Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay

Optimizing the chances of success in the search for epigenetic biomarkers: Embracing genetic variation

The emphasis on clinical translation in biomedical research continues to grow. This focus has been particularly notable in those investigators using epigenetic approaches to decipher the biology of complex behavioral disorders. As a result of these efforts, reproducible findings for several disorders, such as smoking, have been generated, giving rise to hopes that biomarkers for other behavioral illnesses would be forthcoming. Unfortunately, that biomedical cornucopia has not yet materialized. In this editorial, we review progress to date and discuss barriers to generating epigenetic biomarkers for complex behavioral disorders. We highlight the need to incorporate information on genetic variation and develop more powerful bioinformatics tools in order to optimize the likelihood of success. We emphasize that searches should focus on clearly defined, readily distinguishable behavioral constructs and suggest that some well-intentioned methods, such as correction for cellular heterogeneity, may actually impede the identification of clinically relevant biomarkers in peripheral blood. Finally, we describe how the understanding created by the development of these biomarkers may lead to more valid animal models of neuropsychiatric illness. We conclude that the prospects for epigenetic biomarkers for complex disorders are bright, but emphasize that the journey to the clinical implementation of these findings will be a slow, iterative process.

Predicting gene expression in massively parallel reporter assays: A comparative study

In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.

Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression

Expression quantitative trait locus (eQTL) detection has emerged as an important tool for unraveling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and the magnitude of effects. We describe a series of simulation studies designed to evaluate the impact of linkage disequilibrium (LD) on the fine mapping of causal variants with typical eQTL effect sizes. In the presence of multisite regulation, even though between 80 and 90% of modeled eSNPs associate with normally distributed traits, up to 10% of all secondary signals could be statistical artifacts, and at least 5% but up to one-quarter of credible intervals of SNPs within r² > 0.8 of the peak may not even include a causal site. The Bayesian methods eCAVIAR and DAP (Deterministic Approximation of Posteriors) provide only modest improvement in resolution. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine mapping of causal variants needs to be adjusted for multisite influences, as conditional estimates can be highly biased by interference among linked sites, but ultimately experimental verification of individual effects is needed. Presumably similar conclusions apply not just to eQTL mapping, but to multisite influences on fine mapping of most types of quantitative trait.

A Genetic Variant Associated with Five Vascular Diseases Is a Distal Regulator of Endothelin-1 Gene Expression

Genome-wide association studies (GWASs) implicate the PHACTR1 locus (6p24) in risk for five vascular diseases, including coronary artery disease, migraine headache, cervical artery dissection, fibromuscular dysplasia, and hypertension. Through genetic fine mapping, we prioritized rs9349379, a common SNP in the third intron of the PHACTR1 gene, as the putative causal variant. Epigenomic data from human tissue revealed an enhancer signature at rs9349379 exclusively in aorta, suggesting a regulatory function for this SNP in the vasculature. CRISPR-edited stem cell-derived endothelial cells demonstrate rs9349379 regulates expression of endothelin 1 (EDN1), a gene located 600 kb upstream of PHACTR1. The known physiologic effects of EDN1 on the vasculature may explain the pattern of risk for the five associated diseases. Overall, these data illustrate the integration of genetic, phenotypic, and epigenetic analysis to identify the biologic mechanism by which a common, non-coding variant can distally regulate a gene and contribute to the pathogenesis of multiple vascular diseases.

Long-adapter single-strand oligonucleotide probes for the massively multiplexed cloning of kilobase genome regions

As the catalogue of sequenced genomes and metagenomes continues to grow, massively parallel approaches for the comprehensive and functional analysis of gene products and regulatory elements are becoming increasingly valuable. Current strategies to synthesize or clone complex libraries of DNA sequences are limited by the length of the DNA targets, throughput and cost. Here, we show that long-adapter single-strand oligonucleotide (LASSO) probes can capture and clone thousands of kilobase DNA fragments in a single reaction. As a proof-of-principle, we simultaneously cloned >3,000 bacterial open reading frames (ORFs) from E. coli genomic DNA (spanning 400–5,000 bp targets). Targets were enriched up to a median of ~60-fold compared to non-targeted genomic regions. At a cutoff of 3 times the median non-target reads per kilobase of genetic element per million reads, ~75% of the targeted ORFs were successfully captured. We also show that LASSO probes can clone human ORFs from complementary DNA, and an ORF library from a human-microbiome sample. LASSO probes could be used for the preparation of long-read sequencing libraries and for massively multiplexed cloning.

