Variation in the social environment is a fundamental component of many vertebrate societies. In humans and other primates, adverse social environments often translate into lasting physiological costs. The biological mechanisms associated with these effects are therefore of great interest, both for understanding the evolutionary impacts of social behavior and in the context of human health. However, large gaps remain in our understanding of the mechanisms that mediate these effects at the molecular level. Here we addressed these questions by leveraging the power of an experimental system that consisted of 10 social groups of female macaques, in which each individual's social status (i.e., dominance rank) could be experimentally controlled. Using this paradigm, we show that dominance rank results in a widespread, yet plastic, imprint on gene regulation, such that peripheral blood mononuclear cell gene expression data alone predict social status with 80% accuracy. We investigated the mechanistic basis of these effects using cell type-specific gene expression profiling and glucocorticoid resistance assays, which together contributed to rank effects on gene expression levels for 694 (70%) of the 987 rank-related genes. We also explored the possible contribution of DNA methylation levels to these effects, and identified global associations between dominance rank and methylation profiles that suggest epigenetic flexibility in response to status-related behavioral cues. Together, these results illuminate the importance of the molecular response to social conditions, particularly in the immune system, and demonstrate a key role for gene regulation in linking the social environment to individual physiology.

Li et al.(Research Articles, 1 July 2011, p. 53; published online 19 May 2011) reported more than 10,000 mismatches between messenger RNA and DNA sequences from the same individuals, which they attributed to previously unrecognized mechanisms of gene regulation. We found that at least 88% of these sequence mismatches can likely be explained by technical artifacts such as errors in mapping sequencing reads to a reference genome, sequencing errors, and genetic variation.

Differences in gene regulation are thought to play an important role in speciation and adaptation. Comparative genomic studies of gene expression levels have identified a large number of differentially expressed genes among species, and, in a number of cases, also pointed to connections between interspecies differences in gene regulation and differences in ultimate physiological or morphological phenotypes. The mechanisms underlying changes in gene regulation are also being actively studied using comparative genomic approaches. However, the relative importance of different regulatory mechanisms to interspecies differences in gene expression levels is not yet well understood. In particular, it is often difficult to infer causality between apparent differences in regulatory mechanisms and changes in gene expression levels, a challenge that is compounded by the fact that the link between sequence variation and gene regulation is not clear. Indeed, in certain cases, gene regulation can be conserved even when sequences at associated regulatory elements have changed. In this chapter, I examine different genomic approaches to the study of regulatory evolution and the underlying genetic and epigenetic regulatory mechanisms. I try to distinguish between hypothesis-driven and exploratory studies, and argue that the latter class of studies provides valuable information in its own right as well as necessary context for the former. I discuss issues related to study designs and statistical analyses of genomic studies, and review the evidence for natural selection on gene expression levels and associated regulatory mechanisms. Most of the issues that are discussed pertain to the general nature of multivariate genomic data, and thus are often relevant regardless of the technology that is used to collect high-throughput genomic data (for example, microarrays or massively parallel sequencing).

Mapping of expression quantitative trait loci (eQTLs) is an important technique for studying how genetic variation affects gene regulation in natural populations. In a previous study using Illumina expression data from human lymphoblastoid cell lines, we reported that cis-eQTLs are especially enriched around transcription start sites (TSSs) and immediately upstream of transcription end sites (TESs). In this paper, we revisit the distribution of eQTLs using additional data from Affymetrix exon arrays and from RNA sequencing. We confirm that most eQTLs lie close to the target genes; that transcribed regions are generally enriched for eQTLs; that eQTLs are more abundant in exons than introns; and that the peak density of eQTLs occurs at the TSS. However, we find that the intriguing TES peak is greatly reduced or absent in the Affymetrix and RNA-seq data. Instead our data suggest that the TES peak observed in the Illumina data is mainly due to exon-specific QTLs that affect 3' untranslated regions, where most of the Illumina probes are positioned. Nonetheless, we do observe an overall enrichment of eQTLs in exons versus introns in all three data sets, consistent with an important role for exonic sequences in gene regulation.

