Association Studies Research Articles

The Analysis Study of Association of Obesity and Eczema: A Comprehensive Systematic Review

Background: Eczema is a chronic inflammatory skin condition that impacts 10% of the adult population worldwide, whereas obesity represents a significant global health issue. The relationship between eczema and obesity is intricate and differs among various populations. Mechanisms encompass eczema symptoms, avoidance of physical activity, gut microbiome influences, glucocorticoid medications, and factors related to obesity. Examining the relationship between eczema severity and related factors can guide targeted interventions and enhance comprehension of the shared pathways among these conditions. Methods: This systematic review adhered to PRISMA 2020 guidelines, focusing exclusively on full-text articles published in English between 2014 and 2024. Editorials and review articles without a DOI were excluded to ensure the utilization of high-quality sources. A literature review was conducted utilizing reputable databases including ScienceDirect, PubMed, and SagePub to identify relevant studies. Result: An initial search in databases yielded approximately 600 publications relevant to this topic. After a rigorous three-stage screening process, eight studies that met the inclusion criteria were selected for in-depth analysis. These studies underwent comprehensive critical evaluation, allowing in-depth exploration of the relationship between obesity and eczema. This systematic approach ensures that the analysis is based on high-quality evidence that is consistent with the research objectives. Conclusion: Studies demonstrate a clear correlation between moderate eczema and obesity. No significant association was discovered between severe eczema and high BMI. A higher BMI, comorbidities, and behavior may affect the association. Without a dose-response relationship, a bidirectional relationship is possible. To understand the complex association between eczema and obesity, future studies should investigate a non-linear relationship utilizing BMI as a continuous variable.

Deciphering the Role of Calcium Signaling Pathway‐Associated Single Nucleotide Variants in Susceptibility to Hypertension

ABSTRACTBackgroundAs a complex disease, hypertension (HTN) is influenced by both genetic and environmental factors and their interaction. The calcium signaling pathway is known to be involved in the regulation of blood pressure, and dysfunction in this pathway may contribute to the development of hypertension. Genome‐wide association studies (GWAS) have identified several genes in the calcium signaling pathway associated with susceptibility to HTN, including PLCB1, ATP2B1, and ADRB1. The aim of this study was to investigate the possible association between single nucleotide variants (SNVs) in the calcium signaling pathway and HTN.MethodsWe genotyped three SNVs: rs1801253 (ADRB1), rs6108168 (PLCB1), and rs17249754 (ATP2B1) in a population of 131 patients with hypertension and 115 healthy controls from Kermanshah province, Iran.ResultsOur results showed a strong and significant association between the G allele and the GG and CG genotypes of the rs1801253 variant in the ADRB1 gene and susceptibility to hypertension. However, no significant association was found for the other two SNVs. In addition, the presence of the GCG haplotype (alleles from left to right: rs1801253, rs6108168, and rs17249754) appeared to confer a protective role against HTN.ConclusionThis study has made a significant contribution towards enhancing the comprehension of hypertension development, as well as the early identification of individuals who are at risk.

Genetic Predisposition to Hippocampal Atrophy and Risk of Amnestic Mild Cognitive Impairment and Alzheimer’s Dementia

Background: There is a paucity of evidence on the association between genetic propensity for hippocampal atrophy with cognitive outcomes. Therefore, we examined the relationship of the polygenic risk score for hippocampal atrophy (PRShp) with the incidence of amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) as well as the rates of cognitive decline. Methods: Participants were drawn from the population-based HELIAD cohort. Comprehensive neuropsychological assessments were performed at baseline and at follow-up. PRShp was derived from the summary statistics of a large genome-wide association study for hippocampal volume. Cox proportional hazards models as well as generalized estimating equations (GEEs) were used to evaluate the association of PRShp with the combined incidence of aMCI/AD and cognitive changes over time, respectively. All models were adjusted for age, sex, education, and apolipoprotein E (APOE) genotype. Results: Our analysis included 618 older adults, among whom 73 developed aMCI/AD after an average follow-up of 2.96 ± 0.8 years. Each additional SD of PRShp elevated the relative hazard for incident aMCI/AD by 46%. Participants at the top quartile of PRShp had an almost three times higher risk of converting to aMCI/AD compared to the lowest quartile group. Higher PRShp scores were also linked to steeper global cognitive and memory decline. The impact of PRShp was greater among women and younger adults. Conclusions: Our findings support the association of PRShp with aMCI/AD incidence and with global cognitive and memory decline over time. The PRS association was sex- and age-dependent, suggesting that these factors should be considered in genetic modelling for AD.

