Search

East Asian populations exhibit a genetic predisposition to obesity, yet comprehensive research on these traits is limited. We conducted a genome-wide association study (GWAS) with 93,673 Korean subjects to uncover novel genetic loci linked to obesity, examining metrics such as body mass index, waist circumference, body fat ratio, and abdominal fat ratio. Participants were categorized into non-obese, metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) groups. Using advanced computational methods, we developed a multifaceted polygenic risk scores (PRS) model to predict obesity. Our GWAS identified significant genetic effects with distinct sizes and directions within the MHO and MUO groups compared with the non-obese group. Gene-based and gene-set analyses, along with cluster analysis, revealed heterogeneous patterns of significant genes on chromosomes 3 (MUO group) and 11 (MHO group). In analyses targeting genetic predisposition differences based on metabolic health, odds ratios of high PRS compared with medium PRS showed significant differences between non-obese and MUO, and non-obese and MHO. Similar patterns were seen for low PRS compared with medium PRS. These findings were supported by the estimated genetic correlation (0.89 from bivariate GREML). Regional analyses highlighted significant local genetic correlations on chromosome 11, while single variant approaches suggested widespread pleiotropic effects, especially on chromosome 11. In conclusion, our study identifies specific genetic loci and risks associated with obesity in the Korean population, emphasizing the heterogeneous genetic factors contributing to MHO and MUO. [ABSTRACT FROM AUTHOR]

The multikinase inhibitor sorafenib is the standard first-line treatment for advanced hepatocellular carcinoma (HCC), but many patients become sorafenib-resistant (SR). This study investigated the efficacy of another kinase inhibitor, regorafenib (Rego), as a second-line treatment. We produced SR HCC cells, wherein the PI3K-Akt, TNF, cAMP, and TGF-beta signaling pathways were affected. Acute Rego treatment of these cells reversed the expression of genes involved in TGF-beta signaling but further increased the expression of genes involved in PI3K-Akt signaling. Additionally, Rego reversed the expression of genes involved in nucleosome assembly and epigenetic gene expression. Weighted gene co-expression network analysis (WGCNA) revealed four differentially expressed long non-coding RNA (DElncRNA) modules that were associated with the effectiveness of Rego on SR cells. Eleven putative DElncRNAs with distinct expression patterns were identified. We associated each module with DEmRNAs of the same pattern, thus obtaining DElncRNA/DEmRNA co-expression modules. We discuss the potential significance of each module. These findings provide insights and resources for further investigation into the potential mechanisms underlying the response of SR HCC cells to Rego. [ABSTRACT FROM AUTHOR]

Background: Physical restraint (PR) is used to ensure the safety of care recipients. However, this causes an ethical dilemma between the autonomy and dignity of the recipients and the provision of effective treatment by health workers. This study aimed to analyze legal and ethical situations related to the use of PR using written judgments. Methods: This study uses a qualitative retrospective design. Qualitative content analysis was performed on South Korean written judgments. A total of 38 cases from 2015 to 2021 were categorized. The types of court decisions and ethical dilemma situations were examined according to the four principles of bioethics, and the courts' judgments were compared. Results: Written judgments related to PR were classified into three types according to the appropriateness of PR use, the presence or absence of duty of care, and legal negligence. Ethical dilemmas were categorized into three situations depending on whether the four principles of bioethics were followed. The courts' decisions regarding the ethical dilemmas differed depending on the situational factors before and after the use of PR and the conflicting conditions of the ethical principles. Conclusions: Health workers should consider legal and ethical requirements when determining whether to use PR to provide the care recipient with the necessary treatment. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). Results: First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen′s Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. Conclusions: Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods. [ABSTRACT FROM AUTHOR]