Although metastasis is the most lethal attribute of cancer, critical gaps in our knowledge of how cancer cells effectively colonize distant sites remain. For example, little is known about the cellular and molecular events that occur during the timecourse of metastatic colonization. To address this we are using the mitogen-activated protein kinase kinase 4 (MKK4) metastasis suppressor as a tool to identify these events. Specifically, we report a microarray expression-based strategy to identify genes whose transcription is altered in SKOV3ip.1 human ovarian cancer cells that express ectopic MKK4 throughout the course of in vivo metastatic colonization. The majority of genes identified fell into the categories of cytokinesis, cytoskeleton remodeling, and cell adhesion, and their expression was repressed in MKK4-expressing cells relative to vector controls. The greatest transcriptional divergence was concomitant with impaired proliferation at 14 days post injection (dpi). Specifically, 763 genes were differentially expressed (FDR < 0.05) between lesions that expressed ectopic MKK4 and paired controls. In contrast, only seven genes were differentially expressed at the experimental endpoint, when MKK4-expressing and control cells had formed macroscopic metastases. Application of our cohort of differentially expressed genes to three independent clinical datasets demonstrated a strong correlation between our findings and metastatic phenotypes in patient samples. Our results highlight the dynamic nature of metastatic colonization and reinforce the importance of examining both molecular and cellular phenotypes over time when studying metastasis formation.

The mapping of expression quantitative trait loci (eQTLs) has emerged as an important tool for linking genetic variation to changes in gene regulation. However, it remains difficult to identify the causal variants underlying eQTLs, and little is known about the regulatory mechanisms by which they act. Here we show that genetic variants that modify chromatin accessibility and transcription factor binding are a major mechanism through which genetic variation leads to gene expression differences among humans. We used DNase I sequencing to measure chromatin accessibility in 70 Yoruba lymphoblastoid cell lines, for which genome-wide genotypes and estimates of gene expression levels are also available. We obtained a total of 2.7 billion uniquely mapped DNase I-sequencing (DNase-seq) reads, which allowed us to produce genome-wide maps of chromatin accessibility for each individual. We identified 8,902 locations at which the DNase-seq read depth correlated significantly with genotype at a nearby single nucleotide polymorphism or insertion/deletion (false discovery rate = 10%). We call such variants 'DNase I sensitivity quantitative trait loci'(dsQTLs). We found that dsQTLs are strongly enriched within inferred transcription factor binding sites and are frequently associated with allele-specific changes in transcription factor binding. A substantial fraction (16%) of dsQTLs are also associated with variation in the expression levels of nearby genes (that is, these loci are also classified as eQTLs). Conversely, we estimate that as many as 55% of eQTL single nucleotide polymorphisms are also dsQTLs. Our observations indicate that dsQTLs are highly abundant in the human genome and are likely to be important contributors to phenotypic variation.

ABSTRACT: BACKGROUND: Expression quantitative trait loci (eQTLs) are likely to play an important role in the genetics of complex traits; however, their functional basis remains poorly understood. Using the HapMap lymphoblastoid cell lines, we combine 1000 Genomes genotypes and an extensive catalogue of human functional elements to investigate the biological mechanisms that eQTLs perturb. RESULTS: We use a Bayesian hierarchical model to estimate the enrichment of eQTLs in a wide variety of regulatory annotations. We find that approximately 40% of eQTLs occur in open chromatin, and that they are particularly enriched in transcription factor binding sites, suggesting that many directly impact protein-DNA interactions. Analysis of core promoter regions shows that eQTLs also frequently disrupt some known core promoter motifs but, surprisingly, are not enriched in other well-known motifs such as the TATA box. We also show that information from regulatory annotations alone, when weighted by the hierarchical model, can provide a meaningful ranking of the SNPs that are most likely to drive gene expression variation. CONCLUSIONS: Our study demonstrates how regulatory annotation and the association signal derived from eQTL-mapping can be combined into a single framework. We used this approach to further our understanding of the biology that drives human gene expression variation, and of the putatively causal SNPs that underlie it.

