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Use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits
BACKGROUND: Our goal was to identify subgroups of sib pairs from the Framingham Heart Study data set that provided higher evidence of linkage to particular candidate regions for cardiovascular disease traits. The focus of this method is not to claim identification of significant linkage to a particu...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2003
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866504/ https://www.ncbi.nlm.nih.gov/pubmed/14975134 http://dx.doi.org/10.1186/1471-2156-4-S1-S66 |
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author | Costello, Tracy Jennifer Swartz, Michael David Sabripour, Mahyar Gu, Xiangjun Sharma, Rishika Etzel, Carol Jean |
author_facet | Costello, Tracy Jennifer Swartz, Michael David Sabripour, Mahyar Gu, Xiangjun Sharma, Rishika Etzel, Carol Jean |
author_sort | Costello, Tracy Jennifer |
collection | PubMed |
description | BACKGROUND: Our goal was to identify subgroups of sib pairs from the Framingham Heart Study data set that provided higher evidence of linkage to particular candidate regions for cardiovascular disease traits. The focus of this method is not to claim identification of significant linkage to a particular locus but to show that tree models can be used to identify subgroups for use in selected sib-pair sampling schemes. RESULTS: We report results using a novel recursive partitioning procedure to identify subgroups of sib pairs with increased evidence of linkage to systolic blood pressure and other cardiovascular disease-related quantitative traits, using the Framingham Heart Study data set provided by the Genetic Analysis Workshop 13. This procedure uses a splitting rule based on Haseman-Elston regression that recursively partitions sib-pair data into homogeneous subgroups. CONCLUSIONS: Using this procedure, we identified a subgroup definition for use as a selected sib-pair sampling scheme. Using the characteristics that define the subgroup with higher evidence for linkage, we have identified an area of focus for further study. |
format | Text |
id | pubmed-1866504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2003 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-18665042007-05-11 Use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits Costello, Tracy Jennifer Swartz, Michael David Sabripour, Mahyar Gu, Xiangjun Sharma, Rishika Etzel, Carol Jean BMC Genet Proceedings BACKGROUND: Our goal was to identify subgroups of sib pairs from the Framingham Heart Study data set that provided higher evidence of linkage to particular candidate regions for cardiovascular disease traits. The focus of this method is not to claim identification of significant linkage to a particular locus but to show that tree models can be used to identify subgroups for use in selected sib-pair sampling schemes. RESULTS: We report results using a novel recursive partitioning procedure to identify subgroups of sib pairs with increased evidence of linkage to systolic blood pressure and other cardiovascular disease-related quantitative traits, using the Framingham Heart Study data set provided by the Genetic Analysis Workshop 13. This procedure uses a splitting rule based on Haseman-Elston regression that recursively partitions sib-pair data into homogeneous subgroups. CONCLUSIONS: Using this procedure, we identified a subgroup definition for use as a selected sib-pair sampling scheme. Using the characteristics that define the subgroup with higher evidence for linkage, we have identified an area of focus for further study. BioMed Central 2003-12-31 /pmc/articles/PMC1866504/ /pubmed/14975134 http://dx.doi.org/10.1186/1471-2156-4-S1-S66 Text en Copyright © 2003 Costello 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 Costello, Tracy Jennifer Swartz, Michael David Sabripour, Mahyar Gu, Xiangjun Sharma, Rishika Etzel, Carol Jean Use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits |
title | Use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits |
title_full | Use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits |
title_fullStr | Use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits |
title_full_unstemmed | Use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits |
title_short | Use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits |
title_sort | use of tree-based models to identify subgroups and increase power to detect linkage to cardiovascular disease traits |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866504/ https://www.ncbi.nlm.nih.gov/pubmed/14975134 http://dx.doi.org/10.1186/1471-2156-4-S1-S66 |
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