Maya Kasowski

Annotation of functional variation in personal genomes using RegulomeDB

As the sequencing of healthy and disease genomes becomes more commonplace, detailed annotation provides interpretation for individual variation responsible for normal and disease phenotypes. Current approaches focus on direct changes in protein coding genes, particularly nonsynonymous mutations that directly affect the gene product. However, most individual variation occurs outside of genes and, indeed, most markers generated from genome-wide association studies (GWAS) identify variants outside of coding segments. Identification of potential regulatory changes that perturb these sites will lead to a better localization of truly functional variants and interpretation of their effects. We have developed a novel approach and database, RegulomeDB, which guides interpretation of regulatory variants in the human genome. RegulomeDB includes high-throughput, experimental data sets from ENCODE and other sources, as well as computational predictions and manual annotations to identify putative regulatory potential and identify functional variants. These data sources are combined into a powerful tool that scores variants to help separate functional variants from a large pool and provides a small set of putative sites with testable hypotheses as to their function. We demonstrate the applicability of this tool to the annotation of noncoding variants from 69 full sequenced genomes as well as that of a personal genome, where thousands of functionally associated variants were identified. Moreover, we demonstrate a GWAS where the database is able to quickly identify the known associated functional variant and provide a hypothesis as to its function. Overall, we expect this approach and resource to be valuable for the annotation of human genome sequences.

Architecture of the human regulatory network derived from ENCODE data

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

Variation in Transcription Factor Binding Among Humans

Like Father, Like Mother, Like Child Transcriptional regulation is mediated by chromatin structure, which may affect the binding of transcription factors, but the extent of how individual-to-individual genetic variation affects such regulation is not well understood. Kasowski et al. (p. 232, published online 18 March) investigated the binding of two transcription factors across the genomes of human individuals and one chimpanzee. Transcription factor binding was associated with genomic features such as nucleotide variation, insertions and deletions, and copy number variation. Thus, genomic sequence variation affects transcription factor binding and may explain expression difference among individuals. McDaniell et al. (p. 235, published online 18 March) provide a genome-wide catalog of variation in chromatin and transcription factor binding in two parent-child trios of European and African ancestry. Up to 10% of active chromatin binding sites were specific to a set of individuals and were often inherited. Furthermore, variation in active chromatin sites showed heritable allele-specific correlation with variation in gene expression. Transcription factor binding sites vary among individuals and are correlated with differences in expression. Differences in gene expression may play a major role in speciation and phenotypic diversity. We examined genome-wide differences in transcription factor (TF) binding in several humans and a single chimpanzee by using chromatin immunoprecipitation followed by sequencing. The binding sites of RNA polymerase II (PolII) and a key regulator of immune responses, nuclear factor κB (p65), were mapped in 10 lymphoblastoid cell lines, and 25 and 7.5% of the respective binding regions were found to differ between individuals. Binding differences were frequently associated with single-nucleotide polymorphisms and genomic structural variants, and these differences were often correlated with differences in gene expression, suggesting functional consequences of binding variation. Furthermore, comparing PolII binding between humans and chimpanzee suggests extensive divergence in TF binding. Our results indicate that many differences in individuals and species occur at the level of TF binding, and they provide insight into the genetic events responsible for these differences.

The genetic architecture of Down syndrome phenotypes revealed by high-resolution analysis of human segmental trisomies

Down syndrome (DS), or trisomy 21, is a common disorder associated with several complex clinical phenotypes. Although several hypotheses have been put forward, it is unclear as to whether particular gene loci on chromosome 21 (HSA21) are sufficient to cause DS and its associated features. Here we present a high-resolution genetic map of DS phenotypes based on an analysis of 30 subjects carrying rare segmental trisomies of various regions of HSA21. By using state-of-the-art genomics technologies we mapped segmental trisomies at exon-level resolution and identified discrete regions of 1.8–16.3 Mb likely to be involved in the development of 8 DS phenotypes, 4 of which are congenital malformations, including acute megakaryocytic leukemia, transient myeloproliferative disorder, Hirschsprung disease, duodenal stenosis, imperforate anus, severe mental retardation, DS-Alzheimer Disease, and DS-specific congenital heart disease (DSCHD). Our DS-phenotypic maps located DSCHD to a <2-Mb interval. Furthermore, the map enabled us to present evidence against the necessary involvement of other loci as well as specific hypotheses that have been put forward in relation to the etiology of DS—i.e., the presence of a single DS consensus region and the sufficiency of DSCR1 and DYRK1A, or APP, in causing several severe DS phenotypes. Our study demonstrates the value of combining advanced genomics with cohorts of rare patients for studying DS, a prototype for the role of copy-number variation in complex disease.

Extensive variation in chromatin states across humans.

DNA Differences The extent to which genetic variation affects an individuals phenotype has been difficult to predict because the majority of variation lies outside the coding regions of genes. Now, three studies examine the extent to which genetic variation affects the chromatin of individuals with diverse ancestry and genetic variation (see the Perspective by Furey and Sethupathy). Kasowski et al. (p. 750, published online 17 October) examined how genetic variation affects differences in chromatin states and their correlation to histone modifications, as well as more general DNA binding factors. Kilpinen et al. (p. 744, published online 17 October) document how genetic variation is linked to allelic specificity in transcription factor binding, histone modifications, and transcription. McVicker et al. (p. 747, published online 17 October) identified how quantitative trait loci affect histone modifications in Yoruban individuals and established which specific transcription factors affect such modifications. Variability among humans with different ancestry affects chromatin states and gene expression. [Also see Perspective by Furey and Sethupathy] The majority of disease-associated variants lie outside protein-coding regions, suggesting a link between variation in regulatory regions and disease predisposition. We studied differences in chromatin states using five histone modifications, cohesin, and CTCF in lymphoblastoid lines from 19 individuals of diverse ancestry. We found extensive signal variation in regulatory regions, which often switch between active and repressed states across individuals. Enhancer activity is particularly diverse among individuals, whereas gene expression remains relatively stable. Chromatin variability shows genetic inheritance in trios, correlates with genetic variation and population divergence, and is associated with disruptions of transcription factor binding motifs. Overall, our results provide insights into chromatin variation among humans.

Genome-wide map of regulatory interactions in the human genome

Increasing evidence suggests that interactions between regulatory genomic elements play an important role in regulating gene expression. We generated a genome-wide interaction map of regulatory elements in human cells (ENCODE tier 1 cells, K562, GM12878) using Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) experiments targeting six broadly distributed factors. Bound regions covered 80% of DNase I hypersensitive sites including 99.7% of TSS and 98% of enhancers. Correlating this map with ChIP-seq and RNA-seq data sets revealed cohesin, CTCF, and ZNF143 as key components of three-dimensional chromatin structure and revealed how the distal chromatin state affects gene transcription. Comparison of interactions between cell types revealed that enhancer-promoter interactions were highly cell-type-specific. Construction and comparison of distal and proximal regulatory networks revealed stark differences in structure and biological function. Proximal binding events are enriched at genes with housekeeping functions, while distal binding events interact with genes involved in dynamic biological processes including response to stimulus. This study reveals new mechanistic and functional insights into regulatory region organization in the nucleus.

An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues

We present Omni-ATAC, an improved ATAC-seq protocol for chromatin accessibility profiling that works across multiple applications with substantial improvement of signal-to-background ratio and information content. The Omni-ATAC protocol generates chromatin accessibility profiles from archival frozen tissue samples and 50-μm sections, revealing the activities of disease-associated DNA elements in distinct human brain structures. The Omni-ATAC protocol enables the interrogation of personal regulomes in tissue context and translational studies.