The aim of this review is to summarize the key findings from genetic and epidemiological research, which show that autism is a complex disorder resulting from the combination of genetic and environmental factors. Remarkable advances in the knowledge of genetic causes of autism have resulted from the great efforts made in the field of genetics. The identification of specific alleles contributing to the autism spectrum has supplied important pieces for the autism puzzle. However, many questions remain unanswered, and new questions are raised by recent results. Moreover, given the amount of evidence supporting a significant contribution of environmental factors to autism risk, it is now clear that the search for environmental factors should be reinforced. One aspect of this search that has been neglected so far is the study of interactions between genes and environmental factors.

The significance of the environment for health is increasingly being recognized. There is a need for systematic approaches to assessment of environmental factors most relevant to health, health outcomes most influenced by the environment, and the relationships between them, as well as for approaches to representing the results of such assessments in policy deliberations. As a step in the development of such methods, we used findings and data from the environmental protection and public health sectors to develop a set of measures representing topics relevant to children's environmental health. We used a definition of the environment that emphasized contaminants and a process that involved both analytic and deliberative elements. The steps in this process were to a) develop a conceptual framework to depict relationships between environment and health with relevant types of data and information, b) select topic areas of significance for children, c) identify best available data sources and devise measures, d) assess possible surrogate data sources and measures when needed, e) design and implement metrics for computation of measures using specified data elements, f) select graphical representations of measures, g) identify related measures, and h) identify data gaps. Representatives of policy and stakeholder audiences participated in this process. The measures are presented in three groups that reflect contaminants in the environment, contaminants in human tissues, and diseases and disorders. The measures present scientifically based representations of data understandable to stakeholders and policy makers that integrate key information from the health and environment sectors in a consistent format.

Obesity continues to be a health burden to society and new efforts may be needed to combat this epidemic. This study aims to investigate the contribution of parents education and level of income, food environment (grocery stores and fast food restaurants), and built environment (perceived safety, availability/quantity of parks) on childhood obesity. This cross-sectional observational study explored whether parents education and income level, built environment, and food environment can affect children with obesity. Participants were selected from 3 separate elementary schools located in an urban community with higher risk to have children with obesity in Montclair, California. Children living in families with low incomes have 2.31 times greater odds to be affected by obesity than children living in higher income homes. Children whose parents did not feel safe in their neighborhoods had odds of obesity 2.23 times greater than those who reported their neighborhoods as safe. Age also appeared to be a risk factor, and the odds of children affected by obesity among children 8 to 9 years was 0.79, and the odds of being affected by obesity among children 10 to 11 years of age was 0.36, when compared to children 6 to 7 years old. Findings suggest that low family income, perceptions of neighborhoods as unsafe, and young age are associated with higher body mass index (BMI) percentiles among children living in poor neighborhoods in Montclair, California.

How combinations of gene-environment interactions collectively give rise to genotype-environment interactions is not fully understood. To shed light on this problem, we genetically dissected an environment-specific poor growth phenotype in a cross of two budding yeast strains. This phenotype is detectable when certain segregants are grown on ethanol at 37°C ('E37'), a condition that differs from the standard culturing environment in both its carbon source (ethanol as opposed to glucose) and temperature (37°C as opposed to 30°C). Using recurrent backcrossing with phenotypic selection, we identified 16 contributing loci. To examine how these loci interact with each other and the environment, we focused on a subset of four loci that together can lead to poor growth in E37. We measured the growth of all 16 haploid combinations of alleles at these loci in all four possible combinations of carbon source (ethanol or glucose) and temperature (30 or 37°C) in a nearly isogenic population. This revealed that the four loci act in an almost entirely additive manner in E37. However, we also found that these loci have weaker effects when only carbon source or temperature is altered, suggesting that their effect magnitudes depend on the severity of environmental perturbation. Consistent with such a possibility, cloning of three causal genes identified factors that have unrelated functions in stress response. Thus, our results indicate that polymorphisms in stress response can show effects that are intensified by environmental stress, thereby resulting in major genotype-environment interactions when multiple of these variants co-occur.

No abstract available

Interactive analysis notebook environments promise to streamline genomics research through interleaving text, multimedia, and executable code into unified, sharable, reproducible "research narratives." However, current notebook systems require programming knowledge, limiting their wider adoption by the research community. We have developed the GenePattern Notebook environment (http://www.genepattern-notebook.org), to our knowledge the first system to integrate the dynamic capabilities of notebook systems with an investigator-focused, easy-to-use interface that provides access to hundreds of genomic tools without the need to write code.

An individual's response to environmental exposures varies depending on their genotype, which has been termed the gene-environment interaction. The phenotype of cell exposed can also be a key determinant in the response to physiological cues, indicating that a cell-gene-environment interaction may exist. We investigated whether the cellular environment could alter the transcriptional response to environmental chemicals. Publicly available gene expression array data permitted a targeted comparison of the transcriptional response to a unique subclass of environmental chemicals that alter the activity of the estrogen receptor, xenoestrogens.

Evaluation of the educational climate has been highlighted as key to the delivery of high-quality medical education. Health educators across places and educational settings have widely used the Dundee Ready Educational Environment Measure (DREEM) to appraise their institutions' educational climate.

