Variants at IRX4 as prostate cancer expression quantitative trait loci

被引:0
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作者
Xing Xu
Wasay M Hussain
Joseph Vijai
Kenneth Offit
Mark A Rubin
Francesca Demichelis
Robert J Klein
机构
[1] Clinical Genetics Service,Department of Medicine
[2] Memorial Sloan-Kettering Cancer Center,Department of Pathology and Laboratory Medicine
[3] Program in Cancer Biology and Genetics,undefined
[4] Memorial Sloan-Kettering Cancer Center,undefined
[5] Weill Cornell Medical College,undefined
[6] Institute for Computational Biomedicine,undefined
[7] Weill Cornell Medical College,undefined
[8] Centre for Integrative Biology,undefined
[9] CIBIO,undefined
[10] University of Trento,undefined
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关键词
expression quantitative trait loci; eQTL; prostate cancer; GWAS; risk SNPs;
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摘要
Genome-wide association studies (GWAS) have identified numerous prostate cancer-associated risk loci. Some variants at these loci may be regulatory and influence expression of nearby genes. Such loci are known as cis-expression quantitative trait loci (cis-eQTL). As cis-eQTLs are highly tissue-specific, we asked if GWAS-identified prostate cancer risk loci are cis-eQTLs in human prostate tumor tissues. We investigated 50 prostate cancer samples for their genotype at 59 prostate cancer risk-associated single-nucleotide polymorphisms (SNPs) and performed cis-eQTL analysis of transcripts from paired primary tumors within two megabase windows. We tested 586 transcript–genotype associations, of which 27 were significant (false discovery rate ≤10%). An equivalent eQTL analysis of the same prostate cancer risk loci in lymphoblastoid cell lines did not result in any significant associations. The top-ranked cis-eQTL involved the IRX4 (Iroquois homeobox protein 4) transcript and rs12653946, tagged by rs10866528 in our study (P=4.91 × 10−5). Replication studies, linkage disequilibrium, and imputation analyses highlight population specificity at this locus. We independently validated IRX4 as a potential prostate cancer risk gene through cis-eQTL analysis of prostate cancer risk variants. Cis-eQTL analysis in relevant tissues, even with a small sample size, can be a powerful method to expedite functional follow-up of GWAS.
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页码:558 / 563
页数:5
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