Format

Send to

Choose Destination
Prostate. 2010 May 15;70(7):735-44. doi: 10.1002/pros.21106.

Genome-wide linkage analysis of 1,233 prostate cancer pedigrees from the International Consortium for Prostate Cancer Genetics using novel sumLINK and sumLOD analyses.

Author information

1
University of Utah ICPCG Group and Division of Genetic Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah 84108, USA. bryce.christensen@utah.edu

Abstract

BACKGROUND:

Prostate cancer (PC) is generally believed to have a strong inherited component, but the search for susceptibility genes has been hindered by the effects of genetic heterogeneity. The recently developed sumLINK and sumLOD statistics are powerful tools for linkage analysis in the presence of heterogeneity.

METHODS:

We performed a secondary analysis of 1,233 PC pedigrees from the International Consortium for Prostate Cancer Genetics (ICPCG) using two novel statistics, the sumLINK and sumLOD. For both statistics, dominant and recessive genetic models were considered. False discovery rate (FDR) analysis was conducted to assess the effects of multiple testing.

RESULTS:

Our analysis identified significant linkage evidence at chromosome 22q12, confirming previous findings by the initial conventional analyses of the same ICPCG data. Twelve other regions were identified with genome-wide suggestive evidence for linkage. Seven regions (1q23, 5q11, 5q35, 6p21, 8q12, 11q13, 20p11-q11) are near loci previously identified in the initial ICPCG pooled data analysis or the subset of aggressive PC pedigrees. Three other regions (1p12, 8p23, 19q13) confirm loci reported by others, and two (2p24, 6q27) are novel susceptibility loci. FDR testing indicates that over 70% of these results are likely true positive findings. Statistical recombinant mapping narrowed regions to an average of 9 cM.

CONCLUSIONS:

Our results represent genomic regions with the greatest consistency of positive linkage evidence across a very large collection of high-risk PC pedigrees using new statistical tests that deal powerfully with heterogeneity. These regions are excellent candidates for further study to identify PC predisposition genes.

PMID:
20333727
PMCID:
PMC3428045
DOI:
10.1002/pros.21106
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Wiley Icon for PubMed Central
Loading ...
Support Center