Introduction: The literature cites many factors that influence a nurse's decision when choosing their workplace. However, it is unclear which attributes matter the most to newly graduated nurses. The study aimed to identify the relative importance of workplace preference attributes among newly graduated nurses. Design: A cross‐sectional study. Methods: We conducted an online survey and data were collected in June 2022. A total of 1111 newly graduated nurses in South Korea participated. The study employed best–worst scaling to quantify the relative importance of nine workplace preferences and also included questions about participants' willingness to pay for each workplace preferences. The relationships between the relative importance of the workplace attribute and the willingness to pay were determined using a quadrant analysis. Results: The order according to the relative importance of workplace preferences is as follows: salary, working conditions, organizational climate, welfare program, hospital location, hospital level, hospital reputation, professional development, and the chance of promotion. The most important factor, salary, was 16.67 times more important than the least important factor, the chance of promotion, in terms of choosing workplace. In addition, working conditions and organizational climate were recognized as high economic value indicators. Conclusion: Newly graduated nurses nominated better salaries, working conditions, and organizational climate as having a more important role in choosing their workplace. Clinical Relevance: The findings of this study have important implications for institutions and administrators in recruiting and retaining newly graduated nurses. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has sparked a rapid worldwide increase in the utilization of delivery services. This study delves into the experiences of delivery workers as one of the activley developed industries during the COVID-19 pandemic in South Korea and sheds light on the effects of the pandemic on their working conditions. Through in-depth interviews with 10 Korean delivery workers, data analysis employed the hermeneutic phenomenology research method developed by Van Manen. The findings indicate a substantial rise in income levels and a positive societal perception of delivery labor post-COVID-19. The pandemic also attracted many new workers to the industry due to low entry barriers and work flexibility. However, challenges persisted as delivery workers grappled with an uncertain legal status and sometimes jeopardized their safety to boost earnings in shorter time frames. The pivotal role played by delivery workers in enhancing communal quality of life and connectivity during the pandemic cannot be overlooked. As we step into a post-COVID-19 era, comprehensive efforts are needed to enhance the working environment for delivery workers globally. Notably, clarifying the relationship between delivery workers and companies within the novel digital labor landscape is essential, alongside establishing institutional frameworks to safeguard workers' basic rights, including health and safety provisions. [ABSTRACT FROM AUTHOR]

Asthma is a complex heterogeneous disease caused by gene–environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene–environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel (SETDB1 and ZNF8) and five previously reported (DM4C, DOCK8, MMP20, MYL7, and ADCY9) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma. [ABSTRACT FROM AUTHOR]

This study aims to address the question: Why did transition countries enact laws related to social health insurance (SHI) at different times, even though they experienced dissolution of the Soviet Union at the same time in the early 1990s? We used Ragin's fuzzy-set qualitative comparative analysis to investigate the configurations of causal conditions that affected the speed of developing SHI-related legislation in 24 post-socialist countries. The potential causal conditions were health status, economic status, level of governance, level of democracy, issue salience, and number of medical professionals. We found 3 pathways that led to the enactment of SHI-related laws and 1 pathway that inhibits enactment. The key factors impacting enactment of SHI-related laws were non-corrupt governments and realization of democracy. In addition, medical professionals' involvement in policymaking could be the factor to enact SHI-related laws. Further research is needed for more in-depth analysis regarding what the laws specifically include, type of health insurance systems that were adopted based on the laws, and if the legislation contributed toward achieving universal health coverage. [ABSTRACT FROM AUTHOR]

Summary: Background: Asthma‐chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS), which has received much attention, has not been unanimously defined. Objective: In this study, we tried to demonstrate that longitudinally defined ACOS is more useful in the real world than blending patients with asthma and COPD. Methods: The study patients had undergone two consecutive pulmonary function tests measured at least 3 months apart (n = 1889). We selected the patients who had positive bronchodilator response or methacholine provocation tests (n = 959). Next, we defined ACOS as a patient with a persistent airflow obstruction [forced expiratory volume in 1 second (FEV1)/forced vital capacity <0.7] that was identified twice consecutively by an interval of at least 3 months (n = 228). Results: The proportions of patients who were older, male and smokers were significantly higher, and baseline lung function was lower in patients with ACOS. In the longitudinal analysis, the mean change in lung function was high, and a greater decline in FEV1 was observed in patients with ACOS. In addition, we compared ACOS and severe asthma, and we also performed a cluster analysis and compared the results with our definition of ACOS. According to our definition, ACOS is an independent subtype with distinctive characteristics. Finally, a genome‐wide association study (GWAS) was performed to identify genetic variations associated with ACOS, but no significant single nucleotide polymorphisms were identified. Conclusion: Our findings suggest that ACOS should be defined longitudinally and considered as an independent subgroup distinguished by inherited environmental factors rather than as a genetically distinct independent group. [ABSTRACT FROM AUTHOR]

