PADB : Published Association Database

Background  

Although molecular pathway information and the International HapMap Project data can help biomedical researchers to investigate the aetiology of complex diseases more effectively, such information is missing or insufficient in current genetic association databases. In addition, only a few of the environmental risk factors are included as gene-environment interactions, and the risk measures of associations are not indexed in any association databases.     

From 'omics' to complex disease: a systems biology approach to gene-environment interactions in cancer

Background  

Cancer is a complex disease that involves a sequence of gene-environment interactions in a progressive process that cannot occur without dysfunction in multiple systems, including DNA repair, apoptotic and immune functions. Epigenetic mechanisms, responding to numerous internal and external cues in a dynamic ongoing exchange, play a key role in mediating environmental influences on gene expression and tumor development.     

Gene profiling of the erythro- and megakaryoblastic leukaemias induced by the Graffi murine retrovirus

Background

Many common diseases arise from an interaction between environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease.

Methods

We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.

Results

We were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A.

Conclusions

We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Genetic and environmental contributions to weight, height, and BMI from birth to 19 years of age: an international study of over 12,000 twin pairs

Objective

To examine the genetic and environmental influences on variances in weight, height, and BMI, from birth through 19 years of age, in boys and girls from three continents.

Design and Settings

Cross-sectional twin study. Data obtained from a total of 23 twin birth-cohorts from four countries: Canada, Sweden, Denmark, and Australia. Participants were Monozygotic (MZ) and dizygotic (DZ) (same- and opposite-sex) twin pairs with data available for both height and weight at a given age, from birth through 19 years of age. Approximately 24,036 children were included in the analyses.

Results

Heritability for body weight, height, and BMI was low at birth (between 6.4 and 8.7% for boys, and between 4.8 and 7.9% for girls) but increased over time, accounting for close to half or more of the variance in body weight and BMI after 5 months of age in both sexes. Common environmental influences on all body measures were high at birth (between 74.1–85.9% in all measures for boys, and between 74.2 and 87.3% in all measures for girls) and markedly reduced over time. For body height, the effect of the common environment remained significant for a longer period during early childhood (up through 12 years of age). Sex-limitation of genetic and shared environmental effects was observed.

Conclusion

Genetics appear to play an increasingly important role in explaining the variation in weight, height, and BMI from early childhood to late adolescence, particularly in boys. Common environmental factors exert their strongest and most independent influence specifically in pre-adolescent years and more significantly in girls. These findings emphasize the need to target family and social environmental interventions in early childhood years, especially for females. As gene-environment correlation and interaction is likely, it is also necessary to identify the genetic variants that may predispose individuals to obesity.     

Text mining and manual curation of chemical-gene-disease networks for the Comparative Toxicogenomics Database (CTD)

Background  

The Comparative Toxicogenomics Database (CTD) is a publicly available resource that promotes understanding about the etiology of environmental diseases. It provides manually curated chemical-gene/protein interactions and chemical- and gene-disease relationships from the peer-reviewed, published literature. The goals of the research reported here were to establish a baseline analysis of current CTD curation, develop a text-mining prototype from readily available open source components, and evaluate its potential value in augmenting curation efficiency and increasing data coverage.     

Variations in genome-wide gene expression in identical twins – a study of primary osteoblast-like culture from female twins discordant for osteoporosis

Background  

Monozygotic twin pairs who are genetically identical would be potentially useful in gene expression study for specific traits as cases and controls, because there would be much less gene expression variation within pairs compared to two unrelated individuals. However the twin pair has to be discordant for the particular trait or phenotype excluding those resulting from known confounders. Such discordant monozygotic twin pairs are rare and very few studies have explored the potential usefulness of this approach.     

Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap,a nonlinear algorithm

Background  

Life processes are determined by the organism's genetic profile and multiple environmental variables. However the interaction between these factors is inherently non-linear [1]. Microarray data is one representation of the nonlinear interactions among genes and genes and environmental factors. Still most microarray studies use linear methods for the interpretation of nonlinear data. In this study, we apply Isomap, a nonlinear method of dimensionality reduction, to analyze three independent large Affymetrix high-density oligonucleotide microarray data sets.     

Environmental benefits from reusing clothes

Background, aim, and scope  

Clothes are often discarded when much of their potential lifetime is left. Many charitable organizations therefore collect used clothing and resell it as second-hand clothes for example in Eastern Europe or Africa. In this connection, the question arises whether reusing clothes actually results in a decrease of the environmental burden of the life cycle of clothing. The environmental burden of clothing has been studied in several studies. However, most of these studies focus solely on the energy consumption aspects and pay little attention to the potential benefits of diverting used clothing from the waste stream. The aim of the study was to assess the net environmental benefits brought by the disposal of used clothing through charities who return them for second-hand sales assuming that second-hand clothes to some extent replace the purchase of new clothes.     

Socioeconomic status (SES) and children's intelligence (IQ): in a UK-representative sample SES moderates the environmental, not genetic, effect on IQ

Background

The environment can moderate the effect of genes - a phenomenon called gene-environment (GxE) interaction. Several studies have found that socioeconomic status (SES) modifies the heritability of children''s intelligence. Among low-SES families, genetic factors have been reported to explain less of the variance in intelligence; the reverse is found for high-SES families. The evidence however is inconsistent. Other studies have reported an effect in the opposite direction (higher heritability in lower SES), or no moderation of the genetic effect on intelligence.

Methods

Using 8716 twin pairs from the Twins Early Development Study (TEDS), we attempted to replicate the reported moderating effect of SES on children''s intelligence at ages 2, 3, 4, 7, 9, 10, 12 and 14: i.e., lower heritability in lower-SES families. We used a twin model that allowed for a main effect of SES on intelligence, as well as a moderating effect of SES on the genetic and environmental components of intelligence.

Results

We found greater variance in intelligence in low-SES families, but minimal evidence of GxE interaction across the eight ages. A power calculation indicated that a sample size of about 5000 twin pairs is required to detect moderation of the genetic component of intelligence as small as 0.25, with about 80% power - a difference of 11% to 53% in heritability, in low- (−2 standard deviations, SD) and high-SES (+2 SD) families. With samples at each age of about this size, the present study found no moderation of the genetic effect on intelligence. However, we found the greater variance in low-SES families is due to moderation of the environmental effect – an environment-environment interaction.

Conclusions

In a UK-representative sample, the genetic effect on intelligence is similar in low- and high-SES families. Children''s shared experiences appear to explain the greater variation in intelligence in lower SES.     

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background  

The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.     

Interaction between Polymorphisms in Pre-MiRNA Genes and Cooking Oil Fume Exposure on the Risk of Lung Cancer in Chinese Non-Smoking Female Population

Background

Both genetic polymorphisms and environmental risk factors play important roles in the development of human chronic diseases including lung cancer. This is the first case-control study of interaction between polymorphisms in pre-miRNA genes and cooking oil fume exposure on the risk of lung cancer.

Methods

A hospital-based case-control study of 258 cases and 310 controls was conducted. Six polymorphisms in miRNAs were determined by Taqman allelic discrimination method. The gene-environment interactions were assessed on both additive and multiplicative scale. The statistical analyses were performed mostly with SPSS.

