Ian C. McDowell

Distant regulatory effects of genetic variation in multiple human tissues

Understanding the genetics of gene regulation provides information on the cellular mechanisms through which genetic variation influences complex traits. Expression quantitative trait loci, or eQTLs, are enriched for polymorphisms that have been found to be associated with disease risk. While most analyses of human data has focused on regulation of expression by nearby variants (cis-eQTLs), distal or trans-eQTLs may have broader effects on the transcriptome and important phenotypic consequences, necessitating a comprehensive study of the effects of genetic variants on distal gene transcription levels. In this work, we identify trans-eQTLs in the Genotype Tissue Expression (GTEx) project data1, consisting of 449 individuals with RNA-sequencing data across 44 tissue types. We find 81 genes with a trans-eQTL in at least one tissue, and we demonstrate that trans-eQTLs are more likely than cis-eQTLs to have effects specific to a single tissue. We evaluate the genomic and functional properties of trans-eQTL variants, identifying strong enrichment in enhancer elements and Piwi-interacting RNA clusters. Finally, we describe three tissue-specific regulatory loci underlying relevant disease associations: 9q22 in thyroid that has a role in thyroid cancer, 5q31 in skeletal muscle, and a previously reported master regulator near KLF14 in adipose. These analyses provide a comprehensive characterization of trans-eQTLs across human tissues, which contribute to an improved understanding of the tissue-specific cellular mechanisms of regulatory genetic variation.

Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.

Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.

Genetic determinants of adiponectin regulation revealed by pregnancy

This study investigated genetic determinants of adiponectin during pregnancy to reveal novel biology of adipocyte regulation.

Many long intergenic non-coding RNAs distally regulate mRNA gene expression levels

Long intergenic non-coding RNAs (lincRNA) are members of a class of non-protein-coding RNA transcript that has recently been shown to contribute to gene regulatory processes and disease etiology. It has been hypothesized that lincRNAs influence disease risk through the regulation of mRNA transcription [88], possibly by interacting with regulatory proteins such as chromatin-modifying complexes [37, 50]. The hypothesis of the regulation of mRNA by lincRNAs is based on a small number of specific lincRNAs analyses; the cellular roles of lincRNAs regulation have not been catalogued genome-wide. Relative to mRNAs, lincRNAs tend to be expressed at lower levels and in more tissue-specific patterns, making genome-wide studies of their regulatory capabilities difficult [15]. Here we develop a method for Mendelian randomization leveraging expression quantitative trait loci (eQTLs) that regulate the expression levels of lincRNAs (linc-eQTLs) to perform such a study across four primary tissues. We find that linc-eQTLs are largely similar to protein-coding eQTLs (pc-eQTLs) in cis-regulatory element enrichment, which supports the hypothesis that lincRNAs are regulated by the same transcriptional machinery as protein-coding RNAs [15, 80] and validates our linc-eQTLs. We catalog 74 lincRNAs with linc-eQTLs that are in linkage disequilibrium with TASs and are in protein-coding gene deserts; the putative lincRNA-regulated traits are highly enriched for adipose-related traits relative to mRNA-regulated traits.

Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding

Glucocorticoids are potent steroid hormones that regulate immunity and metabolism by activating the transcription factor (TF) activity of glucocorticoid receptor (GR). Previous models have proposed that DNA binding motifs and sites of chromatin accessibility predetermine GR binding and activity. However, there are vast excesses of both features relative to the number of GR binding sites. Thus, these features alone are unlikely to account for the specificity of GR binding and activity. To identify genomic and epigenetic contributions to GR binding specificity and the downstream changes resultant from GR binding, we performed hundreds of genome-wide measurements of TF binding, epigenetic state, and gene expression across a 12-h time course of glucocorticoid exposure. We found that glucocorticoid treatment induces GR to bind to nearly all pre-established enhancers within minutes. However, GR binds to only a small fraction of the set of accessible sites that lack enhancer marks. Once GR is bound to enhancers, a combination of enhancer motif composition and interactions between enhancers then determines the strength and persistence of GR binding, which consequently correlates with dramatic shifts in enhancer activation. Over the course of several hours, highly coordinated changes in TF binding and histone modification occupancy occur specifically within enhancers, and these changes correlate with changes in the expression of nearby genes. Following GR binding, changes in the binding of other TFs precede changes in chromatin accessibility, suggesting that other TFs are also sensitive to genomic features beyond that of accessibility.

