Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES).

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.

Is physical restraint unethical and illegal?: a qualitative analysis of Korean written judgments.

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.

What are the key factors influencing newly graduated nurses' preference for choosing their workplace? A best-worst scaling approach.

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.

Gene-Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors.

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.

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene-Environment Interactions.

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.

Longitudinal analysis to better characterize Asthma-COPD overlap syndrome: Findings from an adult asthma cohort in Korea (COREA).

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.

Hierarchical structural component modeling of microRNA-mRNA integration analysis.

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.

Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes.

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.

Drug response prediction model using a hierarchical structural component modeling method.

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.

Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms.

To gain insight into potential regulatory mechanisms through which the effects of variants at four established type 2 diabetes (T2D) susceptibility loci (CDKAL1, CDKN2A-B, IGF2BP2 and KCNQ1) are mediated, we undertook transancestral fine-mapping in 22 086 cases and 42 539 controls of East Asian, European, South Asian, African American and Mexican American descent. Through high-density imputation and conditional analyses, we identified seven distinct association signals at these four loci, each with allelic effects on T2D susceptibility that were homogenous across ancestry groups. By leveraging differences in the structure of linkage disequilibrium between diverse populations, and increased sample size, we localised the variants most likely to drive each distinct association signal. We demonstrated that integration of these genetic fine-mapping data with genomic annotation can highlight potential causal regulatory elements in T2D-relevant tissues. These analyses provide insight into the mechanisms through which T2D association signals are mediated, and suggest future routes to understanding the biology of specific disease susceptibility loci.

FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes.

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.

Pathway-based approach using hierarchical components of collapsed rare variants.

MOTIVATION: To address 'missing heritability' issue, many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually. However, neglecting correlations between multiple pathways can result in misleading solutions, and pathway-based analyses of large-scale genetic datasets require massive computational burden. We propose a Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates; hence our method considers the correlation of pathways without constraint by a multiple testing problem. RESULTS: Through simulation studies, the proposed method was shown to have higher statistical power than the existing pathway-based methods. In addition, our method was applied to the large-scale whole-exome sequencing data with levels of a liver enzyme using two well-known pathway databases Biocarta and KEGG. This application demonstrated that our method not only identified associated pathways but also successfully detected biologically plausible pathways for a phenotype of interest. These findings were successfully replicated by an independent large-scale exome chip study. AVAILABILITY AND IMPLEMENTATION: An implementation of PHARAOH is available at http://statgen.snu.ac.kr/software/pharaoh/ CONTACT: tspark@stats.snu.ac.kr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Derivation and validation of a risk scoring model to predict advanced colorectal neoplasm in adults of all ages.

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.

FARVAT: a family-based rare variant association test.

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.kr SUPPLEMENTARY INFORMATION: supplementary data are available at Bioinformatics online.

Identification of differentially expressed mRNA/lncRNA modules in acutely regorafenib-treated sorafenib-resistant Huh7 hepatocellular carcinoma cells.

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.

How has COVID-19 affected the work environment of delivery workers?: An interpretative phenomenological analysis.

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.

Educational strategies to encourage participation in health policy for nurses: A systematic review.

OBJECTIVES: To identify evidence on the types of educational interventions that have been developed to encourage participation in health policy for nurses and on the effectiveness of these interventions. DESIGN: A systematic literature review. DATA SOURCES: Six electronic databases (PubMed, EMBASE, Cochrane, CINAHL, Web of Science, and SCOPUS) were searched. REVIEW METHODS: All qualitative and quantitative studies on educational interventions developed to encourage participation in health policy for nurses were included in this study. Two researchers independently reviewed each article for inclusion and assessed the eligibility of the studies. The effects of educational interventions were analyzed through a thematic synthesis for the qualitative studies and data tabulation for the quantitative studies. A comprehensive framework of educational strategies to encourage participation in health policy was developed based on three stages of citizen participation in policy-making. RESULTS: Seven articles were selected for the review: four quantitative studies and three qualitative studies. In all studies that confirmed the educational effect through quantitative design, statistically significant improvement in students' competency on policy participation was observed. In the qualitative study, it was possible to mainly confirm the students' positive feelings toward policy participation education, and three themes were derived: expanding the perspective of policy, understanding the actual feature of policymakers, and improving the possibility of policy participation. The education strategies were synthesized into six types, which were divided into three stages according to the level of citizen participation in policy-making: Listener, Observer, Twitterian, Analyst and Speaker, Interviewer, and Contributor. CONCLUSION: Education on health policy increases the competency and interest of nurses concerning policy participation. We propose systematic policy education according to the policy participation stage. To realize this, nursing educators should secure faculty with relevant expertise and develop a verified curriculum.

Determinants of Legislation on Social Health Insurance in Transition Countries.

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.

Effect of online education on the knowledge on, attitudes towards, and skills in patient safety for nursing students in Korea: a mixed-methods study.

PURPOSE: The purpose of this study was to evaluate the impact of synchronous online education on the patient safety competency (knowledge, attitudes, and skills) of nursing students in Korea and to explore what they thought about this educational method. METHODS: A single-group pre-and post-test design and summative content analysis were implemented. On November 14th, 2020, 110 nursing students completed synchronous online patient safety education. Patient safety competency was measured before and after the intervention using the revised Patient Safety Competency Self-Evaluation tool. The descriptive statistics, paired t-test, and Wilcoxon signed-rank test were used to analyze the data. Students also expressed their opinions about this education based on open-ended questions. RESULTS: All the patient safety competency scores significantly increased after intervention. A summative content analysis of the open-ended questions yielded 5 categories: educational materials, education level, education time, interaction, and educational media. CONCLUSION: This study found that synchronous online patient safety education improves nursing students' knowledge on, attitudes towards, and skills in patient safety. Nursing students also expressed a variety of positive aspects of the online education method. To improve the efficacy of synchronous online patient safety education, there is a need for further empirical studies on the appropriate class duration and difficulty of the content. It is essential to find a way to combine online education with various learning activities.