Background: The Likert scale is an ordinal variable that measures the intensity of responses from research participants. It is widely used not only in social sciences, such as sociology and psychology, but also in survey-based research fields, such as nursing and public health. Among the approaches for handling the Likert-scale data, treating it as a continuous variable has been commonly used because it facilitates the application of parametric statistical methods and interpretation of results. In addition, from a perspective of statistical principle, this type of approach has been widely discussed and considered unproblematic. However, studies exploring the characteristics of the Likert scale in the approach with simulations are relatively rare. Thus, this study aimed to confirm the validity of the approach with simulation that compared the statistical characteristics of the Likert scale variable with those of variables from an assumed continuous latent distribution. Methods: In the Monte Carlo simulation study, the rectified normal distribution was specifically assumed for the continuous latent distribution. Then, type 1 error, statistical power, and correlation were compared across various situations involving both single and multiple variables. Results: From the simulation study, we found that a Likert scale with five or more points exhibited statistical characteristics comparable to those of the variable derived from the latent continuous distribution. Conclusions: Based on the results, we confirmed that the Likert scale can be used as a continuous variable in various parametric statistical methods. The corresponding suggested guidelines are expected to assist researchers in research design, data analysis, and results interpretation.

Variants in the APOE gene region may explain ethnic differences in the association of Alzheimer’s disease (AD) with ε4. Ethnic differences in allele frequencies for three APOE region SNPs (single nucleotide polymorphisms) were identified and tested for association in 19,398 East Asians (EastA), including Koreans and Japanese, 15,836 European ancestry (EuroA) individuals, and 4985 African Americans, and with brain imaging measures of cortical atrophy in sub-samples of Koreans and EuroAs. Among ε4/ε4 individuals, AD risk increased substantially in a dose-dependent manner with the number of APOE promoter SNP rs405509 T alleles in EastAs (TT: OR (odds ratio) = 27.02, p = 8.80 × 10⁻⁹⁴; GT: OR = 15.87, p = 2.62 × 10⁻⁹) and EuroAs (TT: OR = 18.13, p = 2.69 × 10⁻¹⁰⁸; GT: OR = 12.63, p = 3.44 × 10⁻⁶⁴), and rs405509-T homozygotes had a younger onset and more severe cortical atrophy than those with G-allele. Functional experiments using APOE promoter fragments demonstrated that TT lowered APOE expression in human brain and serum. The modifying effect of rs405509 genotype explained much of the ethnic variability in the AD/ε4 association, and increasing APOE expression might lower AD risk among ε4 homozygotes.

The genetic heterogeneity of sensorineural hearing loss is a major hurdle to the efficient discovery of disease-causing genes. We designed a multiphasic analysis of copy number variation (CNV), linkage, and single nucleotide variation (SNV) of whole exome sequencing (WES) data for the efficient discovery of mutations causing nonsyndromic hearing loss (NSHL). From WES data, we identified five distinct CNV loci from a NSHL family, but they were not co-segregated among patients. Linkage analysis based on SNVs identified six candidate loci (logarithm of odds [LOD] >1.5). We selected 15 SNVs that co-segregated with NSHL in the family, which were located in six linkage candidate loci. Finally, the novel variant p.M305T in ACTG1 (DFNA20/26) was selected as a disease-causing variant. Here, we present a multiphasic CNV, linkage, and SNV analysis of WES data for the identification of a candidate mutation causing NSHL. Our stepwise, multiphasic approach enabled us to expedite the discovery of disease-causing variants from a large number of patient variants.

The present study reports two novel genome-wide significant loci for late-onset Alzheimer’s disease (LOAD) identified from APOE ε4 non-carrier subjects of East Asian origin. A genome-wide association study of Alzheimer’s disease was performed in 2,291 Korean seniors in the discovery phase, from the Gwangju Alzheimer’ and Related Dementias (GARD) cohort study. The study was replicated in a Japanese cohort of 1,956 subjects that suggested two novel susceptible SNPs in two genes: LRIG1 and CACNA1A. This study demonstrates that the discovery of AD-associated variants is feasible in non-European ethnic groups using samples comprising fewer subjects from the more homogeneous genetic background.

Background: To evaluate the impact of neutrophil-to-lymphocyte ratios (NLR) as a prognostic factor in predicting treatment outcomes after radiotherapy (RT) for solid tumors. Methods: PubMed and Embase databases were used to search for articles published by February 2019 based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Hazard ratios (HR) with 95% confidence intervals (CI) were used to evaluate the association between NLR levels and treatment outcomes after RT. The primary endpoint was overall survival (OS) rates. Secondary endpoints included progression-free survival, disease-free survival, and disease-specific survival rates. Results: Thirty-eight datasets with a total of 7065 patients were included in the meta-analysis. Patients with high pretreatment NLR demonstrated significantly worse OS with a pooled HR of 1.90 (95% CI 1.66–2.17, P < .001). In patients receiving RT alone, the pooled HR for OS was 1.71 (95% CI 1.44–2.04, P < .001) with no between-study heterogeneity (I ² = 0%, P = .46). Conclusion: Elevated pretreatment NLR is associated with poorer survival in cancer patients undergoing RT. Elevated pretreatment NLR prior to RT initiation may be a useful biomarker to predict treatment outcomes and select a subgroup of patients in need of a more aggressive treatment approach.

Undirected graphical models or Markov random fields have been a popular class of models for representing conditional dependence relationships between nodes. In particular, Markov networks help us to understand complex interactions between genes in biological processes of a cell. Local Poisson models seem to be promising in modeling positive as well as negative dependencies for count data. Furthermore, when zero counts are more frequent than are expected, excess zeros should be considered in the model. We present a penalized Poisson graphical model for zero inflated count data and derive an expectation-maximization (EM) algorithm built on coordinate descent. Our method is shown to be effective through simulated and real data analysis. Results from the simulated data indicate that our method outperforms the local Poisson graphical model in the presence of excess zeros. In an application to a RNA sequencing data, we also investigate the gender effect by comparing the estimated networks according to different genders. Our method may help us in identifying biological pathways linked to sex hormone regulation and thus understanding underlying mechanisms of the gender differences. We have presented a penalized version of zero inflated spatial Poisson regression and derive an efficient EM algorithm built on coordinate descent. We discuss possible improvements of our method as well as potential research directions associated with our findings from the RNA sequencing data.