Interpreting Coronary Artery Disease Risk Through Gene-Environment Interactions in Gene Regulation.

GWAS and eQTL studies identified thousands of genetic variants associated with complex traits and gene expression. Despite the important role of environmental exposures in complex traits, only a limited number of environmental factors were measured in these studies. Measuring molecular phenotypes in tightly controlled cellular environments provides a more tractable setting to study gene-environment interactions in the absence of other confounding variables. We performed RNA-seq and ATAC-seq in endothelial cells exposed to retinoic acid, dexamethasone, caffeine, and selenium to model genetic and environmental effects on gene regulation in the vascular endothelium-a common site of pathology in cardiovascular disease. We found that genes near regions of differentially accessible chromatin were more likely to be differentially expressed [OR = (3.41, 6.52), [Formula: see text]]. Furthermore, we confirmed that environment-specific changes in transcription factor binding are a key mechanism for cellular response to environmental stimuli. Single nucleotide polymorphisms (SNPs) in these transcription response factor footprints for dexamethasone, caffeine, and retinoic acid were enriched in GTEx eQTLs from artery tissues, indicating that these environmental conditions are latently present in GTEx samples. Additionally, SNPs in footprints for response factors in caffeine are enriched in colocalized eQTLs for coronary artery disease (CAD), suggesting a role for caffeine in CAD risk. By combining GWAS, eQTLs, and response genes, we annotated environmental components that can increase or decrease disease risk through changes in gene expression in 43 genes. Interestingly, each treatment may amplify or buffer genetic risk for CAD, depending on the particular SNP or gene considered.

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection.

The availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies, they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of data sets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models-often severely. We find that selection can lead to incorrect inferences of population size changes when none have occurred. Moreover, we show that linked selection can lead to incorrect demographic model selection, when multiple demographic scenarios are compared. We argue that natural populations may experience the amount of recent positive selection required to skew inferences. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.

Functional Annotation and Comparative Analysis of a Zygopteran Transcriptome.

In this paper we present a de novo assembly of the transcriptome of the damselfly (Enallagma hageni) through the use of 454 pyrosequencing. E. hageni is a member of the suborder Zygoptera, in the order Odonata, and Odonata organisms form the basal lineage of the winged insects (Pterygota). To date, sequence data used in phylogenetic analysis of Enallagma species have been derived from either mitochondrial DNA or ribosomal nuclear DNA. This Enallagma transcriptome contained 31,661 contigs that were assembled and translated into 14,813 individual open reading frames. Using these data, we constructed an extensive dataset of 634 orthologous nuclear protein-encoding genes across 11 species of Arthropoda and used Bayesian techniques to elucidate the position of Enallagma in the arthropod phylogenetic tree. Additionally, we demonstrated that the Enallagma transcriptome contains 169 genes that are evolving at rates that differ relative to those of the rest of the transcriptome (29 accelerated and 140 decreased), and, through multiple Gene Ontology searches and clustering methods, we present the first functional annotation of any palaeopteran's transcriptome in the literature.