Structured time-dependent inverse regression (STIR).

We propose and study structured time-dependent inverse regression (STIR), a novel sufficient dimension reduction model, to analyze longitudinally measured, correlated biomarkers in relation to an outcome. The time structure is accommodated in an inverse regression model for the markers that can be applied both to equally and unequally spaced time points for each sample. The inverse regression structure also naturally accommodates retrospectively sampled markers, that is, markers measured in case-control studies. We estimate the corresponding linear combinations of the markers, the reduction, using least squares. We show that under additional distributional assumptions the reduction contains sufficient information about the outcome. In extensive simulations the STIR linear combinations perform well in predictive models based on samples of realistic size. A Wald-type test for association of a particular marker with outcome at any time point based on the STIR reduction has better power overall than assessing associations based on logistic or linear regression models that include all longitudinally measured markers as independent predictors. As illustrations we estimate the STIR reductions for a cohort study of diabetes and hyperlipidemia and a case-control study of brain cancer with multiple longitudinally measured biomarkers. We assess the STIR reductions' predictive performance and identify outcome-associated biomarkers.

Tuberculosis infection and lung adenocarcinoma: Mendelian randomization and pathway analysis of genome-wide association study data from never-smoking Asian women.

We investigated whether genetic susceptibility to tuberculosis (TB) influences lung adenocarcinoma development among never-smokers using TB genome-wide association study (GWAS) results within the Female Lung Cancer Consortium in Asia. Pathway analysis with the adaptive rank truncated product method was used to assess the association between a TB-related gene-set and lung adenocarcinoma using GWAS data from 5512 lung adenocarcinoma cases and 6277 controls. The gene-set consisted of 31 genes containing known/suggestive associations with genetic variants from previous TB-GWAS. Subsequently, we followed-up with Mendelian Randomization to evaluate the association between TB and lung adenocarcinoma using three genome-wide significant variants from previous TB-GWAS in East Asians. The TB-related gene-set was associated with lung adenocarcinoma (p = 0.016). Additionally, the Mendelian Randomization showed an association between TB and lung adenocarcinoma (OR = 1.31, 95% CI: 1.03, 1.66, p = 0.027). Our findings support TB as a causal risk factor for lung cancer development among never-smoking Asian women.

Comparing two health literacy measurements used for assessing older adults' medication adherence.

AIMS AND OBJECTIVES: To compare two health literacy measurements' ability to assess older adults' medication adherence by using the Korean Health Literacy Screening Questions (KHLSQ) and the Modified Korean Functional Health Literacy Test (M-KFHLT), and to identify an appropriate health literacy measurement. BACKGROUND: Lower health literacy has been associated with poorer medication adherence. Thus, health professionals should evaluate the available health literacy assessment instruments they are using and choose an appropriate instrument to assess health literacy to increase older adults' medication adherence. DESIGN: Following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist, a descriptive, cross-sectional study was conducted. METHODS: From November 2017-May 2018, 116 community-dwelling older adults were recruited in South Korea. Questionnaires were completed during a face-to-face interview with each participant in a private room; health literacy was assessed using the KHLSQ and the M-KFHLT and medication adherence using the Morisky Medication Adherence Scale. RESULTS: Health literacy assessed using the KHLSQ was found to be a predictor of medication adherence, but was not a predictor when measured by the M-KFHLT. Having low income, multiple chronic diseases and vision problems were also significant factors related to medication adherence. CONCLUSIONS: This study suggests that health literacy was negatively associated with medication adherence. Furthermore, KHLSQ is an appropriate tool for healthcare providers to use when assessing health literacy to predict older adults' medication adherence. RELEVANCE TO CLINICAL PRACTICE: This finding indicated that healthcare providers should select an appropriate health literacy measurement that suits their purposes and the population they serve, particularly for older adults.

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package.

Association between GWAS-identified lung adenocarcinoma susceptibility loci and EGFR mutations in never-smoking Asian women, and comparison with findings from Western populations.