Pre-established Chromatin Interactions Mediate the Genomic Response to Glucocorticoids

The glucocorticoid receptor (GR) is a hormone-inducible transcription factor involved in metabolic and anti-inflammatory gene expression responses. To investigate what controls interactions between GR binding sites and their target genes, we used in situ Hi-C to generate high-resolution, genome-wide maps of chromatin interactions before and after glucocorticoid treatment. We found that GR binding to the genome typically does not cause new chromatin interactions to target genes but instead acts through chromatin interactions that already exist prior to hormone treatment. Both glucocorticoid-induced and glucocorticoid-repressed genes increased interactions with distal GR binding sites. In addition, while glucocorticoid-induced genes increased interactions with transcriptionally active chromosome compartments, glucocorticoid-repressed genes increased interactions with transcriptionally silent compartments. Lastly, while the architectural DNA-binding proteins CTCF and RAD21 were bound to most chromatin interactions, we found that glucocorticoid-responsive chromatin interactions were depleted for CTCF binding but enriched for RAD21. Together, these findings offer new insights into the mechanisms underlying GC-mediated gene activation and repression.

A long-range flexible billboard model of gene activation

ABSTRACT Gene regulation is fundamentally important for the coordination of diverse biologic processes including homeostasis and responses to developmental and environmental stimuli. Transcription factor (TF) binding sites are one of the major functional subunits of gene regulation. They are arranged in cis-regulatory modules (CRMs) that can be more active than the sum of their individual effects. Recently, we described a mechanism of glucocorticoid (GC)-induced gene regulation in which the glucocorticoid receptor (GR) binds coordinately to multiple CRMs that are 10s of kilobases apart in the genome. In those results, the minority of GR binding sites appear to involve direct TF:DNA interactions. Meanwhile, other GR binding sites in a cluster interact with those direct binding sites to tune their gene regulatory activity. Here, we consider the implications of those and related results in the context of existing models of gene regulation. Based on our analyses, we propose that the billboard and regulatory grammar models of cis-regulatory element activity be expanded to consider the influence of long-range interactions between cis-regulatory modules.

Clustering gene expression time series data using an infinite Gaussian process mixture model

Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.

Transversions have larger regulatory effects than transitions

BackgroundTransversions (Tv’s) are more likely to alter the amino acid sequence of proteins than transitions (Ts’s), and local deviations in the Ts:Tv ratio are indicative of evolutionary selection on genes. Whether the two different types of mutations have different effects in non-protein-coding sequences remains unknown. Genetic variants primarily impact gene expression by disrupting the binding of transcription factors (TFs) and other DNA-binding proteins. Because Tv’s cause larger changes in the shape of a DNA backbone, we hypothesized that Tv’s would have larger impacts on TF binding and gene expression.ResultsHere, we provide multiple lines of evidence demonstrating that Tv’s have larger impacts on regulatory DNA including analyses of TF binding motifs and allele-specific TF binding. In these analyses, we observed a depletion of Tv’s within TF binding motifs and TF binding sites. Using massively parallel population-scale reporter assays, we also provided empirical evidence that Tv’s have larger effects than Ts’s on the activity of human gene regulatory elements.ConclusionsTv’s are more likely to disrupt TF binding, resulting in larger changes in gene expression. Although the observed differences are small, these findings represent a novel, fundamental property of regulatory variation. Understanding the features of functional non-coding variation could be valuable for revealing the genetic underpinnings of complex traits and diseases in future studies.