Rare and common genetic variants underlying the risk of Hirschsprung’s disease

Abstract Hirschsprung’s disease (HSCR) is a congenital enteric neuropathic disorder characterized by high heritability (>80%) and polygenic inheritance (>20 genes). The previous genome-wide association studies (GWAS) identified several common variants associated with HSCR and demonstrated increased predictive performance for HSCR risk in Europeans using a genetic risk score, there remains a notable gap in knowledge regarding Chinese populations. We conducted whole exome sequencing in a HSCR case cohort in Chinese. By using the common controls (505 controls from 1KG EAS and 10 588 controls from ChinaMAP), we conducted GWAS for the common variants in the exome and gene-based association for rare variants. We further validated the associated variants and genes in replicated samples and in vitro and vivo experiments. We identified one novel gene PLK5 by GWAS and suggested 45 novel putative genes based the gene-based test. By using genetic variant at RET and PLK5, we constructed a genetic risk score that could identify the individuals with very high genetic risk for HSCR. Compared with patients with zero or one risk allele from the three variants, the risk for HSCR was 36.61 times higher with six alleles. In addition, we delineated a HSCR risk gene landscape that encompasses 57 genes, which explains 88.5% and 54.5% of HSCR in Chinese and European, respectively. In summary, this study improved the understanding of genetic architecture of HSCR and provided a risk prediction approach for HSCR in the Chinese.

Mendelian Randomization Analysis Reveals a Causal Relationship Between Membranous Nephropathy and the Gut Microbiome

Background: With the increasing prevalence of membranous nephropathy (MN), the gut microbiome (GM) is increasingly implicated in its cause, yet the intricate mechanisms remain unclear. Whether changes in the diversity and richness of gut microbial populations among MN patients contribute to disease prevalence is still unanswered, necessitating further exploration into the potential causative link between the GM and MN. Methods: We conducted a comprehensive bidirectional Mendelian randomization (MR) study. We selected 211 bacterial taxa using Genome-wide association study (GWAS) data provided by the MiBioGen consortium, while GWAS data relevant to MN were obtained from ebi-a-GCST010005. The inverse-variance weighted (IVW) method was the primary technique used to delineate the causal relationship between exposures and outcomes. To confirm the robustness of our results, we used additional methods, including MR-Egger, weighted median, simple mode, and weighted mode. Sensitivity analyses included tests for pleiotropy, heterogeneity, and leave-one-out sensitivity to ensure the integrity of our conclusions. Finally, reverse MR analyses were conducted to assess the likelihood of reverse causality. Results: Using various analytical methods, including the IVW approach, MR-Egger, weighted median, simple mode, and weighted mode, our study identified six microbial taxa with a statistically significant causal link to MN, as indicated by p-values less than 0.05. The implicated taxa are Butyrivibrio (OR= 1.25, 95 % CI: 1.001-1.565, P = 0.048), Butyricicoccus (OR = 2.15, 95 % CI: 1.005-4.621, P = 0.048), Catenibacterium (OR = 1.49, 95 % CI: 1.043-2.134, P = 0.028), Ruminiclostridium5 (OR = 1.78, 95 % CI: 1.140-2.763, P = 0.03), RuminococcaceaeUCG003 (OR = 1.78, 95 % CI: 1.140-2.763, P = 0.011) and Bacillales (OR = 1.52, 95 % CI: 1.135-2.025, P = 0.005). Each of these taxa has been established as a risk factor for MN. Notably, Ruminococcaceae UCG-003 and Bacillales were identified as having a bidirectional causal relationship with the disease. Conclusion: Our MR study has revealed a causal link between six microbial taxa and MN, highlighting their potential involvement in the disease's development. These findings represent an initial step into this complex field and underscore the need for more in-depth research.