To evaluate associations by EGFR mutation status for lung adenocarcinoma risk among never-smoking Asian women, we conducted a meta-analysis of 11 loci previously identified in genome-wide association studies (GWAS). Genotyping in an additional 10,780 never-smoking cases and 10,938 never-smoking controls from Asia confirmed associations with eight known single nucleotide polymorphisms (SNPs). Two new signals were observed at genome-wide significance (P < 5 × 10-8), namely, rs7216064 (17q24.3, BPTF), for overall lung adenocarcinoma risk, and rs3817963 (6p21.3, BTNL2) which is specific to cases with EGFR mutations. In further sub-analyses by EGFR status, rs9387478 (ROS1/DCBLD1) and rs2179920 (HLA-DPB1) showed stronger estimated associations in EGFR-positive compared to EGFR-negative cases. Comparison of the overall associations with published results in Western populations revealed that the majority of these findings were distinct, underscoring the importance of distinct contributing factors for smoking and non-smoking lung cancer. Our results extend the catalogue of regions associated with lung adenocarcinoma in non-smoking Asian women and highlight the importance of how the germline could inform risk for specific tumour mutation patterns, which could have important translational implications.

Meta-analysis of genome-wide association studies identifies multiple lung cancer susceptibility loci in never-smoking Asian women.

Genome-wide association studies (GWAS) of lung cancer in Asian never-smoking women have previously identified six susceptibility loci associated with lung cancer risk. To further discover new susceptibility loci, we imputed data from four GWAS of Asian non-smoking female lung cancer (6877 cases and 6277 controls) using the 1000 Genomes Project (Phase 1 Release 3) data as the reference and genotyped additional samples (5878 cases and 7046 controls) for possible replication. In our meta-analysis, three new loci achieved genome-wide significance, marked by single nucleotide polymorphism (SNP) rs7741164 at 6p21.1 (per-allele odds ratio (OR) = 1.17; P = 5.8 × 10(-13)), rs72658409 at 9p21.3 (per-allele OR = 0.77; P = 1.41 × 10(-10)) and rs11610143 at 12q13.13 (per-allele OR = 0.89; P = 4.96 × 10(-9)). These findings identified new genetic susceptibility alleles for lung cancer in never-smoking women in Asia and merit follow-up to understand their biological underpinnings.

A unified genetic association test robust to latent population structure for a count phenotype.

Confounding caused by latent population structure in genome-wide association studies has been a big concern despite the success of genome-wide association studies at identifying genetic variants associated with complex diseases. In particular, because of the growing interest in association mapping using count phenotype data, it would be interesting to develop a testing framework for genetic associations that is immune to population structure when phenotype data consist of count measurements. Here, I propose a solution for testing associations between single nucleotide polymorphisms and a count phenotype in the presence of an arbitrary population structure. I consider a classical range of models for count phenotype data. Under these models, a unified test for genetic associations that protects against confounding was derived. An algorithm was developed to efficiently estimate the parameters that are required to fit the proposed model. I illustrate the proposed approach using simulation studies and an empirical study. Both simulated and real-data examples suggest that the proposed method successfully corrects population structure.

Molecular mechanisms of Dicer: endonuclease and enzymatic activity.

The enzyme Dicer is best known for its role as a riboendonuclease in the small RNA pathway. In this canonical role, Dicer is a critical regulator of the biogenesis of microRNA and small interfering RNA, as well as a growing number of additional small RNAs derived from various sources. Emerging evidence demonstrates that Dicer's endonuclease role extends beyond the generation of small RNAs; it is also involved in processing additional endogenous and exogenous substrates, and is becoming increasingly implicated in regulating a variety of other cellular processes, outside of its endonuclease function. This review will describe the canonical and newly identified functions of Dicer.

Probabilistic models of genetic variation in structured populations applied to global human studies.

MOTIVATION: Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes without requiring a higher-level admixture interpretation. RESULTS: We formulate two general probabilistic models, and we propose computationally efficient algorithms to estimate them. First, we show how principal component analysis can be utilized to estimate a general model that includes the well-known Pritchard-Stephens-Donnelly admixture model as a special case. Noting some drawbacks of this approach, we introduce a new 'logistic factor analysis' framework that seeks to directly model the logit transformation of probabilities underlying observed genotypes in terms of latent variables that capture population structure. We demonstrate these advances on data from the Human Genome Diversity Panel and 1000 Genomes Project, where we are able to identify SNPs that are highly differentiated with respect to structure while making minimal modeling assumptions. AVAILABILITY AND IMPLEMENTATION: A Bioconductor R package called lfa is available at http://www.bioconductor.org/packages/release/bioc/html/lfa.html CONTACT: jstorey@princeton.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Testing calibration of risk models at extremes of disease risk.