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene–gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene–Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP–SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 × SPOCK1) and (LINGO2 × ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website (http://statgen.snu.ac.kr/software/hiscom-ggi). [ABSTRACT FROM AUTHOR]

Background: Component-based structural equation modeling methods are now widely used in science, business, education, and other fields. This method uses unobservable variables, i.e., “latent” variables, and structural equation model relationships between observable variables. Here, we applied this structural equation modeling method to biologically structured data. To identify candidate drug-response biomarkers, we first used proteomic peptide-level data, as measured by multiple reaction monitoring mass spectrometry (MRM-MS), for liver cancer patients. MRM-MS is a highly sensitive and selective method for proteomic targeted quantitation of peptide abundances in complex biological samples. Results: We developed a component-based drug response prediction model, having the advantage that it first combines collapsed peptide-level data into protein-level information, facilitating subsequent biological interpretation. Our model also uses an alternating least squares algorithm, to efficiently estimate both coefficients of peptides and proteins. This approach also considers correlations between variables, without constraint, by a multiple testing problem. Using estimated peptide and protein coefficients, we selected significant protein biomarkers by permutation testing, resulting in our model for predicting liver cancer response to the tyrosine kinase inhibitor sorafenib. Conclusions: Using data from a cohort of liver cancer patients, we then “fine-tuned” our model to successfully predict drug responses, as demonstrated by a high area under the curve (AUC) score. Such drug response prediction models may eventually find clinical translation in identifying individual patients likely to respond to specific therapies. [ABSTRACT FROM AUTHOR]

We examined the constitutional provisions on the right to health in the Western Pacific region countries and compared universal health coverage (UHC) achievement. In 9 of the 11 countries, the constitution had provisions related to health rights, of which 7 countries also included details related to the health care system. Additionally, 5 countries also had provisions for the vulnerable. The countries with weak state obligation and no clear provisions on health rights (China and Laos) all recorded low UHC achievement scores. Australia and Malaysia, which do not have constitutional provisions regarding health, have achieved high UHC achievement scores. Constitution is the supreme law of a country and the basis for developing and implementing health and medical laws and policies. In addition, laws or constitutions that regulate the rights to health can help gain access to health care. Follow-up research related to the constitutional right to health will be needed. [ABSTRACT FROM AUTHOR]

Background: Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification. Results: It is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration ("HisCoM-mimi") model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods. Conclusion: As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation. [ABSTRACT FROM AUTHOR]

Background: As one possible solution to the "missing heritability" problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways. Results: Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset. Conclusions: Herein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously. [ABSTRACT FROM AUTHOR]

Background and Aims Little is known about how to include adults < 50 years in colonoscopy screening. This study aimed to derive a risk-scoring model incorporating laboratory indicators for metabolic risks to predict advanced colorectal neoplasia (ACN) in asymptomatic Korean adults both younger and older than 50 years. Methods In this cross-sectional study, 70 812 consecutive adult recipients of a screening colonoscopy in a single health check-up center in Korea between 2003 and 2012 were enrolled. A risk score model was developed using multiple logistic regression model and internally validated. Results Overall prevalence of ACN was 1.4% (956/70 812). A 15-point score model was developed to comprise age, sex, family history of colorectal cancer, smoking, body mass index, serum levels of fasting glucose, low-density lipoprotein cholesterol, and carcinoembryonic antigen. Based on the score, the validation cohort could be categorized into five risk groups (low, borderline, moderate, high, and very high) with an ACN prevalence of 0.7%, 1.3%, 2.7%, 6.6%, and 13.2%, respectively. Compared with the borderline risk group, the low-risk group showed a 50.3% reduced risk of ACN. Meanwhile, the moderate, high, and very high risk groups showed 2, 5, and 10-fold increased risk of ACN. The score showed significantly superior discriminative power than the Asian-Pacific colorectal screening score ( P = 0.003). Conclusions Our scoring model based on both clinical and laboratory risk factors is useful for the prediction of ACN. This score may be used to include adults < 50 years in colonoscopy screening. [ABSTRACT FROM AUTHOR]