Migraine Genetic Susceptibility Does Not Strongly Influence Migraine Characteristics and Outcomes in a Treated, Real-World, Community Cohort

Background/Objectives: Migraine is a common neurological disorder with highly variable characteristics. While genome-wide association studies have identified genetic risk factors that implicate underlying pathways, the influence of genetic susceptibility on disease characteristics or treatment response is incompletely understood. We examined the relationships between a previously developed standardized integrative migraine polygenic genetic risk score (PRS) and migraine characteristics in a real-world, treated patient cohort. Methods: This retrospective cohort study used covariate-adjusted regression to comprehensively evaluate associations between the PRS and clinical characteristics in 1653 treated migraine cases with European ancestry at baseline and, in 800 cases, after one year. Cases were deeply phenotyped by neurologists during extensive interviews, using structured clinical documentation tools to record ~200 discrete data elements. Results: In treated patients, higher standardized PRS showed associations with two common migraine symptoms: photophobia (odds ratio [confidence interval]: 1.33 [1.13–1.56], p = 0.001) and stabbing pain (1.21 [1.08–1.36], p = 0.001]; both retained significance at Q = 0.05. Associations with phonophobia, nausea, emesis, and unilateral headache had similar effect sizes but did not survive correction for multiple tests. In this population, the PRS was not associated with other symptoms of migraine attacks, objective measures of migraine disability, frequency, severity, average duration, time-to-peak intensity of migraine attacks, chronification, emergency department visits, triptan responsiveness, or changes at follow-up. Conclusions: In treated patients, genetic risk was associated with common migraine symptoms but not with the severity of migraine characteristics or treatment outcomes. This suggests that in treated patients, other genetic and non-genetic factors influence migraine symptom severity and disease course more strongly than genetic susceptibility.

GestaltGAN: synthetic photorealistic portraits of individuals with rare genetic disorders

Abstract The facial gestalt (overall facial morphology) is a characteristic clinical feature in many genetic disorders that is often essential for suspecting and establishing a specific diagnosis. Therefore, publishing images of individuals affected by pathogenic variants in disease-associated genes has been an important part of scientific communication. Furthermore, medical imaging data is also crucial for teaching and training deep-learning models such as GestaltMatcher. However, medical data is often sparsely available, and sharing patient images involves risks related to privacy and re-identification. Therefore, we explored whether generative neural networks can be used to synthesize accurate portraits for rare disorders. We modified a StyleGAN architecture and trained it to produce artificial condition-specific portraits for multiple disorders. In addition, we present a technique that generates a sharp and detailed average patient portrait for a given disorder. We trained our GestaltGAN on the 20 most frequent disorders from the GestaltMatcher database. We used REAL-ESRGAN to increase the resolution of portraits from the training data with low-quality and colorized black-and-white images. To augment the model’s understanding of human facial features, an unaffected class was introduced to the training data. We tested the validity of our generated portraits with 63 human experts. Our findings demonstrate the model’s proficiency in generating photorealistic portraits that capture the characteristic features of a disorder while preserving patient privacy. Overall, the output from our approach holds promise for various applications, including visualizations for publications and educational materials and augmenting training data for deep learning.

Joint analysis of genome-wide cross-trait and multi-omics reveals molecular mechanisms of inflammatory bowel disease and nominates its novel therapeutic genes.

Inflammatory bowel disease (IBD) with the two predominant endophenotypes-Crohn's disease (CD) and ulcerative colitis (UC)-represents a group of chronic gastrointestinal inflammatory conditions. Since most genetic associations with IBD are often limited to independent subtypes, we reported a genome-wide association study (GWAS) cross-trait analysis combined with CD and UC to enhance statistical power. Initially, we detected 256 association signals at 54 genomic susceptibility loci and further characterized the functionality of variants within these regions. Subsequently, we revealed tissue and cell-specific heritability enrichment, particularly in whole blood, small intestine terminal ileum, spleen, lung, and colon transverse. Leveraging multi-omics datasets, we adopted a two-pronged approach comprising summary data-based Mendelian randomization (SMR) and transcriptome-wide association study (TWAS) to pinpoint likely causal genes and variants. Further, RNA-seq analysis facilitated the evaluation of differential expression and co-expression in intestinal tissues. Through a multi-stage prioritization strategy, compelling evidence for putative targets was nominated; notably highlighting several potential susceptibility genes such as IL27 and SBNO2. Finally, we utilized Mendelian randomization (MR) analysis with diverse datasets to verify the convergence of these two endophenotype-driven genes. Our investigation yields valuable insights to inform genetic mechanisms in IBD and reveal potential causal gene targets.

