analysis found a total of 28 significant SNPs affecting amino acid content. Through gene annotation, a total of 20 candidate genes were screened. In addition, Through LD analysis and enrichment analysis, we considered that SERAC1, CACNA2D3 and SLC38A1 genes are important candidate genes affecting amino acid content in duck breast muscle.

Associations between varicose veins and heart failure: A genetic correlation and mendelian randomization study.

Varicose veins and heart failure (HF) are increasingly prevalent. Although numbers of observational studies have indicated that varicose veins might contribute to the risk of HF, the causal relationship between them remains unclear due to the uncontrolled confounding factors and reverse causation bias. Therefore, this study aimed to explore the potential causal relationship between varicose veins and HF. Based on publicly released genome-wide association studies (GWAS), gene correlation was assessed using linkage disequilibrium score (LDSC) regression, and we conducted a two-sample Mendelian randomization (TSMR) analysis to infer the causal relationship. We performed the Inverse variance weighted (IVW) method as the primary analysis, and used Weighted median, MR-Egger, weighted mode, simple mode, and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods to detect and correct for horizontal pleiotropy. LDSC revealed there was a positive genetic correlation between varicose veins and HF (rg = 0.1726184, Se = 0.04511803, P = .0001). The results of the IVW method indicated that genetically predicted varicose veins were associated with an increased risk of HF (odds ratio (OR) = 1.03; 95% confidence interval (CI): 1.01-1.06; P = .009). Our findings illustrated the significant causal effect of varicose veins on HF, suggesting that people with varicose veins might have a higher risk of HF. The results provided a novel and important perspective into the development mechanism of HF.

Genomic analysis uncovers novel candidate genes related to adaptation to tropical climates and milk production traits in native goats.

BACKGROUND: Since domestication, both evolutionary forces and human selection have played crucial roles in producing adaptive and economic traits, resulting in animal breeds that have been selected for specific climates and different breeding goals. Pakistani goat breeds have acquired genomic adaptations to their native climate conditions, such as tropical and hot climates. In this study, using next-generation sequencing data, we aimed to assess the signatures of positive selection in three native Pakistani goats, known as milk production breeds, that have been well adapted to their local climate. RESULTS: To explore the genomic relationship between studied goat populations and their population structure, whole genome sequence data from native goat populations in Pakistan (n = 26) was merged with available worldwide goat genomic data (n = 184), resulting in a total dataset of 210 individuals. The results showed a high genetic correlation between Pakistani goats and samples from North-East Asia. Across all populations analyzed, a higher linkage disequilibrium (LD) level (- 0.59) was found in the Pakistani goat group at a genomic distance of 1 Kb. Our findings from admixture analysis (K = 5 and K = 6) showed no evidence of shared genomic ancestry between Pakistani goats and other goat populations from Asia. The results from genomic selection analysis revealed several candidate genes related to adaptation to tropical/hot climates (such as; KITLG, HSPB9, HSP70, HSPA12B, and HSPA12B) and milk production related-traits (such as IGFBP3, LPL, LEPR, TSHR, and ACACA) in Pakistani native goat breeds. CONCLUSIONS: The results from this study shed light on the structural variation in the DNA of the three native Pakistani goat breeds. Several candidate genes were discovered for adaptation to tropical/hot climates, immune responses, and milk production traits. The identified genes could be exploited in goat breeding programs to select efficient breeds for tropical/hot climate regions.

Accuracy of genomic prediction using multiple Atlantic salmon populations.

