Single nucleotide polymorphisms of one-carbon metabolism and cancers of the esophagus, stomach, and liver in a Chinese population.

One-carbon metabolism (folate metabolism) is considered important in carcinogenesis because of its involvement in DNA synthesis and biological methylation reactions. We investigated the associations of single nucleotide polymorphisms (SNPs) in folate metabolic pathway and the risk of three GI cancers in a population-based case-control study in Taixing City, China, with 218 esophageal cancer cases, 206 stomach cancer cases, 204 liver cancer cases, and 415 healthy population controls. Study participants were interviewed with a standardized questionnaire, and blood samples were collected after the interviews. We genotyped SNPs of the MTHFR, MTR, MTRR, DNMT1, and ALDH2 genes, using PCR-RFLP, SNPlex, or TaqMan assays. To account for multiple comparisons and reduce the chances of false reports, we employed semi-Bayes (SB) shrinkage analysis. After shrinkage and adjusting for potential confounding factors, we found positive associations between MTHFR rs1801133 and stomach cancer (any T versus C/C, SB odds-ratio [SBOR]: 1.79, 95% posterior limits: 1.18, 2.71) and liver cancer (SBOR: 1.51, 95% posterior limits: 0.98, 2.32). There was an inverse association between DNMT1 rs2228612 and esophageal cancer (any G versus A/A, SBOR: 0.60, 95% posterior limits: 0.39, 0.94). In addition, we detected potential heterogeneity across alcohol drinking status for ORs relating MTRR rs1801394 to esophageal (posterior homogeneity P = 0.005) and stomach cancer (posterior homogeneity P = 0.004), and ORs relating MTR rs1805087 to liver cancer (posterior homogeneity P = 0.021). Among non-alcohol drinkers, the variant allele (allele G) of these two SNPs was inversely associated with the risk of these cancers; while a positive association was observed among ever-alcohol drinkers. Our results suggest that genetic polymorphisms related to one-carbon metabolism may be associated with cancers of the esophagus, stomach, and liver. Heterogeneity across alcohol consumption status of the associations between MTR/MTRR polymorphisms and these cancers indicates potential interactions between alcohol drinking and one-carbon metabolic pathway

Temporal and Spatial Variability of Precipitation from Observations and Models

Principal component analysis (PCA) is utilized to explore the temporal and spatial variability of precipitation from GPCP and a CAM5 simulation from 1979 to 2010. In the tropical region, the interannual variability of tropical precipitation is characterized by two dominant modes (El Niño and El Niño Modoki). The first and second modes of tropical GPCP precipitation capture 31.9% and 15.6% of the total variance, respectively. The first mode has positive precipitation anomalies over the western Pacific and negative precipitation anomalies over the central and eastern Pacific. The second mode has positive precipitation anomalies over the central Pacific and negative precipitation anomalies over the western and eastern Pacific. Similar variations are seen in the first two modes of tropical precipitation from a CAM5 simulation, although the magnitudes are slightly weaker than in the observations. Over the Northern Hemisphere (NH) high latitudes, the first mode, capturing 8.3% of the total variance of NH GPCP precipitation, is related to the northern annular mode (NAM). During the positive phase of NAM, there are negative precipitation anomalies over the Arctic and positive precipitation anomalies over the midlatitudes. Over the Southern Hemisphere (SH) high latitudes, the first mode, capturing 13.2% of the total variance of SH GPCP precipitation, is related to the southern annular mode (SAM). During the positive phase of the SAM, there are negative precipitation anomalies over the Antarctic and positive precipitation anomalies over the midlatitudes. The CAM5 precipitation simulation demonstrates similar results to those of the observations. However, they do not capture both the high precipitation anomalies over the northern Pacific Ocean or the position of the positive precipitation anomalies in the SH

