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Title
Comprehensive resequence analysis of a 123-kb region of chromosome 11q13 associated with prostate cancer.
Pubmed ID
22468268 (View this publication on the PubMed website)
Publication
Prostate. 2012 Apr; Volume 72 (Issue 5): Pages 476-86
Authors
Chung CC, Boland J, Yeager M, Jacobs KB, Zhang X, Deng Z, Matthews C, Berndt SI, Chanock SJ
Affiliations
  • Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Abstract

BACKGROUND: Genome-wide association studies of prostate cancer have identified single nucleotide polymorphism (SNP) markers in a region of chromosome 11q13.3 in men of European decent. A fine-mapping analysis with tag SNPs in the cancer genetic markers of susceptibility study identified three independent loci, marked by rs10896438, rs12793759, and rs10896449. This study further annotates common and uncommon variation across this region.

METHODS: A next generation resequence analysis of a 122.9-kb region of 11q13.3(68,642,755-68,765,690) was conducted in 78 unrelated individuals of European background,1 CEPH trio, and 1 YRI trio.

RESULTS: In total, 644 polymorphic loci were identified by our sequence analysis. Of these,166 variants—118 SNPs and 48 insertion-deletion polymorphisms (indels)—were novel,namely not present in the 1000 Genomes or International HapMap Projects. We identified 22,25, 6, and 4 variants strongly correlated (r2 ≥ 0.8) with rs10896438, rs10896449, rs12793759,and rs11228565, respectively. HapMap SNPs were in linkage disequilibrium (r2 ≥ 0.8) with 48%, 69%, 14%, and 60% of SNPs marking bins by rs10896438, rs10896449, rs12793759, and rs11228565, respectively.

CONCLUSIONS: Our next generation resequence analysis compliments publicly available datasets of European descent (HapMap, build 28 and 1000 Genome, Pilot 1, October 2010),underscoring the value of targeted resequence analysis prior to initiating functional studies based on public databases alone. Increasing the number of common variants enables investigators to better prioritize variants for functional studies designed to uncover the biological basis of the direct association(s) in the region.

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