BACKGROUND: The accuracy of genomic prediction is partly determined by the size of the reference population. In Atlantic salmon breeding programs, four parallel populations often exist, thus offering the opportunity to increase the size of the reference set by combining these populations. By allowing a reduction in the number of records per population, multi-population prediction can potentially reduce cost and welfare issues related to the recording of traits, particularly for diseases. In this study, we evaluated the accuracy of multi- and across-population prediction of breeding values for resistance to amoebic gill disease (AGD) using all single nucleotide polymorphisms (SNPs) on a 55K chip or a selected subset of SNPs based on the signs of allele substitution effect estimates across populations, using both linear and nonlinear genomic prediction (GP) models in Atlantic salmon populations. In addition, we investigated genetic distance, genetic correlation estimated based on genomic relationships, and persistency of linkage disequilibrium (LD) phase across these populations. RESULTS: The genetic distance between populations ranged from 0.03 to 0.07, while the genetic correlation ranged from 0.19 to 0.99. Nonetheless, compared to within-population prediction, there was limited or no impact of combining populations for multi-population prediction across the various models used or when using the selected subset of SNPs. The estimates of across-population prediction accuracy were low and to some extent proportional to the genetic correlation estimates. The persistency of LD phase between adjacent markers across populations using all SNP data ranged from 0.51 to 0.65, indicating that LD is poorly conserved across the studied populations. CONCLUSIONS: Our results show that a high genetic correlation and a high genetic relationship between populations do not guarantee a higher prediction accuracy from multi-population genomic prediction in Atlantic salmon.

Comparative whole-genome resequencing to uncover selection signatures linked to litter size in Hu Sheep and five other breeds.

Hu sheep (HS), a breed of sheep carrying the FecB mutation gene, is known for its "year-round estrus and multiple births" and is an ideal model for studying the high fecundity mechanisms of livestock. Through analyzing and comparing the genomic selection features of Hu sheep and other sheep breeds, we identified a series of candidate genes that may play a role in Hu sheep's high fecundity mechanisms. In this study, we conducted whole-genome resequencing on six breeds and screened key mutations significantly correlated with high reproductive traits in sheep. Notably, the CC2D1B gene was selected by the fixation index (FST) and the cross-population composite likelihood ratio (XP-CLR) methods in HS and other five breeds. It was worth noting that the CC2D1B gene in HS was different from that in other sheep breeds, and seven missense mutations have been identified. Furthermore, the linkage disequilibrium (LD) analysis revealed a strong linkage disequilibrium in this specific gene region. Subsequently, by performing different grouping based on FecB genotypes in Hu sheep, genome-wide selective signal analysis screened several genes related to reproduction, such as BMPR1B and PPM1K. Besides, FST analysis identified functional genes related to reproductive traits, including RHEB, HSPA2, PPP1CC, HVCN1, and CCDC63. Additionally, a missense mutation was found in the CCDC63 gene and the haplotype was different between the high reproduction (HR) group and low reproduction (LR) group in HS. In summary, we discovered genetic differentiation among six distinct breeding sheep breeds at the whole genome level. Additionally, we identified a set of genes which were associated with reproductive performance in Hu sheep and visualized how these genes differed in different breeds. These findings laid a theoretical foundation for understanding genetic mechanisms behind high prolific traits in sheep.

Comparative Analysis of Virulence and Molecular Diversity of Puccinia striiformis f. sp. tritici Isolates Collected in 2016 and 2023 in the Western Region of China.

Puccinia striiformis f. sp. tritici (Pst) is adept at overcoming resistance in wheat cultivars, through variations in virulence in the western provinces of China. To apply disease management strategies, it is essential to understand the temporal and spatial dynamics of Pst populations. This study aimed to evaluate the virulence and molecular diversity of 84 old Pst isolates, in comparison to 59 newer ones. By using 19 Chinese wheat differentials, we identified 98 pathotypes, showing virulence complexity ranging from 0 to 16. Associations between 23 Yr gene pairs showed linkage disequilibrium and have the potential for gene pyramiding. The new Pst isolates had a higher number of polymorphic alleles (1.97), while the older isolates had a slightly higher number of effective alleles, Shannon's information, and diversity. The Gansu Pst population had the highest diversity (uh = 0.35), while the Guizhou population was the least diverse. Analysis of molecular variance revealed that 94% of the observed variation occurred within Pst populations across the four provinces, while 6% was attributed to differences among populations. Overall, Pst populations displayed a higher pathotypic diversity of H > 2.5 and a genotypic diversity of 96%. This underscores the need to develop gene-pyramided cultivars to enhance the durability of resistance.

