The effects of common structural variants on 3D chromatin structure

- We hypothesize that common structural variation (SV) in the human population can disrupt regulatory sequences and thereby influence TAD formation.
- To determine the effects of SVs on 3D chromatin organization, we performed chromosome conformation capture sequencing (Hi-C) of lymphoblastoid cell lines from 19 subjects for which SVs had been previously characterized in the 1000 genomes project.
- quantitative chromatin interactions (contacts) within 240 kb of the deletion, and we specifically tested the.
- Some deletions at TBs, including a 80 kb deletion of the genes CFHR1 and CFHR3, had detectable effects on chromatin contacts.
- Large inversions in the population had a distinguishable signature characterized by a rearrangement of contacts that span its breakpoints..
- Conclusions: Our study demonstrates that common SVs in the population impact long-range chromatin structure, and deletions and inversions have distinct signatures.
- Genome-wide analysis of chromatin conformation in large cohorts will be needed to quantify the influence of common SVs on chromatin structure..
- little is known about patterns of topological variation in the population and the underlying genetic mechanisms..
- 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0.
- 2 Beyster Center for Genomics of Psychiatric Diseases, Department of Psychiatry, UCSD, San Diego, CA, USA.
- 3 Department of Cellular and Molecular Medicine, UCSD, San Diego, CA, USA Full list of author information is available at the end of the article.
- Specif- ically we sought to test the hypothesis that deletions of the boundary regions between adjacent TADs could re- sult in large scale alterations in chromatin conformation..
- The average number of contacts is shown for subjects who were homozygous for the deletion (Fig.
- The regional effects of the CFHR3/1 deletion on TAD structure was examined in more detail by correlating counts with genotype for all elements of the contact matrix using linear regression controlling for ancestry and sex.
- The resulting correlation matrix is visualized as a heatmap of the regression coefficients (Fig.
- Maps of chromatin interaction surrounding an 80 kb deletion of the CFHR3 and CFHR1 genes (hg19 position chr are depicted by averaging the counts within the contact matrices of subjects homozygous for the deletion haplotype ( N = 3, Panel a) and subjects homozygous for the reference haplotype ( N = 12, Panel b).
- A portion of the CFHR3/1 deletion overlaps with multiple annotated segmental duplications (SDs) which could potentially confound the mapping of Hi-C read pairs.
- To more rigorously determine the association of dele- tions with chromatin conformation, we used a linear re- gression model to test for the effects of deletions on chromatin contacts.
- The effects of dele- tions on chromatin conformation were then tested for.
- 2 Testing the effect of common deletions on chromatin conformation.
- The effect of the deletion on chromatin conformation was investigated by linear regression, showing a significant effect in the span region ( p -value: 0.002, Panel b) and no effect in the flank region (Panel c).
- Large deletions have the strongest effect in the span region while the contribution from small deletions is non-existent (Panel d).
- Large deletions show a smaller effect in the flank region (Panel e).
- Other potential confounders were evaluated, includ- ing surrogate variables, to account for unknown sources of noise (see methods), however including these add- itional covariates did not reduce the overall inflation of the test statistic (Additional file 1: Fig.
- The effect of the CFHR3/1 deletion on spanning contacts was statisti- cally significant (Fig.
- 2 b, p-value: 0.002), but the dele- tion did not have a significant effect on the number of contacts in the flanking regions that overlap with the ad- jacent TADs (Fig.
- We next sought to extend the analysis of Hi-C data to all common deletions in the phase 3 release of the 1000 genomes project [10].
- The magnitude of the genetic effects was assessed based on genomic infla- tion of the test statistic (λ).
- The magnitude of the effect of large dele- tions on the spanning contacts was greater than for small deletions (Kolmogorov-Smirnov test, p-value:.
- Given that the effects of com- mon deletions on chromatin conformation are driven by large deletions, our subsequent analyses focused on this subset of SVs..
- We therefore hypothesized that deletions could have more dramatic effects on chromatin con- formation when they occur in TAD boundaries.
