Urinary microbiome-based metagenomic signature for the noninvasive diagnosis of hepatocellular carcinoma.

BACKGROUND: Gut microbial dysbiosis is implicated in chronic liver disease and hepatocellular carcinoma (HCC), but the role of microbiomes from various body sites remains unexplored. We assessed disease-specific alterations in the urinary microbiome in HCC patients, investigating their potential as diagnostic biomarkers. METHODS: We performed cross-sectional analyses of urine samples from 471 HCC patients and 397 healthy controls and validated the results in an independent cohort of 164 HCC patients and 164 healthy controls. Urinary microbiomes were analyzed by 16S rRNA gene sequencing. A microbial marker-based model distinguishing HCC from controls was built based on logistic regression, and its performance was tested. RESULTS: Microbial diversity was significantly reduced in the HCC patients compared with the controls. There were significant differences in the abundances of various bacteria correlated with HCC, thus defining a urinary microbiome-derived signature of HCC. We developed nine HCC-associated genera-based models with robust diagnostic accuracy (area under the curve [AUC], 0.89; balanced accuracy, 81.2%). In the validation, this model detected HCC with an AUC of 0.94 and an accuracy of 88.4%. CONCLUSIONS: The urinary microbiome might be a potential biomarker for the detection of HCC. Further clinical testing and validation of these results are needed in prospective studies.

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models.

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as genes and proteins. However, most pathway analysis methods neglect the complex nonlinear relationship between biological factors and pathways. In this study, we propose a Deep-learning pathway analysis using Hierarchical structured CoMponent models (DeepHisCoM) that utilize deep learning to consider a nonlinear complex contribution of biological factors to pathways by constructing a multilayered model which accounts for hierarchical biological structure. Through simulation studies, DeepHisCoM was shown to have a higher power in the nonlinear pathway effect and comparable power for the linear pathway effect when compared to the conventional pathway methods. Application to hepatocellular carcinoma (HCC) omics datasets, including metabolomic, transcriptomic and metagenomic datasets, demonstrated that DeepHisCoM successfully identified three well-known pathways that are highly associated with HCC, such as lysine degradation, valine, leucine and isoleucine biosynthesis and phenylalanine, tyrosine and tryptophan. Application to the coronavirus disease-2019 (COVID-19) single-nucleotide polymorphism (SNP) dataset also showed that DeepHisCoM identified four pathways that are highly associated with the severity of COVID-19, such as mitogen-activated protein kinase (MAPK) signaling pathway, gonadotropin-releasing hormone (GnRH) signaling pathway, hypertrophic cardiomyopathy and dilated cardiomyopathy. Codes are available at https://github.com/chanwoo-park-official/DeepHisCoM.

Oxidative stress in patients with coronavirus disease and end-stage renal disease: a pilot study.

BACKGROUND: Oxidative stress, an imbalance between reactive oxygen species production and antioxidant capacity, increases in patients with coronavirus disease (COVID-19) or renal impairment. We investigated whether combined COVID-19 and end-stage renal disease (ESRD) would increase oxidative stress levels compared to each disease alone. METHODS: Oxidative stress was compared among three groups. Two groups comprised patients with COVID-19 referred to the hospital with or without renal impairment (COVID-ESRD group [n = 18]; COVID group [n = 17]). The third group (ESRD group [n = 18]) comprised patients without COVID-19 on maintenance hemodialysis at a hospital. RESULTS: The total oxidative stress in the COVID-ESRD group was lower than in the COVID group (p = 0.047). The total antioxidant status was higher in the COVID-ESRD group than in the ESRD (p < 0.001) and COVID (p < 0.001) groups after controlling for covariates. The oxidative stress index was lower in the COVID-ESRD group than in the ESRD (p = 0.001) and COVID (p < 0.001) groups. However, the three oxidative parameters did not differ significantly between the COVID and COVID-ESRD groups. CONCLUSIONS: The role of reactive oxygen species in the pathophysiology of COVID-19 among patients withESRD appears to be non-critical. Therefore, the provision of supplemental antioxidants may not confer a therapeutic advantage, particularly in cases of mild COVID-19 in ESRD patients receiving hemodialysis. Nonetheless, this area merits further research.

