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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| Format: | Online |
| Language: | English |
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Frontiers Media S.A.
2013
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3747326/ |
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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. 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. 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. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Nelson, Quentin Agarwal, Neeraj Stephenson, Robert Cannon-Albright, Lisa A. |
| spellingShingle |
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 |
| author_facet |
Nelson, Quentin Agarwal, Neeraj Stephenson, Robert Cannon-Albright, Lisa A. |
| author_sort |
Nelson, Quentin |
| title |
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_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_sort |
population-based analysis of clustering identifies a strong genetic contribution to lethal prostate cancer |
| 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. |
| publisher |
Frontiers Media S.A. |
| publishDate |
2013 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3747326/ |
| _version_ |
1612004096044171264 |