Genetically predicted causal link between the plasma lipidome and pancreatic diseases: a bidirectional Mendelian randomization study

BackgroundRecent studies have increasingly emphasized the strong correlation between the lipidome and the risk of pancreatic diseases. To determine causality, a Mendelian randomization (MR) analysis was performed to identify connections between the lipidome and pancreatic diseases.MethodsStatistics from a genome-wide association study of the plasma lipidome, which included a diverse array of 179 lipid species, were obtained from the GeneRISK cohort study with 7,174 participants. Genetic associations with four types of pancreatitis and pancreatic cancer were sourced from the R11 release of the FinnGen consortium. Two pancreatitis datasets from UK Biobank were employed as the validation cohort. MR analysis was conducted to assess the relationship between the genetically predicted plasma lipidome and these pancreatic diseases. Inverse variance weighted was adopted as the main statistical method. Bayesian weighted MR was employed for further verification. The MR-Egger intercept test for pleiotropy and Cochrane's Q statistics test for heterogeneity were performed to ensure the robustness.ResultsMR analysis yielded significant evidence that 26, 25, 2, and 19 lipid species were correlated with diverse outcomes of pancreatitis, and 8 lipid species were correlated with pancreatic cancer. Notably, sterol ester (27:1/20:2) levels (OR: 0.84, 95% CI: 0.78–0.90, P = 5.79 × 10−7) were significantly associated with acute pancreatitis, and phosphatidylcholine (17:0_20:4) levels (OR: 0.89, 95% CI: 0.84–0.94, P = 1.78 × 10−4) and sterol ester (27:1/20:4) levels (OR: 0.90, 95% CI: 0.86–0.95, P = 2.71 × 10−4) levels were significantly associated with chronic pancreatitis after the Bonferroni-corrected test. As for validation, 14 and 9 lipid species were correlated with acute and chronic pancreatitis of UK Biobank. Some lipid classes showed significant effects both in the FinnGen consortium and UK Biobank datasets.ConclusionsThe findings of this study indicate a potential genetic predisposition linking the plasma lipidome to pancreatic diseases and good prospects for future pancreatic disease clinical trials.

Missing Regulation Between Genetic Association and Transcriptional Abundance for Hypercholesterolemia Genes

Background: Low-density lipoprotein cholesterol (LDL-C) is a well-established risk factor for cardiovascular disease, and it plays a causal role in the development of atherosclerosis. Genome-wide association studies (GWASs) have successfully identified hundreds of genetic variants associated with LDL-C. Most of these risk loci fall in non-coding regions of the genome, and it is unclear how these non-coding variants affect circulating lipid levels. One hypothesis is that genetically mediated variation in transcript abundance, detected via the analysis of expressed quantitative trait loci (eQTLs), is key to the biologic function of causal variants. Here, we investigate the hypothesis that non-coding GWAS risk variants affect the homeostatic expression of a nearby putatively causal gene for serum LDL-C levels. Methods: We establish a set of twenty-one expert-curated and validated genes implicated in hypercholesterolemia via dose-dependent pharmacologic modulation in human adults, for which the relevant tissue type has been established. We show that the expression of these LDL-C genes is impacted by eQTLs in relevant tissues and that there are significant genomic-risk loci in LDL-GWAS near these causal genes. We evaluate, using statistical colocalization, whether a single variant or set of variants in each genetic locus is responsible for the GWAS and eQTL signals. Results: Genome-wide association study results for serum LDL-C levels demonstrate that the 402 identified genomic-risk loci for LDL-C are highly enriched for known causal genes for LDL-C (OR 527, 95% CI 126–5376, p < 2.2 × 10−16). However, we find limited evidence for colocalization between GWAS signals near validated hypercholesterolemia genes and eQTLs in relevant tissues (colocalization rate of 26% at a locus-level colocalization probability > 50%). Conclusions: Our results highlight the complexity of genetic regulatory effects for causal hypercholesterolemia genes; we suggest that context-responsive eQTLs may explain the effects of non-coding GWAS hits that do not overlap with standard eQTLs.