Gene-environment interaction in type 2 diabetes in Korean cohorts: Interaction of a type 2 diabetes polygenic risk score with triglyceride and cholesterol on fasting glucose levels.

Type 2 diabetes (T2D) is caused by genetic and environmental factors as well as gene-environment interactions. However, these interactions have not been systematically investigated. We analyzed these interactions for T2D and fasting glucose levels in three Korean cohorts, HEXA, KARE, and CAVAS, using the baseline data with a multiple regression model. Two polygenic risk scores for T2D (PRST2D ) and fasting glucose (PRSFG ) were calculated using 488 and 82 single nucleotide polymorphisms (SNP) for T2D and fasting glucose, respectively, which were extracted from large-scaled genome-wide association studies with multiethnic data. Both lifestyle risk factors and T2D-related biochemical measurements were assessed. The effect of interactions between PRST2D -triglyceride (TG) and PRST2D -total cholesterol (TC) on fasting glucose levels was observed as follows: ß ± SE = 0.0005 ± 0.0001, p = 1.06 × 10-19 in HEXA, ß ± SE = 0.0008 ± 0.0001, p = 2.08 × 10-8 in KARE for TG; ß ± SE = 0.0006 ± 0.0001, p = 2.00 × 10-6 in HEXA, ß ± SE = 0.0020 ± 0.0004, p = 2.11 × 10-6 in KARE, ß ± SE = 0.0007 ± 0.0004, p = 0.045 in CAVAS for TC. PRST2D -based classification of the participants into four groups showed that the fasting glucose levels in groups with higher PRST2D were more adversely affected by both the TG and TC. In conclusion, blood TG and TC levels may affect the fasting glucose level through interaction with T2D genetic factors, suggesting the importance of consideration of gene-environment interaction in the preventive medicine of T2D.

High-Performance Self-Powered Quantum Dot Infrared Photodetector with Azide Ion Solution Treated Electron Transport Layer.

The demand for self-powered photodetectors (PDs) capable of NIR detection without external power is growing with the advancement of NIR technologies such as LIDAR and object recognition. Lead sulfide quantum dot-based photodetectors (PbS QPDs) excel in NIR detection; however, their self-powered operation is hindered by carrier traps induced by surface defects and unfavorable band alignment in the zinc oxide nanoparticle (ZnO NP) electron-transport layer (ETL). In this study, an effective azide-ion (N3 - ) treatment is introduced on a ZnO NP ETL to reduce the number of traps and improve the band alignment in a PbS QPD. The ZnO NP ETL treated with azide ions exhibited notable improvements in carrier lifetime and mobility as well as an enhanced internal electric field within the thin-film heterojunction of the ZnO NPs and PbS QDs. The azide-ion-treated PbS QPD demonstrated a increase in short-circuit current density upon NIR illumination, marking a responsivity of 0.45 A W-1 , specific detectivity of 4 × 1011 Jones at 950 nm, response time of 8.2 µs, and linear dynamic range of 112 dB.

Kernel-based hierarchical structural component models for pathway analysis.

MOTIVATION: Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex. RESULTS: To model complex effects including non-linear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models non-linear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies. AVAILABILITY AND IMPLEMENTATION: The HisCoM-Kernel software is freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/. The RNA-seq data underlying this article are available at https://xena.ucsc.edu/, and the others will be shared on reasonable request to the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method.

MOTIVATION: Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. RESULTS: Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. AVAILABILITY AND IMPLEMENTATION: The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Longitudinal study investigating serum metabolites and their association with type 2 diabetes risk in a Korean population.

AIM: The lack of longitudinal metabolomics data and the statistical techniques to analyse them has limited the understanding of the metabolite levels related to type 2 diabetes (T2D) onset. Thus, we carried out logistic regression analysis and simultaneously proposed new approaches based on residuals of multiple logistic regression and geometric angle-based clustering for the analysis in T2D onset-specific metabolic changes. MATERIALS AND METHODS: We used the sixth, seventh and eighth follow-up data from 2013, 2015 and 2017 among the Korea Association REsource (KARE) cohort data. Semi-targeted metabolite analysis was performed using ultraperformance liquid chromatography/triple quadrupole-mass spectrometry systems. RESULTS: As the results from the multiple logistic regression and a single metabolite in a logistic regression analysis varied dramatically, we recommend using models that consider potential multicollinearity among metabolites. The residual-based approach particularly identified neurotransmitters or related precursors as T2D onset-specific metabolites. By using geometric angle-based pattern clustering studies, ketone bodies and carnitines are observed as disease-onset specific metabolites and separated from others. CONCLUSION: To treat patients with early-stage insulin resistance and dyslipidaemia when metabolic disorders are still reversible, our findings may contribute to a greater understanding of how metabolomics could be used in disease intervention strategies during the early stages of T2D.