Causal relationship between ulcerative colitis and male infertility: A two-sample Mendelian randomization study.

AIMS: To explore the causal relationship between ulcerative colitis (UC) and male infertility using Mendelian randomization method with single nucleotide polymorphism (SNP) as the instrumental variables. METHODS: Genetic loci closely associated with UC were extracted as instrumental variables and male infertility was the outcome variable in pooled data from the gene-wide association study (GWAS),which was derived from European ethnic groups. The UC data(ebi-a-GCST003045) contained a total sample size of 27432 individuals and 110944 SNPs, and the male infertility data(finn-b-N14_MALEINFERT) contained a total sample size of 73479 individuals and 16377329 SNPs. The SNPs highly correlated with UC were screened from ebi-a-GCST003045(P<5×10-8 as the screening condition, the linkage disequilibrium coefficient was 0.001,and the width of the linkage disequilibrium area was 10000 kb).SNPs related to male infertility from finn-b-N14_MALEINFERT (the minimum r2>0.8,replacing the missing SNPs with SNPs with high linkage, and deleting SNPs without substitution sites) were extracted. MR analysis was performed using MR-Egger regression, the weighted median and the inverse-variance weighted (IVW) respectively, and the causal relationship between UC and male infertility was evaluated by OR and 95% CI, and the Egger-intercept method was used to test for horizontal multiplicity, and the sensitivity analysis was performed using "leave-one-out method". Finally, we used Bayesian Weighted Mendelian Randomization (BWMR) approach to test the results of MR study. RESULTS: A total of 86 SNPs were included as IVs, with OR and 95% CI of 1.095(0.820~1.462)、1.059(0.899~1.248)、1.125(1.002~1.264) for MR-Egger, the weighted median and IVW results respectively, and P value of less than 0.05 for IVW, indicating that a causal relationship between UC and male infertility was causally related. The results of MR analysis combined with BWMR analysis also showed positive genetic causal relationship between UC and male infertility.MR-Egger regression showed an intercept of -2.21×10-3 with a standard error of 0.006 and P = 0.751, there was no horizontal pleiotropy for the IVs of exposure factors. Heterogeneity tests showed no heterogeneity and the results of the "leave-one-out" sensitivity analysis were stable. CONCLUSION: There is a causal association between UC and male infertility, which increases the risk of developing male infertility.

Falls, fracture and frailty risk in multiple sclerosis: a Mendelian Randomization study to identify shared genetics.

INTRODUCTION: Patients with multiple sclerosis (MS) commonly present musculoskeletal disorders characterized by lower bone mineral density (BMD) and muscle weakness. However, the underlying etiology remains unclear. Our objective is to identify shared pleiotropic genetic effects and estimate the causal relationship between MS and musculoskeletal disorders. MATERIALS AND METHODS: We conducted linkage disequilibrium score regression (LDSR), colocalization, and Mendelian randomization (MR) analyses using summary statistics from recent large-scale genome-wide association studies (GWAS), encompassing MS, falls, fractures, and frailty. Additional MR analyses explored the causal relationship with musculoskeletal risk factors, such as BMD, lean mass, grip strength, and vitamin D. RESULTS: We observed a moderate genetic correlation between MS and falls (RG = 0.10, P-value = 0.01) but not between MS with fracture or frailty in the LDSR analyses. MR revealed MS had no causal association with fracture and frailty but a moderate association with falls (OR: 1.004, FDR q-value = 0.018). We further performed colocalization analyses using nine SNPs that exhibited significant associations with both MS and falls in MR. Two SNPs (rs7731626 on ANKRD55 and rs701006 on OS9 gene) showed higher posterior probability of colocalization (PP.H4 = 0.927), suggesting potential pleiotropic effects between MS and falls. The nine genes are associated with central nervous system development and inflammation signaling pathways. CONCLUSION: We found potential pleiotropic genetic effects between MS and falls. However, our analysis did not reveal a causal relationship between MS and increased risks of falls, fractures, or frailty. This suggests that the musculoskeletal disorders frequently reported in MS patients in clinical studies are more likely attributed to secondary factors associated with disease progression and treatment, rather than being directly caused by MS itself.