- A visualization of the change in chro- matin structure is illustrated by averaging each element of the contact matrix within 240 kb of a deletion across loci in TB/NonTB categories separately (Fig.
- NonTB deletions we observe an increase in the number of deletion spanning contacts (Fig.
- Using the deletion-spanning contacts for 80 large common de- letions as a measure of TAD fusion, we examined whether there was a correlation between the fusion score of the deletion and the coefficient from the regression..
- We found no correlation of the predicted fusion scores with the observed effects of these deletions on spanning contacts (Additional file 4: Fig.
- Our results suggest that large SVs have detectable ef- fects on chromatin conformation.
- Since the above ana- lysis focused on deletions, it did not assess the largest common SVs known to exist in the population, which include large inversions of 8p23.1 (3.87 Mb) and 7q11.1 (2.45 Mb).
- To characterize the effects of large inversions on chromatin conformation, inversion genotypes were obtained from single-cell strand sequencing (Strand-seq) of a subset of 9 subjects in the 1000 genomes project [17], and the correlation of chromatin contacts across the region was visualized (Fig.
- The most dramatic effects of the inversion involve contacts that span the in- version breakpoints, denoted by the black triangle, and these effects span distances >.
- The availability of a full assembly of the 8p23.1 inversion haplotype [18] enabled us to map TAD structure of the in- version haplotype by directly mapping Hi-C data of sub- jects that were homozygous for the 8p23.1 inversion to the inversion haplotype.
- TAD structures of the reference and inver- sion haplotypes were similar, and the same 5 TADs were defined.
- Of the 2180 common deletions from our analysis and 5128 SV-eQTLs that were previ- ously identified in another study [20], 75 common dele- tions tested in this study correspond to SV-eQTLs, and these were larger on average with an average length of 5.98 kb compared to the rest of the 2105 deletions which had an average length of 2.5 kb.
- A Wilcoxon rank sum test was performed between these two groups to deter- mine if there was a significant difference between the regression p -value distribution of the deletions with SV- eQTLs and the regression p-value distribution of deletions without SV-eQTLs in the span region.
- How- ever, SVs that were driving eQTLs did not have stronger effects on chromatin contacts (p-value: 0.45).
- Hi-C has enabled discoveries related to understanding the structural and functional basis of the genome.
- The most dramatic example was a common deletion polymorphism at CFHR3/1, which results in the gain of contacts that span a broad region betweem two adjacent TADs.
- An increase in the number of contacts between two distinct TADs is an effect reminiscent of.
- Deletions not present at TAD boundaries have positive values in the span region (Panel b).
- Deletions that intersect TAD boundaries do not have a unique trend in the span or flank region (Panel c).
- Deletions that remove TAD boundaries and cause TAD fusion may be under negative selection in the population and would therefore tend to be rare..
- Well-powered characterization of the effects of SVs on chromatin structure and gene regulation would therefore require Hi-C characterization of common variants in lar- ger samples combined with targeted Hi-C and RNA se- quencing of patient samples with specific rare disease associated variants..
- Large common inversions have distinct effects on chromatin interactions that span the inversion break- points, and these effects can extend for distances >.
- Our analysis has shown that large common SVs can in- fluence local 3D chromatin structure, and the strength and direction of the observed effect varies by locus.
- 4 Long range effects of a large 8p23 inversion on chromatin conformation.
- Read ends were aligned to hg19 with BWA- MEM v0.7.8 [23] and in the case of split alignments, the five-prime-most alignment was used as the primary alignment.
- The bins of the contact matrix that “span”.
- Quantifying effects of common deletions on TAD structure.
- Quantitative effects of deletions on chromatin conform- ation were tested by Ordinary Least Squares Regression (OLSR) using Python.
- First, bins that overlapped with SVs were masked and specific deletion-flanking and de- letion-spanning target regions were defined within 240 kb (six 40 kb bins) on either side of the deletion (Fig.
