Variation in Transcription Factor Binding Among Humans

By Maya M Kasowski, Fabian Grubert, Christopher Heffelfinger, M. Hariharan, A. Asabere, S. Waszak, L. Habegger, Joel S. Rozowsky, Minyi Shi, Alexander E. Urban and 7 more. Science, 2010

617 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/65aed7c9604756d7284e18dae1a31588522c07b3

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Citations in the corpus

ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs (2014, methodology): “To provide an example assessment of the accuracy of ISMARA’s genome-wide target predictions, we compared the predicted targets of NFKB1/REL/RELA with targets identified through ChIP-seq inlymphoblastoidcelllinesderivedfrom10individualsofAfrican, European, and Asian ancestry (Kasowski et al. 2010).”
Next-Generation Genomics: an Integrative Approach (2010, positive): “For example, a study by Snyder and colleagues showed that SNPs found in binding regions for RNAPII and nuclear factor-κB (NF-κB) accounted for individual variability in gene expression level”
Differential expression analysis for sequence count data (2010, methodology): “Tests for differential expression that are based on a Poisson model, such as those discussed in References [6,7,20,22,23] should thus be interpreted with caution, as they may severely under-estimate the effect of biological variability, in particular for highly expressed genes.”
Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes (2012, background): “Comparison of SNPs that alter binding patterns of NFkB and Pol II sites (Kasowski et al., 2010), also revealed a number of other interesting regulatory variants, some of which are associated with human disease (e.g., EDIL) (Sun et al., 2010) (Figure S1B).”
AlleleSeq: analysis of allele-specific expression and binding in a network framework (2011, methodology): “A natural area of recent focus has been looking at the degree of functional genomic differences across the human population (Gregg et al , 2010a,b; Kasowski et al , 2010; McDaniell et al , 2010; Montgomery et al , 2010; Pickrell et al , 2010).”
Uncovering the roles of rare variants in common disease through whole-genome sequencing (2010, background): “Partially in response to this assumption, genome-wide patterns of gene expression have been characterized in multiple tissue types, and many common variants that influence gene expression have been identifie”
New methods for separating causes from effects in genomics data (2012, neutral): “RELA [22,41,42] 1 1,420 3,657 3,657 931”
Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence (2011, background): “, even for central components of the transcription machinery such as RNA polymerase I”
A User's Guide to the Encyclopedia of DNA Elements (ENCODE) (2011, methodology): “The genotype and sequence data from GM12878 generated by the 1,000 Genomes Project are being integrated with sequence data from ENCODE chromatin, transcription, TF occupancy, DNA methylation, and other assays to facilitate recognition of functional allelic variation, a significant contributor to phe”
A framework for incorporating evolutionary genomics into biodiversity conservation and management (2015, background): “The Poolseq approach does not allow the data for different individuals to be separated post-sequencing, but it is highly cost-effective for assessing population structure [63], genetic distance [64-66] and genomewide patterns of heterozygosity.”
Annotation of functional variation in personal genomes using RegulomeDB (2012, background): “The study authors demonstrate a decrease in NFKB binding with the polymorphism and conclude that this variant is likely to influence TNFAIP3 expression by decreasing factor binding in response to pro-inflammatory signals.”
Linking disease associations with regulatory information in the human genome (2012, background): “Experimental evidence shows that the presence of SNPs in these regions leads to differences in transcription factor binding between individuals (Kasowski et al. 2010).”