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Updated: Apr 17, 2026
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
A simple-to-use method incorporating genomic markers into prostate cancer risk prediction tools facilitated future
Sonja Grill1, Mahdi Fallah2, Robin J Leach3
1Department of Life Sciences of the Technical University Munich, Liesel-Beckmann-Str. 2, 85354 Freising, Germany.
Objectives:
To incorporate single-nucleotide polymorphisms (SNPs) into the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC).
Study Design And Setting:
A multivariate random-effects meta-analysis of likelihood ratios (LRs) for 30 validated SNPs was performed, allowing the incorporation of linkage disequilibrium. LRs for an SNP were defined as the ratio of the probability of observing the SNP in prostate cancer cases relative to controls and estimated by published allele or genotype frequencies. LRs were multiplied by the PCPTRC prior odds of prostate cancer to provide updated posterior odds.
Results:
In the meta-analysis (prostate cancer cases/controls = 386,538/985,968), all but two of the SNPs had at least one statistically significant allele LR (P < 0.05). The two SNPs with the largest LRs were rs16901979 [LR = 1.575 for one risk allele, 2.552 for two risk alleles (homozygous)] and rs1447295 (LR = 1.307 and 1.887, respectively).
Conclusion:
The substantial investment in genome-wide association studies to discover SNPs associated with prostate cancer risk and the ability to integrate these findings into the PCPTRC allows investigators to validate these observations, to determine the clinical impact, and to ultimately improve clinical practice in the early detection of the most common cancer in men.