- The genomic inflation factor (λ) was used to determine how much of the effect could be attributable to con- founding variables such as ethnicity or other unobserved noise in the data that could be captured with surrogate variables.
- the rest of the study and regression coefficients for all loci were displayed in a boxplot (Fig.
- Hi-C chromatin interactions for the bins that overlap the inversion and 62 bins on each side of the inversion were extracted.
- All 2180 common deletions were first annotated with summary statistics from the regression analysis by reporting a p -value and regression coefficient describing the effect of the variant on both the flank region and span region.
- The SVs were then intersected with the TAD boundaries previously defined in the methods and defined as overlapping that TAD boundary if the inter- section was at least 1 bp.
- An empty element in the table represents no overlap with a TAD boundary.
- To determine which covariates reduce the bias in the linear regression model, the effect of common deletions on chromatin conformation was tested for 6 different models, with each model adding an extra covariate term (Panel A).
- P -values of the regression for each deletion in the span (Panel B) and flank (Panel C) region display how the chosen model still has inflation despite the low genomic inflation factor that can be attributed to real effects.
- 2180 common deletions from 19 individuals in the 1000 Genomes Project were annotated with TAD boundaries, eQTLs, and GWAS hits.
- To investigate the effect of these deletions on chromatin conformation, a linear regression was performed between genotype and the median number of chromatin interactions within the flank and span region of each deletion.
- Ancestry principal components and sex were used as covariates in the regression model.
- Masking segmental duplications does not change the effects of deletions on chromatin conformation.
- To determine if the effects on chromatin conformation are driven by segmental duplications (SD), a separate analysis was conducted for all large common deletions after masking every SD found within the deletion or in the flank regions.
- A Wilcoxon rank-sum test was per- formed for each group against a null distribution and the results are con- sistent with the analysis that did not involve SD masking, showing that the effects of deletions on chromatin contacts are not driven by segmen- tal duplications.
- Linear regression coefficients in the span region do not correlate with TAD fusion score.
- We generated the TAD fusion score for our 80 large common deletions and compared the result with the linear regression coefficients in the span region.
- Long range effects of 7q11.1 inversion on chromatin conformation.
- The effect of the 7q11.1 inversion on chromatin conformation is similar to the effects of the 8p23.1 inversion, where the most dramatic effects involve contacts that span the inversion breakpoints.
- We thank Bing Ren and David Gorkin for the generation of the Hi-C data and Yunjiang Qiu for pre-processing the Hi-C contact matrices.
- Collaborating authors of the Human Genome Structural Variation Consortium (HGSVC):.
- 1 Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA.
- 4 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
- 17 Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
- 28 Beyster Center for Genomics of Psychiatric Diseases, Department of Psychiatry Uni- versity of California San Diego, La Jolla, CA 92093, USA.
- 31 Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA.
- 46 Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
- 47 Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, USA.
- 50 Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
- HGSVC provided SV calls on the study cohort of 19 subjects and provided a patched version of the genome containing the inversion haplotype of the 8p23.1 inversion.
- and supported the sequencing of the 9 samples (GM19238, GM19239, GM19240, HG00512, HG00513, HG00514, HG00731, HG00732 and HG00733).
- NHGRI played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript..
- Details and genotypes for common deletions are provided in the supplementary materials.
- 1 Department of Electrical and Computer Engineering, UCSD, San Diego, CA, USA.
- 3 Department of Cellular and Molecular Medicine, UCSD, San Diego, CA, USA.
- 4 Department of Pediatrics, UCSD, San Diego, CA, USA..
- Comprehensive mapping of long-range interactions reveals folding principles of the human genome.
- Paired-end mapping reveals extensive structural variation in the human genome.
- Structural variation in the 3D genome.
- Interchromosomal core duplicons drive both evolutionary instability and disease susceptibility of the chromosome 8p23.1 region.
- Common DNA sequence variation influences 3-dimensional conformation of the human genome.
- A simulation study of the number of events per variable in logistic regression analysis

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