HYPERREFLECTIVE FOCI AS IMPORTANT PROGNOSTIC INDICATORS OF PROGRESSION OF RETINITIS PIGMENTOSA.

PURPOSE: To investigate the presence and clinical relevance of hyperreflective foci (HRFs) in retinitis pigmentosa. METHODS: Seventy seven retinitis pigmentosa cases were retrospectively reviewed. The 10-mm wide cross-line macular scans in optical coherence tomography were acquired. Hyperreflective foci were classified according to the location in optical coherence tomography: outer layers within the macula (HRF-outer-central), macular border beyond the central 3 mm (HRF-outer-perifoveal), and choroid (HRF-choroidal). The visual acuity at baseline, at 12 months, and other fundus characteristics were collected. RESULTS: The mean logMAR best-corrected visual acuity decreased from 0.59 ± 0.66 (20/78 in Snellen) to 0.74 ± 0.81 (20/106 in Snellen) in 1 year. Sixty-six (42.9%), 105 (68.2%), and 98 (63.6%) eyes were classified to HRF-outer-central, HRF-outer-perifoveal, and HRF-choroidal group, respectively. Hyperreflective foci were positively correlated with poorer vision, central macular thinning, and ellipsoid zone disruption (all P < 0.001). Worse vision was associated with older age, macular involvement, and the coexistence of two or three HRF groups (P = 0.014, 0.047, 0.019, <0.001, respectively). Hyperreflective foci developed more frequently in patients with thick choroid than in those with thin choroid. The coexistence of three HRF groups was correlated with quicker visual deterioration (P = 0.034). CONCLUSION: Hyperreflective foci are common in retinitis pigmentosa and can be a negative prognostic indicator of macular thickness and visual preservation. Thick choroid was associated with all groups of HRFs, especially HRF-choroidal.

Influence of parental behavior on myopigenic behaviors and risk of myopia: analysis of nationwide survey data in children aged 3 to 18 years.

BACKGROUND: Preventive parental behavior may play an important role in the outcomes of children's myopia. We investigated associations between parental behavior and children's myopia status and daily activities using data from the most recent myopia survey in Taiwan. METHODS: In total, 3845 children aged 3 to 18 years who completely responded to the questionnaire were included (total score ranging from 0 to 75). A score of ≥ 50 was considered to indicate beneficial parental behavior. Time allocation data for near-work activities, using electronic devices, and outdoor activities were collected using a separate self-reported questionnaire. Associations between beneficial parental behavior and children's myopia status and activity patterns were analyzed and stratified by school level. RESULTS: Beneficial parental behavior was positively associated with children's myopia in the overall samples [adj. odds ratio (OR): 1.31, 95% confidence interval (CI): 1.08-1.59, p = 0.006)] and at the elementary school level (adj. OR: 1.43, 95% CI: 1.11-1.83, p = 0.005). However, a negative association with high myopia was observed in the overall samples (adj. OR: 0.71, 95% CI: 0.50-0.99, p = 0.049) and high school level (adj. OR: 0.62, 95% CI: 0.41-0.92, p = 0.02). Beneficial parental behavior was associated with less time spent on near work (≥ 180 min/day) and electronic device use (≥ 60 min/day), but not with outdoor activities. CONCLUSION: In Taiwan, children's myopia is associated with higher rate of parents' beneficial behaviors, which suggests that regular vision surveillance is necessary to promote better parental behavior toward children's eye care. Certain parental practices may influence children's behavior pattern and reduce the risk of children's high myopia development in the long run.

Evolution of the Prevalence of Myopia among Taiwanese Schoolchildren: A Review of Survey Data from 1983 through 2017.

