Functional Dyadicity and Heterophilicity of Gene-Gene Interactions in Statistical Epistasis Networks

Background: The interaction effect among multiple genetic factors, i.e. epistasis, plays an important role in explaining susceptibility on common human diseases and phenotypic traits. The uncertainty over the number of genetic attributes involved in interactions poses great challenges in genetic association studies and calls for advanced bioinformatics methodologies. Network science has gained popularity in modeling genetic interactions thanks to its structural characterization of large numbers of entities and their complex relationships. However, little has been done on functionally interpreting statistically inferred epistatic interactions using networks. Results: In this study, we propose to characterize gene functional properties in the context of interaction network structure. We used Gene Ontology (GO) to functionally annotate genes as vertices in a statistical epistasis network, and quantitatively characterize the correlation between the distribution of gene functional properties and the network structure by measuring dyadicity and heterophilicity of each functional category in the network. These two parameters quantify whether genetic interactions tend to occur more frequently for genes from the same functional category, i.e. dyadic effect, or more frequently for genes from across different functional categories, i.e. heterophilic effect. Conclusions: By applying this framework to a population-based bladder cancer dataset, we were able to identify several GO categories that have significant dyadicity or heterophilicity associated with bladder cancer susceptibility. Thus, our informatics framework suggests a new methodology for embedding functional analysis in network modeling of statistical epistasis in genetic association studies

Genomic and proteomic profiling of responses to toxic metals in human lung cells.

Examining global effects of toxic metals on gene expression can be useful for elucidating patterns of biological response, discovering underlying mechanisms of toxicity, and identifying candidate metal-specific genetic markers of exposure and response. Using a 1,200 gene nylon array, we examined changes in gene expression following low-dose, acute exposures of cadmium, chromium, arsenic, nickel, or mitomycin C (MMC) in BEAS-2B human bronchial epithelial cells. Total RNA was isolated from cells exposed to 3 M Cd(II) (as cadmium chloride), 10 M Cr(VI) (as sodium dichromate), 3 g/cm2 Ni(II) (as nickel subsulfide), 5 M or 50 M As(III) (as sodium arsenite), or 1 M MMC for 4 hr. Expression changes were verified at the protein level for several genes. Only a small subset of genes was differentially expressed in response to each agent: Cd, Cr, Ni, As (5 M), As (50 M), and MMC each differentially altered the expression of 25, 44, 31, 110, 65, and 16 individual genes, respectively. Few genes were commonly expressed among the various treatments. Only one gene was altered in response to all four metals (hsp90), and no gene overlapped among all five treatments. We also compared low-dose (5 M, noncytotoxic) and high-dose (50 M, cytotoxic) arsenic treatments, which surprisingly, affected expression of almost completely nonoverlapping subsets of genes, suggesting a threshold switch from a survival-based biological response at low doses to a death response at high doses

Detecting Gene-Gene Interactions Using a Permutation-Based Random Forest Method

Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions

Drinking-Water Arsenic Exposure Modulates Gene Expression in Human Lymphocytes from a U.S. Population

Background: Arsenic exposure impairs development and can lead to cancer, cardiovascular disease, and diabetes. The mechanism underlying these effects remains unknown. Primarily because of geologic sources of contamination, drinking-water arsenic levels are above the current recommended maximum contaminant level of 10 μg/L in the northeastern, western, and north central regions of the United States. Objectives: We investigated the effects of arsenic exposure, defined by internal biomarkers at levels relevant to the United States and similarly exposed populations, on gene expression. Methods: We conducted separate Affymetrix microarray-based genomewide analyses of expression patterns. Peripheral blood lymphocyte samples from 21 controls interviewed (1999–2002) as part of a case–control study in New Hampshire were selected based on high- versus low-level arsenic exposure levels. Results: The biologic functions of the transcripts that showed statistically significant abundance differences between high- and low-arsenic exposure groups included an overrepresentation of genes involved in defense response, immune function, cell growth, apoptosis, regulation of cell cycle, T-cell receptor signaling pathway, and diabetes. Notably, the high-arsenic exposure group exhibited higher levels of several killer cell immunoglobulin-like receptors that inhibit natural killer cell activity. Conclusions: These findings define biologic changes that occur with chronic arsenic exposure in humans and provide leads and potential targets for understanding and monitoring the pathogenesis of arsenic-induced diseases

