Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C

Dryden, Nicola H.; Broome, Laura R.; Dudbridge, Frank; Johnson, Nichola; Orr, Nick; Schoenfelder, Stefan; Nagano, Takashi; Andrews, Simon; Wingett, Steven W.; Kozarewa, Iwanka; Assiotis, Ioannis; Fenwick, Kerry; Maguire, Sarah; Campbell, James; Natrajan, Rachael; Lambros, Maryou; Perrakis, Eleni; Ashworth, Alan; Fraser, Peter; Fletcher, Olivia

doi:10.1101/gr.175034.114

Cited by 243 publications

(257 citation statements)

References 76 publications

Supporting

Mentioning

241

Contrasting

Unclassified

Order By: Relevance

“…Several genome wide association studies (GWAS) have identified the 2q35 region as associated with breast cancer risk (2)(3)(4)(5). Recent work has revealed the complexity of chromatin interactions in the 2q35 region (6) and its involvement in transcriptional regulation of neighbouring gene, IGFBP5 (MIM: 146734). The 2q35 risk locus falls within a 400Kb gene desert bounded by genes TNP1 (MIM: 190231) and DIRC3 (MIM: 608262), nearby two members of the insulin growth factor binding protein family, IGFBP5 and IGFBP2 (MIM: 146731).…”

Section: Introductionmentioning

confidence: 99%

An intergenic risk locus containing an enhancer deletion in 2q35 modulates breast cancer risk by deregulating IGFBP5 expression

Wyszynski¹,

Hong²,

Lam³

et al. 2016

Hum. Mol. Genet.

Self Cite

View full text Add to dashboard Cite

Breast cancer is the most diagnosed malignancy and the second leading cause of cancer mortality in females. Previous association studies have identified variants on 2q35 associated with the risk of breast cancer. To identify functional susceptibility loci for breast cancer, we interrogated the 2q35 gene desert for chromatin architecture and functional variation correlated with gene expression. We report a novel intergenic breast cancer risk locus containing an enhancer copy number variation (enCNV; deletion) located approximately 400Kb upstream to IGFBP5, which overlaps an intergenic ERa-bound enhancer that loops to the IGFBP5 promoter. The enCNV is correlated with modified ERa binding and monoallelic-repression of IGFBP5 following oestrogen treatment. We investigated the association of enCNV genotype with breast cancer in 1,182 cases and 1,362 controls, and replicate our findings in an independent set of 62,533 cases and 60,966 controls from 41 case control studies and 11 GWAS. We report a dose-dependent inverse association of 2q35 enCNV genotype (percopy OR ¼ 0.68 95%CI 0.55-0.83, P ¼ 0.0002; replication OR ¼ 0.77 95% CI 0.73-0.82, P ¼ 2.1 Â 10 À19 ) and identify 13 additional linked variants (r 2 > 0.8) in the 20Kb linkage block containing the enCNV (P ¼ 3.2 Â 10 À15 À 5.6 Â 10 À17 ). These associations were independent of previously reported 2q35 variants, rs13387042/rs4442975 and rs16857609, and were stronger for ER-positive than ER-negative disease. Together, these results suggest that 2q35 breast cancer risk loci may be mediating their effect through IGFBP5.

show abstract

Section: Introductionmentioning

confidence: 99%

An intergenic risk locus containing an enhancer deletion in 2q35 modulates breast cancer risk by deregulating IGFBP5 expression

Wyszynski¹,

Hong²,

Lam³

et al. 2016

Hum. Mol. Genet.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Generally, stronger fixation conditions (i.e., higher formaldehyde concentration or longer fixation time) may increase the risk of higher background noise in single-cell Hi-C data. For dissociation to single cells and cell permeabilization, additional steps such as trypsin treatment or Dounce homogenizing may be necessary for some cell types 17 .…”

