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Combining multiple family-based association studies
While high-throughput genotyping technologies are becoming readily available, the merit of using these technologies to perform genome-wide association studies has not been established. One major concern is that for studies of complex diseases and traits, the whole-genome approach requires such large...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367479/ https://www.ncbi.nlm.nih.gov/pubmed/18466508 |
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author | Tang, Hua Peng, Jie Wang, Pei Coram, Marc Hsu, Li |
author_facet | Tang, Hua Peng, Jie Wang, Pei Coram, Marc Hsu, Li |
author_sort | Tang, Hua |
collection | PubMed |
description | While high-throughput genotyping technologies are becoming readily available, the merit of using these technologies to perform genome-wide association studies has not been established. One major concern is that for studies of complex diseases and traits, the whole-genome approach requires such large sample sizes that both recruitment and genotyping pose considerable challenge. Here we propose a novel statistical method that boosts the effective sample size by combining data obtained from several studies. Specifically, we consider a situation in which various studies have genotyped non-overlapping subjects at largely non-overlapping sets of markers. Our approach, which exploits the local linkage disequilibrium structure without assuming an explicit population model, opens up the possibility of improving statistical power by incorporating existing data into future association studies. |
format | Text |
id | pubmed-2367479 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-23674792008-05-06 Combining multiple family-based association studies Tang, Hua Peng, Jie Wang, Pei Coram, Marc Hsu, Li BMC Proc Proceedings While high-throughput genotyping technologies are becoming readily available, the merit of using these technologies to perform genome-wide association studies has not been established. One major concern is that for studies of complex diseases and traits, the whole-genome approach requires such large sample sizes that both recruitment and genotyping pose considerable challenge. Here we propose a novel statistical method that boosts the effective sample size by combining data obtained from several studies. Specifically, we consider a situation in which various studies have genotyped non-overlapping subjects at largely non-overlapping sets of markers. Our approach, which exploits the local linkage disequilibrium structure without assuming an explicit population model, opens up the possibility of improving statistical power by incorporating existing data into future association studies. BioMed Central 2007-12-18 /pmc/articles/PMC2367479/ /pubmed/18466508 Text en Copyright © 2007 Tang 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 Tang, Hua Peng, Jie Wang, Pei Coram, Marc Hsu, Li Combining multiple family-based association studies |
title | Combining multiple family-based association studies |
title_full | Combining multiple family-based association studies |
title_fullStr | Combining multiple family-based association studies |
title_full_unstemmed | Combining multiple family-based association studies |
title_short | Combining multiple family-based association studies |
title_sort | combining multiple family-based association studies |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367479/ https://www.ncbi.nlm.nih.gov/pubmed/18466508 |
work_keys_str_mv | AT tanghua combiningmultiplefamilybasedassociationstudies AT pengjie combiningmultiplefamilybasedassociationstudies AT wangpei combiningmultiplefamilybasedassociationstudies AT corammarc combiningmultiplefamilybasedassociationstudies AT hsuli combiningmultiplefamilybasedassociationstudies |