Drinking-Water Arsenic Exposure Modulates Gene Expression in Human Lymphocytes from a U.S. Population

Background: Arsenic exposure impairs development and can lead to cancer, cardiovascular disease, and diabetes. The mechanism underlying these effects remains unknown. Primarily because of geologic sources of contamination, drinking-water arsenic levels are above the current recommended maximum contaminant level of 10 μg/L in the northeastern, western, and north central regions of the United States. Objectives: We investigated the effects of arsenic exposure, defined by internal biomarkers at levels relevant to the United States and similarly exposed populations, on gene expression. Methods: We conducted separate Affymetrix microarray-based genomewide analyses of expression patterns. Peripheral blood lymphocyte samples from 21 controls interviewed (1999–2002) as part of a case–control study in New Hampshire were selected based on high- versus low-level arsenic exposure levels. Results: The biologic functions of the transcripts that showed statistically significant abundance differences between high- and low-arsenic exposure groups included an overrepresentation of genes involved in defense response, immune function, cell growth, apoptosis, regulation of cell cycle, T-cell receptor signaling pathway, and diabetes. Notably, the high-arsenic exposure group exhibited higher levels of several killer cell immunoglobulin-like receptors that inhibit natural killer cell activity. Conclusions: These findings define biologic changes that occur with chronic arsenic exposure in humans and provide leads and potential targets for understanding and monitoring the pathogenesis of arsenic-induced diseases

Lung Cancer in a U.S. Population with Low to Moderate Arsenic Exposure

BackgroundLittle is known about the carcinogenic potential of arsenic in areas with low to moderate concentrations of arsenic (< 100 microg/L) in drinking water.ObjectivesWe examined associations between arsenic and lung cancer.MethodsA population-based case-control study of primary incident lung cancer was conducted in 10 counties in two U.S. states, New Hampshire and Vermont. The study included 223 lung cancer cases and 238 controls, each of whom provided toenail clippings for arsenic exposure measurement by inductively coupled-plasma mass spectrometry. We estimated odds ratios (ORs) of the association between arsenic exposure and lung cancer using unconditional logistic regression with adjustment for potential confounders (age, sex, race/ethnicity, smoking pack-years, education, body mass index, fish servings per week, and toenail selenium level).ResultsArsenic exposure was associated with small-cell and squamous-cell carcinoma of the lung [OR = 2.75; 95% confidence interval (CI), 1.00-7.57] for toenail arsenic concentration > or = 0.114 microg/g, versus < 0.05 microg/g. A history of lung disease (bronchitis, chronic obstructive pulmonary disease, or fibrosis) was positively associated with lung cancer (OR = 2.86; 95% CI, 1.39-5.91). We also observed an elevated risk of lung cancer among participants with a history of lung disease and toenail arsenic > or = 0.05 microg/g (OR = 4.78; 95% CI, 1.87-12.2) than among individuals with low toenail arsenic and no history of lung disease.ConclusionAlthough this study supports the possibility of an increased risk of specific lung cancer histologic types at lower levels of arsenic exposure, we recommend large-scale population-based studies

Hyper-Methylated Loci Persisting from Sessile Serrated Polyps to Serrated Cancers