Section: Applications Advantages and Limitations Of Single-cell Hi-cmentioning

confidence: 99%

Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell

et al. 2015

Self Cite

View full text Add to dashboard Cite

Hi-c is a powerful method that provides pairwise information on genomic regions in spatial proximity in the nucleus. Hi-c requires millions of cells as input and, as genome organization varies from cell to cell, a limitation of Hi-c is that it only provides a population average of genome conformations. We developed single-cell Hi-c to create snapshots of thousands of chromatin interactions that occur simultaneously in a single cell. to adapt Hi-c to single-cell analysis, we modified the protocol to include in-nucleus ligation. this enables the isolation of single nuclei carrying Hi-c-ligated Dna into separate tubes, followed by reversal of cross-links, capture of biotinylated ligation junctions on streptavidin-coated magnetic beads and pcr amplification of single-cell Hi-c libraries. the entire laboratory protocol can be carried out in 1 week, and although we have demonstrated its use in mouse t helper (t H 1) cells, it should be applicable to any cell type or species for which standard Hi-c has been successful. We also developed an analysis pipeline to filter noise and assess the quality of data sets in a few hours. although the interactome maps produced by single-cell Hi-c are sparse, the data provide useful information to understand cellular variability in nuclear genome organization and chromosome structure. standard wet and dry laboratory skills in molecular biology and computational analysis are required. of cell fixation. Therefore, the data provide the opportunity to analyze snapshots of chromosome conformations from individual cells capturing cellular heterogeneity, which may reflect their dynamic behavior. For example, we showed in our singlecell Hi-C study on mouse T H 1 cells that individual chromosomes maintain topological domain organization at the megabase scale, but that chromosome structures vary from cell to cell at larger scales 13 . An important point to note is that single-cell Hi-C data are sparse, and a potential concern is that the variability in interactome maps derived from individual cells could be because of nonuniform sampling of the interactome from each cell. However, proper statistical analyses can rule out such explanations for the observations.A distinct advantage of single-cell or single-molecule Hi-C data (as in the case of the X chromosome in male cells) is the ability to apply 3D modeling approaches. For example, the interaction data from the single-copy male X chromosome was used to derive distance restraints to calculate 3D models of chromosomes, upon which genomic and epigenomic information can be projected for the study of spatial patterns of such features 13 .Currently, a major limiting factor for the single-cell Hi-C technique is the sparsity of data or genome coverage. We detected up to ~30,000 unique interaction pairs per single cell. The theoretical number of distinct mappable interaction pairs in a mouse diploid cell is ~1.2 million, indicating that the genome coverage from the richest data sets was ~2.5%. Although coverage was low, it was uniform, suggesting that ...

show abstract

“…A number of such modifications have been published (Fig. 3b), called Capture C, Capture Hi-C (CHi-C), HiCap, and targeted chromatin capture (T2C) (Dryden et al 2014;Hughes et al 2014;Jager et al 2015;Kolovos et al 2014;Mifsud et al 2015;Sahlen et al 2015;Schoenfelder et al 2015). Most of these assays are based on hybridization of sequencing adaptor-ligated 3C (Capture C) and Hi-C samples (CHi-C and HiCap) respectively, followed by pull-down using biotinylated RNA molecules (120 nucleotides).…”

Section: Technical Limitations and Further Improvements In Hi-c Technmentioning

confidence: 99%

Chromosome conformation capture technologies and their impact in understanding genome function

2016

View full text Add to dashboard Cite

It has been more than a decade since the first chromosome conformation capture (3C) assay was described. The assay was originally devised to measure the frequency with which two genomic loci interact within the three-dimensional (3D) nuclear space. Over time, this method has evolved both qualitatively and quantitatively, from detection of pairwise interaction of two unique loci to generating maps for the global chromatin interactome. Combined with the analysis of the epigenetic chromatin context, these advances led to the unmasking of general genome folding principles. The evolution of 3C-based methods has been supported first by the revolution in ChIP and then by sequencingbased approaches, methods that were primarily tools to study the unidimensional genome. The gradual improvement of 3C-based methods illustrates how the field adapted to the need to gradually address more subtle questions, beginning with enquiries of reductionist nature to reach more holistic perspectives, as the technology advanced, in a process that is greatly improving our knowledge on genome behavior and regulation. Here, we describe the evolution of 3C and other 3C-based methods for the analysis of chromatin interactions, along with a brief summary of their contribution in uncovering the significance of the threedimensional world within the nucleus. We also discuss their inherent limitations and caveats in order to provide a critical view of the power and the limits of this technology.

show abstract

Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C

Cited by 243 publications

References 76 publications

An intergenic risk locus containing an enhancer deletion in 2q35 modulates breast cancer risk by deregulating IGFBP5 expression

An intergenic risk locus containing an enhancer deletion in 2q35 modulates breast cancer risk by deregulating IGFBP5 expression

Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell

Chromosome conformation capture technologies and their impact in understanding genome function

Contact Info

Product

Resources

About