Effects of Type 1 Diabetes Risk Alleles on Immune Cell Gene Expression

Genetic studies have identified 61 variants associated with the risk of developing Type 1 Diabetes (T1D). The functions of most of the non-HLA (Human Leukocyte Antigen) genetic variants remain unknown. We found that only 16 of these risk variants could potentially be linked to a protein-coding change. Therefore, we investigated whether these variants affected susceptibility by regulating changes in gene expression. To do so, we examined whole transcriptome profiles of 600 samples from the Type 1 Diabetes Genetics Consortium (T1DGC). These comprised four different immune cell types (Epstein-Barr virus (EBV)-transformed B cells, either basal or after stimulation; and cluster of differentiation (CD)4+ and CD8+ T cells). Many of the T1D-associated risk variants regulated expression of either neighboring (cis-) or distant (trans-) genes. In brief, 24 of the non-HLA T1D variants affected the expression of 31 nearby genes (cis) while 25 affected 38 distant genes (trans). The effects were highly significant (False Discovery Rate p < 0.001). In addition, we searched in public databases for expression effects of T1D single nucleotide polymorphisms (SNPs) in other immune cell types such as CD14+ monocytes, lipopolysaccharide (LPS) stimulated monocytes, and CD19+ B cells. In this paper, we review the (expression quantitative trait loci (eQTLs) associated with each of the 60 T1D variants and provide a summary of the genes impacted by T1D risk alleles in various immune cells. We then review the methodological steps involved in analyzing the function of genome wide association studies (GWAS)-identified variants, with emphasis on those affecting gene expression. We also discuss recent advancements in the methodologies and their advantages. We conclude by suggesting future study designs that will aid in the study of T1D risk variants.

Recent advances in high-throughput approaches to dissect enhancer function

The regulation of gene transcription in higher eukaryotes is accomplished through the involvement of transcription start site (TSS)-proximal (promoters) and -distal (enhancers) regulatory elements. It is now well acknowledged that enhancer elements play an essential role during development and cell differentiation, while genetic alterations in these elements are a major cause of human disease. Many strategies have been developed to identify and characterize enhancers. Here, we discuss recent advances in high-throughput approaches to assess enhancer activity, from the well-established massively parallel reporter assays to the recent clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-based technologies. We highlight how these approaches contribute toward a better understanding of enhancer function, eventually leading to the discovery of new types of regulatory sequences, and how the alteration of enhancers can affect transcriptional regulation.

Challenges and progress in interpretation of non-coding genetic variants associated with human disease

Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.

Genetic insights into juvenile idiopathic arthritis derived from deep whole genome sequencing

Deep whole genome sequencing (WGS) allows for the comprehensive study of genetic landscapes at finer resolution than array based methods. We conducted deep WGS on children with the polyarticular form of juvenile idiopathic arthritis (JIA), using 2 independent cohorts to ascertain the sequencing fidelity. Genome wide SNP density analysis identified 18 SNP hotspots with comparison to the 1000 Genome Projects (1KGP) data. A subset of the genes adjacent to SNP hotspots showed statistically significant enrichment in immunological processes. Genes adjacent to indel hotspots were functionally related to G-protein coupled signaling pathways. Further analyses elucidated significantly more JIA SNPs with regulatory potential compared to 1KGP data. Furthermore, SNPs located within linkage disequibilium (LD) blocks containing previously identified JIA-associated SNPs demonstrated higher regulation potential compared to SNPs outside LD blocks. We also demonstrated enrichment of novel JIA variants in histone modification peaks and DNase hypersensitivity sites in B cells. This study greatly expands the number of genetic variants that may contribute to JIA and give us some clues into what may trigger this disease. To date, this study is the first deep WGS effort on children with JIA and provides useful genetic resources for research communities particularly in understanding JIA etiology.

Cellular network perturbations by disease-associated variants

Genetic and genome-wide association studies (GWAS) have identified a myriad of human disease-associated genomic variants. However, these studies do not reveal the mechanisms by which these variants perturb cellular networks, a necessary step to intervene and improve disease outcomes. This has been challenging because multiple variants are present in haplotype blocks, thereby confounding the identification of causal variants, and because most reside in noncoding regions. Here, we review recent advances in the identification of functional variants and gene-variant associations. In addition, we examine approaches used to study perturbations in protein-protein and protein-DNA interactions associated with disease, and discuss how these perturbations affect cellular networks.

