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EFBAT: exact family-based association tests

BACKGROUND: Family-based association tests are important tools for investigating genetic risk factors of complex diseases. These tests are especially valuable for being robust to population structure. We introduce a tool, EFBAT, which performs exact family-based tests of association for X-chromosome...

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Detalles Bibliográficos
Autores principales: Schneiter, Kady, Degnan, James H, Corcoran, Christopher, Xu, Xin, Laird, Nan
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2234422/
https://www.ncbi.nlm.nih.gov/pubmed/18096066
http://dx.doi.org/10.1186/1471-2156-8-86
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author Schneiter, Kady
Degnan, James H
Corcoran, Christopher
Xu, Xin
Laird, Nan
author_facet Schneiter, Kady
Degnan, James H
Corcoran, Christopher
Xu, Xin
Laird, Nan
author_sort Schneiter, Kady
collection PubMed
description BACKGROUND: Family-based association tests are important tools for investigating genetic risk factors of complex diseases. These tests are especially valuable for being robust to population structure. We introduce a tool, EFBAT, which performs exact family-based tests of association for X-chromosome and autosomal biallelic markers. RESULTS: The program EFBAT extends a network algorithm previously applied to autosomal markers to include the X-chromosome and to perform tests of association under the null hypotheses "no association, no linkage" and "no association in the presence of linkage" under additive, dominant and recessive genetic models. These tests are valid regardless of patterns of missing familial data. CONCLUSION: The general framework for performing exact family-based association tests has been usefully extended to the X-chromosome, particularly for the hypothesis of "no association in the presence of linkage" and for different genetic models.
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spelling pubmed-22344222008-02-08 EFBAT: exact family-based association tests Schneiter, Kady Degnan, James H Corcoran, Christopher Xu, Xin Laird, Nan BMC Genet Software BACKGROUND: Family-based association tests are important tools for investigating genetic risk factors of complex diseases. These tests are especially valuable for being robust to population structure. We introduce a tool, EFBAT, which performs exact family-based tests of association for X-chromosome and autosomal biallelic markers. RESULTS: The program EFBAT extends a network algorithm previously applied to autosomal markers to include the X-chromosome and to perform tests of association under the null hypotheses "no association, no linkage" and "no association in the presence of linkage" under additive, dominant and recessive genetic models. These tests are valid regardless of patterns of missing familial data. CONCLUSION: The general framework for performing exact family-based association tests has been usefully extended to the X-chromosome, particularly for the hypothesis of "no association in the presence of linkage" and for different genetic models. BioMed Central 2007-12-20 /pmc/articles/PMC2234422/ /pubmed/18096066 http://dx.doi.org/10.1186/1471-2156-8-86 Text en Copyright © 2007 Schneiter 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 Software
Schneiter, Kady
Degnan, James H
Corcoran, Christopher
Xu, Xin
Laird, Nan
EFBAT: exact family-based association tests
title EFBAT: exact family-based association tests
title_full EFBAT: exact family-based association tests
title_fullStr EFBAT: exact family-based association tests
title_full_unstemmed EFBAT: exact family-based association tests
title_short EFBAT: exact family-based association tests
title_sort efbat: exact family-based association tests
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2234422/
https://www.ncbi.nlm.nih.gov/pubmed/18096066
http://dx.doi.org/10.1186/1471-2156-8-86
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