Risk-prediction models need careful calibration to ensure they produce unbiased estimates of risk for subjects in the underlying population given their risk-factor profiles. As subjects with extreme high or low risk may be the most affected by knowledge of their risk estimates, checking the adequacy of risk models at the extremes of risk is very important for clinical applications. We propose a new approach to test model calibration targeted toward extremes of disease risk distribution where standard goodness-of-fit tests may lack power due to sparseness of data. We construct a test statistic based on model residuals summed over only those individuals who pass high and/or low risk thresholds and then maximize the test statistic over different risk thresholds. We derive an asymptotic distribution for the max-test statistic based on analytic derivation of the variance-covariance function of the underlying Gaussian process. The method is applied to a large case-control study of breast cancer to examine joint effects of common single nucleotide polymorphisms (SNPs) discovered through recent genome-wide association studies. The analysis clearly indicates a non-additive effect of the SNPs on the scale of absolute risk, but an excellent fit for the linear-logistic model even at the extremes of risks.

Interactions between household air pollution and GWAS-identified lung cancer susceptibility markers in the Female Lung Cancer Consortium in Asia (FLCCA).

We previously carried out a multi-stage genome-wide association study (GWAS) on lung cancer among never smokers in the Female Lung Cancer Consortium in Asia (FLCCA) (6,609 cases, 7,457 controls) that identified novel susceptibility loci at 10q25.2, 6q22.2, and 6p21.32, and confirmed two previously identified loci at 5p15.33 and 3q28. Household air pollution (HAP) attributed to solid fuel burning for heating and cooking, is the leading cause of the overall disease burden in Southeast Asia, and is known to contain lung carcinogens. To evaluate the gene-HAP interactions associated with lung cancer in loci independent of smoking, we analyzed data from studies participating in FLCCA with fuel use information available (n = 3; 1,731 cases; 1,349 controls). Coal use was associated with a 30% increased risk of lung cancer (OR 1.3, 95% CI 1.0-1.6). Among the five a priori SNPs identified by our GWAS, two showed a significant interaction with coal use (HLA Class II rs2395185, p = 0.02; TP63 rs4488809 (rs4600802), p = 0.04). The risk of lung cancer associated with coal exposure varied with the respective alleles for these two SNPs. Our observations provide evidence that genetic variation in HLA Class II and TP63 may modify the association between HAP and lung cancer risk. The roles played in the cell cycle and inflammation pathways by the proteins encoded by these two genes provide biological plausibility for these interactions; however, additional replication studies are needed in other non-smoking populations.

Jackknife-based gene-gene interactiontests for untyped SNPs.

BACKGROUND: Testing gene-gene interaction in genome-wide association studies generally yields lower power than testing marginal association. Meta-analysis that combines different genotyping platforms is one method used to increase power when assessing gene-gene interactions, which requires a test for interaction on untyped SNPs. However, to date, formal statistical tests for gene-gene interaction on untyped SNPs have not been thoroughly addressed. The key concern for gene-gene interaction testing on untyped SNPs located on different chromosomes is that the pair of genes might not be independent and the current generation of imputation methods provides imputed genotypes at the marginal accuracy. RESULTS: In this study we address this challenge and describe a novel method for testing gene-gene interaction on marginally imputed values of untyped SNPs. We show that our novel Wald-type test statistics for interactions with and without constraints in the interaction parameters follow the asymptotic distributions which are the same as those of the corresponding tests for typed SNPs. Through simulations, we show that the proposed tests properly control type I error and are more powerful than the extension of the classical dosage method to interaction tests. The increase in power results from a proper correction for the uncertainty in imputation through the variance estimator using the jackknife, one of resampling techniques. We apply the method to detect interactions between SNPs on chromosomes 5 and 15 on lung cancer data. The inclusion of the results at the untyped SNPs provides a much more detailed information at the regions of interest. CONCLUSIONS: As demonstrated by the simulation studies and real data analysis, our approaches outperform the application of traditional dosage method to detection of gene-gene interaction in terms of power while providing control of the type I error.

