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Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environme...

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Detalles Bibliográficos
Autores principales: Kim, Sung, Zhang, Kui, Sun, Fengzhu
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2003
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866530/
https://www.ncbi.nlm.nih.gov/pubmed/14975077
http://dx.doi.org/10.1186/1471-2156-4-S1-S9
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author Kim, Sung
Zhang, Kui
Sun, Fengzhu
author_facet Kim, Sung
Zhang, Kui
Sun, Fengzhu
author_sort Kim, Sung
collection PubMed
description Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.
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spelling pubmed-18665302007-05-11 Detecting susceptibility genes in case-control studies using set association Kim, Sung Zhang, Kui Sun, Fengzhu BMC Genet Proceedings Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype. BioMed Central 2003-12-31 /pmc/articles/PMC1866530/ /pubmed/14975077 http://dx.doi.org/10.1186/1471-2156-4-S1-S9 Text en Copyright © 2003 Kim 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
Kim, Sung
Zhang, Kui
Sun, Fengzhu
Detecting susceptibility genes in case-control studies using set association
title Detecting susceptibility genes in case-control studies using set association
title_full Detecting susceptibility genes in case-control studies using set association
title_fullStr Detecting susceptibility genes in case-control studies using set association
title_full_unstemmed Detecting susceptibility genes in case-control studies using set association
title_short Detecting susceptibility genes in case-control studies using set association
title_sort detecting susceptibility genes in case-control studies using set association
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866530/
https://www.ncbi.nlm.nih.gov/pubmed/14975077
http://dx.doi.org/10.1186/1471-2156-4-S1-S9
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