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SeqBreed: a python tool to evaluate genomic prediction in complex scenarios
BACKGROUND: Genomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experime...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7008576/ https://www.ncbi.nlm.nih.gov/pubmed/32039696 http://dx.doi.org/10.1186/s12711-020-0530-2 |
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author | Pérez-Enciso, Miguel Ramírez-Ayala, Lino C. Zingaretti, Laura M. |
author_facet | Pérez-Enciso, Miguel Ramírez-Ayala, Lino C. Zingaretti, Laura M. |
author_sort | Pérez-Enciso, Miguel |
collection | PubMed |
description | BACKGROUND: Genomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experiments, including genome-wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible forward simulator programmed in python3. RESULTS: SeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes that are determined by any number of causal loci. SeqBreed implements several GP methods, including genomic best linear unbiased prediction (GBLUP), single-step GBLUP, pedigree-based BLUP, and mass selection. We illustrate its functionality with Drosophila genome reference panel (DGRP) sequence data and with tetraploid potato genotype data. CONCLUSIONS: SeqBreed is a flexible and easy to use tool that can be used to optimize GP or genome-wide association studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation, and examples are available at https://github.com/miguelperezenciso/SeqBreed. |
format | Online Article Text |
id | pubmed-7008576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70085762020-02-13 SeqBreed: a python tool to evaluate genomic prediction in complex scenarios Pérez-Enciso, Miguel Ramírez-Ayala, Lino C. Zingaretti, Laura M. Genet Sel Evol Software BACKGROUND: Genomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experiments, including genome-wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible forward simulator programmed in python3. RESULTS: SeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes that are determined by any number of causal loci. SeqBreed implements several GP methods, including genomic best linear unbiased prediction (GBLUP), single-step GBLUP, pedigree-based BLUP, and mass selection. We illustrate its functionality with Drosophila genome reference panel (DGRP) sequence data and with tetraploid potato genotype data. CONCLUSIONS: SeqBreed is a flexible and easy to use tool that can be used to optimize GP or genome-wide association studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation, and examples are available at https://github.com/miguelperezenciso/SeqBreed. BioMed Central 2020-02-10 /pmc/articles/PMC7008576/ /pubmed/32039696 http://dx.doi.org/10.1186/s12711-020-0530-2 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Software Pérez-Enciso, Miguel Ramírez-Ayala, Lino C. Zingaretti, Laura M. SeqBreed: a python tool to evaluate genomic prediction in complex scenarios |
title | SeqBreed: a python tool to evaluate genomic prediction in complex scenarios |
title_full | SeqBreed: a python tool to evaluate genomic prediction in complex scenarios |
title_fullStr | SeqBreed: a python tool to evaluate genomic prediction in complex scenarios |
title_full_unstemmed | SeqBreed: a python tool to evaluate genomic prediction in complex scenarios |
title_short | SeqBreed: a python tool to evaluate genomic prediction in complex scenarios |
title_sort | seqbreed: a python tool to evaluate genomic prediction in complex scenarios |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7008576/ https://www.ncbi.nlm.nih.gov/pubmed/32039696 http://dx.doi.org/10.1186/s12711-020-0530-2 |
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