Genome-wide association studies are enriched for interacting genes

BackgroundWith recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.ResultsWe used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.ConclusionsOur findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

Advancing Genetic Engineering through AI: Sequencing and Editing Innovations

As an important part of modern biotechnology, genetic engineering is widely used in fields such as disease treatment, agricultural improvement, and environmental protection. Gene sequencing technology, especially next-generation sequencing technology, provides a powerful tool for studying biological genetic information. However, with the rapid growth of genomic data, how to efficiently and accurately analyze and apply this huge data has become a major challenge facing genetic engineering. In recent years, artificial intelligence (AI) technology, especially deep learning, has been widely used in the automated processing of large-scale data and has shown great potential in genetic engineering. AI technology not only shows advantages in gene editing optimization, genetic variation detection and genome association analysis, but also significantly improves the efficiency and accuracy of genetic data analysis. Although AI has brought many conveniences in genetic engineering, challenges such as technology transparency, data quality issues, and ethics and privacy protection still need to be solved. This article explores the application of artificial intelligence in genetic engineering sequencing and data analysis, analyzes how AI can improve the efficiency and accuracy of genetic data analysis, and discusses the potential contribution of AI in gene editing and precision medicine. As AI continues to develop, it is expected to play an increasingly important role in fields such as genomics, gene editing, and precision medicine, and provide more effective strategies for future disease treatment

Depression symptom-specific genetic associations in clinically diagnosed and proxy case Alzheimer’s disease

Abstract Depression is a risk factor for the later development of Alzheimer’s disease (AD), but evidence for the genetic relationship is mixed. Assessing depression symptom-specific genetic associations may better clarify this relationship. To address this, we conducted genome-wide meta-analysis (a genome-wide association study, GWAS) of the nine depression symptom items, plus their sum score, on the Patient Health Questionnaire (PHQ-9) (GWAS-equivalent N: 224,535–308,421) using data from UK Biobank, the GLAD study and PROTECT, identifying 37 genomic risk loci. Using six AD GWASs with varying proportions of clinical and proxy (family history) case ascertainment, we identified 20 significant genetic correlations with depression/depression symptoms. However, only one of these was identified with a clinical AD GWAS. Local genetic correlations were detected in 14 regions. No statistical colocalization was identified in these regions. However, the region of the transmembrane protein 106B gene (TMEM106B) showed colocalization between multiple depression phenotypes and both clinical-only and clinical + proxy AD. Mendelian randomization and polygenic risk score analyses did not yield significant results after multiple testing correction in either direction. Our findings do not demonstrate a causal role of depression/depression symptoms on AD and suggest that previous evidence of genetic overlap between depression and AD may be driven by the inclusion of family history-based proxy cases/controls. However, colocalization at TMEM106B warrants further investigation.

Causal relationships between plasma lipidome and diabetic neuropathy: a Mendelian randomization study

BackgroundDyslipidemia is closely related to diabetic neuropathy. This study examined the potential causal relationship involving 179 lipid species and the disease.MethodsThe pooled data on 179 lipid species and diabetic neuropathy were obtained from previous genome-wide association studies (GWAS). A Mendelian Randomization (MR) method was employed to investigate the potential causal link, and the robustness of the findings was confirmed through comprehensive sensitivity analyses.ResultsGenetically, phosphatidylcholine might be associated with the risk of diabetic neuropathy. Upon adjusting for multiple comparisons, higher levels of phosphatidylcholine (16:0_20:2) (OR = 0.82, 95%CI: 0.73-0.91; P < 0.001, FDR = 0.033) and phosphatidylcholine (16:1_18:1) (OR = 0.77, 95%CI: 0.67-0.88; P < 0.001, FDR = 0.019) are associated with a decreased risk of diabetic neuropathy. Further multivariable MR (MVMR) analysis demonstrated the effect of genetically predicted phosphatidylcholine (16:1_18:1) remained after adjusting for body mass index (BMI) and glycated hemoglobin (HbA1c). Sensitivity assessments have confirmed the robustness of these findings, revealing no evidence of heterogeneity or pleiotropy.ConclusionOur research linked certain lipid species with diabetic neuropathy risk, suggesting that targeting lipids could be a therapeutic strategy in clinical trials addressing this condition.