Understanding Human Autoimmunity and Autoinflammation Through Transcriptomics

Transcriptomics, the high-throughput characterization of RNAs, has been instrumental in defining pathogenic signatures in human autoimmunity and autoinflammation. It enabled the identification of new therapeutic targets in IFN-, IL-1- and IL-17-mediated diseases. Applied to immunomonitoring, transcriptomics is starting to unravel diagnostic and prognostic signatures that stratify patients, track molecular changes associated with disease activity, define personalized treatment strategies, and generally inform clinical practice. Herein, we review the use of transcriptomics to define mechanistic, diagnostic, and predictive signatures in human autoimmunity and autoinflammation. We discuss some of the analytical approaches applied to extract biological knowledge from high-dimensional data sets. Finally, we touch upon emerging applications of transcriptomics to study eQTLs, B and T cell repertoire diversity, and isoform usage.

Accurate eQTL prioritization with an ensemble-based framework

We present a novel ensemble-based computational framework, EnsembleExpr, that achieved the best performance in the Fourth Critical Assessment of Genome Interpretation expression quantitative trait locus "(eQTL)-causal SNPs" challenge for identifying eQTLs and prioritizing their gene expression effects. eQTLs are genome sequence variants that result in gene expression changes and are thus prime suspects in the search for contributions to the causality of complex traits. When EnsembleExpr is trained on data from massively parallel reporter assays, it accurately predicts reporter expression levels from unseen regulatory sequences and identifies sequence variants that exhibit significant changes in reporter expression. Compared with other state-of-the-art methods, EnsembleExpr achieved competitive performance when applied on eQTL datasets determined by other protocols. We envision EnsembleExpr to be a resource to help interpret noncoding regulatory variants and prioritize disease-associated mutations for downstream validation.

Gene Regulatory Elements, Major Drivers of Human Disease

Gene expression changes, the driving forces for cellular diversity in multicellular organisms, are regulated by a diverse set of gene regulatory elements that direct transcription in specific cells. Mutations in these elements, ranging from chromosomal aberrations to single-nucleotide polymorphisms, are a major cause of human disease. However, we currently have a very limited understanding of how regulatory element genotypes lead to specific phenotypes. In this review, we discuss the various methods of regulatory element identification, the different types of mutations they harbor, and their impact on human disease. We highlight how these variations can affect transcription of multiple genes in gene regulatory networks. In addition, we describe how novel technologies, such as massively parallel reporter assays and CRISPR/Cas9 genome editing, are beginning to provide a better understanding of the functional roles that these elements have and how their alteration can lead to specific phenotypes.

Large, Diverse Population Cohorts of hiPSCs and Derived Hepatocyte-like Cells Reveal Functional Genetic Variation at Blood Lipid-Associated Loci

Genome-wide association studies have struggled to identify functional genes and variants underlying complex phenotypes. We recruited a multi-ethnic cohort of healthy volunteers (n = 91) and used their tissue to generate induced pluripotent stem cells (iPSCs) and hepatocyte-like cells (HLCs) for genome-wide mapping of expression quantitative trait loci (eQTLs) and allele-specific expression (ASE). We identified many eQTL genes (eGenes) not observed in the comparably sized Genotype-Tissue Expression project's human liver cohort (n = 96). Focusing on blood lipid-associated loci, we performed massively parallel reporter assays to screen candidate functional variants and used genome-edited stem cells, CRISPR interference, and mouse modeling to establish rs2277862-CPNE1, rs10889356-DOCK7, rs10889356-ANGPTL3, and rs10872142-FRK as functional SNP-gene sets. We demonstrated HLC eGenes CPNE1, VKORC1, UBE2L3, and ANGPTL3 and HLC ASE gene ACAA2 to be lipid-functional genes in mouse models. These findings endorse an iPSC-based experimental framework to discover functional variants and genes contributing to complex human traits.

Transcription factor-DNA binding: beyond binding site motifs

Sequence-specific transcription factors (TFs) regulate gene expression by binding to cis-regulatory elements in promoter and enhancer DNA. While studies of TF-DNA binding have focused on TFs' intrinsic preferences for primary nucleotide sequence motifs, recent studies have elucidated additional layers of complexity that modulate TF-DNA binding. In this review, we discuss technological developments for identifying TF binding preferences and highlight recent discoveries that elaborate how TF interactions, local DNA structure, and genomic features influence TF-DNA binding. We highlight novel approaches for characterizing functional binding site motifs that promise to inform our understanding of how TF binding controls gene expression and ultimately contributes to phenotype.

cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant’s regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test.

