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Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models

Due to increasing demand for new advanced crops, considerable efforts have been made to explore the improvement of stress and disease resistance cultivar traits through the study of wild crops. When both wild and interspecific hybrid materials are available, a common approach has been to study two t...

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Autores principales: Shin, Min-Gyoung, Nuzhdin, Sergey V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450460/
https://www.ncbi.nlm.nih.gov/pubmed/34552613
http://dx.doi.org/10.3389/fgene.2021.684882
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author Shin, Min-Gyoung
Nuzhdin, Sergey V.
author_facet Shin, Min-Gyoung
Nuzhdin, Sergey V.
author_sort Shin, Min-Gyoung
collection PubMed
description Due to increasing demand for new advanced crops, considerable efforts have been made to explore the improvement of stress and disease resistance cultivar traits through the study of wild crops. When both wild and interspecific hybrid materials are available, a common approach has been to study two types of materials separately and simply compare the quantitative trait locus (QTL) regions. However, combining the two types of materials can potentially create a more efficient method of finding predictive QTLs. In this simulation study, we focused on scenarios involving causal marker expression suppressed by trans-regulatory mechanisms, where the otherwise easily lost associated signals benefit the most from combining the two types of data. A probabilistic sampling approach was used to prioritize consistent genotypic phenotypic patterns across both types of data sets. We chose random forest and gradient boosting to apply the prioritization scheme and found that both facilitated the investigation of predictive causal markers in most of the biological scenarios simulated.
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spelling pubmed-84504602021-09-21 Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models Shin, Min-Gyoung Nuzhdin, Sergey V. Front Genet Genetics Due to increasing demand for new advanced crops, considerable efforts have been made to explore the improvement of stress and disease resistance cultivar traits through the study of wild crops. When both wild and interspecific hybrid materials are available, a common approach has been to study two types of materials separately and simply compare the quantitative trait locus (QTL) regions. However, combining the two types of materials can potentially create a more efficient method of finding predictive QTLs. In this simulation study, we focused on scenarios involving causal marker expression suppressed by trans-regulatory mechanisms, where the otherwise easily lost associated signals benefit the most from combining the two types of data. A probabilistic sampling approach was used to prioritize consistent genotypic phenotypic patterns across both types of data sets. We chose random forest and gradient boosting to apply the prioritization scheme and found that both facilitated the investigation of predictive causal markers in most of the biological scenarios simulated. Frontiers Media S.A. 2021-09-06 /pmc/articles/PMC8450460/ /pubmed/34552613 http://dx.doi.org/10.3389/fgene.2021.684882 Text en Copyright © 2021 Shin and Nuzhdin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Shin, Min-Gyoung
Nuzhdin, Sergey V.
Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models
title Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models
title_full Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models
title_fullStr Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models
title_full_unstemmed Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models
title_short Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models
title_sort interspecific sample prioritization can improve qtl detection with tree-based predictive models
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450460/
https://www.ncbi.nlm.nih.gov/pubmed/34552613
http://dx.doi.org/10.3389/fgene.2021.684882
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