Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes

<p>Abstract</p> <p>Background</p> <p>Network modeling of whole transcriptome expression data enables characterization of complex epistatic (gene-gene) interactions that underlie cellular functions. Though numerous methods have been proposed and successfully implemented to develop these networks, there are no formal methods for comparing differences in network connectivity patterns as a function of phenotypic trait.</p> <p>Results</p> <p>Here we describe a novel approach for quantifying the differences in gene-gene connectivity patterns across disease states based on Graphical Gaussian Models (GGMs). We compare the posterior probabilities of connectivity for each gene pair across two disease states, expressed as a posterior odds-ratio (postOR) for each pair, which can be used to identify network components most relevant to disease status. The method can also be generalized to model differential gene connectivity patterns within previously defined gene sets, gene networks and pathways. We demonstrate that the GGM method reliably detects differences in network connectivity patterns in datasets of varying sample size. Applying this method to two independent breast cancer expression data sets, we identified numerous reproducible differences in network connectivity across histological grades of breast cancer, including several published gene sets and pathways. Most notably, our model identified two gene hubs (MMP12 and CXCL13) that each exhibited differential connectivity to more than 30 transcripts in both datasets. Both genes have been previously implicated in breast cancer pathobiology, but themselves are not differentially expressed by histologic grade in either dataset, and would thus have not been identified using traditional differential gene expression testing approaches. In addition, 16 curated gene sets demonstrated significant differential connectivity in both data sets, including the matrix metalloproteinases, PPAR alpha sequence targets, and the PUFA synthesis pathway.</p> <p>Conclusions</p> <p>Our results suggest that GGM can be used to formally evaluate differences in global interactome connectivity across disease states, and can serve as a powerful tool for exploring the molecular events that contribute to disease at a systems level.</p

Association between health examination items and body mass index among school children in Hualien, Taiwan

BACKGROUND: To assess the prevalence of obesity and major physical examination items including dental caries, myopia, pinworm, hematuria, and proteinuria among school children in Hualien, Taiwan. In addition, the health status differences between gender, grader, levels of residence urbanization, and body mass index (BMI) were examined. METHODS: Cross-sectional studies with a total of 11,080 students (age, 7–14 years) in grades 1, 4, and 7 were evaluated for weight, height, routine physical examination, and urine analysis during the 2010 Student Health Examination in Hualien. Frequencies, Chi-square test, and logistic regression were conducted using SPSS. RESULTS: Of the 11,080 students evaluated, 1357 (12.2%) were overweight, and 1421 (12.8%) were obese. There were significant differences in overweight/obese prevalence by gender, by grader, and by levels of residence urbanization. Dental caries, myopia, and obesity were the most prevalent health problems among these students (75.6%, 33.0%, and 12.8%, respectively). In crude and adjusted analyses, research results showed that there were significant differences in the prevalence of major physical examination items between different gender, grader, levels of residence urbanization, and BMI groups. Girls had a higher prevalence of dental caries, myopia, and hematuria than boys (all p < 0.01), whereas boys had a higher prevalence of pinworm than girls (p = 0.02). Students in higher grades had significantly higher prevalence of myopia, hematuria, and proteinuria (all p < 0.01), whereas students in lower grades had higher prevalence of dental caries and pinworm (p < 0.01). Students with abnormal BMI had lower prevalence of pinworm (p < 0.01). Students residing in suburban and rural areas had higher prevalence of dental caries, pinworm, and hematuria (all p < 0.01), and lower prevalence of myopia than students residing in urban areas (all p < 0.01). CONCLUSION: Routine health examination provides an important way to detect students’ health problems. Our study elucidated major health problems among school children in Hualien, Taiwan. In addition, the results also indicated that the prevalence of health problems had a significant relationship with gender, grader, levels of residence urbanization, and BMI. It is suggested that school health interventions should consider students’ health profiles along with their risk factors status in planning

A Temporal Frequent Itemset-Based Clustering Approach For Discovering Event Episodes From News Sequence

When performing environmental scanning, organizations typically deal with a numerous of events and topics about their core business, relevant technique standards, competitors, and market, where each event or topic to monitor or track generally is associated with many news documents. To reduce information overload and information fatigues when monitoring or tracking such events, it is essential to develop an effective event episode discovery mechanism for organizing all news documents pertaining to an event of interest. In this study, we propose the time-adjoining frequent itemset-based event-episode discovery (TAFIED) technique. Based on the frequent itemset-based hierarchical clustering (FIHC) approach, our proposed TAFIED further considers the temporal characteristic of news articles, including the burst, novelty, and temporal proximity of features in an event episode, when discovering event episodes from the sequence of news articles pertaining to a specific event. Using the traditional feature-based HAC, HAC with a time-decaying function (HAC+TD), and FIHC techniques as performance benchmarks, our empirical evaluation results suggest that the proposed TAFIED technique outperforms all evaluation benchmarks in cluster recall and cluster precision

