Identification of breast cancer associated variants that modulate transcription factor binding

Liu, Yunxian; Walavalkar, Ninad M.; Dozmorov, Mikhail G.; Rich, Stephen S.; Civelek, Mete; Guertin, Kristin A.

doi:10.1371/journal.pgen.1006761

Cited by 43 publications

(43 citation statements)

References 76 publications

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“…The SNP resulted in a significant allelic imbalance between alleles C and T, nearly 79% and 21%, respectively in a set of MC7 cell lines ( Supplementary Figure 5 and Supplementary Table 13). Our findings corresponded to the results for rs8103622 in (36). Moreover, we checked the presence of DHSs and CTCF signals for the remaining tissue types: blood, liver and myeloid.…”

Section: Enrichment Of Genetic Variantssupporting

confidence: 87%

funMotifs: Tissue-specific transcription factor motifs

Smolinska-Garbulowska

Marzouka

et al. 2019

Preprint

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Transcription factors (TF) regulate gene expression by binding to specific sequences known as motifs. A bottleneck in our knowledge of gene regulation is the lack of functional characterization of TF motifs, which is mainly due to the large number of predicted TF motifs, and tissue specificity of TF binding. We built a framework to identify tissue-specific functional motifs (funMotifs) across the genome based on thousands of annotation tracks obtained from large-scale genomics projects including ENCODE, RoadMap Epigenomics and FANTOM. The annotations were weighted using a logistic regression model trained on regulatory elements obtained from massively parallel reporter assays. Overall, genome-wide predicted motifs of 519 TFs were characterized across fifteen tissue types. funMotifs summarizes the weighted annotations into a functional activity score for each of the predicted motifs. funMotifs enabled us to measure tissue specificity of different TFs and to identify candidate functional variants in TF motifs from the 1000 genomes project, the GTEx project, the GWAS catalogue, and in 2,515 cancer samples from the Pan-cancer analysis of whole genome sequences (PCAWG) cohort. To enable researchers annotate genomic variants or regions of interest, we have implemented a command-line pipeline and a web-based interface that can publicly be accessed on: http://bioinf.icm.uu.se/funmotifs.

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Section: Enrichment Of Genetic Variantssupporting

confidence: 87%

funMotifs: Tissue-specific transcription factor motifs

Smolinska-Garbulowska

Marzouka

et al. 2019

Preprint

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“…Our method differs from existing single-locus GWAS methods 11,12,103 in that it enables stronger statements about causality and mechanism. Regarding causality, this is because a consistent genome-wide directional effect of SNPs predicted to affect TF binding due to sequence change (across a large set of TF binding sites; see Table 1a) is less susceptible to pleiotropy, LD, and allelic heterogeneity 103,105 .…”

Section: Discussionmentioning

confidence: 99%

“…However, there are instances in which experimental data allow us not only to identify SNPs that affect a biological process, but also to predict which SNP alleles promote the process and which SNP alleles hinder it, thereby enabling us to assess whether there is a systematic association between SNP alleles' direction of effect on the process and their direction of effect on a trait. Transcription factor (TF) binding, which plays a major role in human disease 1,[8][9][10][11][12] , represents an important case in which such signed functional annotations are available: because TFs have a tendency to bind to specific DNA sequences, it is possible to estimate whether the sequence change introduced by a SNP allele will increase or decrease binding of a TF 1,[13][14][15][16][17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Reshef