Clinical features of COVID-19 among patients with end-stage renal disease on hemodialysis in the context of high vaccination coverage during the omicron surge period: a retrospective cohort study.

BACKGROUND: We determined the clinical presentation and outcomes of the Omicron variant of severe acute respiratory syndrome coronavirus 2 infection in hemodialysis patients and identified the risk factors for severe coronavirus disease (COVID-19) and mortality in the context of high vaccination coverage. METHODS: This was a retrospective cohort study involving hemodialysis patients who were vaccinated against COVID-19 during March-September 2022, when the Omicron variant was predominant, and the COVID-19 vaccination rate was high. The proportion of people with severe COVID-19 or mortality was evaluated using univariate logistic regression. RESULTS: Eighty-three (78.3%) patients had asymptomatic/mild symptoms, 10 (9.4%) had moderate symptoms, and 13 (12.3%) had severe symptoms. Six (5.7%) patients required intensive care admission, two (1.9%) required mechanical ventilation, and one (0.9%) was kept on high-flow nasal cannula. Of the five (4.7%) mortality cases, one was directly attributed to COVID-19 and four to pre-existing comorbidities. Risk factors for both severe COVID-19 and mortality were advanced age; number of comorbidities; cardiovascular diseases; increased levels of aspartate transaminase, lactate dehydrogenase, blood urea nitrogen/creatinine ratio, brain natriuretic peptide, and red cell distribution; and decreased levels of hematocrit and albumin. Moreover, the number of COVID-19 vaccinations wasa protective factor against both severe disease and mortality. CONCLUSIONS: Clinical features of hemodialysis patients during the Omicron surge with high COVID-19 vaccination coverage were significant for low mortality. The risk features for severe COVID-19 or mortality were similar to those in the pre-Omicron period in the context of low vaccination coverage.

Statistical modeling of health space based on metabolic stress and oxidative stress scores.

BACKGROUND: Health space (HS) is a statistical way of visualizing individual's health status in multi-dimensional space. In this study, we propose a novel HS in two-dimensional space based on scores of metabolic stress and of oxidative stress. METHODS: These scores were derived from three statistical models: logistic regression model, logistic mixed effect model, and proportional odds model. HSs were developed using Korea National Health And Nutrition Examination Survey data with 32,140 samples. To evaluate and compare the performance of the HSs, we also developed the Health Space Index (HSI) which is a quantitative performance measure based on the approximate 95% confidence ellipses of HS. RESULTS: Through simulation studies, we confirmed that HS from the proportional odds model showed highest power in discriminating health status of individual (subject). Further validation studies were conducted using two independent cohort datasets: a health examination dataset from Ewha-Boramae cohort with 862 samples and a population-based cohort from the Korea association resource project with 3,199 samples. CONCLUSIONS: These validation studies using two independent datasets successfully demonstrated the usefulness of the proposed HS.

Spatial rank-based multifactor dimensionality reduction to detect gene-gene interactions for multivariate phenotypes.

