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Mapping epistatic quantitative trait loci

BACKGROUND: How to map quantitative trait loci (QTL) with epistasis efficiently and reliably has been a persistent problem for QTL mapping analysis. There are a number of difficulties for studying epistatic QTL. Linkage can impose a significant challenge for finding epistatic QTL reliably. If multip...

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Autores principales: Laurie, Cecelia, Wang, Shengchu, Carlini-Garcia, Luciana Aparecida, Zeng, Zhao-Bang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226885/
https://www.ncbi.nlm.nih.gov/pubmed/25367219
http://dx.doi.org/10.1186/s12863-014-0112-9
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author Laurie, Cecelia
Wang, Shengchu
Carlini-Garcia, Luciana Aparecida
Zeng, Zhao-Bang
author_facet Laurie, Cecelia
Wang, Shengchu
Carlini-Garcia, Luciana Aparecida
Zeng, Zhao-Bang
author_sort Laurie, Cecelia
collection PubMed
description BACKGROUND: How to map quantitative trait loci (QTL) with epistasis efficiently and reliably has been a persistent problem for QTL mapping analysis. There are a number of difficulties for studying epistatic QTL. Linkage can impose a significant challenge for finding epistatic QTL reliably. If multiple QTL are in linkage and have interactions, searching for QTL can become a very delicate issue. A commonly used strategy that performs a two-dimensional genome scan to search for a pair of QTL with epistasis can suffer from low statistical power and also may lead to false identification due to complex linkage disequilibrium and interaction patterns. RESULTS: To tackle the problem of complex interaction of multiple QTL with linkage, we developed a three-stage search strategy. In the first stage, main effect QTL are searched and mapped. In the second stage, epistatic QTL that interact significantly with other identified QTL are searched. In the third stage, new epistatic QTL are searched in pairs. This strategy is based on the consideration that most genetic variance is due to the main effects of QTL. Thus by first mapping those main-effect QTL, the statistical power for the second and third stages of analysis for mapping epistatic QTL can be maximized. The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage. The model search criterion is empirically and dynamically evaluated by using a score-statistic based resampling procedure. We demonstrate through simulations that the method has good power and low false positive in the identification of QTL and epistasis. CONCLUSION: This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern. The method has been implemented in the user-friendly computer software Windows QTL Cartographer. This will greatly facilitate the application of the method for QTL mapping data analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-014-0112-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-42268852014-11-12 Mapping epistatic quantitative trait loci Laurie, Cecelia Wang, Shengchu Carlini-Garcia, Luciana Aparecida Zeng, Zhao-Bang BMC Genet Research Article BACKGROUND: How to map quantitative trait loci (QTL) with epistasis efficiently and reliably has been a persistent problem for QTL mapping analysis. There are a number of difficulties for studying epistatic QTL. Linkage can impose a significant challenge for finding epistatic QTL reliably. If multiple QTL are in linkage and have interactions, searching for QTL can become a very delicate issue. A commonly used strategy that performs a two-dimensional genome scan to search for a pair of QTL with epistasis can suffer from low statistical power and also may lead to false identification due to complex linkage disequilibrium and interaction patterns. RESULTS: To tackle the problem of complex interaction of multiple QTL with linkage, we developed a three-stage search strategy. In the first stage, main effect QTL are searched and mapped. In the second stage, epistatic QTL that interact significantly with other identified QTL are searched. In the third stage, new epistatic QTL are searched in pairs. This strategy is based on the consideration that most genetic variance is due to the main effects of QTL. Thus by first mapping those main-effect QTL, the statistical power for the second and third stages of analysis for mapping epistatic QTL can be maximized. The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage. The model search criterion is empirically and dynamically evaluated by using a score-statistic based resampling procedure. We demonstrate through simulations that the method has good power and low false positive in the identification of QTL and epistasis. CONCLUSION: This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern. The method has been implemented in the user-friendly computer software Windows QTL Cartographer. This will greatly facilitate the application of the method for QTL mapping data analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-014-0112-9) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-04 /pmc/articles/PMC4226885/ /pubmed/25367219 http://dx.doi.org/10.1186/s12863-014-0112-9 Text en © Laurie et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Laurie, Cecelia
Wang, Shengchu
Carlini-Garcia, Luciana Aparecida
Zeng, Zhao-Bang
Mapping epistatic quantitative trait loci
title Mapping epistatic quantitative trait loci
title_full Mapping epistatic quantitative trait loci
title_fullStr Mapping epistatic quantitative trait loci
title_full_unstemmed Mapping epistatic quantitative trait loci
title_short Mapping epistatic quantitative trait loci
title_sort mapping epistatic quantitative trait loci
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4226885/
https://www.ncbi.nlm.nih.gov/pubmed/25367219
http://dx.doi.org/10.1186/s12863-014-0112-9
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