PURPOSE: The aim of this study was to evaluate the changes in the prevalence of myopia in Taiwanese schoolchildren over the past few decades and to analyze the risk factors for myopia. DESIGN: Analysis of 8 consecutive population-based myopia surveys conducted from 1983 through 2017. PARTICIPANTS: An average of 8917 (5019-11 656) schoolchildren 3 to 18 years of age were selected using stratified systematic cluster sampling or by probability proportional to size sampling. METHODS: All participants underwent complete ophthalmic evaluations. Three drops of 0.5% tropicamide were used to obtain the cycloplegic refractive status of each participant. Questionnaires were used to acquire participant data from the 1995, 2005, 2010, and 2016 surveys. MAIN OUTCOME MEASURES: Prevalence of myopia (spherical equivalence of ≤-0.25 diopter [D]) and high myopia (≤-6.0 D) was assessed. Multivariate analyses of risk factors were conducted. RESULTS: The prevalence of myopia among all age groups increased steadily. From 1983 through 2017, the weighted prevalence increased from 5.37% (95% confidence interval [CI], 3.50%-7.23%) to 25.41% (95% CI, 21.27%-29.55%) for 7-year-olds (P = 0.001 for trend) and from 30.66% (95% CI, 26.89%-34.43%) to 76.67% (95% CI, 72.94%-80.40%) for 12-year-olds (P = 0.001 for trend). The prevalence of high myopia also increased from 1.39% (95% CI, 0.43%-2.35%) to 4.26% (95% CI, 3.35%-5.17%) for 12-year-olds (P = 0.008 for trend) and from 4.37% (95% CI, 2.91%-5.82%) to 15.36% (95% CI, 13.78%-16.94%) for 15-year-olds (P = 0.039 for trend). In both the 2005 and 2016 survey samples, children who spent less than 180 minutes daily on near-work activities showed significantly lower risks for myopia developing (<60 minutes: odds ratio [OR], 0.48 and 0.56; 60-180 minutes: OR, 0.69 and 0.67). In the 2016 survey, spending more than 60 minutes daily on electronic devices was associated significantly with both myopia and high myopia (OR, 2.43 and 2.31). CONCLUSIONS: The prevalence of myopia among schoolchildren increased rapidly from 1983 through 2017 in Taiwan. The major risk factors are older age and time spent on near-work activities. Use of electronic devices increased the amount of time spent on near-work and may increase the risk of developing myopia.

Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis.

Copy number variants (CNVs) are the gain or loss of DNA segments in the genome that can vary in dosage and length. CNVs comprise a large proportion of variation in human genomes and impact health conditions. To detect rare CNV associations, kernel-based methods have been shown to be a powerful tool due to their flexibility in modeling the aggregate CNV effects, their ability to capture effects from different CNV features, and their accommodation of effect heterogeneity. To perform a kernel association test, a CNV locus needs to be defined so that locus-specific effects can be retained during aggregation. However, CNV loci are arbitrarily defined and different locus definitions can lead to different performance depending on the underlying effect patterns. In this work, we develop a new kernel-based test called CONCUR (i.e., copy number profile curve-based association test) that is free from a definition of locus and evaluates CNV-phenotype associations by comparing individuals' copy number profiles across the genomic regions. CONCUR is built on the proposed concepts of "copy number profile curves" to describe the CNV profile of an individual, and the "common area under the curve (cAUC) kernel" to model the multi-feature CNV effects. The proposed method captures the effects of CNV dosage and length, accounts for the numerical nature of copy numbers, and accommodates between- and within-locus etiological heterogeneity without the need to define artificial CNV loci as required in current kernel methods. In a variety of simulation settings, CONCUR shows comparable or improved power over existing approaches. Real data analyses suggest that CONCUR is well powered to detect CNV effects in the Swedish Schizophrenia Study and the Taiwan Biobank.

Network hub-node prioritization of gene regulation with intra-network association.

BACKGROUND: To identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based on the topology of the pathway network or the correlation between them. RESULTS: Here we develop a pathway activity score incorporating the marginal (local) effects of gene nodes as well as intra-network affinity measures. This score summarizes the expression levels in a gene-set/pathway for each sample, with weights on local and network information, respectively. The score is next used to examine the impact of each node through a leave-one-out evaluation. To illustrate the procedure, two cancer studies, one involving RNA-Seq from breast cancer patients with high-grade ductal carcinoma in situ and one microarray expression data from ovarian cancer patients, are used to assess the performance of the procedure, and to compare with existing methods, both ones that do and do not take into consideration correlation and network information. The hub nodes identified by the proposed procedure in the two cancer studies are known influential genes; some have been included in standard treatments and some are currently considered in clinical trials for target therapy. The results from simulation studies show that when marginal effects are mild or weak, the proposed procedure can still identify causal nodes, whereas methods relying only on marginal effect size cannot. CONCLUSIONS: The NetworkHub procedure proposed in this research can effectively utilize the network information in combination with local effects derived from marker values, and provide a useful and complementary list of recommendations for prioritizing causal hubs.

