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Optimization of BSA-seq experiment for QTL mapping
Deep sequencing-based bulked segregant analysis (BSA-seq) has become a popular approach for quantitative trait loci (QTL) mapping in recent years. Effective statistical methods for BSA-seq have been developed, but how to design a suitable experiment for BSA-seq remains unclear. In this paper, we sho...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727994/ https://www.ncbi.nlm.nih.gov/pubmed/34791194 http://dx.doi.org/10.1093/g3journal/jkab370 |
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author | Huang, Likun Tang, Weiqi Wu, Weiren |
author_facet | Huang, Likun Tang, Weiqi Wu, Weiren |
author_sort | Huang, Likun |
collection | PubMed |
description | Deep sequencing-based bulked segregant analysis (BSA-seq) has become a popular approach for quantitative trait loci (QTL) mapping in recent years. Effective statistical methods for BSA-seq have been developed, but how to design a suitable experiment for BSA-seq remains unclear. In this paper, we show in theory how the major experimental factors (including population size, pool proportion, pool balance, and generation) and the intrinsic factors of a QTL (including heritability and degree of dominance) affect the power of QTL detection and the precision of QTL mapping in BSA-seq. Increasing population size can improve the power and precision, depending on the QTL heritability. The best proportion of each pool in the population is around 0.25. So, 0.25 is generally applicable in BSA-seq. Small pool proportion can greatly reduce the power and precision. Imbalance of pool pair in size also causes decrease of the power and precision. Additive effect is more important than dominance effect for QTL mapping. Increasing the generation of filial population produced by selfing can significantly increase the power and precision, especially from F(2) to F(3). These findings enable researchers to optimize the experimental design for BSA-seq. A web-based program named BSA-seq Design Tool is available at http://124.71.74.135/BSA-seqDesignTool/ and https://github.com/huanglikun/BSA-seqDesignTool. |
format | Online Article Text |
id | pubmed-8727994 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-87279942022-01-05 Optimization of BSA-seq experiment for QTL mapping Huang, Likun Tang, Weiqi Wu, Weiren G3 (Bethesda) Investigation Deep sequencing-based bulked segregant analysis (BSA-seq) has become a popular approach for quantitative trait loci (QTL) mapping in recent years. Effective statistical methods for BSA-seq have been developed, but how to design a suitable experiment for BSA-seq remains unclear. In this paper, we show in theory how the major experimental factors (including population size, pool proportion, pool balance, and generation) and the intrinsic factors of a QTL (including heritability and degree of dominance) affect the power of QTL detection and the precision of QTL mapping in BSA-seq. Increasing population size can improve the power and precision, depending on the QTL heritability. The best proportion of each pool in the population is around 0.25. So, 0.25 is generally applicable in BSA-seq. Small pool proportion can greatly reduce the power and precision. Imbalance of pool pair in size also causes decrease of the power and precision. Additive effect is more important than dominance effect for QTL mapping. Increasing the generation of filial population produced by selfing can significantly increase the power and precision, especially from F(2) to F(3). These findings enable researchers to optimize the experimental design for BSA-seq. A web-based program named BSA-seq Design Tool is available at http://124.71.74.135/BSA-seqDesignTool/ and https://github.com/huanglikun/BSA-seqDesignTool. Oxford University Press 2021-11-15 /pmc/articles/PMC8727994/ /pubmed/34791194 http://dx.doi.org/10.1093/g3journal/jkab370 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Investigation Huang, Likun Tang, Weiqi Wu, Weiren Optimization of BSA-seq experiment for QTL mapping |
title | Optimization of BSA-seq experiment for QTL mapping |
title_full | Optimization of BSA-seq experiment for QTL mapping |
title_fullStr | Optimization of BSA-seq experiment for QTL mapping |
title_full_unstemmed | Optimization of BSA-seq experiment for QTL mapping |
title_short | Optimization of BSA-seq experiment for QTL mapping |
title_sort | optimization of bsa-seq experiment for qtl mapping |
topic | Investigation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727994/ https://www.ncbi.nlm.nih.gov/pubmed/34791194 http://dx.doi.org/10.1093/g3journal/jkab370 |
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