Cargando…

New insights into trait introgression with the look-ahead intercrossing strategy

Trait introgression (TI) can be a time-consuming and costly task that typically requires multiple generations of backcrossing (BC). Usually, the aim is to introduce one or more alleles (e.g. QTLs) from a single donor into an elite recipient, both of which are fully inbred. This article studies the p...

Descripción completa

Detalles Bibliográficos
Autores principales: Ni, Zheng, Moeinizade, Saba, Kusmec, Aaron, Hu, Guiping, Wang, Lizhi, Schnable, Patrick S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085795/
https://www.ncbi.nlm.nih.gov/pubmed/36821776
http://dx.doi.org/10.1093/g3journal/jkad042
_version_ 1785022011670528000
author Ni, Zheng
Moeinizade, Saba
Kusmec, Aaron
Hu, Guiping
Wang, Lizhi
Schnable, Patrick S
author_facet Ni, Zheng
Moeinizade, Saba
Kusmec, Aaron
Hu, Guiping
Wang, Lizhi
Schnable, Patrick S
author_sort Ni, Zheng
collection PubMed
description Trait introgression (TI) can be a time-consuming and costly task that typically requires multiple generations of backcrossing (BC). Usually, the aim is to introduce one or more alleles (e.g. QTLs) from a single donor into an elite recipient, both of which are fully inbred. This article studies the potential advantages of incorporating intercrossing (IC) into TI programs when compared with relying solely on the traditional BC framework. We simulate a TI breeding pipeline using 3 previously proposed selection strategies for the traditional BC scheme and 3 modified strategies that allow IC. Our proposed look-ahead intercrossing method (LAS-IC) combines look-ahead Monte Carlo simulations, intercrossing, and additional selection criteria to improve computational efficiency. We compared the efficiency of the 6 strategies across 5 levels of resource availability considering the generation when the major QTLs have been successfully introduced into the recipient and a desired background recovery rate reached. Simulations demonstrate that the inclusion of intercrossing in a TI program can substantially increase efficiency and the probability of success. The proposed LAS-IC provides the highest probability of success across the different scenarios using fewer resources compared with BC-only strategies.
format Online
Article
Text
id pubmed-10085795
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-100857952023-04-12 New insights into trait introgression with the look-ahead intercrossing strategy Ni, Zheng Moeinizade, Saba Kusmec, Aaron Hu, Guiping Wang, Lizhi Schnable, Patrick S G3 (Bethesda) Investigation Trait introgression (TI) can be a time-consuming and costly task that typically requires multiple generations of backcrossing (BC). Usually, the aim is to introduce one or more alleles (e.g. QTLs) from a single donor into an elite recipient, both of which are fully inbred. This article studies the potential advantages of incorporating intercrossing (IC) into TI programs when compared with relying solely on the traditional BC framework. We simulate a TI breeding pipeline using 3 previously proposed selection strategies for the traditional BC scheme and 3 modified strategies that allow IC. Our proposed look-ahead intercrossing method (LAS-IC) combines look-ahead Monte Carlo simulations, intercrossing, and additional selection criteria to improve computational efficiency. We compared the efficiency of the 6 strategies across 5 levels of resource availability considering the generation when the major QTLs have been successfully introduced into the recipient and a desired background recovery rate reached. Simulations demonstrate that the inclusion of intercrossing in a TI program can substantially increase efficiency and the probability of success. The proposed LAS-IC provides the highest probability of success across the different scenarios using fewer resources compared with BC-only strategies. Oxford University Press 2023-02-23 /pmc/articles/PMC10085795/ /pubmed/36821776 http://dx.doi.org/10.1093/g3journal/jkad042 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the 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
Ni, Zheng
Moeinizade, Saba
Kusmec, Aaron
Hu, Guiping
Wang, Lizhi
Schnable, Patrick S
New insights into trait introgression with the look-ahead intercrossing strategy
title New insights into trait introgression with the look-ahead intercrossing strategy
title_full New insights into trait introgression with the look-ahead intercrossing strategy
title_fullStr New insights into trait introgression with the look-ahead intercrossing strategy
title_full_unstemmed New insights into trait introgression with the look-ahead intercrossing strategy
title_short New insights into trait introgression with the look-ahead intercrossing strategy
title_sort new insights into trait introgression with the look-ahead intercrossing strategy
topic Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085795/
https://www.ncbi.nlm.nih.gov/pubmed/36821776
http://dx.doi.org/10.1093/g3journal/jkad042
work_keys_str_mv AT nizheng newinsightsintotraitintrogressionwiththelookaheadintercrossingstrategy
AT moeinizadesaba newinsightsintotraitintrogressionwiththelookaheadintercrossingstrategy
AT kusmecaaron newinsightsintotraitintrogressionwiththelookaheadintercrossingstrategy
AT huguiping newinsightsintotraitintrogressionwiththelookaheadintercrossingstrategy
AT wanglizhi newinsightsintotraitintrogressionwiththelookaheadintercrossingstrategy
AT schnablepatricks newinsightsintotraitintrogressionwiththelookaheadintercrossingstrategy