Motivation: Individuals in each family are genetically more homogeneous than unrelated individuals, and family-based designs are often recommended for the analysis of rare variants. However, despite the importance of family-based samples analysis, few statistical methods for rare variant association analysis are available.Results: In this report, we propose a FAmily-based Rare Variant Association Test (FARVAT). FARVAT is based on the quasi-likelihood of whole families, and is statistically and computationally efficient for the extended families. FARVAT assumed that families were ascertained with the disease status of family members, and incorporation of the estimated genetic relationship matrix to the proposed method provided robustness under the presence of the population substructure. Depending on the choice of working matrix, our method could be a burden test or a variance component test, and could be extended to the SKAT-O-type statistic. FARVAT was implemented in C++, and application of the proposed method to schizophrenia data and simulated data for GAW17 illustrated its practical importance.Availability: The software calculates various statistics for the analysis of related samples, and it is freely downloadable from http://healthstats.snu.ac.kr/software/farvat.Contact: won1@snu.ac.kr or tspark@stats.snu.ac.krSupplementary information: supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Recent investigations such as a more powerful quasi-likelihoods score test (MQLS) statistic have enabled the efficient association analysis with related samples. Although those approaches are robust against the mis-specified phenotypic distribution and covariance structure, it has been shown that MQLS statistic becomes violated under the presence of the population substructure if the level of population substructure depends on the genomic location. In this report, we propose a new statistical method which combines EIGENSTRAT approach and MQLS-statistic. The proposed method was evaluated with simulation data under various scenarios and we found that proposed method performs better than the traditional methods such as transmission disequilibrium test. The proposed method was applied to genetic association analysis for body mass index with Framingham heart study, and we found that rs1121980 and rs9940128 in the linkage block in FTO gene are associated with the body mass index. [ABSTRACT FROM AUTHOR]

The causal relationship between genes and diseases has been investigated with the development of DNA sequence. Polymorphisms incorporated in the HapMap Project have enabled fine mapping with linkage disequilibrium (LD) and prior clustering of the haplotypes on the basis of a similarity measure has often been performed in an attempt to capture coalescent events because they can reduce the amount of computation. However an inappropriate choice of similarity measure can lead to wrong conclusions and we propose a new haplotype-based clustering algorithm for fine-scale mapping by using a Bayesian partition model. To handle phase-unknown genotypes, we propose a new algorithm based on a Metropolized Gibbs sampler and it is implemented in C++. Our simulation studies found that the proposed method improves the accuracy of the estimator for the disease susceptibility locus. We illustrated the practical implication of the new analysis method by an application to fine-scale mapping of CYP2D6 in drug metabolism. [ABSTRACT FROM AUTHOR]

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the "missing heritability" problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data. [ABSTRACT FROM AUTHOR]

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios. [ABSTRACT FROM AUTHOR]

Background: A Mendelian transmission produces phenotypic and genetic relatedness between family members, giving family-based analytical methods an important role in genetic epidemiological studies—from heritability estimations to genetic association analyses. With the advance in genotyping technologies, whole-genome sequence data can be utilized for genetic epidemiological studies, and family-based samples may become more useful for detecting de novo mutations. However, genetic analyses employing family-based samples usually suffer from the complexity of the computational/statistical algorithms, and certain types of family designs, such as incorporating data from extended families, have rarely been used. Results: We present a Workbench for Integrated Superfast Association studies for Related Data (WISARD) programmed in C/C++. WISARD enables the fast and a comprehensive analysis of SNP-chip and next-generation sequencing data on extended families, with applications from designing genetic studies to summarizing analysis results. In addition, WISARD can automatically be run in a fully multithreaded manner, and the integration of R software for visualization makes it more accessible to non-experts. Conclusions: Comparison with existing toolsets showed that WISARD is computationally suitable for integrated analysis of related subjects, and demonstrated that WISARD outperforms existing toolsets. WISARD has also been successfully utilized to analyze the large-scale massive sequencing dataset of chronic obstructive pulmonary disease data (COPD), and we identified multiple genes associated with COPD, which demonstrates its practical value. [ABSTRACT FROM AUTHOR]