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Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

KEY MESSAGE: Polypoid crop breeders can balance resources between density and sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture. ABSTRACT: The autopolyploid nature of potato and swe...

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Autores principales: Gemenet, Dorcus C., Lindqvist-Kreuze, Hannele, De Boeck, Bert, da Silva Pereira, Guilherme, Mollinari, Marcelo, Zeng, Zhao-Bang, Craig Yencho, G., Campos, Hugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567692/
https://www.ncbi.nlm.nih.gov/pubmed/32876753
http://dx.doi.org/10.1007/s00122-020-03673-2
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author Gemenet, Dorcus C.
Lindqvist-Kreuze, Hannele
De Boeck, Bert
da Silva Pereira, Guilherme
Mollinari, Marcelo
Zeng, Zhao-Bang
Craig Yencho, G.
Campos, Hugo
author_facet Gemenet, Dorcus C.
Lindqvist-Kreuze, Hannele
De Boeck, Bert
da Silva Pereira, Guilherme
Mollinari, Marcelo
Zeng, Zhao-Bang
Craig Yencho, G.
Campos, Hugo
author_sort Gemenet, Dorcus C.
collection PubMed
description KEY MESSAGE: Polypoid crop breeders can balance resources between density and sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture. ABSTRACT: The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene-action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental F(1) population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: (i) do polyploid crop breeders need to invest more for additional sequencing depth? (ii) how many markers are required to make selection decisions? (iii) does considering non-additive genetic effects improve predictive ability (PA)? (iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction in the type of populations we analyzed. We also show that considering dosage information and models considering only additive effects had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00122-020-03673-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-75676922020-10-19 Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops Gemenet, Dorcus C. Lindqvist-Kreuze, Hannele De Boeck, Bert da Silva Pereira, Guilherme Mollinari, Marcelo Zeng, Zhao-Bang Craig Yencho, G. Campos, Hugo Theor Appl Genet Original Article KEY MESSAGE: Polypoid crop breeders can balance resources between density and sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture. ABSTRACT: The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene-action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental F(1) population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: (i) do polyploid crop breeders need to invest more for additional sequencing depth? (ii) how many markers are required to make selection decisions? (iii) does considering non-additive genetic effects improve predictive ability (PA)? (iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction in the type of populations we analyzed. We also show that considering dosage information and models considering only additive effects had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00122-020-03673-2) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-09-02 2020 /pmc/articles/PMC7567692/ /pubmed/32876753 http://dx.doi.org/10.1007/s00122-020-03673-2 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/.
spellingShingle Original Article
Gemenet, Dorcus C.
Lindqvist-Kreuze, Hannele
De Boeck, Bert
da Silva Pereira, Guilherme
Mollinari, Marcelo
Zeng, Zhao-Bang
Craig Yencho, G.
Campos, Hugo
Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops
title Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops
title_full Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops
title_fullStr Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops
title_full_unstemmed Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops
title_short Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops
title_sort sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567692/
https://www.ncbi.nlm.nih.gov/pubmed/32876753
http://dx.doi.org/10.1007/s00122-020-03673-2
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