Probabilistic prioritization of candidate pathway association with pathway score.

BACKGROUND: Current methods for gene-set or pathway analysis are usually designed to test the enrichment of a single gene-set. Once the analysis is carried out for each of the sets under study, a list of significant sets can be obtained. However, if one wishes to further prioritize the importance or strength of association of these sets, no such quantitative measure is available. Using the magnitude of p-value to rank the pathways may not be appropriate because p-value is not a measure for strength of significance. In addition, when testing each pathway, these analyses are often implicitly affected by the number of differentially expressed genes included in the set and/or affected by the dependence among genes. RESULTS: Here we propose a two-stage procedure to prioritize the pathways/gene-sets. In the first stage we develop a pathway-level measure with three properties. First, it contains all genes (differentially expressed or not) in the same set, and summarizes the collective effect of all genes per sample. Second, this pathway score accounts for the correlation between genes by synchronizing their correlation directions. Third, the score includes a rank transformation to enhance the variation among samples as well as to avoid the influence of extreme heterogeneity among genes. In the second stage, all scores are included simultaneously in a Bayesian logistic regression model which can evaluate the strength of association for each set and rank the sets based on posterior probabilities. Simulations from Gaussian distributions and human microarray data, and a breast cancer study with RNA-Seq are considered for demonstration and comparison with other existing methods. CONCLUSIONS: The proposed summary pathway score provides for each sample an overall evaluation of gene expression in a gene-set. It demonstrates the advantages of including all genes in the set and the synchronization of correlation direction. The simultaneous utilization of all pathway-level scores in a Bayesian model not only offers a probabilistic evaluation and ranking of the pathway association but also presents good accuracy in identifying the top-ranking pathways. The resulting recommendation list of ranked pathways can be a reference for potential target therapy or for future allocation of research resources.

Distinct seromarkers predict different milestones of chronic hepatitis B progression.

UNLABELLED: Spontaneous seroclearance of hepatitis B e antigen (HBeAg) and hepatitis B virus (HBV) DNA undetectability are important milestones of chronic hepatitis B and major treatment endpoints of antiviral therapy. This study investigated the role of serum hepatitis B surface antigen (HBsAg) levels and established models for predicting HBeAg seroclearance and HBV DNA undetectability. A total of 2,139 HBsAg-seropositive, anti-HCV-seronegative, and treatment-naïve participants without liver cirrhosis at study entry were included. Spontaneous HBeAg seroclearance and HBV DNA undetectability were analyzed in 431 HBeAg-seropositive participants and 1,708 HBeAg-seronegative participants, respectively. Regression coefficients of predictors in Cox proportional hazard models were converted into integer scores for predicting seroclearance and predictive accuracy was assessed with time-dependent receiver operating characteristic (ROC) curves. The HBV DNA level was the most important predictor of HBeAg seroclearance but serum HBsAg level was the most significant predictor of HBV DNA undetectability. Compared to individuals with HBsAg levels ≥ 10,000 IU/mL, the multivariate-adjusted rate ratio (95% confidence interval) of HBV DNA undetectability was 1.20 (0.62-2.30), 2.49 (1.31-4.75), and 6.08 (3.19-11.61) for those with serum HBsAg levels of 1,000-9,999, 100-999, and <100 IU/mL, respectively. The area under the ROC curve (AUROC) of the prediction models for predicting the 5- and 10-year probabilities of HBeAg seroclearance and HBV DNA undetectability were 0.85 (0.80-0.90) and 0.78 (0.73-0.83) for HBeAg seroclearance, and 0.77 (0.72-0.82) and 0.73 (0.70-0.76) for HBV DNA undetectability. CONCLUSION: Prediction models incorporating important host and virus factors can predict HBeAg seroclearance and HBV DNA undetectability. Serum HBsAg levels rather than HBV DNA is the most important predictor of spontaneous HBV DNA undetectability. Serum HBsAg levels should be monitored in the management of patients with HBeAg-seronegative chronic hepatitis B.

A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.