Results

The combination of the risk genotypes of five miRNA SNPs (miR-146a rs2910164, miR-196a2 rs11614913, miR-608 rs4919510, miR-27a rs895819 and miR-423 rs6505162) with risk factor (cooking oil fume exposure) contributed to a significantly higher risk of lung cancer, and the corresponding ORs (95% confidence intervals) were 1.91(1.04-3.52), 1.94 (1.16-3.25), 2.06 (1.22-3.49), 1.76 (1.03-2.98) and 2.13 (1.29-3.51). The individuals with both risk genotypes of miRNA SNPs and exposure to risk factor (cooking oil fumes) were in a higher risk of lung cancer than persons with only one of the two risk factors (ORs were 1.91, 1.05 and 1.41 for miR-146a rs2910164, ORs were 1.94, 1.23 and 1.34 for miR-196a2 rs11614913, ORs were 2.06, 1.41 and 1.68 for miR-608 rs4919510, ORs were 1.76, 0.82 and 1.07 for miR-27a rs895819, and ORs were 2.13, 1.15 and 1.02 for miR-423 rs6505162, respectively). All the measures of biological interaction indicate that there were not indeed biological interactions between the six SNPs of miRNAs and exposure to cooking oil fumes on an additive scale. Logistic models suggested that the gene-environment interactions were not statistically significant on a multiplicative scale.

Conclusions

The interactions between miRNA SNPs and cooking oil fume exposure suggested by ORs of different combination were not statistically significant.     

MetAssimulo:Simulation of Realistic NMR Metabolic Profiles

Background  

Probing the complex fusion of genetic and environmental interactions, metabolic profiling (or metabolomics/metabonomics), the study of small molecules involved in metabolic reactions, is a rapidly expanding 'omics' field. A major technique for capturing metabolite data is ¹H-NMR spectroscopy and this yields highly complex profiles that require sophisticated statistical analysis methods. However, experimental data is difficult to control and expensive to obtain. Thus data simulation is a productive route to aid algorithm development.     

A comparison of internal validation techniques for multifactor dimensionality reduction

Background  

It is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data.     

Inferring the conservative causal core of gene regulatory networks

Background  

Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.     

Genetic and potential non-genetic benefits increase offspring fitness of polyandrous females in non-resource based mating system

Background  

The adaptive significance of female polyandry is currently under considerable debate. In non-resource based mating systems, indirect, i.e. genetic benefits have been proposed to be responsible for the fitness gain from polyandry. We studied the benefits of polyandry in the Arctic charr (Salvelinus alpinus) using an experimental design in which the material investments by the sires and maternal environmental effects were controlled.     

Peripheral and gastrointestinal immune systems of healthy cattle raised outdoors at pasture or indoors on a concentrate-based ration

Background  

Despite an increasing preference of consumers for beef produced from more extensive pasture-based production systems and potential human health benefits from the consumption of such beef, data regarding the health status of animals raised on pasture are limited. The objective of this study was to characterise specific aspects of the bovine peripheral and the gastrointestinal muscosal immune systems of cattle raised on an outdoor pasture system in comparison to animals raised on a conventional intensive indoor concentrate-based system.     

Interactions between Environmental Factors and Melatonin Receptor Type 1A Polymorphism in Relation to Oral Cancer Susceptibility and Clinicopathologic Development

Background

The purpose of this study was to explore the combined effect of melatonin receptor type 1A (MTNR1A) gene polymorphisms and exposure to environmental carcinogens on the susceptibility and clinicopathological characteristics of oral cancer.

Methodology and Principal Findings

Three polymorphisms of the MTNR1A gene from 618 patients with oral cancer and 560 non-cancer controls were analyzed by real-time polymerase chain reaction (PCR). The CTA haplotype of the studied MTNR1A polymorphisms (rs2119882, rs13140012, rs6553010) was related to a higher risk of oral cancer. Moreover, MTNR1A gene polymorphisms exhibited synergistic effects of environmental factors (betel quid and tobacco use) on the susceptibility of oral cancer. Finally, oral-cancer patients with betel quid-chewing habit who had T/T allele of MTNR1A rs13140012 were at higher risk for developing an advanced clinical stage and lymph node metastasis.

Conclusion

These results support gene-environment interactions of MTNR1A polymorphisms with smoking and betel quid-chewing habits possibly altering oral-cancer susceptibility and metastasis.     

Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions

Background  

The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.