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3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations.

ABSTRACT: The Hi-C method is widely used to study the functional roles of the three-dimensional (3D) architecture of genomes. Here, we integrate Hi-C, whole-genome sequencing (WGS) and RNA-seq to study the 3D genome architecture of multiple myeloma (MM) and how it associates with genomic variation and gene expression. Our results show that Hi-C interaction matrices are biased by copy number variations (CNVs) and can be used to detect CNVs. Also, combining Hi-C and WGS data can improve the detection of translocations. We find that CNV breakpoints significantly overlap with topologically associating domain (TAD) boundaries. Compared to normal B cells, the numbers of TADs increases by 25% in MM, the average size of TADs is smaller, and about 20% of genomic regions switch their chromatin A/B compartment types. In summary, we report a 3D genome interaction map of aneuploid MM cells and reveal the relationship among CNVs, translocations, 3D genome reorganization, and gene expression regulation.

SUBMITTER: Wu P

PROVIDER: S-EPMC5715138 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations.

Wu Pengze P Li Tingting T Li Ruifeng R Jia Lumeng L Zhu Ping P Liu Yifang Y Chen Qing Q Tang Daiwei D Yu Yuezhou Y Li Cheng C

Nature communications 20171205 1

The Hi-C method is widely used to study the functional roles of the three-dimensional (3D) architecture of genomes. Here, we integrate Hi-C, whole-genome sequencing (WGS) and RNA-seq to study the 3D genome architecture of multiple myeloma (MM) and how it associates with genomic variation and gene expression. Our results show that Hi-C interaction matrices are biased by copy number variations (CNVs) and can be used to detect CNVs. Also, combining Hi-C and WGS data can improve the detection of tra ...[more]

PMID: 29203764

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Project description:Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information.To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B).aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub https://github.com/FJD-CEPH/aCNViewer and Docker https://hub.docker.com/r/fjdceph/acnviewer/.aCNViewer@cephb.fr.

Project description:The populations of the Arabian Peninsula remain the least represented in public genetic databases, both in terms of single nucleotide variants and of larger genomic mutations. We present the first high-resolution copy number variation (CNV) map for a Gulf Arab population, using a hybrid approach that integrates array genotyping intensity data and next-generation sequencing reads to call CNVs in the Qatari population.CNVs were detected in 97 unrelated Qatari individuals by running two calling algorithms on each of two primary datasets: high-resolution genotyping (Illumina Omni 2.5M) and high depth whole-genome sequencing (Illumina PE 100bp). The four call-sets were integrated to identify high confidence CNV regions, which were subsequently annotated for putative functional effect and compared to public databases of CNVs in other populations. The availability of genome sequence was leveraged to identify tagging SNPs in high LD with common deletions in this population, enabling their imputation from genotyping experiments in the future.Genotyping intensities and genome sequencing data from 97 Qataris were analyzed with four different algorithms and integrated to discover 16,660 high confidence CNV regions (CNVRs) in the total population, affecting ~28 Mb in the median Qatari genome. Up to 40% of all CNVs affected genes, including novel CNVs affecting Mendelian disease genes, segregating at different frequencies in the 3 major Qatari subpopulations, including those with Bedouin, Persian/South Asian, and African ancestry. Consistent with high consanguinity levels in the Bedouin subpopulation, we found an increased burden for homozygous deletions in this group. In comparison to known CNVs in the comprehensive Database of Genomic Variants, we found that 5% of all CNVRs in Qataris were completely novel, with an enrichment of CNVs affecting several known chromosomal disorder loci and genes known to regulate sugar metabolism and type 2 diabetes in the Qatari cohort. Finally, we leveraged the availability of genome sequence to find suitable tagging SNPs for common deletions in this population.We combine four independently generated datasets from 97 individuals to study CNVs for the first time at high-resolution in a Gulf Arab population.

Dataset Information

3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations.

Publications

3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations.

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