BACKGROUND: Identifying interaction effects between genes is one of the main tasks of genome-wide association studies aiming to shed light on the biological mechanisms underlying complex diseases. Multifactor dimensionality reduction (MDR) is a popular approach for detecting gene-gene interactions that has been extended in various forms to handle binary and continuous phenotypes. However, only few multivariate MDR methods are available for multiple related phenotypes. Current approaches use Hotelling's T2 statistic to evaluate interaction models, but it is well known that Hotelling's T2 statistic is highly sensitive to heavily skewed distributions and outliers. RESULTS: We propose a robust approach based on nonparametric statistics such as spatial signs and ranks. The new multivariate rank-based MDR (MR-MDR) is mainly suitable for analyzing multiple continuous phenotypes and is less sensitive to skewed distributions and outliers. MR-MDR utilizes fuzzy k-means clustering and classifies multi-locus genotypes into two groups. Then, MR-MDR calculates a spatial rank-sum statistic as an evaluation measure and selects the best interaction model with the largest statistic. Our novel idea lies in adopting nonparametric statistics as an evaluation measure for robust inference. We adopt tenfold cross-validation to avoid overfitting. Intensive simulation studies were conducted to compare the performance of MR-MDR with current methods. Application of MR-MDR to a real dataset from a Korean genome-wide association study demonstrated that it successfully identified genetic interactions associated with four phenotypes related to kidney function. The R code for conducting MR-MDR is available at https://github.com/statpark/MR-MDR . CONCLUSIONS: Intensive simulation studies comparing MR-MDR with several current methods showed that the performance of MR-MDR was outstanding for skewed distributions. Additionally, for symmetric distributions, MR-MDR showed comparable power. Therefore, we conclude that MR-MDR is a useful multivariate non-parametric approach that can be used regardless of the phenotype distribution, the correlations between phenotypes, and sample size.

Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species.

DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.

Association of an IGHV3-66 gene variant with Kawasaki disease.

In a meta-analysis of three GWAS for susceptibility to Kawasaki disease (KD) conducted in Japan, Korea, and Taiwan and follow-up studies with a total of 11,265 subjects (3428 cases and 7837 controls), a significantly associated SNV in the immunoglobulin heavy variable gene (IGHV) cluster in 14q33.32 was identified (rs4774175; OR = 1.20, P = 6.0 × 10-9). Investigation of nonsynonymous SNVs of the IGHV cluster in 9335 Japanese subjects identified the C allele of rs6423677, located in IGHV3-66, as the most significant reproducible association (OR = 1.25, P = 6.8 × 10-10 in 3603 cases and 5731 controls). We observed highly skewed allelic usage of IGHV3-66, wherein the rs6423677 A allele was nearly abolished in the transcripts in peripheral blood mononuclear cells of both KD patients and healthy adults. Association of the high-expression allele with KD strongly indicates some active roles of B-cells or endogenous immunoglobulins in the disease pathogenesis. Considering that significant association of SNVs in the IGHV region with disease susceptibility was previously known only for rheumatic heart disease (RHD), a complication of acute rheumatic fever (ARF), these observations suggest that common B-cell related mechanisms may mediate the symptomology of KD and ARF as well as RHD.

Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study.

BACKGROUND: Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. OBJECTIVE: This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. METHODS: This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. RESULTS: Out of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. CONCLUSIONS: Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

Clinical Characteristics and Outcomes of COVID-19 Cohort Patients in Daegu Metropolitan City Outbreak in 2020.

BACKGROUND: A coronavirus disease 2019 (COVID-19) outbreak started in February 2020 and was controlled at the end of March 2020 in Daegu, the epicenter of the coronavirus outbreak in Korea. The aim of this study was to describe the clinical course and outcomes of patients with COVID-19 in Daegu. METHODS: In collaboration with Daegu Metropolitan City and Korean Center for Diseases Control, we conducted a retrospective, multicenter cohort study. Demographic, clinical, treatment, and laboratory data, including viral RNA detection, were obtained from the electronic medical records and cohort database and compared between survivors and non-survivors. We used univariate and multi-variable logistic regression methods and Cox regression model and performed Kaplan-Meier analysis to determine the risk factors associated with the 28-day mortality and release from isolation among the patients. RESULTS: In this study, 7,057 laboratory-confirmed patients with COVID-19 (total cohort) who had been diagnosed from February 18 to July 10, 2020 were included. Of the total cohort, 5,467 were asymptomatic to mild patients (77.4%) (asymptomatic 30.6% and mild 46.8%), 985 moderate (14.0%), 380 severe (5.4%), and 225 critical (3.2%). The mortality of the patients was 2.5% (179/7,057). The Cox regression hazard model for the patients with available clinical information (core cohort) (n = 2,254) showed the risk factors for 28-day mortality: age > 70 (hazard ratio [HR], 4.219, P = 0.002), need for O2 supply at admission (HR, 2.995; P = 0.001), fever (> 37.5°C) (HR, 2.808; P = 0.001), diabetes (HR, 2.119; P = 0.008), cancer (HR, 3.043; P = 0.011), dementia (HR, 5.252; P = 0.008), neurological disease (HR, 2.084; P = 0.039), heart failure (HR, 3.234; P = 0.012), and hypertension (HR, 2.160; P = 0.017). The median duration for release from isolation was 33 days (interquartile range, 24.0-46.0) in survivors. The Cox proportional hazard model for the long duration of isolation included severity, age > 70, and dementia. CONCLUSION: Overall, asymptomatic to mild patients were approximately 77% of the total cohort (asymptomatic, 30.6%). The case fatality rate was 2.5%. Risk factors, including older age, need for O2 supply, dementia, and neurological disorder at admission, could help clinicians to identify COVID-19 patients with poor prognosis at an early stage.