A resampling-based approach to share reference panels.

For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference panels have become larger and more diverse, leading to improvements in imputation accuracy. However, the latest generation of reference panels is subject to restrictions on data sharing due to concerns about privacy, limiting their usefulness for genotype imputation. In this context, here we propose RESHAPE, a method that employs a recombination Poisson process on a reference panel to simulate the genomes of hypothetical descendants after multiple generations. This data transformation helps to protect against re-identification threats and preserves data attributes, such as linkage disequilibrium patterns and, to some degree, identity-by-descent sharing, allowing for genotype imputation. Our experiments on gold-standard datasets show that simulated descendants up to eight generations can serve as reference panels without substantially reducing genotype imputation accuracy.

Exploring the interconnected between type 2 diabetes mellitus and nonalcoholic fatty liver disease: Genetic correlation and Mendelian randomization analysis.

Epidemiological and clinical studies have indicated a higher risk of nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM), implying a potentially shared genetic etiology, which is still less explored. Genetic links between T2DM and NAFLD were assessed using linkage disequilibrium score regression and pleiotropic analysis under composite null hypothesis. European GWAS data have identified shared genes, whereas SNP-level pleiotropic analysis under composite null hypothesis has explored pleiotropic loci. generalized gene-set analysis of GWAS data determines pleiotropic pathways and tissue enrichment using eQTL mapping to identify associated genes. Mendelian randomization analysis was used to investigate the causal relationship between NAFLD and T2DM. Linkage disequilibrium score regression analysis revealed a strong genetic correlation between T2DM and NAFLD, and identified 24 pleiotropic loci. These single-nucleotide polymorphisms are primarily involved in biosynthetic regulation, RNA biosynthesis, and pancreatic development. generalized gene-set analysis of GWAS data analysis revealed significant enrichment in multiple brain tissues. Gene mapping using these 3 methods led to the identification of numerous pleiotropic genes, with differences observed in liver and kidney tissues. These genes were mainly enriched in pancreas, brain, and liver tissues. The Mendelian randomization method indicated a significantly positive unidirectional causal relationship between T2DM and NAFLD. Our study identified a shared genetic structure between NAFLD and T2DM, providing new insights into the genetic pathogenesis and mechanisms of NAFLD and T2DM comorbidities.

Discussion on the relationship between gut microbiota and glioma through Mendelian randomization test based on the brain gut axis.

BACKGROUND: In the realm of Gut-Brain axis research, existing evidence points to a complex bidirectional regulatory mechanism between gut microbiota and the brain. However, the question of whether a causal relationship exists between gut microbiota and specific types of brain tumors, such as gliomas, remains unresolved. To address this gap, we employed publicly available Genome-Wide Association Study (GWAS) and MIOBEN databases, conducting an in-depth analysis using Two-Sample Mendelian Randomization (MR). METHOD: We carried out two sets of MR analyses. The preliminary analysis included fewer instrumental variables due to a high genome-wide statistical significance threshold (5×10-8). To enable a more comprehensive and detailed analysis, we adjusted the significance threshold to 1×10-5. We performed linkage disequilibrium analysis (R2 <0.001, clumping distance = 10,000kb) and detailed screening of palindromic SNPs, followed by MR analysis and validation through sensitivity analysis. RESULTS: Our findings reveal a causal relationship between gut microbiota and gliomas. Further confirmation via Inverse Variance Weighting (IVW) identified eight specific microbial communities related to gliomas. Notably, the Peptostreptococcaceae and Olsenella communities appear to have a protective effect, reducing glioma risk. CONCLUSION: This study not only confirms the causal link between gut microbiota and gliomas but also suggests a new avenue for future glioma treatment.