A sequential methodology for the rapid identification and characterization of breast cancer-associated functional SNPs

Yihan Zhao, Di Wu, Danli Jiang, Xiaoyu Zhang, Ting Wu, Jing Cui, Min Qian, Jean Zhao, Steffi Oesterreich, Wei Sun, Toren Finkel and Gang Li ()
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Yihan Zhao: University of Pittsburgh
Di Wu: University of North Carolina at Chapel Hill
Danli Jiang: University of Pittsburgh
Xiaoyu Zhang: University of Pittsburgh
Ting Wu: University of Pittsburgh
Jing Cui: Brigham and Women’s Hospital
Min Qian: East China Normal University
Jean Zhao: DFCI
Steffi Oesterreich: University of Pittsburgh School of Medicine
Wei Sun: University of Pittsburgh Medical Center
Toren Finkel: University of Pittsburgh
Gang Li: University of Pittsburgh

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract GWAS cannot identify functional SNPs (fSNP) from disease-associated SNPs in linkage disequilibrium (LD). Here, we report developing three sequential methodologies including Reel-seq (Regulatory element-sequencing) to identify fSNPs in a high-throughput fashion, SDCP-MS (SNP-specific DNA competition pulldown-mass spectrometry) to identify fSNP-bound proteins and AIDP-Wb (allele-imbalanced DNA pulldown-Western blot) to detect allele-specific protein:fSNP binding. We first apply Reel-seq to screen a library containing 4316 breast cancer-associated SNPs and identify 521 candidate fSNPs. As proof of principle, we verify candidate fSNPs on three well-characterized loci: FGFR2, MAP3K1 and BABAM1. Next, using SDCP-MS and AIDP-Wb, we rapidly identify multiple regulatory factors that specifically bind in an allele-imbalanced manner to the fSNPs on the FGFR2 locus. We finally demonstrate that the factors identified by SDCP-MS can regulate risk gene expression. These data suggest that the sequential application of Reel-seq, SDCP-MS, and AIDP-Wb can greatly help to translate large sets of GWAS data into biologically relevant information.

Date: 2020
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DOI: 10.1038/s41467-020-17159-8

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