Do we need to reconsider the CMAM admission and discharge criteria?; an analysis of CMAM data in South Sudan.

BACKGROUND: Weight-for-height Z-score (WHZ) and Mid Upper Arm Circumference (MUAC) are both commonly used as acute malnutrition screening criteria. However, there exists disparity between the groups identified as malnourished by them. Thus, here we aim to investigate the clinical features and linkage with chronicity of the acute malnutrition cases identified by either WHZ or MUAC. Besides, there exists evidence indicating that fat restoration is disproportionately rapid compared to that of muscle gain in hospitalized malnourished children but related research at community level is lacking. In this study we suggest proxy measure to inspect body composition restoration responding to malnutrition management among the malnourished children. METHODS: The data of this study is from World Vision South Sudan's emergency nutrition program from 2006 to 2012 (4443 children) and the nutrition survey conducted in 2014 (3367 children). The study investigated clinical presentations of each type of severe acute malnutrition (SAM) by WHZ (SAM-WHZ) or MUAC (SAM-MUAC), and analysed correlation between each malnutrition and chronic malnutrition. Furthermore, we explored the pattern of body composition restoration during the recovery phase by comparing the relative velocity of MUAC3 with that of weight gain. RESULTS: As acutely malnourished children identified by MUAC more often share clinical features related to chronic malnutrition and minimal overlapping with malnourished children by WHZ, Therefore, MUAC only screening in the nutrition program would result in delayed identification of the malnourished children. CONCLUSIONS: The relative velocity of MUAC3 gain was suggested as a proxy measure for volume increase, and it was more prominent than that of weight gain among the children with SAM by WHZ and MUAC over all the restoring period. Based on this we made a conjecture about dominant fat mass gain over the period of CMAM program. Also, considering initial weight gain could be ascribed to fat mass increase, the current discharge criteria would leave the malnourished children at risk of mortality even after treatment due to limited restoration of muscle mass. Given this, further research should be followed including assessment of body composition for evidence to recapitulate and reconsider the current admission and discharge criteria for CMAM program.

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.

Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components.

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.

BALLI: Bartlett-adjusted likelihood-based linear model approach for identifying differentially expressed genes with RNA-seq data.

BACKGROUND: Transcriptomic profiles can improve our understanding of the phenotypic molecular basis of biological research, and many statistical methods have been proposed to identify differentially expressed genes (DEGs) under two or more conditions with RNA-seq data. However, statistical analyses with RNA-seq data are often limited by small sample sizes, and global variance estimates of RNA expression levels have been utilized as prior distributions for gene-specific variance estimates, making it difficult to generalize the methods to more complicated settings. We herein proposed a Bartlett-Adjusted Likelihood-based LInear mixed model approach (BALLI) to analyze more complicated RNA-seq data. The proposed method estimates the technical and biological variances with a linear mixed-effects model, with and without adjusting small sample bias using Bartlkett's corrections. RESULTS: We conducted extensive simulations to compare the performance of BALLI with those of existing approaches (edgeR, DESeq2, and voom). Results from the simulation studies showed that BALLI correctly controlled the type-1 error rates at various nominal significance levels and produced better statistical power and precision estimates than those of other competing methods in various scenarios. Furthermore, BALLI was robust to variation of library size. It was also successfully applied to Holstein milk yield data, illustrating its practical value. CONCLUSIONS;: BALLI is statistically more efficient and valid than existing methods, and we conclude that it is useful for identifying DEGs in RNA-seq analysis.

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.