Although serrated polyps were historically considered to pose little risk, it is now understood that progression down the serrated pathway could account for as many as 15%–35% of colorectal cancers. The sessile serrated adenoma/polyp (SSA/P) is the most prevalent pre-invasive serrated lesion. Our objective was to identify the CpG loci that are persistently hyper-methylated during serrated carcinogenesis, from the early SSA/P lesion through the later cancer phases of neoplasia development. We queried the loci hyper-methylated in serrated cancers within our rightsided SSA/Ps from the New Hampshire Colonoscopy Registry, using the Illumina Infinium Human Methylation 450 k panel to comprehensively assess the DNA methylation status. We identified CpG loci and regions consistently hyper-methylated throughout the serrated carcinogenesis spectrum, in both our SSA/P specimens and in serrated cancers. Hyper-methylated CpG loci included the known the tumor suppressor gene RET (p = 5.72 x 10−10), as well as loci in differentially methylated regions for GSG1L, MIR4493, NTNG1, MCIDAS, ZNF568, and RERG. The hyper-methylated loci that we identified help characterize the biology of SSA/P development, and could be useful as therapeutic targets, or for future identification of patients who may benefit from shorter surveillance intervals

Environmental And Occupational Exposures And Amyotrophic Lateral Sclerosis In New England

Background: Recent data provide support for the concept that potentially modifiable exposures are responsible for sporadic amyotrophic lateral sclerosis (ALS). Objective: To evaluate environmental and occupational exposures as risk factors for sporadic ALS. Methods: We performed a case control study of ALS among residents of New England, USA. The analysis compared questionnaire responses from 295 patients with a confirmed ALS diagnosis to those of 225 controls without neurodegenerative illness. Results: Self-reported job-or hobby-related exposure to one or more chemicals, such as pesticides, solvents, or heavy metals, increased the risk of ALS (adjusted OR 2.51; 95% CI 1.64-3.89). Industries with a higher toxicant exposure potential (construction, manufacturing, mechanical, military, or painting) were associated with an elevated occupational risk (adjusted OR 3.95; 95% CI 2.04-8.30). We also identified increases in the risk of ALS associated with frequent participation in water sports, particularly waterskiing (adjusted OR 3.89; 95% C11.97-8.44). Occupation and waterskiing both retained independent statistical significance in a composite model containing age, gender, and smoking status. Conclusions: Our study contributes to a growing body of literature implicating occupational-and hobby-related toxicant exposures in ALS etiology. These epidemiologic study results also provide motivation for future evaluation of water-body-related risk factors. (C) 2017 S. Karger AG, Base

Functional Genomics Annotation of a Statistical Epistasis Network Associated with Bladder Cancer Susceptibility

Background: Several different genetic and environmental factors have been identified as independent risk factors for bladder cancer in population-based studies. Recent studies have turned to understanding the role of gene-gene and gene-environment interactions in determining risk. We previously developed the bioinformatics framework of statistical epistasis networks (SEN) to characterize the global structure of interacting genetic factors associated with a particular disease or clinical outcome. By applying SEN to a population-based study of bladder cancer among Caucasians in New Hampshire, we were able to identify a set of connected genetic factors with strong and significant interaction effects on bladder cancer susceptibility. Findings: To support our statistical findings using networks, in the present study, we performed pathway enrichment analyses on the set of genes identified using SEN, and found that they are associated with the carcinogen benzo[a]pyrene, a component of tobacco smoke. We further carried out an mRNA expression microarray experiment to validate statistical genetic interactions, and to determine if the set of genes identified in the SEN were differentially expressed in a normal bladder cell line and a bladder cancer cell line in the presence or absence of benzo[a]pyrene. Significant nonrandom sets of genes from the SEN were found to be differentially expressed in response to benzo[a]pyrene in both the normal bladder cells and the bladder cancer cells. In addition, the patterns of gene expression were significantly different between these two cell types. Conclusions: The enrichment analyses and the gene expression microarray results support the idea that SEN analysis of bladder in population-based studies is able to identify biologically meaningful statistical patterns. These results bring us a step closer to a systems genetic approach to understanding cancer susceptibility that integrates population and laboratory-based studies