Copy number variation genotyping using family information

Abstract Background In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies. Results To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments. Conclusions In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.http://deepblue.lib.umich.edu/bitstream/2027.42/112374/1/12859_2012_Article_5896.pd

Copy number variation genotyping using family information

BACKGROUND: In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies. RESULTS: To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments. CONCLUSIONS: In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs

Gene expression network analyses in response to air pollution exposures in the trucking industry

Background: Exposure to air pollution, including traffic-related pollutants, has been associated with a variety of adverse health outcomes, including increased cardiopulmonary morbidity and mortality, and increased lung cancer risk. Methods: To better understand the cellular responses induced by air pollution exposures, we performed genome-wide gene expression microarray analysis using whole blood RNA sampled at three time-points across the work weeks of 63 non-smoking employees at 10 trucking terminals in the northeastern US. We defined genes and gene networks that were differentially activated in response to PM2.5 (particulate matter ≤ 2.5 microns in diameter) and elemental carbon (EC) and organic carbon (OC). Results: Multiple transcripts were strongly associated (padj < 0.001) with pollutant levels (48, 260, and 49 transcripts for EC, OC, and PM2.5, respectively), including 63 that were statistically significantly correlated with at least two out of the three exposures. These genes included many that have been implicated in ischemic heart disease, chronic obstructive pulmonary disease (COPD), lung cancer, and other pollution-related illnesses. Through the combination of Gene Set Enrichment Analysis and network analysis (using GeneMANIA), we identified a core set of 25 interrelated genes that were common to all three exposure measures and were differentially expressed in two previous studies assessing gene expression attributable to air pollution. Many of these are members of fundamental cancer-related pathways, including those related to DNA and metal binding, and regulation of apoptosis and also but include genes implicated in chronic heart and lung diseases. Conclusions: These data provide a molecular link between the associations of air pollution exposures with health effects. Electronic supplementary material The online version of this article (doi:10.1186/s12940-016-0187-z) contains supplementary material, which is available to authorized users

Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD

Background: The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes. Results: We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches. Conclusion: Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms

Synthesis and Applications of Cyano-Vinylene-Based Polymers Containing Cyclopentadithiophene and Dithienosilole Units for Photovoltaic Cells

ABSTRACT: Two b-cyano-thiophenevinylene-based polymers containing cyclopentadithiophene (CPDT-CN) and dithienosilole (DTS-CN) units were synthesized via Stille coupling reaction with Pd(PPh 3 ) 4 as a catalyst. The effects of the bridged atoms (C and Si) and cyano-vinylene groups on their thermal, optical, electrochemical, charge transporting, and photovoltaic properties were investigated. Both polymers possessed the highest occupied molecular orbital (HOMO) levels of about À5.30 eV and the lowest unoccupied molecular orbital (LUMO) levels of about À3.60 eV, and covered broad absorption ranges with narrow optical band gaps (ca. 1.6 eV). The bulk heterojunction polymer solar cell (PSC) devices containing an active layer o

A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism

<p>Abstract</p> <p>Background</p> <p>Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes.</p> <p>Results</p> <p>Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3) identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4) evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2) and Interleukin 1B (IL1B), two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics.</p> <p>Conclusion</p> <p>Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.</p

The CD4+ T-cell transcriptome and serum IgE in asthma: IL17RB and the role of sex

<p>Abstract</p> <p>Background</p> <p>The relationships between total serum IgE levels and gene expression patterns in peripheral blood CD4+ T cells (in all subjects and within each sex specifically) are not known.</p> <p>Methods</p> <p>Peripheral blood CD4+ T cells from 223 participants from the Childhood Asthma Management Program (CAMP) with simultaneous measurement of IgE. Total RNA was isolated, and expression profiles were generated with Illumina HumanRef8 v2 BeadChip arrays. Modeling of the relationship between genome-wide gene transcript levels and IgE levels was performed in all subjects, and stratified by sex.</p> <p>Results</p> <p>Among all subjects, significant evidence for association between gene transcript abundance and IgE was identified for a single gene, the interleukin 17 receptor B (IL17RB), explaining 12% of the variance (r²) in IgE measurement (p value = 7 × 10^-7, 9 × 10^-3after adjustment for multiple testing). Sex stratified analyses revealed that the correlation between IL17RB and IgE was restricted to males only (r²= 0.19, p value = 8 × 10^-8; test for sex-interaction p < 0.05). Significant correlation between gene transcript abundance and IgE level was not found in females. Additionally we demonstrated substantial sex-specific differences in IgE when considering multi-gene models, and in canonical pathway analyses of IgE level.</p> <p>Conclusions</p> <p>Our results indicate that IL17RB may be the only gene expressed in CD4+ T cells whose transcript measurement is correlated with the variation in IgE level in asthmatics. These results provide further evidence sex may play a role in the genomic regulation of IgE.</p