High-throughput screening of prostate cancer risk loci by single nucleotide polymorphisms sequencing

Peng Zhang, Ji-Han Xia, Jing Zhu, Ping Gao, Yi-Jun Tian, Meijun Du, Yong-Chen Guo, Sufyan Suleman, Qin Zhang, Manish Kohli, Lori S. Tillmans, Stephen N. Thibodeau, Amy J. French, James R. Cerhan, Li-Dong Wang (), Gong-Hong Wei () and Liang Wang ()
Additional contact information
Peng Zhang: The First Affiliated Hospital of Zhengzhou University
Ji-Han Xia: University of Oulu
Jing Zhu: Medical College of Wisconsin
Ping Gao: University of Oulu
Yi-Jun Tian: Medical College of Wisconsin
Meijun Du: Medical College of Wisconsin
Yong-Chen Guo: Medical College of Wisconsin
Sufyan Suleman: University of Oulu
Qin Zhang: University of Oulu
Manish Kohli: Mayo Clinic
Lori S. Tillmans: Mayo Clinic, 200 First Street SW
Stephen N. Thibodeau: Mayo Clinic, 200 First Street SW
Amy J. French: Mayo Clinic, 200 First Street SW
James R. Cerhan: Mayo Clinic
Li-Dong Wang: The First Affiliated Hospital of Zhengzhou University
Gong-Hong Wei: University of Oulu
Liang Wang: Medical College of Wisconsin

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract Functional characterization of disease-causing variants at risk loci has been a significant challenge. Here we report a high-throughput single-nucleotide polymorphisms sequencing (SNPs-seq) technology to simultaneously screen hundreds to thousands of SNPs for their allele-dependent protein-binding differences. This technology takes advantage of higher retention rate of protein-bound DNA oligos in protein purification column to quantitatively sequence these SNP-containing oligos. We apply this technology to test prostate cancer-risk loci and observe differential allelic protein binding in a significant number of selected SNPs. We also test a unique application of self-transcribing active regulatory region sequencing (STARR-seq) in characterizing allele-dependent transcriptional regulation and provide detailed functional analysis at two risk loci (RGS17 and ASCL2). Together, we introduce a powerful high-throughput pipeline for large-scale screening of functional SNPs at disease risk loci.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-018-04451-x Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04451-x

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-04451-x

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().