Motivation: Differential network (D-Net) analysis has attracted great attention in systems biology for its ability to identify genetic variations in response to different conditions. Current approaches either estimate the condition-specific networks separately followed by post-procedures to determine the differential edges or estimate the D-Net directly. Both types of analysis overlook the probabilistic inference and can only provide deterministic inference of the edges. Results: Here, we propose a Bayesian solution and translate the probabilistic estimation in the regression model to an inferential D-Net analysis for genetic association and classification studies. The proposed PRobabilistic Interaction for Differential Edges (PRIDE) focuses on inferring the D-Net with uncertainty so that the existence of the differential edges can be evaluated with probability and even prioritized if comparison among these edges is of interest. The performance of the proposed model is compared with state-of-the-art methods in simulations and is demonstrated in glioblastoma and breast cancer studies. The proposed PRIDE performs comparably to or outperforms most existing tools under deterministic evaluation criteria. Additionally, it offers the unique advantages, including prioritizing the differential edges with probabilities, highlighting the relative importance of hub nodes, and identifying potential sub-networks in a D-Net. Availability and implementation: All the data analyzed in this research can be downloaded at https://xenabrowser.net/datapages/. The R code for implementing PRIDE is available at https://github.com/YJGene0806/PRIDE_Code.

Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions.

BACKGROUND: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. METHOD: We defined people aged 51-90 years who were free from ischemic heart disease (ICD9: 410-414) in 1996-2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). RESULTS: People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. CONCLUSIONS: Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.

Probabilistic edge inference of gene networks with markov random field-based bayesian learning.

Current algorithms for gene regulatory network construction based on Gaussian graphical models focuses on the deterministic decision of whether an edge exists. Both the probabilistic inference of edge existence and the relative strength of edges are often overlooked, either because the computational algorithms cannot account for this uncertainty or because it is not straightforward in implementation. In this study, we combine the Bayesian Markov random field and the conditional autoregressive (CAR) model to tackle simultaneously these two tasks. The uncertainty of edge existence and the relative strength of edges can be measured and quantified based on a Bayesian model such as the CAR model and the spike-and-slab lasso prior. In addition, the strength of the edges can be utilized to prioritize the importance of the edges in a network graph. Simulations and a glioblastoma cancer study were carried out to assess the proposed model's performance and to compare it with existing methods when a binary decision is of interest. The proposed approach shows stable performance and may provide novel structures with biological insights.

The misuse of distributional assumptions in functional class scoring gene-set and pathway analysis.

Gene-set analysis (GSA) is a standard procedure for exploring potential biological functions of a group of genes. The development of its methodology has been an active research topic in recent decades. Many GSA methods, when newly proposed, rely on simulation studies to evaluate their performance with an implicit assumption that the multivariate expression values are normally distributed. This assumption is commonly adopted in GSAs, particularly those in the group of functional class scoring (FCS) methods. The validity of the normality assumption, however, has been disputed in several studies, yet no systematic analysis has been carried out to assess the effect of this distributional assumption. Our goal in this study is not to propose a new GSA method but to first examine if the multi-dimensional gene expression data in gene sets follow a multivariate normal (MVN) distribution. Six statistical methods in three categories of MVN tests were considered and applied to a total of 24 RNA data sets. These RNA values were collected from cancer patients as well as normal subjects, and the values were derived from microarray experiments, RNA sequencing, and single-cell RNA sequencing. Our first finding suggests that the MVN assumption is not always satisfied. This assumption does not hold true in many applications tested here. In the second part of this research, we evaluated the influence of non-normality on the statistical power of current FCS methods, both parametric and nonparametric ones. Specifically, the scenario of mixture distributions representing more than one population for the RNA values was considered. This second investigation demonstrates that the non-normality distribution of the RNA values causes a loss in the statistical power of these GSA tests, especially when subtypes exist. Among the FCS GSA tools examined here and among the scenarios studied in this research, the N-statistics outperform the others. Based on the results from these two investigations, we conclude that the assumption of MVN should be used with caution when evaluating new GSA tools, since this assumption cannot be guaranteed and violation may lead to spurious results, loss of power, and incorrect comparison between methods. If a newly proposed GSA tool is to be evaluated, we recommend the incorporation of a wide range of multivariate non-normal distributions or sampling from large databases if available.

Mitochondrial DNA methylation profiling of the human prefrontal cortex and nucleus accumbens: correlations with aging and drug use.

