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A method dealing with a large number of correlated traits in a linkage genome scan
We propose a method to perform linkage genome scans for many correlated traits in the Genetic Analysis Workshop 15 (GAW15) data. The proposed method has two steps: first, we use a clustering method to find the tight clusters of the traits and use the first principal component (PC) of the traits in e...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367490/ https://www.ncbi.nlm.nih.gov/pubmed/18466587 |
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author | Feng, Tao Zhang, Shuanglin Sha, Qiuying |
author_facet | Feng, Tao Zhang, Shuanglin Sha, Qiuying |
author_sort | Feng, Tao |
collection | PubMed |
description | We propose a method to perform linkage genome scans for many correlated traits in the Genetic Analysis Workshop 15 (GAW15) data. The proposed method has two steps: first, we use a clustering method to find the tight clusters of the traits and use the first principal component (PC) of the traits in each cluster to represent the cluster; second, we perform a linkage scan for each cluster by using the representative trait of the cluster. The results of applying the method to the GAW15 Problem 1 data indicate that most of the traits in the same cluster have the same regulators, and the representative trait measure, the first PC, can explain a large part of the total variation of all the traits in each cluster. Furthermore, considering one cluster of traits at a time may yield more linkage signals than considering traits individually. |
format | Text |
id | pubmed-2367490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-23674902008-05-06 A method dealing with a large number of correlated traits in a linkage genome scan Feng, Tao Zhang, Shuanglin Sha, Qiuying BMC Proc Proceedings We propose a method to perform linkage genome scans for many correlated traits in the Genetic Analysis Workshop 15 (GAW15) data. The proposed method has two steps: first, we use a clustering method to find the tight clusters of the traits and use the first principal component (PC) of the traits in each cluster to represent the cluster; second, we perform a linkage scan for each cluster by using the representative trait of the cluster. The results of applying the method to the GAW15 Problem 1 data indicate that most of the traits in the same cluster have the same regulators, and the representative trait measure, the first PC, can explain a large part of the total variation of all the traits in each cluster. Furthermore, considering one cluster of traits at a time may yield more linkage signals than considering traits individually. BioMed Central 2007-12-18 /pmc/articles/PMC2367490/ /pubmed/18466587 Text en Copyright © 2007 Feng et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Feng, Tao Zhang, Shuanglin Sha, Qiuying A method dealing with a large number of correlated traits in a linkage genome scan |
title | A method dealing with a large number of correlated traits in a linkage genome scan |
title_full | A method dealing with a large number of correlated traits in a linkage genome scan |
title_fullStr | A method dealing with a large number of correlated traits in a linkage genome scan |
title_full_unstemmed | A method dealing with a large number of correlated traits in a linkage genome scan |
title_short | A method dealing with a large number of correlated traits in a linkage genome scan |
title_sort | method dealing with a large number of correlated traits in a linkage genome scan |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367490/ https://www.ncbi.nlm.nih.gov/pubmed/18466587 |
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