Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set

S. N. Thibodeau (), A. J. French, S. K. McDonnell, J. Cheville, S. Middha, L. Tillmans, S. Riska, S. Baheti, M. C. Larson, Z. Fogarty, Y. Zhang, N. Larson, A. Nair, D. O’Brien, L. Wang and D J. Schaid
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S. N. Thibodeau: Mayo Clinic College of Medicine
A. J. French: Mayo Clinic College of Medicine
S. K. McDonnell: Mayo Clinic College of Medicine
J. Cheville: Mayo Clinic College of Medicine
S. Middha: Mayo Clinic College of Medicine
L. Tillmans: Mayo Clinic College of Medicine
S. Riska: Mayo Clinic College of Medicine
S. Baheti: Mayo Clinic College of Medicine
M. C. Larson: Mayo Clinic College of Medicine
Z. Fogarty: Mayo Clinic College of Medicine
Y. Zhang: University of Maryland School of Medicine
N. Larson: Mayo Clinic College of Medicine
A. Nair: Mayo Clinic College of Medicine
D. O’Brien: Mayo Clinic College of Medicine
L. Wang: Medical College of Wisconsin
D J. Schaid: Mayo Clinic College of Medicine

Nature Communications, 2015, vol. 6, issue 1, 1-10

Abstract: Abstract Multiple studies have identified loci associated with the risk of developing prostate cancer but the associated genes are not well studied. Here we create a normal prostate tissue-specific eQTL data set and apply this data set to previously identified prostate cancer (PrCa)-risk SNPs in an effort to identify candidate target genes. The eQTL data set is constructed by the genotyping and RNA sequencing of 471 samples. We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals. We analyse cis-acting associations where the transcript is located within 2 Mb (±1 Mb) of the risk SNP interval. Of all SNP–gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates. Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes. This study provides a rich resource to study biological mechanisms underlying genetic risk to PrCa.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9653

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DOI: 10.1038/ncomms9653

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