BACKGROUND: Despite the brain's high demand for energy, research on its epigenetics focuses on nuclear methylation, and much of the mitochondrial DNA methylation remains seldom investigated. With a focus on the nucleus accumbens (NAcc) and the prefrontal cortex (PFC), we aimed to identify the mitochondrial methylation signatures for (1) distinguishing the two brain areas, (2) correlating with aging, and (3) reflecting the influence of illicit drugs on the brain. RESULT: We collected the brain tissue in the NAcc and the PFC from the deceased individuals without (n = 39) and with (n = 14) drug use and used whole-genome bisulfite sequencing to cover cytosine sites in the mitochondrial genome. We first detected differential methylations between the NAcc and the PFC in the nonusers group (P = 3.89 × 10-9). These function-related methylation differences diminished in the drug use group due to the selective alteration in the NAcc. Then, we found the correlation between the methylation levels and the chronological ages in the nonusers group (R2 = 0.34 in the NAcc and 0.37 in the PFC). The epigenetic clocks in illicit drug users, especially in the ketamine users, were accelerated in both brain regions by comparison with the nonusers. Finally, we summarized the effect of the illicit drugs on the methylation, which could significantly differentiate the drug users from the nonusers (AUC = 0.88 in the NAcc, AUC = 0.94 in the PFC). CONCLUSION: The mitochondrial methylations were different between different brain areas, generally accumulated with aging, and sensitive to the effects of illicit drugs. We believed this is the first report to elucidate comprehensively the importance of mitochondrial DNA methylation in human brain.

Combining an evolution-guided clustering algorithm and haplotype-based LRT in family association studies.

BACKGROUND: With the completion of the international HapMap project, many studies have been conducted to investigate the association between complex diseases and haplotype variants. Such haplotype-based association studies, however, often face two difficulties; one is the large number of haplotype configurations in the chromosome region under study, and the other is the ambiguity in haplotype phase when only genotype data are observed. The latter complexity may be handled based on an EM algorithm with family data incorporated, whereas the former can be more problematic, especially when haplotypes of rare frequencies are involved. Here based on family data we propose to cluster long haplotypes of linked SNPs in a biological sense, so that the number of haplotypes can be reduced and the power of statistical tests of association can be increased. RESULTS: In this paper we employ family genotype data and combine a clustering scheme with a likelihood ratio statistic to test the association between quantitative phenotypes and haplotype variants. Haplotypes are first grouped based on their evolutionary closeness to establish a set containing core haplotypes. Then, we construct for each family the transmission and non-transmission phase in terms of these core haplotypes, taking into account simultaneously the phase ambiguity as weights. The likelihood ratio test (LRT) is next conducted with these weighted and clustered haplotypes to test for association with disease. This combination of evolution-guided haplotype clustering and weighted assignment in LRT is able, via its core-coding system, to incorporate into analysis both haplotype phase ambiguity and transmission uncertainty. Simulation studies show that this proposed procedure is more informative and powerful than three family-based association tests, FAMHAP, FBAT, and an LRT with a group consisting exclusively of rare haplotypes. CONCLUSIONS: The proposed procedure takes into account the uncertainty in phase determination and in transmission, utilizes the evolutionary information contained in haplotypes, reduces the dimension in haplotype space and the degrees of freedom in tests, and performs better in association studies. This evolution-guided clustering procedure is particularly useful for long haplotypes containing linked SNPs, and is applicable to other haplotype-based association tests. This procedure is now implemented in R and is free for download.

An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data.

Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.

Metabolic Syndromes as Important Comorbidities in Patients of Inherited Retinal Degenerations: Experiences from the Nationwide Health Database and a Large Hospital-Based Cohort.

This study aimed to evaluate the medical and socioeconomic impacts of IRDs using the nationwide health database and a large hospital-based cohort. This retrospective cross-sectional cohort study used data from the nationwide National Health Insurance Research Database (NHIRD). All patients with IRD from January 2012 to December 2016 were selected from the NHIRD and matched with the general population at a ratio of 1:4. All variables, including comorbidities, medications, service utilization, and medical costs, within 1 year from the date of the IRD diagnosis, were analyzed. Disability data were retrieved from the Taiwan Inherited retinal degeneration Project (TIP), a medical center-based database. A total of 4447 and 17,788 subjects from the nationwide database were included in the IRD and control groups, respectively. The Charlson comorbidity index score was higher in the IRD group (0.74:0.52, p < 0.001). Yearly visits to the ophthalmology clinic were more frequent in the IRD group (6.80:1.06, p < 0.001), particularly to tertiary medical centers (p < 0.001). The IRD group showed greater odds ratios (OR) for metabolic syndrome-related comorbidities, including hypertension (OR = 1.18, 95% confidence interval (CI) 1.10 to 1.26) and diabetes (OR = 1.32, 95% CI 1.21 to 1.45), and double the average yearly medical cost (2104.3 vs. 1084.6 USD, p < 0.001) and ten times the yearly ophthalmology cost (369.1 vs. 36.1 USD, p < 0.001). The average disability level was 54.17% for all subjects. This study revealed the large medical and socioeconomic impacts of IRD on not only patients with IRD, but also their family members and the whole society.