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294-91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits-12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs-one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic-covariate and residual-covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

The transition from episodic migraine to chronic migraine, migraine chronification, is usually a gradual process, which involves multiple risk factors. To date, studies of the genetic risk factors for chronic migraine have focused primarily on candidate-gene approaches using healthy individuals as controls.

The atmosphere is part of the environment with which the human organism is permanently confronted. Epidemiological research investigates the occurrence of effects on morbidity and mortality due to heat, cold, air pollution and changes in the weather. Concentrating on aspects of the environment relevant for medical questions, three major complexes of effects can be discriminated: the complex conditions of heat exchange, the direct biological effects of solar radiation, and air pollution. Biometeorological knowledge can serve to assess the atmospheric environment, and can also be of help in the field of preventive planning, to conserve and develop the climate as a natural resource with regard to man's health, well-being and performance.

No abstract available

bioBakery is a meta'omic analysis environment and collection of individual software tools with the capacity to process raw shotgun sequencing data into actionable microbial community feature profiles, summary reports, and publication-ready figures. It includes a collection of pre-configured analysis modules also joined into workflows for reproducibility.

Next generation biofuels including longer-chain alcohols such as butanol are attractive as renewable, high-energy fuels. A barrier to microbial production of butanols is the increased toxicity compared to ethanol; however, the cellular targets and microbial defense mechanisms remain poorly understood, especially under anaerobic conditions used frequently in industry. Here we took a comparative approach to understand the response of Saccharomyces cerevisiae to 1-butanol, isobutanol, or ethanol, across three genetic backgrounds of varying tolerance in aerobic and anaerobic conditions. We find that strains have different growth properties and alcohol tolerances with and without oxygen availability, as well as unique and common responses to each of the three alcohols. Our results provide evidence for strain-by-alcohol-by-oxygen interactions that moderate how cells respond to alcohol stress.

Evidence based health policy, such as that put forward in the European Food and Nutrition Action Plan 2015-2020 and the WHO Global Action Plan on the Prevention and Control of Noncommunicable Diseases, has a role in curbing the consumption of unhealthful foods and drink. We ask how countries are performing in the adoption of these policies and how the comprehensiveness of their food environment policies explains variations in consumption of unhealthful products across Europe.

Perception of undesirable features may inhibit built environment use for physical activity among underserved families with children at risk for obesity.

Interactions among genes and the environment are a common source of phenotypic variation. To characterize the interplay between genetics and the environment at single nucleotide resolution, we quantified the genetic and environmental interactions of four quantitative trait nucleotides (QTN) that govern yeast sporulation efficiency. We first constructed a panel of strains that together carry all 32 possible combinations of the 4 QTN genotypes in 2 distinct genetic backgrounds. We then measured the sporulation efficiencies of these 32 strains across 8 controlled environments. This dataset shows that variation in sporulation efficiency is shaped largely by genetic and environmental interactions. We find clear examples of QTN:environment, QTN: background, and environment:background interactions. However, we find no QTN:QTN interactions that occur consistently across the entire dataset. Instead, interactions between QTN only occur under specific combinations of environment and genetic background. Thus, what might appear to be a QTN:QTN interaction in one background and environment becomes a more complex QTN:QTN:environment:background interaction when we consider the entire dataset as a whole. As a result, the phenotypic impact of a set of QTN alleles cannot be predicted from genotype alone. Our results instead demonstrate that the effects of QTN and their interactions are inextricably linked both to genetic background and to environmental variation.

Occurrence of pharmaceuticals, especially antibiotics in the environment increased attention to their environmental fate. Hydrolysis is one of two abiotic processes by which compounds are degraded in the environment. According to authors knowledge this is the first study investigating hydrolytic degradation of nitrofurantoin at pH-values normally found in the environment. Nitrofurantoin hydrolytic degradation appeared to be much slower at acidic (pH 4) solution compared to neutral (pH 7) and alkaline (pH 9) solutions at all three investigated temperatures (20 °C, 40 °C and 60 °C). In all cases nitrofurantoin hydrolysis followed the first-order kinetics with half-lives ranged from 0.5 days at pH 9 and 60 °C to 3.9 years at pH 4 and 20 °C. Temperature dependence of the hydrolysis rate constant was quantified by Arrhenius equation; obtained Ea values were as follows: 100.7 kJ mol-1 at pH 4, 111.2 kJ mol-1 at pH 7 and 102.3 kJ mol-1 at pH 9. Increase in hydrolysis rate constants for each 10 °C increase in temperature were 3.4, 3.9 and 3.5 at pH 4, pH 7 and pH 9, respectively. The structures of hydrolytic degradation products were determined and degradation pathways were suggested. Three main processes occurred depending on pH-values: protonation of the nitrofurantoin followed by cleavage of the NN single bond, heterocyclic non-aromatic ring cleavage, and reduction of the non-aromatic heterocyclic ring.

The sound fed to a loudspeaker may significantly differ from that reaching the ear of the listener. The transformation from one to the other consists of spectral distortions with strong dependence on the relative locations of the speaker and the listener as well as on the geometry of the environment. With the increased importance of research in awake, freely-moving animals in large arenas, it becomes important to understand how animal location influences the corresponding spectral distortions.

High-quality draft genomes are now easy to generate, as sequencing and assembly costs have dropped dramatically. However, building a user-friendly searchable Web site and database for a newly annotated genome is not straightforward. Here we present Badger, a lightweight and easy-to-install genome exploration environment designed for next generation non-model organism genomes.