Interaction effect of PTEN and CDKN1B chromosomal regions on prostate cancer linkage

Jianfeng Xu, Carl D. Langefeld, S. Lilly Zheng, Elizabeth M. Gillanders, Bao Li Chang, Sarah D. Isaacs, Adrienne H. Williams, Kathy E. Wiley, Latchezar Dimitrov, Deborah A. Meyers, Patrick C. Walsh, Jeffrey M. Trent, William B. Isaacs

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


The tumor suppressor functions of PTEN and CDKN1B have been extensively characterized. Recent data from mouse models suggest that, for some organs, the combined action of both PTEN and CDKN1B has a stronger tumor suppressor function than each alone; for the prostate, heterozygous knockout of both genes leads to 100% penetrance for prostate cancer. To assess whether such an interaction contributes to an increased risk of prostate cancer in humans, we performed a series of epistatic PTEN and CDKN1B interaction analyses in a collection of 188 high-risk hereditary prostate cancer families. Two different analytical approaches were performed; a nonparametric linkage (NPL) regression analysis that simultaneously models allele sharing at these two regions in all families, and an ordered subset analysis (OSA) that assesses linkage evidence at a target region in a subset of families based on the magnitude of allele sharing at the reference region. The strongest evidence of interaction effect was observed at 10q23-24 and 12p11-13 from both the NPL regression analysis (P=0.0002) in all families and the OSA analyses in subsets of families. A LOD-delta of 3.15 (P=0.01) was observed at 10q23-24 among 54 families with the highest NPL scores at 12p11-13, and a LOD-delta of 2.63 (P=0.02) was observed at 12p11-13 among 34 families with the highest NPL scores at 10q23-24. The evidence for the interaction was stronger when using additional fine-mapping markers in the PTEN (10q23) and CDKN1B (12p13) regions. Our data are consistent with epistatic interactions between the PTEN and CDKN1B genes affecting risk for prostate cancer and demonstrate the utility of modeling epistatic effects in linkage analysis to detect susceptibility genes of complex diseases.

Original languageEnglish (US)
Pages (from-to)255-262
Number of pages8
JournalHuman Genetics
Issue number3
StatePublished - Aug 2004
Externally publishedYes

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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