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A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer

Background: Prostate cancer is a common and often deadly cancer. Decades of study have yet to identify genes that explain much familial prostate cancer. Traditional linkage analysis of pedigrees has yielded results that are rarely validated. We hypothesize that there are rare segregating variants re...

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Autores principales: Nelson, Quentin, Agarwal, Neeraj, Stephenson, Robert, Cannon-Albright, Lisa A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3747326/
https://www.ncbi.nlm.nih.gov/pubmed/23970893
http://dx.doi.org/10.3389/fgene.2013.00152
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author Nelson, Quentin
Agarwal, Neeraj
Stephenson, Robert
Cannon-Albright, Lisa A.
author_facet Nelson, Quentin
Agarwal, Neeraj
Stephenson, Robert
Cannon-Albright, Lisa A.
author_sort Nelson, Quentin
collection PubMed
description Background: Prostate cancer is a common and often deadly cancer. Decades of study have yet to identify genes that explain much familial prostate cancer. Traditional linkage analysis of pedigrees has yielded results that are rarely validated. We hypothesize that there are rare segregating variants responsible for high-risk prostate cancer pedigrees, but recognize that within-pedigree heterogeneity is responsible for significant noise that overwhelms signal. Here we introduce a method to identify homogeneous subsets of prostate cancer, based on cancer characteristics, which show the best evidence for an inherited contribution. Methods: We have modified an existing method, the Genealogical Index of Familiality (GIF) used to show evidence for significant familial clustering. The modification allows a test for excess familial clustering of a subset of prostate cancer cases when compared to all prostate cancer cases. Results: Consideration of the familial clustering of eight clinical subsets of prostate cancer cases compared to the expected familial clustering of all prostate cancer cases identified three subsets of prostate cancer cases with evidence for familial clustering significantly in excess of expected. These subsets include prostate cancer cases diagnosed before age 50 years, prostate cancer cases with body mass index (BMI) greater than or equal to 30, and prostate cancer cases for whom prostate cancer contributed to death. Conclusions: This analysis identified several subsets of prostate cancer cases that cluster significantly more than expected when compared to all prostate cancer familial clustering. A focus on high-risk prostate cancer cases or pedigrees with these characteristics will reduce noise and could allow identification of the rare predisposition genes or variants responsible.
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spelling pubmed-37473262013-08-22 A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer Nelson, Quentin Agarwal, Neeraj Stephenson, Robert Cannon-Albright, Lisa A. Front Genet Genetics Background: Prostate cancer is a common and often deadly cancer. Decades of study have yet to identify genes that explain much familial prostate cancer. Traditional linkage analysis of pedigrees has yielded results that are rarely validated. We hypothesize that there are rare segregating variants responsible for high-risk prostate cancer pedigrees, but recognize that within-pedigree heterogeneity is responsible for significant noise that overwhelms signal. Here we introduce a method to identify homogeneous subsets of prostate cancer, based on cancer characteristics, which show the best evidence for an inherited contribution. Methods: We have modified an existing method, the Genealogical Index of Familiality (GIF) used to show evidence for significant familial clustering. The modification allows a test for excess familial clustering of a subset of prostate cancer cases when compared to all prostate cancer cases. Results: Consideration of the familial clustering of eight clinical subsets of prostate cancer cases compared to the expected familial clustering of all prostate cancer cases identified three subsets of prostate cancer cases with evidence for familial clustering significantly in excess of expected. These subsets include prostate cancer cases diagnosed before age 50 years, prostate cancer cases with body mass index (BMI) greater than or equal to 30, and prostate cancer cases for whom prostate cancer contributed to death. Conclusions: This analysis identified several subsets of prostate cancer cases that cluster significantly more than expected when compared to all prostate cancer familial clustering. A focus on high-risk prostate cancer cases or pedigrees with these characteristics will reduce noise and could allow identification of the rare predisposition genes or variants responsible. Frontiers Media S.A. 2013-08-20 /pmc/articles/PMC3747326/ /pubmed/23970893 http://dx.doi.org/10.3389/fgene.2013.00152 Text en Copyright © 2013 Nelson, Agarwal, Stephenson and Cannon-Albright. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Nelson, Quentin
Agarwal, Neeraj
Stephenson, Robert
Cannon-Albright, Lisa A.
A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer
title A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer
title_full A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer
title_fullStr A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer
title_full_unstemmed A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer
title_short A population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer
title_sort population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3747326/
https://www.ncbi.nlm.nih.gov/pubmed/23970893
http://dx.doi.org/10.3389/fgene.2013.00152
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