DNA Methylation and SNPs in VCX are Correlated with Sex Differences in the Response to Chronic Hepatitis B

The study was conducted to explore the mechanisms of sex differences in the response to chronic hepatitis B(CHB) in terms of DNA methylation, SNP genotype, and gene expression. Genomic DNA was isolated from peripheral blood mononuclear cells(PBMCs) of CHB patients and healthy controls and evaluated using the Human Methylation 450 K Assay. The DNA methylation level at hg37 chromosome(CHR) X: 7810800 was further validated using pyrosequencing.SNP genotypes, VCX m RNA expression of PBMCs, and plasma VCX protein concentration were further examined using SNaPshot, RT-qPCR, and Western blot, respectively. Results showed that a total of 5529 CpG loci were differentially methylated between male and female CHB patients. DNA methylation level and CC+CT frequency at CHR X: 7810800,VCX mRNA expression of PBMCs, and plasma VCX protein concentration were higher in female than in male CHB patients. The CHR X: 7810800 locus was hypermethylated in CHB patients with CC+CT genotypes in comparison with those with the TT genotype. In cases of CC+CT genotypes, VCX mRNA expression was negatively correlated with the DNA methylation level. CHB patients with higher levels of HBV DNA, AST, and GGT or higher GPRI scores exhibited lower VCX expression. In conclusion, SNPs and DNA methylation at the CHR X: 7810800 locus cooperatively regulate VCX expression in CHB. The upregulated VCX expression in female CHB patients might represent a mechanism of protection from more severe liver dysfunction and extensive fibrosis, as observed in male CHB patients.     

Joint rare variant association test of the average and individual effects for sequencing studies

For many complex traits, single nucleotide polymorphisms (SNPs) identified from genome-wide association studies (GWAS) only explain a small percentage of heritability. Next generation sequencing technology makes it possible to explore unexplained heritability by identifying rare variants (RVs). Existing tests designed for RVs look for optimal strategies to combine information across multiple variants. Many of the tests have good power when the true underlying associations are either in the same direction or in opposite directions. We propose three tests for examining the association between a phenotype and RVs, where two of them jointly consider the common association across RVs and the individual deviations from the common effect. On one hand, similar to some of the best existing methods, the individual deviations are modeled as random effects to borrow information across multiple RVs. On the other hand, unlike the existing methods which pool individual effects towards zero, we pool them towards a possibly non-zero common effect by adding a pooled variant into the model. The common effect and the individual effects are jointly tested. We show through extensive simulations that at least one of the three tests proposed here is the most powerful or very close to being the most powerful in various settings of true models. This is appealing in practice because the direction and size of the true effects of the associated RVs are unknown. Researchers can apply the developed tests to improve power under a wide range of true models.     

Microsatellite Markers and Genetic Diversity of Four Scleractinian Corals

Russian Journal of Marine Biology - Eighteen microsatellite sequences were isolated from Platygyra acuta through magnetic bead hybridization enrichment method, and the site primers were used to...     

Nonparametric estimation of age-at-onset distributions from censored kin-cohort data

We present a nonparametric estimator of genotype-specific age-at-onsetdistributions from kin-cohort data. Standard error calculationsare derived and the methodology is illustrated through an analysisof the influence of mutations of the Parkin gene on Parkinson'sdisease. Semiparametric efficiency considerations are brieflydiscussed.     

Prediction-based structured variable selection through the receiver operating characteristic curves

In many clinical settings, a commonly encountered problem is to assess accuracy of a screening test for early detection of a disease. In these applications, predictive performance of the test is of interest. Variable selection may be useful in designing a medical test. An example is a research study conducted to design a new screening test by selecting variables from an existing screener with a hierarchical structure among variables: there are several root questions followed by their stem questions. The stem questions will only be asked after a subject has answered the root question. It is therefore unreasonable to select a model that only contains stem variables but not its root variable. In this work, we propose methods to perform variable selection with structured variables when predictive accuracy of a diagnostic test is the main concern of the analysis. We take a linear combination of individual variables to form a combined test. We then maximize a direct summary measure of the predictive performance of the test, the area under a receiver operating characteristic curve (AUC of an ROC), subject to a penalty function to control for overfitting. Since maximizing empirical AUC of the ROC of a combined test is a complicated nonconvex problem (Pepe, Cai, and Longton, 2006, Biometrics62, 221-229), we explore the connection between the empirical AUC and a support vector machine (SVM). We cast the problem of maximizing predictive performance of a combined test as a penalized SVM problem and apply a reparametrization to impose the hierarchical structure among variables. We also describe a penalized logistic regression variable selection procedure for structured variables and compare it with the ROC-based approaches. We use simulation studies based on real data to examine performance of the proposed methods. Finally we apply developed methods to design a structured screener to be used in primary care clinics to refer potentially psychotic patients for further specialty diagnostics and treatment.     

Genotyping of Giardia duodenalis Isolates from Dogs in Guangdong,China Based on Multi-Locus Sequence

This study aimed to identify the assemblages (or subassemblages) of Giardia duodenalis by using normal or nested PCR based on 4 genetic loci: glutamate dehydrogenase (gdh), triose phosphate isomerase (tpi), β-giardin (bg), and small subunit ribosomal DNA (18S rRNA) genes. For this work, a total of 216 dogs'' fecal samples were collected in Guangdong, China. The phylogenetic trees were constructed with MEGA5.2 by using the neighbor-joining method. Results showed that 9.7% (21/216) samples were found to be positive; moreover, 10 samples were single infection (7 isolates assemblage A, 2 isolates assemblage C, and 1 isolate assemblage D) and 11 samples were mixed infections where assemblage A was predominant, which was potentially zoonotic. These findings showed that most of the dogs in Guangdong were infected or mixed-infected with assemblage A, and multi-locus sequence typing could be the best selection for the genotype analysis of dog-derived Giardia isolates.     

台湾海峡夏秋季游泳动物资源分布及群落结构

根据夏季和秋季在台湾海峡进行的底拖网调查数据,分析了台湾海峡游泳动物资源密度指数分布及群落结构.结果表明,台湾海峡夏季平均资源密度指数高于秋季,分别为8.50和6.94kg·h^-1;夏秋季出现游泳动物种类分别为80种和91种,秋季丰富度、多样性、均匀度均高于夏季,夏秋季平均多样性指数分别为2.0466和2.3964;相对重要性指数IRI大于200的优势种夏季有5种,为带鱼、发光鲷、短尾大眼鲷、花斑蛇鲻、中国枪乌贼,秋季有6种,为带鱼、七星底灯鱼、刺鲳、中国枪乌贼、蓝圆_鲹、鹿斑鲾;夏季优势种的聚集强度比秋季强.