The association between type 2 diabetes and asthma incidence: a longitudinal analysis considering genetic susceptibility

BackgroundThe prevalence of type 2 diabetes (T2D) and asthma is rising, yet evidence regarding the relationship between T2D and asthma, particularly in the context of genetic predispositions, remains limited.MethodsThis study utilized data from the UK Biobank longitudinal cohort, involving 388,775 participants. A polygenic risk score (PRS) for asthma was derived from genome-wide association studies summary. Cox regression models were used to assess the association between T2D and asthma, incorporating the asthma PRS.ResultsOver a median follow-up of 13.62 years, 10,211 asthma cases were documented. After adjusting for age, sex, current smoking status, and other confounding variables, T2D was significantly associated with an increased risk of developing asthma (Hazard Ratios [HR] 1.16, 95% confidence interval [CI] 1.06–1.26). This association remained significant after further adjustments for genetic susceptibility to asthma. Furthermore, T2D increased the risk of developing asthma across both high and low genetic risk groups.ConclusionsT2D is associated with an increased risk of developing asthma, irrespective of genetic susceptibility. These findings underscore the importance of incorporating glucose regulation strategies into asthma prevention efforts.

Gut microbiota, metabolites, and cytokines in relation to the risk of prostate cancer in the Asian population

PurposeStudies have shown that gut microbiota is involved in the tumorigenesis and development of prostate cancer. We aimed to perform a comprehensive analysis of causal associations of gut microbiota, metabolites, and cytokines with prostate cancer in the Asian population.Patients and methodsGenome-wide association study (GWAS) summary datasets were collected from the public databases. There were 418 bacterial traits, 452 metabolites, 91 cytokines, 5408 cases of prostate cancer from East Asia, and 109,347 controls included. Mendelian randomization (MR) analyses were performed to investigate their causal relationships. Sensitivity analyses were conducted to test the reliability of MR results. Furthermore, the FinnGen database was used to assess the generalizability of our findings based on Asians.ResultsThere were a total of 17 bacterial traits, 28 metabolites (including 2 microbiota-associated metabolites), and 9 cytokines to be significantly associated with prostate cancer in Asians (P < 0.05). Further MR analyses of these positive results indicated that G_Ruminococcaceae UCG014/TNFSF10 axis, G_Anaerofilum/TNFRSF14 axis, G_Erysipelotrichaceae UCG003/TNFSF10 axis, and P_Proteobacteria/cholesterol axis were key signaling pathways involved in the progression of prostate cancer. Notably, G_Ruminococcaceae UCG014/TNFSF10 axis and G_Anaerofilum/TNFRSF14 axis were found to act as protective factors, while the other two signaling axes played a crucial role in promoting the progression of prostate cancer. Sensitivity analyses further confirmed the reliability of our findings. Using the European population as outcome, we further assessed the generalizability of our conclusions and found limited applicability to Europeans.ConclusionsWe found that there were causal associations of gut microbiota, metabolites, and cytokines with prostate cancer in Asians. The causal effects of gut microbiota on prostate cancer were partially mediated by metabolites and cytokines. These findings might contribute to the development of new therapeutic strategies for prostate cancer.

The association between primary frozen shoulder and serum lipids may be overestimated: evidence based on retrospective observational studies and Mendelian randomization

BackgroundPrevious studies have shown that dyslipidemia is significantly associated with primary frozen shoulder and may be a risk factor for the development of primary frozen shoulder. However, these findings may be biased by a number of confounding factors. We investigated the association between serum lipids and primary frozen shoulder by retrospective analysis and two-sample Mendelian randomization (MR) methods.MethodsThis retrospective observational study included 284 patients with primary frozen shoulder diagnosed from October 2020 to October 2023 at four centers as the experimental group. Patients with diabetes and thyroid dysfunction were excluded. The control group consisted of age- and sex-matched people who underwent a health checkup. We compared total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) between the two groups. Genetic variants for the serum lipids and frozen shoulder were then extracted from large-scale genome-wide association studies. Causal effects were assessed using Inverse variance weighting (IVW), Weighted median, MR-Egger, simple and weighted models in MR analysis.ResultsThe analysis showed that after excluding patients with diabetes and thyroid dysfunction, the serum lipids (TC, TG, HDL, and LDL) in the primary frozen shoulder group were no different from those of normal individuals. None of the MR methods found significant causal evidence between them.ConclusionsDyslipidemia in patients with primary frozen shoulder may be influenced by confounding factors such as diabetes and thyroid dysfunction. These findings deepen our understanding of primary frozen shoulder risk factors.