Predicting enhancer activity and variant impact using gkm-SVM

We participated in the Critical Assessment of Genome Interpretation eQTL challenge to further test computational models of regulatory variant impact and their association with human disease. Our prediction model is based on a discriminative gapped-kmer SVM (gkm-SVM) trained on genome-wide chromatin accessibility data in the cell type of interest. The comparisons with massively parallel reporter assays (MPRA) in lymphoblasts show that gkm-SVM is among the most accurate prediction models even though all other models used the MPRA data for model training, and gkm-SVM did not. In addition, we compare gkm-SVM with other MPRA datasets and show that gkm-SVM is a reliable predictor of expression and that deltaSVM is a reliable predictor of variant impact in K562 cells and mouse retina. We further show that DHS (DNase-I hypersensitive sites) and ATAC-seq (assay for transposase-accessible chromatin using sequencing) data are equally predictive substrates for training gkm-SVM, and that DHS regions flanked by H3K27Ac and H3K4me1 marks are more predictive than DHS regions alone.

Systematic dissection of genomic features determining transcription factor binding and enhancer function

Enhancers regulate gene expression through the binding of sequence-specific transcription factors (TFs) to cognate motifs. Various features influence TF binding and enhancer function-including the chromatin state of the genomic locus, the affinities of the binding site, the activity of the bound TFs, and interactions among TFs. However, the precise nature and relative contributions of these features remain unclear. Here, we used massively parallel reporter assays (MPRAs) involving 32,115 natural and synthetic enhancers, together with high-throughput in vivo binding assays, to systematically dissect the contribution of each of these features to the binding and activity of genomic regulatory elements that contain motifs for PPARγ, a TF that serves as a key regulator of adipogenesis. We show that distinct sets of features govern PPARγ binding vs. enhancer activity. PPARγ binding is largely governed by the affinity of the specific motif site and higher-order features of the larger genomic locus, such as chromatin accessibility. In contrast, the enhancer activity of PPARγ binding sites depends on varying contributions from dozens of TFs in the immediate vicinity, including interactions between combinations of these TFs. Different pairs of motifs follow different interaction rules, including subadditive, additive, and superadditive interactions among specific classes of TFs, with both spatially constrained and flexible grammars. Our results provide a paradigm for the systematic characterization of the genomic features underlying regulatory elements, applicable to the design of synthetic regulatory elements or the interpretation of human genetic variation.

[High-throughput approaches to study cis-regulating elements]

Gene expression in higher eukaryotes is regulated through the involvement of transcription start site (TSS)-proximal (promoters) and -distal (enhancers) regulatory elements. Enhancer elements play an essential role during development and cell differentiation, while genetic alterations in these elements are a major cause of human disease. Here, we discuss recent advances in high-throughput approaches to identify and characterize enhancer elements, from the well-established massively parallel reporter assays to the recent clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-based technologies. We discuss how these approaches contribute toward a better understanding of enhancer function in normal and pathological conditions.

Decoding the role of regulatory element polymorphisms in complex disease

Genetic variation in gene regulatory elements contributes to diverse human diseases, ranging from rare and severe developmental defects to common and complex diseases such as obesity and diabetes. Early examples of regulatory mechanisms of human diseases involve large chromosomal rearrangements that change the regulatory connections within the genome. Single nucleotide variants in regulatory elements can also contribute to disease, potentially via demonstrated associations with changes in transcription factor binding, enhancer activity, post-translational histone modifications, long-range enhancer-promoter interactions, or RNA polymerase recruitment. Establishing causality between non-coding genetic variants, gene regulation, and disease has recently become more feasible with advances in genome-editing and epigenome-editing technologies. As establishing causal regulatory mechanisms of diseases becomes routine, functional annotation of target genes is likely to emerge as a major bottleneck for translation into patient benefits. In this review, we discuss the history and recent advances in understanding the regulatory mechanisms of human disease, and new challenges likely to be encountered once establishing those mechanisms becomes rote.