Effects of education in an obesity control program for obese homemakers on body fat and flexibility in Korea.

The study aims to investigate the effects of education in an obesity-control program (EOCP) on the percentage of body fat and flexibility in obese women in Korea. Women with over 30% body fat were offered EOCP between July 2012 and October 2012. The EOCP consisted of an educational program, exercise program, and counseling. The numbers of participants both in the EOCP and control group were 17. The study was analyzed by Wilcoxon signed-rank test using the Statistical Analysis System package. The EOCP participants presented statistically significant increases in the degree of forward trunk flexion, but only the percentage of the body fat showed differences within the EOCP group. The EOCP improved flexibility in obese women, and can be used in local obesity-control programs.

Risk prediction and interaction analysis using polygenic risk score of type 2 diabetes in a Korean population.

Joint modelling of genetic and environmental risk factors can provide important information to predict the risk of type 2 diabetes (T2D). Therefore, to predict the genetic risk of T2D, we constructed a polygenic risk score (PRS) using genotype data of one Korean cohort, KARE (745 cases and 2549 controls), and the genome-wide association study summary statistics of Biobank Japan. We evaluated the performance of PRS in an independent Korean cohort, HEXA (5684 cases and 35,703 controls). Individuals with T2D had a significantly higher mean PRS than controls (0.492 vs. - 0.078, p ≈ 0 ). PRS predicted the risk of T2D with an AUC of 0.658 (95% CI 0.651-0.666). We also evaluated interaction between PRS and waist circumference (WC) in the HEXA cohort. PRS exhibited a significant sub-multiplicative interaction with WC (ORinteraction 0.991, 95% CI 0.987-0.995, pinteraction = 4.93 × 10-6) in T2D. The effect of WC on T2D decreased as PRS increased. The sex-specific analyses produced similar interaction results, revealing a decreased WC effect on T2D as the PRS increased. In conclusion, the risk of WC for T2D may differ depending on PRS and those with a high PRS might develop T2D with a lower WC threshold. Our findings are expected to improve risk prediction for T2D and facilitate the identification of individuals at an increased risk of T2D.

A role for human Dicer in pre-RISC loading of siRNAs.

RNA interference is a powerful mechanism for sequence-specific inhibition of gene expression. It is widely known that small interfering RNAs (siRNAs) targeting the same region of a target-messenger RNA can have widely different efficacies. In efforts to better understand the siRNA features that influence knockdown efficiency, we analyzed siRNA interactions with a high-molecular weight complex in whole cell extracts prepared from two different cell lines. Using biochemical tools to study the nature of the complex, our results demonstrate that the primary siRNA-binding protein in the whole cell extracts is Dicer. We find that Dicer is capable of discriminating highly functional versus poorly functional siRNAs by recognizing the presence of 2-nt 3' overhangs and the thermodynamic properties of 2-4 bp on both ends of effective siRNAs. Our results suggest a role for Dicer in pre-selection of effective siRNAs for handoff to Ago2. This initial selection is reflective of the overall silencing potential of an siRNA.

Multiplexed Ultrasensitive Sample-to-Answer RT-LAMP Chip for the Identification of SARS-CoV-2 and Influenza Viruses.

Prompt on-site diagnosis of SARS-CoV-2 with other respiratory infections will have minimized the global impact of the COVID-19 pandemic through rapid, effective management. However, no such multiplex point-of-care (POC) chip has satisfied a suitable sensitivity of gold-standard nucleic acid amplification tests (NAATs). Here, a rapid multiplexed ultrasensitive sample-to-answer loop-mediated isothermal amplification (MUSAL) chip operated by simple LED-driven photothermal amplification to detect six targets from single-swab sampling is presented. First, the MUSAL chip allows ultrafast on-chip sample preparation with ≈500-fold preconcentration at a rate of 1.2 mL min-1 . Second, the chip enables contamination-free amplification using autonomous target elution into on-chip reagents by photothermal activation. Finally, the chip accomplishes multiplexed on-chip diagnostics of SARS-CoV-2 and influenza viruses with a limit of detection (LoD) of 0.5 copies µL-1 . The rapid, ultrasensitive, cost-effective sample-to-answer chip with a multiplex capability will allow timely management of various pandemics situations that may be faced shortly.