3D genomic features across >50 diverse cell types reveal insights into the genomic architecture of childhood obesity

The prevalence of childhood obesity is increasing worldwide, along with the associated common comorbidities of type 2 diabetes and cardiovascular disease in later life. Motivated by evidence for a strong genetic component, our prior genome-wide association study (GWAS) efforts for childhood obesity revealed 19 independent signals for the trait; however, the mechanism of action of these loci remains to be elucidated. To molecularly characterize these childhood obesity loci, we sought to determine the underlying causal variants and the corresponding effector genes within diverse cellular contexts. Integrating childhood obesity GWAS summary statistics with our existing 3D genomic datasets for 57 human cell types, consisting of high-resolution promoter-focused Capture-C/Hi-C, ATAC-seq, and RNA-seq, we applied stratified LD score regression and calculated the proportion of genome-wide SNP heritability attributable to cell type-specific features, revealing pancreatic alpha cell enrichment as the most statistically significant. Subsequent chromatin contact-based fine-mapping was carried out for genome-wide significant childhood obesity loci and their linkage disequilibrium proxies to implicate effector genes, yielded the most abundant number of candidate variants and target genes at the BDNF, ADCY3, TMEM18, and FTO loci in skeletal muscle myotubes and the pancreatic beta-cell line, EndoC-BH1. One novel implicated effector gene, ALKAL2 – an inflammation-responsive gene in nerve nociceptors – was observed at the key TMEM18 locus across multiple immune cell types. Interestingly, this observation was also supported through colocalization analysis using expression quantitative trait loci (eQTL) derived from the Genotype-Tissue Expression (GTEx) dataset, supporting an inflammatory and neurologic component to the pathogenesis of childhood obesity. Our comprehensive appraisal of 3D genomic datasets generated in a myriad of different cell types provides genomic insights into pediatric obesity pathogenesis.

Effect of FABP4 Gene Polymorphisms on Fatty Acid Composition, Chemical Composition, and Carcass Traits in Sonid Sheep

Fatty acids (FAs) are a group of organic compounds that are regulated by polygenic and environmental factors and affect the taste, nutritional value, and quality of meat. Lamb meat is rich in FAs required by the human body, which has directed more attention to sheep research and meat production. The fatty acid-binding protein 4 (FABP4) gene is considered a candidate gene that can affect FA composition in livestock. Therefore, the aim of this study was to screen for genetic polymorphisms of FABP4 and confirm the association between these polymorphisms and FAs, chemical composition, and carcass traits in Sonid lambs. The results of the association study showed that g.57764667T>C, g.57764436T>G, g.57764242G>A, and g.57757988A>G were associated with the composition of certain long-chain FAs, and g.57764242G>A, g.57764436T>G, and g.57758026G>A were associated with free amino acid levels. In addition, g.57764667T>C and g.57757988A>G were associated with carcass weight and live weight in Sonid lambs. Therefore, the polymorphisms of the FABP4 gene are expected to be a genetic selection marker for superior traits in Sonid sheep breeding, which also provides new insights into how the ovine FABP4 gene affects traits of lamb quality.

Cross-validation of Previously Identified Marker Trait Associations for Sclerotinia Stem Rot Resistance in Soybean

Genome-wide association studies (GWAS) face several design and methodological challenges that complicate the identification of genuine marker-trait associations (MTAs) for complex traits in crops. A post-GWAS genomic assessment of previously reported MTAs on Chromosomes 2 and 9 for Sclerotinia stem rot (SSR) resistance in soybean was conducted using an independent cross-validation panel. Results showed a significantly higher frequency of resistance (R) alleles and lower frequency of susceptible (S) alleles in partially resistant (PR) cultivars. This study presents promising findings regarding the utility of previously identified SSR resistance MTAs and introduces a valuable new approach for post-GWAS MTA validation.