Finucane

Kelley

et al. 2017

Preprint

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Biological interpretation of GWAS data frequently involves analyzing unsigned genomic annotations comprising SNPs involved in a biological process and assessing enrichment for disease signal. However, it is often possible to generate signed annotations quantifying whether each SNP allele promotes or hinders a biological process, e.g., binding of a transcription factor (TF). Directional effects of such annotations on disease risk enable stronger statements about causal mechanisms of disease than enrichments of corresponding unsigned annotations. Here we introduce a new method, signed LD profile regression, for detecting such directional effects using GWAS summary statistics, and we apply the method using 382 signed annotations reflecting predicted TF binding. We show via theory and simulations that our method is well-powered and is well-calibrated even when TF binding sites co-localize with other enriched regulatory elements, which can confound unsigned enrichment methods. We apply our method to 12 molecular traits and recover many known relationships including positive associations between gene expression and genome-wide binding of RNA polymerase II, NF-κB, and several ETS family members, as well as between known chromatin modifiers and their respective chromatin marks. Finally, we apply our method to 46 diseases and complex traits (average N = 289, 617) and identify 77 significant associations at per-trait FDR < 5%, representing 12 independent signals. Our results include a positive association between educational attainment and genome-wide binding of BCL11A, consistent with recent work linking BCL11A hemizygosity to intellectual disability; a negative association between lupus risk and genome-wide binding of CTCF, which has been shown to suppress myeloid differentiation; and a positive association between Crohn's disease (CD) risk and genome-wide binding of IRF1, an immune regulator that lies inside a CD GWAS locus and has eQTLs that increase CD risk. Our method provides a new way to leverage functional data to draw inferences about causal mechanisms of disease.

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“…Although we found no specific enrichment for variants in CTCF elements, numerous were within CTCF peaks. Recent evidence has shown that disruption of CTCF binding by common variants plays a role in determining the severity of influenza and breast cancer 64,65 , thus it likely represents a less common, yet important molecular mechanism. Additionally, modelling of rs10758656 showed near complete loss of open chromatin.…”

Section: Discussionmentioning

confidence: 99%

An integrated platform to systematically identify causal variants and genes for polygenic human traits

Downes

Hill

Nußbaum

et al. 2019

Preprint

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33Japan 34 35 ABSTRACT 37 Genome-wide association studies (GWAS) have identified over 150,000 links between 38 common genetic variants and human traits or complex diseases. Over 80% of these 39 associations map to polymorphisms in non-coding DNA. Therefore, the challenge is 40 to identify disease-causing variants, the genes they affect, and the cells in which 41 these effects occur. We have developed a platform using ATAC-seq, DNaseI 42 footprints, NG Capture-C and machine learning to address this challenge. Applying 43 this approach to red blood cell traits identifies a significant proportion of known 44 causative variants and their effector genes, which we show can be validated by direct 45 in vivo modelling.Identification of the variation of the genome that determines the risk of common chronic and 48 infectious diseases informs on their primary causes, which leads to preventative or 49 therapeutic approaches and insights. Whilst genome-wide association studies (GWASs) 50 have identified thousands of chromosome regions 1 , the identification of the causal genes, 51 variants and cell types remains a major bottleneck. This is due to three major features of the 52 genome and its complex association with disease susceptibility. Trait-associated variants 53 are often tightly associated, through linkage disequilibrium (LD), with tens or hundreds of 54 other variants, mostly single-nucleotide polymorphisms (SNPs), any one or more of which 55 could be causal; the majority (>85%) the variants identified in GWAS lie within the non-56 coding genome 2 . Although non-coding regions are increasingly well annotated, many 57 variants do not correspond to known regulatory elements, and even when they do, it is rarely 58 known which genes these elements control, and in which cell types. New technical 59 approaches to link variants to the genes they control are rapidly improving but are often 60 limited by their sensitivity and resolution [3][4][5][6] ; and because so few causal variants have been 61 unequivocally linked to the genes they affect, the mechanisms by which non-coding variants 62 alter gene expression remain unknown in all but a few cases; and, third, the complexity of 63 gene regulation and cell/cell interactions means that knowing when in development, in which 64 cell type, in which activation state, and within which pathway(s) a causal variant exerts its 65 effect is usually impossible to predict. Although significant progress is being made, currently, 66 none of these problems has been adequately solved. 68Here, we have developed an integrated platform of experimental and computational 69 methods to prioritise likely causal variants, link them to the genes they regulate, and 70 determine the mechanism by which they alter gene function. To illustrate the approach we 71 have initially focussed on a single haematopoietic lineage: the development of mature red 72 blood cells (RBC), for which all stages of lineage specification and differentiation from a 73 haematopoietic stem cell to a RBC are known, and can be r...

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Identification of breast cancer associated variants that modulate transcription factor binding

Cited by 43 publications

References 76 publications

funMotifs: Tissue-specific transcription factor motifs

funMotifs: Tissue-specific transcription factor motifs

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

An integrated platform to systematically identify causal variants and genes for polygenic human traits

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