Rare variants discovery by extensive whole-genome sequencing of the Han Chinese population in Taiwan: Applications to cardiovascular medicine.

Introduction: A population-specific genomic reference is important for research and clinical practice, yet it remains unavailable for Han Chinese (HC) in Taiwan. Objectives: We report the first whole genome sequencing (WGS) database of HC (1000 Taiwanese genome (1KTW-WGS)) and demonstrate several applications to cardiovascular medicine. Methods: Whole genomes of 997 HC were sequenced to at least 30X depth. A total of 20,117 relatively healthy HC individuals were genotyped using a customized Axiom GWAS array. We performed a genome-wide genotype imputation technique using IMPUTE2. Results: We identified 26.7 million single-nucleotide variants (SNVs) and 4.2 million insertions-deletions. Of the SNVs, 16.1% were novel relative to dbSNP (build 152), and 34.2% were novel relative to gnomAD. A total of 18,450 healthy HC individuals were genotyped using a customized Genome-Wide Association Study (GWAS) array. We identified hypertension-associated variants and developed a hypertension prediction model based on the correlation between the WGS data and GWAS data (combined clinical and genetic models, AUC 0.887), and also identified 3 novel hyperlipidemia-associated variants. Each individual carried an average of 16.42 (SD = 3.72) disease-causing variants. Additionally, we established an online SCN5A (an important cardiac gene) database that can be used to explore racial differences. Finally, pharmacogenetics studies identified HC population-specific SNVs in genes (CYP2C9 and VKORC1) involved in drug metabolism and blood clotting. Conclusion: This research demonstrates the benefits of constructing a population-specific genomic reference database for precision medicine.

Increased risk of QT prolongation associated with atherosclerotic diseases in arseniasis-endemic area in southwestern coast of Taiwan.

Chronic arsenic exposure has been documented to be associated with various cardiovascular diseases. We aimed to investigate 1) the increased risk of QT prolongation in chronic arsenic exposure, and 2) the relationships of cardiac repolarization (QT interval duration) with ischemic heart disease and carotid atherosclerosis. We studied 280 men and 355 women living in the endemic area of arseniasis in southwestern Taiwan. QT intervals in electrocardiogram and carotid intima-media thickness (IMT) by ultrasonography were measured. Ischemic heart disease was diagnosed by history or abnormal electrocardiogram. Significant associations of the corrected QT interval (QTc) duration with ischemic heart disease and carotid intima-medium thickness and plaque were observed after adjustment for various risk factors in the multiple linear regression analysis (all p values <0.05). Three indices of chronic arsenic exposure were all significantly associated with the risk of QTc prolongation showing dose-response relationships (p<0.001). Chronic arsenic exposure was dose-dependently associated with the risk of QTc prolongation. Ischemic heart disease and carotid atherosclerosis were significantly associated with QTc intervals in chronic arsenic exposure. QTc prolongation might be suggested as an early biomarker for ischemic heart disease or carotid atherosclerosis in population with previous exposure to arsenic.

Significantly higher frequencies of presence of serum autoantibodies in Chinese patients with oral lichen planus.

