Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry

Corradin, Olivia; Cohen, Andrea J.; Luppino, Jennifer M.; Bayles, Ian; Schumacher, Fredrick; Scacheri, Peter C.

doi:10.1038/ng.3674

Cited by 57 publications

(51 citation statements)

References 71 publications

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“…Genes were defined according to NCBI build 37 (hg19/GRCh37) coordinates, with the inclusion of a 50 kb flanking region added to the transcription start/stop positions. These flanking regions were added to genomic regions as polymorphisms in these 5′ and 3′ regions often influence gene regulation and expression not only for the nearest gene but for other nearby genes too (Corradin et al 2016; Guo and Jamison 2005; Brodie et al 2016; Schork et al 2013). MAGMA estimates LD patterns for each gene using an ancestry-matched reference file; specifically the reference files composed of data for the 503 unrelated individuals of European ancestry from Phase 3 of the 1000 Genomes Project.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide association studies for corneal and refractive astigmatism in UK Biobank demonstrate a shared role for myopia susceptibility loci

Shah

Guggenheim

2018

Hum Genet

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Previous studies have suggested that naturally occurring genetic variation contributes to the risk of astigmatism. The purpose of this investigation was to identify genetic markers associated with corneal and refractive astigmatism in a large-scale European ancestry cohort (UK Biobank) who underwent keratometry and autorefraction at an assessment centre. Genome-wide association studies for corneal and refractive astigmatism were performed in individuals of European ancestry (N = 86,335 and 88,005 respectively), with the mean corneal astigmatism or refractive astigmatism in fellow eyes analysed as a quantitative trait (dependent variable). Genetic correlation between the two traits was calculated using LD Score regression. Gene-based and gene-set tests were carried out using MAGMA. Single marker-based association tests for corneal astigmatism identified four genome-wide significant loci (P < 5 × 10−8) near the genes ZC3H11B (1q41), LINC00340 (6p22.3), HERC2/OCA2 (15q13.1) and NPLOC4/TSPAN10 (17q25.3). Three of these loci also demonstrated genome-wide significant association with refractive astigmatism: LINC00340, HERC2/OCA2 and NPLOC4/TSPAN10. The genetic correlation between corneal and refractive astigmatism was 0.85 (standard error = 0.068, P = 1.37 × 10−35). Here, we have undertaken the largest genome-wide association studies for corneal and refractive astigmatism to date and identified four novel loci for corneal astigmatism, two of which were also novel loci for refractive astigmatism. These loci have previously demonstrated association with axial length (ZC3H11B), myopia (NPLOC4), spherical equivalent refractive error (LINC00340) and eye colour (HERC2). The shared role of these novel candidate genes for astigmatism lends further support to the shared genetic susceptibility of myopia and astigmatism.Electronic supplementary materialThe online version of this article (10.1007/s00439-018-1942-8) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Genome-wide association studies for corneal and refractive astigmatism in UK Biobank demonstrate a shared role for myopia susceptibility loci

Shah

Guggenheim

2018

Hum Genet

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“…However, regulatory variants can affect phenotypes by changing the expression of target genes up to several megabases (mb) away [10][11][12][13], well beyond their LD block (median length ≈ 1-2kb, Supplementary Table 1b). This prompted Corradin and colleagues to conclude that a gene's regulatory program is not related to local haplotype structure [14]. Even when a GWAS SNP is in LD with a gene that has a strong biological link to the phenotype, the causal variant may be in a nearby non-coding region regulating a different gene [15,16].…”

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confidence: 99%

Most regulatory interactions are not in linkage disequilibrium

Whalen

2018

Preprint

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Linkage disequilibrium (LD) and genomic proximity are commonly used to map non-coding variants to genes, despite increasing examples of causal variants outside the LD block of the gene they regulate. We compared chromatin contacts in 22 cell types to LD across billions of pairs of loci in the human genome and found no concordance, even at genomic distances below 25 kilobases where both tend to be high. Gene expression and ontology data suggest that chromatin contacts identify regulatory variants more reliably than do LD and genomic proximity. We conclude that the genomic architectures of genetic and physical interactions are independent, with important implications for gene regulatory evolution and precision medicine. 2. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/272245 doi: bioRxiv preprint first posted online Feb. 26, 2018; Genetic variants ranging from large scale chromosomal rearrangements to single nucleotide polymorphisms (SNPs) can impact gene function by altering exonic sequence or by changing gene regulation. Recent studies estimate that 93% of disease-associated variants are in non-coding DNA [1] and 60% of causal variants map to regulatory elements [2], accounting for 79% of phenotypic variance [3]. Additionally, diseaseassociated variants are enriched in regulatory regions [4], especially those from tissues relevant to the phenotype [5]. Functionally annotating non-coding variants and correctly mapping causal variants to the genes and pathways they affect is critical for understanding disease mechanisms and using genetics in precision medicine [6][7][8][9].Common practice associates non-coding variants with the closest gene promoter or promoters within the same LD block. However, regulatory variants can affect phenotypes by changing the expression of target genes up to several megabases (mb) away [10][11][12][13], well beyond their LD block (median length ≈ 1-2kb, Supplementary Table 1b). This prompted Corradin and colleagues to conclude that a gene's regulatory program is not related to local haplotype structure [14]. Even when a GWAS SNP is in LD with a gene that has a strong biological link to the phenotype, the causal variant may be in a nearby non-coding region regulating a different gene [15,16]. Highlighting the long range of regulatory interactions, recent work in T cells found that only 14% of 684 autoimmune variants targeted their closest gene; 86% skipped one or more intervening genes to reach their target, and 64% of variants interacted with multiple genes [17]. Thus, many non-coding variants are far away and in low LD with the promoters they regulate.Distal non-coding variants can cause changes in gene regulation and phenotypes via three-dimensional (3D) chromatin interactions. For example, the obesity-associated FTO variant (rs1421085) was found to disrupt an ARID5...

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“…Many technologies based on crosslinking and ligation of spatially closed genomic regions, such as Hi-C [42,43] and chromatin conformation capture by paired-end tag sequencing (ChIA-Pet) [44], have emerged to identify target genes affected by noncoding variants. For example, a recent study examining the chromatin state effects of variants associated with autoimmune diseases found that these variants alter gene expression by disrupting the physical interactions between enhancers and promoters [45]. Computational methods have also been used to identify gene-disease and gene-variant associations.…”

Section: Regulatory Variantsmentioning

confidence: 99%

Cellular network perturbations by disease-associated variants

Sewell

Bass

2017

Current Opinion in Systems Biology

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Genetic and genome-wide association studies (GWAS) have identified a myriad of human disease-associated genomic variants. However, these studies do not reveal the mechanisms by which these variants perturb cellular networks, a necessary step to intervene and improve disease outcomes. This has been challenging because multiple variants are present in haplotype blocks, thereby confounding the identification of causal variants, and because most reside in noncoding regions. Here, we review recent advances in the identification of functional variants and gene-variant associations. In addition, we examine approaches used to study perturbations in protein-protein and protein-DNA interactions associated with disease, and discuss how these perturbations affect cellular networks.

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Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry

Cited by 57 publications

References 71 publications

Genome-wide association studies for corneal and refractive astigmatism in UK Biobank demonstrate a shared role for myopia susceptibility loci

Genome-wide association studies for corneal and refractive astigmatism in UK Biobank demonstrate a shared role for myopia susceptibility loci

Most regulatory interactions are not in linkage disequilibrium

Cellular network perturbations by disease-associated variants

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