H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility

Giambartolomei, Claudia; Seo, Ji-Heui; Schwarz, Tommer; Freund, Malika K.; Johnson, Ruth; Spisák, Sándor; Baca, Sylvan C.; Gusev, Alexander; Mancuso, Nicholas; Paşaniuc, Bogdan; Freedman, Matthew L.

doi:10.1016/j.ajhg.2021.11.007

Cited by 43 publications

(66 citation statements)

References 92 publications

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“…With the modified design we chose to test 289 PCa risk-associated SNPs which are located on 184 non-overlapping segments that include 50% (70/140) of the previously published PCa risk-associated loci (Supplementary Table 1; Supplementary Table 2) (5). These regions were selected as they are potential enhancers which have both H3K27Ac and chromosomal looping to a gene promoter (51). In addition to these risk-associated SNPs we designed the DNA capture to also target 25 strong enhancers (52) (positive control) and 25 inactive regions (negative control) as experimental controls (Figure 1.A; Supplementary Table 3) .…”

Section: Resultsmentioning

confidence: 99%

“…Supporting these plasmid-based results, those SNPs with bi-allelic enhancer activity commonly affected target gene expression. Using previously published enhancer-promoter interactions from H3K27Ac-HiChIP (51), we found that 20 of 36 “hit” PCa risk-associated SNPs correlated with altered expression of the target gene (Supplementary Table 7) . Interestingly, we found 2 significant SNPs (rs13265330; 2.2-fold, FDR=9.25×10 −6 , rs11782388; 1.47-fold FDR=0.07) that were located ~10kb downstream of NKX3-1, a gene involved in early stages of prostate tumorigenesis (54).…”

Section: Resultsmentioning

confidence: 99%

“…We picked 289 SNPs ( Supplementary Table 2 ) which are located in enhancer regions that have H3K27Ac signal and chromosomal looping to a gene promoter (51) as well as 50 control regions (25 positive and 25 negative control). All capture regions can be found in (Supplementary Table 3) .…”

Section: Methodsmentioning

confidence: 99%

“…Similar to previous comparison, anchor BED file then intersected by 308 SNP BED file (Supplementary Table 4) . We obtained eQTL information from Supplementary Table 6 from (51) work. Specifically, those genes that are TRUE for both eGene_TCGA and eGene_THIB columns.…”

Section: Methodsmentioning

confidence: 99%

“…We obtained HiChIP-H3k27Ac chromosome interaction data paired-end BED file (BEDPE) from previous work (51). Each line of this file contains one interaction consisting of anchorA and anchorB.…”

Section: Methodsmentioning

confidence: 99%

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Optimized high-throughput screening of non-coding variants identified from genome-wide association studies

Morova¹,

Ding

Huang³

et al. 2022

Preprint

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The vast majority of disease-associated single nucleotide polymorphisms identified from genome-wide association study (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and number of variants tested per loci. Using this strategy, we interrogated 70 of 140 known prostate cancer (PCa) risk-associated loci and demonstrated that 26 (37%) of them harbor 36 SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Optimized high-throughput screening of non-coding variants identified from genome-wide association studies

Morova¹,

Ding

Huang³

et al. 2022

Preprint

Self Cite

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Bayesian Genetic Colocalization Test of Two Traits Using coloc

2022

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Genetic colocalization is an approach for determining whether a genetic variant at a particular locus is shared across multiple phenotypes. Genome-wide association studies (GWAS) have successfully mapped genetic variants associated with thousands of complex traits and diseases. However, a large proportion of GWAS signals fall in non-coding regions of the genome, making functional interpretation a challenge. Colocalization relies on a Bayesian framework that can integrate summary statistics, for example those derived from GWAS and expression quantitative trait loci (eQTL) mapping, to assess whether two or more independent association signals at a region of interest are consistent with a shared causal variant. The results from a colocalization analysis may be used to evaluate putative causal relationships between omics-based molecular measurements and a complex disease, and can generate hypotheses that may be followed up by tailored experiments. In this article, we present an easy and straightforward protocol for conducting a Bayesian test for colocalization of two traits using the 'coloc' package in R with summary-level results derived from GWAS and eQTL studies. We also provide general guidelines that can assist in the interpretation of findings generated from colocalization analyses.

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