BACKGROUND: Previous studies have shown the presence of serum anti-nuclear (ANA), anti-smooth muscle (SMA), anti-mitochondrial (AMA), anti-gastric parietal cell (GPCA), anti-thyroglobulin (TGA) and anti-thyroid microsomal autoantibodies (TMA) in small groups of patients with oral lichen planus (OLP). METHODS: In this study, the serum levels of ANA, SMA, AMA, GPCA, TGA and TMA were measured in a group of 320 Chinese OLP patients and 53 healthy control subjects to assess whether Chinese OLP patients had significantly higher frequencies of serum autoantibodies than healthy control subjects and to assess which risk factors had a significant influence on the possession of a specific serum autoantibody in OLP patients. RESULTS: We found that autoantibodies were present in 195 (60.9%) of the 320 OLP patients. The frequencies of presence of serum ANA (28.1%), GPCA (26.3%), TGA (21.3%) and TMA (24.4%) in OLP patients were significantly higher than those (5.7%, 1.9%, 1.9% and 1.9%, respectively) in healthy control subjects (all P-values were < 0.005). Forty-one (12.8%) OLP patients also had anti-hepatitis C virus antibody (HCVA) in their sera. The multivariate logistic regression found that major erosive OLP (EOLP) [odds ratio (OR) = 1.786], TGA/TMA-positivity (OR = 2.517), and HCVA-positivity (OR = 2.214) were significant risk factors to influence ANA-positivity in OLP patients. Moreover, only major EOLP (OR = 1.879) and ANA-positivity (OR = 2.581) were significant risk factors to influence TGA/TMA-positivity in OLP patients. CONCLUSIONS: There are significantly higher frequencies of presence of ANA, GPCA, TGA and TMA in Chinese OLP patients than in healthy control subjects.

Use of ecstasy and other psychoactive substances among school-attending adolescents in Taiwan: national surveys 2004-2006.

BACKGROUND: With the backdrop of a global ecstasy epidemic, this study sought to examine the trend, correlates, and onset sequence of ecstasy use among adolescents in Taiwan, where a well-established gateway drug such as marijuana is much less popular. METHODS: A multistage probability survey of school-attending adolescents in grades 7, 9, 10, and 12, aged 11-19 years, was conducted in 2004, 2005, and 2006. A self-administered anonymous questionnaire elicited response rates ranging from 94.3% to 96.6%. The sample sizes were 18232 respondents in 2004, 17986 in 2005, and 17864 in 2006. RESULTS: In terms of lifetime prevalence and incidence, ecstasy and ketamine by and large appeared as the first and second commonly used illegal drugs, respectively, among middle (grades 7 and 9) and high school students (grades 10 and 12) during the 3-year survey period; however, this order was reversed in the middle school-aged students starting in 2006. Having sexual experience, tobacco use, and betel nut use were factors consistently associated with the onset of ecstasy use across years. The majority of ecstasy users had been involved in polydrug use, such as the use of ketamine (41.4%-53.5%), marijuana (12.7%-18.7%), and methamphetamine (4.2%-9.5%). CONCLUSION: From 2004 to 2006, a decline was noted in the prevalence and incidence rate of ecstasy, a leading illegal drug used by school-attending adolescents in Taiwan since the early 2000s. The emerging ketamine use trend may warrant more attention in the future.

Globally temporal transitions of blood lead levels of preschool children across countries of different categories of Human Development Index.

In the past decades, efforts to eliminate lead from gasoline, paint and drinking water around the world have substantially reduced human blood lead levels. This study was therefore aimed at examining the global temporal trends in the blood lead levels of preschool children by the category of UN Human Development Index (HDI). In total, 103 blood lead records were retrieved from 51 articles searched from PubMed and Google Scholar, with study subjects aged up to 8years old. Collected preschool children blood lead levels were plotted chronologically by HDI category and their reciprocals were used in regression analysis against calendar year to establish their temporal transition trends in the past decades. Results show that the modes of blood lead level of the preschool children were reduced from 4-6µg/dL to 0.8-1.5µg/dL, from 6-15µg/dL to 3-6µg/dL and from 12-16 to 5-6µg/dL for the very high HDI countries, the high HDI countries and the medium/low HDI countries, respectively. The highest correlation coefficient, 0.849, between the reciprocal of blood lead level and the calendar year was found for the very high HDI countries. Based on the regression lines, the predicted preschool children mean blood lead levels in the year of 2030 are 0.74µg/dL, 2.21µg/dL and 2.86µg/dL, respectively, for the very high HDI countries, the high HDI countries and the medium/low HDI countries. Persistent differences in blood lead level prevailed among countries of different HDI category, suggesting the effects of disparities and inequalities, at the state level, on preschool children blood lead levels. Further action is warranted to reduce the already low environmental lead exposure to eliminate the developmental burden of lead on children through (1) identification of individual local factors for lead exposure and (2) averting health disparity and inequalities at the state level.