Factors Influencing Frontline Screening Nurses' Depression as a Long-Term Impact of COVID-19.

Frontline screening nurses experienced exhaustion and depressive symptoms as a long-term impact of COVID-19. This study aimed to explore fatigue, depression, and empowerment among frontline screening nurses and examine the factors influencing depression. This was a descriptive cross-sectional study. The study included 140 frontline screening nurses in South Korea recruited from February to March 2021. The measures included a fatigue scale, the Text of Items Measuring Empowerment (TIME), and the Center for Epidemiological Studies Depression Scale (CES-D). The STROBE checklist was used for reporting aspects of the cross-sectional design. Frontline screening nurses showed high fatigue scores (M = 3.47, SD = 0.55), and 55.7% (n = 78) of them were depressed and had low empowerment scores (M = 3.53, SD = 0.69). Empowerment and fatigue were predictors of depression. Increased empowerment and decreased fatigue were important in decreasing depression. Therefore, efforts to provide sufficient staffing, screening for depression, and listening to nurses' voices are necessary.

Evolution of Cell-Type-Specific RNA Aptamers via Live Cell-Based SELEX.

Live cell-based SELEX (Systematic Evolution of Ligand EXponential enrichment) is a promising approach for identifying aptamers that can selectively bind to a cell-surface receptor or recognize a particular target cell population. In particular, it offers a facile selection strategy for some special cell-surface proteins that are originally glycosylated or heavily posttranslationally modified and are unavailable in their native/active conformation after in vitro expression and purification. In this chapter, we describe a generalized procedure for evolution of cell type-specific RNA aptamers targeting a cell membrane bound target by combining the live cell-based SELEX strategy with high-throughput sequencing (HTS) and bioinformatics analysis.

The Effect of Dicer Knockout on RNA Interference Using Various Dicer Substrate Small Interfering RNA (DsiRNA) Structures.

Small interfering RNAs (siRNAs) are artificial molecules used to silence genes of interest through the RNA interference (RNAi) pathway, mediated by the endoribonuclease Dicer. Dicer-substrate small interfering RNAs (DsiRNAs) are an alternative to conventional 21-mer siRNAs, with an increased effectiveness of up to 100-fold compared to traditional 21-mer designs. DsiRNAs have a novel asymmetric design that allows them to be processed by Dicer into the desired conventional siRNAs. DsiRNAs are a useful tool for sequence-specific gene silencing, but the molecular mechanism underlying their increased efficacy is not precisely understood. In this study, to gain a deeper understanding of Dicer function in DsiRNAs, we designed nicked DsiRNAs with and without tetra-loops to target a specific mRNA sequence, established a Dicer knockout in the HCT116 cell line, and analyzed the efficacy of various DsiRNAs on RNAi-mediated gene silencing activity. The gene silencing activity of all DsiRNAs was reduced in Dicer knockout cells. We demonstrated that tetra-looped DsiRNAs exhibited increased efficacy for gene silencing, which was mediated by Dicer protein. Thus, this study improves our understanding of Dicer function, a key component of RNAi silencing, which will inform RNAi research and applications.

KIM-1/TIM-1 is a Receptor for SARS-CoV-2 in Lung and Kidney.

SARS-CoV-2 precipitates respiratory distress by infection of airway epithelial cells and is often accompanied by acute kidney injury. We report that Kidney Injury Molecule-1/T cell immunoglobulin mucin domain 1 (KIM-1/TIM-1) is expressed in lung and kidney epithelial cells in COVID-19 patients and is a receptor for SARS-CoV-2. Human and mouse lung and kidney epithelial cells express KIM-1 and endocytose nanoparticles displaying the SARS-CoV-2 spike protein (virosomes). Uptake was inhibited by anti-KIM-1 antibodies and TW-37, a newly discovered inhibitor of KIM-1-mediated endocytosis. Enhanced KIM-1 expression by human kidney tubuloids increased uptake of virosomes. KIM-1 binds to the SARS-CoV-2 Spike protein in vitro . KIM-1 expressing cells, not expressing angiotensin-converting enzyme 2 (ACE2), are permissive to SARS-CoV-2 infection. Thus, KIM-1 is an alternative receptor to ACE2 for SARS-CoV-2. KIM-1 targeted therapeutics may prevent and/or treat COVID-19.