Against spyware using CAPTCHA in graphical password scheme

Text-based password schemes have inherent security and usability problems, leading to the development of graphicalpassword schemes. However, most of these alternate schemes are vulnerable to spyware attacks. We propose a new scheme, using CAPTCHA (Completely Automated Public Turing tests to tell Computers and Humans Apart) that retaining the advantages of graphical password schemes, while simultaneously raising the cost of adversaries by orders of magnitude. Furthermore, some primary experiments are conducted and the results indicate that the usability should be improved in the future work

Performance of Polygenic Scores for Predicting Phobic Anxiety

Context Anxiety disorders are common, with a lifetime prevalence of 20% in the U.S., and are responsible for substantial burdens of disability, missed work days and health care utilization. To date, no causal genetic variants have been identified for anxiety, anxiety disorders, or related traits. Objective: To investigate whether a phobic anxiety symptom score was associated with 3 alternative polygenic risk scores, derived from external genome-wide association studies of anxiety, an internally estimated agnostic polygenic score, or previously identified candidate genes. Design: Longitudinal follow-up study. Using linear and logistic regression we investigated whether phobic anxiety was associated with polygenic risk scores derived from internal, leave-one out genome-wide association studies, from 31 candidate genes, and from out-of-sample genome-wide association weights previously shown to predict depression and anxiety in another cohort. Setting and Participants: Study participants (n = 11,127) were individuals from the Nurses' Health Study and Health Professionals Follow-up Study. Main Outcome Measure: Anxiety symptoms were assessed via the 8-item phobic anxiety scale of the Crown Crisp Index at two time points, from which a continuous phenotype score was derived. Results: We found no genome-wide significant associations with phobic anxiety. Phobic anxiety was also not associated with a polygenic risk score derived from the genome-wide association study beta weights using liberal p-value thresholds; with a previously published genome-wide polygenic score; or with a candidate gene risk score based on 31 genes previously hypothesized to predict anxiety. Conclusion: There is a substantial gap between twin-study heritability estimates of anxiety disorders ranging between 20–40% and heritability explained by genome-wide association results. New approaches such as improved genome imputations, application of gene expression and biological pathways information, and incorporating social or environmental modifiers of genetic risks may be necessary to identify significant genetic predictors of anxiety

Genome-wide polygenic scoring for a 14-year long-term average depression phenotype

Background: Despite moderate heritability estimates for depression-related phenotypes, few robust genetic predictors have been identified. Potential explanations for this discrepancy include the use of phenotypic measures taken from a single time point, rather than integrating information over longer time periods via multiple assessments, and the possibility that genetic risk is shaped by multiple loci with small effects. Methods: We developed a 14-year long-term average depression measure based on 14 years of follow-up in the Nurses' Health Study (NHS; N = 6989 women). We estimated polygenic scores (PS) with internal whole-genome scoring (NHS-GWAS-PS). We also constructed PS by applying two external PS weighting algorithms from independent samples, one previously shown to predict depression (GAIN-MDD-PS) and another from the largest genome-wide analysis currently available (PGC-MDD-PS). We assessed the association of all three PS with our long-term average depression phenotype using linear, logistic, and quantile regressions. Results: In this study, the three PS approaches explained at most 0.2% of variance in the long-term average phenotype. Quantile regressions indicated PS had larger impacts at higher quantiles of depressive symptoms. Quantile regression coefficients at the 75th percentile were at least 40% larger than at the 25th percentile in all three polygenic scoring algorithms. The interquartile range comparison suggested the effects of PS significantly differed at the 25th and 75th percentiles of the long-term depressive phenotype for the PGC-MDD-PS (P = 0.03), and this difference also reached borderline statistical significance for the GAIN-MDD-PS (P = 0.05). Conclusions: Integrating multiple phenotype assessments spanning 14 years and applying different polygenic scoring approaches did not substantially improve genetic prediction of depression. Quantile regressions suggested the effects of PS may be largest at high quantiles of depressive symptom scores, presumably among people with additional, unobserved sources of vulnerability to depression