Systematic identification of regulatory variants associated with cancer risk

Abstract

BackgroundMost cancer risk-associated single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) are noncoding and it is challenging to assess their functional impacts. To systematically identify the SNPs that affect gene expression by modulating activities of distal regulatory elements, we adapt the self-transcribing active regulatory region sequencing (STARR-seq) strategy, a high-throughput technique to functionally quantify enhancer activities.ResultsFrom 10,673 SNPs linked with 996 cancer risk-associated SNPs identified in previous GWAS studies, we identify 575 SNPs in the fragments that positively regulate gene expression, and 758 SNPs in the fragments with negative regulatory activities. Among them, 70 variants are regulatory variants for which the two alleles confer different regulatory activities. We analyze in depth two regulatory variants—breast cancer risk SNP rs11055880 and leukemia risk-associated SNP rs12142375—and demonstrate their endogenous regulatory activities on expression of ATF7IP and PDE4B genes, respectively, using a CRISPR-Cas9 approach.ConclusionsBy identifying regulatory variants associated with cancer susceptibility and studying their molecular functions, we hope to help the interpretation of GWAS results and provide improved information for cancer risk assessment.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1322-z) contains supplementary material, which is available to authorized users.

A systematic evaluation of the design, orientation, and sequence context dependencies of massively parallel reporter assays

“…Instead of relying on measurement of a conventional reporter, MPRAs characterize each element in a multiplex fashion, through sequence-based quantification of barcodes incorporated into the RNA, each associated with a different element 10 . MPRAs (a term we use broadly to encompass related methods including STARR-seq 12 and lentiMPRAs 13 ) have facilitated the scalable study of putative regulatory elements for goals ranging from functional annotation [12][13][14] to variant effect prediction 10,11,[15][16][17][18] to evolutionary reconstruction 19,20 .…”

Systematic identification of human SNPs affecting regulatory element activity

“…MPRAs have previously been used successfully to generate initial lists of SNPs that can alter the activity of regulatory elements [20][21][22][23][24] . These earlier efforts, however, covered only a tiny proportion of the total collection of human SNPs.…”

“…Large-scale versions of this approach are referred to as Massively Parallel Reporter Assays (MPRAs). So far, MPRAs have been successfully applied to screen tens of thousands of SNPs [20][21][22][23][24] . Each of these studies has yielded tens to at most several hundreds of SNPs that significantly alter promoter or enhancer activity.…”

Computational Assessment of the Regulation-Modulating Potential for Noncoding Variants

“…The performance was evaluated on a pathogenic dataset with 2,135 variants (Supplementary Table 7). d , The performance comparison of CARMEN to other tools 26 bubbles colored by F1 score. The thresholds of the different tools were obtained from respective official websites or reference papers (Supplementary Table 10).…”

“…The thresholds of the different tools were obtained from respective official websites or reference papers (Supplementary Table 10). e , The recall of different tools on the known expression-modulating variants 26 . The X axis indicates the proportion of variants from the significant expression-modulating variants that were predicted as positive variants by the different tools.…”