Recent genome-wide association studies (GWAS) have identified a number of novel genetic associations with complex human diseases. In spite of these successes, results from GWAS generally explain only a small proportion of disease heritability, an observation termed the 'missing heritability problem'. Several sources for the missing heritability have been proposed, including the contribution of many common variants with small individual effect sizes, which cannot be reliably found using the standard GWAS approach. The goal of our study was to explore a complimentary approach, which combines GWAS results with functional data in order to identify novel genetic associations with small effect sizes. To do so, we conducted a GWAS for lymphocyte count, a physiologic quantitative trait associated with asthma, in 462 Hutterites. In parallel, we performed a genome-wide gene expression study in lymphoblastoid cell lines from 96 Hutterites. We found significant support for genetic associations using the GWAS data when we considered variants near the 193 genes whose expression levels across individuals were most correlated with lymphocyte counts. Interestingly, these variants are also enriched with signatures of an association with asthma susceptibility, an observation we were able to replicate. The associated loci include genes previously implicated in asthma susceptibility as well as novel candidate genes enriched for functions related to T cell receptor signaling and adenosine triphosphate synthesis. Our results, therefore, establish a new set of asthma susceptibility candidate genes. More generally, our observations support the notion that many loci of small effects influence variation in lymphocyte count and asthma susceptibility.

Tuberculosis (TB) is a major public health problem. One-third of the world's population is estimated to be infected with Mycobacterium tuberculosis (MTB), the etiological agent causing TB, and active disease kills nearly 2 million individuals worldwide every year. Several lines of evidence indicate that interindividual variation in susceptibility to TB has a heritable component, yet we still know little about the underlying genetic architecture. To address this, we performed a genome-wide mapping study of loci that are associated with functional variation in immune response to MTB. Specifically, we characterized transcript and protein expression levels and mapped expression quantitative trait loci (eQTL) in primary dendritic cells (DCs) from 65 individuals, before and after infection with MTB. We found 198 response eQTL, namely loci that were associated with variation in gene expression levels in either untreated or MTB-infected DCs, but not both. These response eQTL are associated with natural regulatory variation that likely affects (directly or indirectly) host interaction with MTB. Indeed, when we integrated our data with results from a genome-wide association study (GWAS) for pulmonary TB, we found that the response eQTL were more likely to be genetically associated with the disease. We thus identified a number of candidate loci, including the MAPK phosphatase DUSP14 in particular, that are promising susceptibility genes to pulmonary TB.

Comparative genomic studies in primates have yielded important insights into the evolutionary forces that shape genetic diversity and revealed the likely genetic basis for certain species-specific adaptations. To date, however, these studies have focused on only a small number of species. For the majority of non-human primates, including some of the most critically endangered, genome-level data are not yet available. In this study, we have taken the first steps towards addressing this gap by sequencing RNA from the livers of multiple individuals from each of 16 mammalian species, including humans and 11 non-human primates. Of the non-human primate species, five are lemurs and two are lorisoids, for which little or no genomic data were previously available. To analyze these data, we developed a method for de novo assembly and alignment of orthologous gene sequences across species. We assembled an average of 5,721 genes per species, and characterized diversity and divergence of both gene sequences and gene expression levels. We identified patterns of variation that are consistent with the action of positive or directional selection, including an 18-fold enrichment of peroxisomal genes among genes whose regulation likely evolved under directional selection in the ancestral primate lineage. Importantly, we found no relationship between genetic diversity and endangered status, with the two most endangered species in our study, the black and white ruffed lemur and the Coquerels sifaka, having the highest genetic diversity among all primates. Our observations imply that many endangered lemur populations still harbor considerable genetic variation. Timely efforts to conserve these species alongside their habitats have therefore strong potential to achieve long-term success.