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It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

Whole genome analysis in large samples from a single population is needed to provide adequate power to assess relative strengths of natural selection across different functional components of the genome. In this study, we analyzed next-generation sequencing data from 962 European Americans, and found that as expected approximately 60% of the top 1% of positive selection signals lie in intergenic regions, 33% in intronic regions, and slightly over 1% in coding regions. Several detailed functional annotation categories in intergenic regions showed statistically significant enrichment in positively selected loci when compared to the null distribution of the genomic span of ENCODE categories. There was a significant enrichment of purifying selection signals detected in enhancers, transcription factor binding sites, microRNAs and target sites, but not on lincRNA or piRNAs, suggesting different evolutionary constraints for these domains. Loci in "repressed or low activity regions" and loci near or overlapping the transcription start site were the most significantly over-represented annotations among the top 1% of signals for positive selection.

Following the dispersal out of Africa, where hominins evolved in warm environments for millions of years, our species has colonised different climate zones of the world, including high latitudes and cold environments. The extent to which human habitation in (sub-)Arctic regions has been enabled by cultural buffering, short-term acclimatization and genetic adaptations is not clearly understood. Present day indigenous populations of Siberia show a number of phenotypic features, such as increased basal metabolic rate, low serum lipid levels and increased blood pressure that have been attributed to adaptation to the extreme cold climate. In this study we introduce a dataset of 200 individuals from ten indigenous Siberian populations that were genotyped for 730,525 SNPs across the genome to identify genes and non-coding regions that have undergone unusually rapid allele frequency and long-range haplotype homozygosity change in the recent past. At least three distinct population clusters could be identified among the Siberians, each of which showed a number of unique signals of selection. A region on chromosome 11 (chr11:66-69 Mb) contained the largest amount of clustering of significant signals and also the strongest signals in all the different selection tests performed. We present a list of candidate cold adaption genes that showed significant signals of positive selection with our strongest signals associated with genes involved in energy regulation and metabolism (CPT1A, LRP5, THADA) and vascular smooth muscle contraction (PRKG1). By employing a new method that paints phased chromosome chunks by their ancestry we distinguish local Siberian-specific long-range haplotype signals from those introduced by admixture.

BACKGROUND:Localising regulatory variants that control gene expression is a challenge for genome research. Several studies have recently identified non-coding polymorphisms associated with inter-individual differences in gene expression. These approaches rely on the identification of signals of association against a background of variation due to other genetic and environmental factors. A complementary approach is to use an Allele-Specific Expression (ASE) assay, which is more robust to the effects of environmental variation and trans-acting genetic factors. METHODOLOGY/PRINCIPAL FINDINGS:Here we apply an ASE method which utilises heterozygosity within an individual to compare expression of the two alleles of a gene in a single cell. We used individuals from three HapMap population groups and analysed the allelic expression of genes with cis-regulatory regions previously identified using total gene expression studies. We were able to replicate the results in five of the six genes tested, and refined the cis- associated regions to a small number of variants. We also showed that by using multi-populations it is possible to refine the associated cis-effect DNA regions. CONCLUSIONS/SIGNIFICANCE:We discuss the efficacy and drawbacks of both total gene expression and ASE approaches in the discovery of cis-acting variants. We show that the ASE approach has significant advantages as it is a cleaner representation of cis-acting effects. We also discuss the implication of using different populations to map cis-acting regions and the importance of finding regulatory variants which contribute to human phenotypic variation.