A Compendium of Chromatin Contact Maps Reveals Spatially Active Regions in the Human Genome

Schmitt, Anthony; Hu, Maowen; Jung, Inkyung; Xu, Zheng; Qiu, Yunjiang; Tan, Catherine; Li, Yun; Lin, Shin; Lin, Yiing; Barr, Cathy L.; Ren, Bing

doi:10.1016/j.celrep.2016.10.061

Cited by 804 publications

(1,021 citation statements)

References 52 publications

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“…We further expanded this analysis using the recently published Hi-C data in 14 human primary tissues and two cell lines (Supplemental Table S4; Schmitt et al 2016). Because biological replicates are not available for all the samples, our analysis focused on quantifying the relationships between tissues or cells.…”

Section: Wwwgenomeorgmentioning

confidence: 99%

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

et al. 2017

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Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.

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Section: Wwwgenomeorgmentioning

confidence: 99%

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

et al. 2017

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“…1f). For each of these three conditions, two biological replicates were generated; 5 out of 6 replicates produced >25 million interactions at >10kb separation, on par with other primary tissue Hi-C ( 27 , Supplementary Tables 1–2). The contact maps obtained from each biological replicate showed extensive similarities (Supplementary Data 2), allowing us to pool the two replicate datasets to generate Hi-C maps for the three different conditions.…”

mentioning

confidence: 99%

Two independent modes of chromatin organization revealed by cohesin removal

Schwarzer

Abdennur

Goloborodko

et al. 2017

Nature

1,052

114

1,115

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Imaging and chromosome conformation capture studies have revealed several layers of chromosome organization, including segregation into megabase-large active and inactive compartments, and partitioning into sub-megabase domains (TADs). Yet, it remains unclear how these layers of organization form, interact with one another and impact genome functions. Here, we show that deletion of the cohesin-loading factor Nipbl, in mouse liver, leads to a dramatic reorganization of chromosomal folding. TADs and associated peaks vanish globally, even in the absence of transcriptional changes. In contrast, compartmental segregation is preserved and even reinforced. Strikingly, the disappearance of TADs unmasks a finer compartment structure that accurately reflects the underlying epigenetic landscape. These observations demonstrate that the 3D organization of the genome results from the interplay of two independent mechanisms: 1) cohesin-independent segregation of the genome into fine-scale compartments, defined by chromatin state; 2) cohesin-dependent formation of TADs, possibly by loop extrusion, which contributes to guide distant enhancers to their target genes.

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“…Subsequently we used chromatin interaction maps 28 for aorta, and left-and right-ventricle tissue samples to look for interactions between the regulatory regions, to which AS risk variants mapped, and promoters.…”

mentioning

confidence: 99%

Genome-wide analysis yields new loci associating with aortic valve stenosis

Helgadóttir

Þorleifsson

Grétarsdóttir

et al. 2017

Preprint

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Aortic valve stenosis (AS) is the most common valvular heart disease, characterized by a thickened and calcified valve causing left ventricular outflow obstruction. Severe AS is a significant cause of morbidity and mortality, affecting approximately 5% of those over 70 years of age1,2,3. Little is known about the genetics of AS, although recently a variant at the LPA locus4 and a rare MYH6 missense variant were found to associate with AS5. We report a large genome-wide association study (GWAS) with a follow-up in up to 7,307 AS cases and 801,073 controls. We identified two new AS loci, on chromosome 1p21 near PALMD (rs7543130; OR=1.20, P=1.2×10−22) and on chromosome 2q22 in TEX41 (rs1830321; OR=1.15, P=1.8×10−13). Rs7543130 also associates with bicuspid aortic valve (BAV) (OR=1.28, P=6.6×10−10) and aortic root diameter (P=1.30×10−8) and rs1830321 associates with BAV (OR=1.12, P=5.3×10−3 and coronary artery disease (CAD) (OR=1.05, P=9.3×10−5). These results indicate that AS is partly rooted in the same processes as cardiac development and atherosclerosis.

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A Compendium of Chromatin Contact Maps Reveals Spatially Active Regions in the Human Genome

Cited by 804 publications

References 52 publications

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

Two independent modes of chromatin organization revealed by cohesin removal

Genome-wide analysis yields new loci associating with aortic valve stenosis

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