Targeted AID-mediated mutagenesis (TAM) enables efficient genomic diversification in mammalian cells

A large number of genetic variants have been associated with human diseases. However, the lack of a genetic diversification approach has impeded our ability to interrogate functions of genetic variants in mammalian cells. Current screening methods can only be used to disrupt a gene or alter its expression. Here we report the fusion of activation-induced cytidine deaminase (AID) with nuclease-inactive clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (dCas9) for efficient genetic diversification, which enabled high-throughput screening of functional variants. Guided by single guide (sg)RNAs, dCas9-AID-P182X (AIDx) directly changed cytidines or guanines to the other three bases independent of AID hotspot motifs, generating a large repertoire of variants at desired loci. Coupled with a uracil-DNA glycosylase inhibitor, dCas9-AIDx converted targeted cytidines specifically to thymines, creating specific point mutations. By targeting BCR-ABL with dCas9-AIDx, we efficiently identified known and new mutations conferring imatinib resistance in chronic myeloid leukemia cells. Thus, targeted AID-mediated mutagenesis (TAM) provides a forward genetic tool to screen for gain-of-function variants at base resolution.

Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions

Massively parallel reporter assays (MPRAs) enable nucleotide-resolution dissection of transcriptional regulatory regions, such as enhancers, but only few regions at a time. Here we present a combined experimental and computational approach, Systematic high-resolution activation and repression profiling with reporter tiling using MPRA (Sharpr-MPRA), that allows high-resolution analysis of thousands of regions simultaneously. Sharpr-MPRA combines dense tiling of overlapping MPRA constructs with a probabilistic graphical model to recognize functional regulatory nucleotides, and to distinguish activating and repressive nucleotides, using their inferred contribution to reporter gene expression. We used Sharpr-MPRA to test 4.6 million nucleotides spanning 15,000 putative regulatory regions tiled at 5-nucleotide resolution in two human cell types. Our results recovered known cell-type-specific regulatory motifs and evolutionarily conserved nucleotides, and distinguished known activating and repressive motifs. Our results also showed that endogenous chromatin state and DNA accessibility are both predictive of regulatory function in reporter assays, identified retroviral elements with activating roles, and uncovered 'attenuator' motifs with repressive roles in active chromatin.

The power of multiplexed functional analysis of genetic variants

New technologies have recently enabled saturation mutagenesis and functional analysis of nearly all possible variants of regulatory elements or proteins of interest in single experiments. Here we discuss the past, present, and future of such multiplexed (functional) assays for variant effects (MAVEs). MAVEs provide detailed insight into sequence-function relationships, and they may prove critical for the prospective clinical interpretation of genetic variants.

CRISPR Screens to Discover Functional Noncoding Elements

A major challenge in genomics is to identify functional elements in the noncoding genome. Recently, pooled clustered regularly interspersed palindromic repeat (CRISPR) mutagenesis screens of noncoding regions have emerged as a novel method for finding elements that impact gene expression and phenotype/disease-relevant biological processes. Here we review and compare different approaches for high-throughput dissection of noncoding elements.

Systematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell Traits

Genome-wide association studies (GWAS) have successfully identified thousands of associations between common genetic variants and human disease phenotypes, but the majority of these variants are non-coding, often requiring genetic fine-mapping, epigenomic profiling, and individual reporter assays to delineate potential causal variants. We employ a massively parallel reporter assay (MPRA) to simultaneously screen 2,756 variants in strong linkage disequilibrium with 75 sentinel variants associated with red blood cell traits. We show that this assay identifies elements with endogenous erythroid regulatory activity. Across 23 sentinel variants, we conservatively identified 32 MPRA functional variants (MFVs). We used targeted genome editing to demonstrate endogenous enhancer activity across 3 MFVs that predominantly affect the transcription of SMIM1, RBM38, and CD164. Functional follow-up of RBM38 delineates a key role for this gene in the alternative splicing program occurring during terminal erythropoiesis. Finally, we provide evidence for how common GWAS-nominated variants can disrupt cell-type-specific transcriptional regulatory pathways.

Pooled ChIP-Seq Links Variation in Transcription Factor Binding to Complex Disease Risk

Cis-regulatory elements such as transcription factor (TF) binding sites can be identified genome-wide, but it remains far more challenging to pinpoint genetic variants affecting TF binding. Here, we introduce a pooling-based approach to mapping quantitative trait loci (QTLs) for molecular-level traits. Applying this to five TFs and a histone modification, we mapped thousands of cis-acting QTLs, with over 25-fold lower cost compared to standard QTL mapping. We found that single genetic variants frequently affect binding of multiple TFs, and CTCF can recruit all five TFs to its binding sites. These QTLs often affect local chromatin and transcription but can also influence long-range chromosomal contacts, demonstrating a role for natural genetic variation in chromosomal architecture. Thousands of these QTLs have been implicated in genome-wide association studies, providing candidate molecular mechanisms for many disease risk loci and suggesting that TF binding variation may underlie a large fraction of human phenotypic variation.