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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-8450460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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