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How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data

The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F(2) population were genotyped with the DArTseq (sequencing-based diversity...

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Autores principales: Góralska, Magdalena, Bińkowski, Jan, Lenarczyk, Natalia, Bienias, Anna, Grądzielewska, Agnieszka, Czyczyło-Mysza, Ilona, Kapłoniak, Kamila, Stojałowski, Stefan, Myśków, Beata
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593958/
https://www.ncbi.nlm.nih.gov/pubmed/33053706
http://dx.doi.org/10.3390/ijms21207501
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author Góralska, Magdalena
Bińkowski, Jan
Lenarczyk, Natalia
Bienias, Anna
Grądzielewska, Agnieszka
Czyczyło-Mysza, Ilona
Kapłoniak, Kamila
Stojałowski, Stefan
Myśków, Beata
author_facet Góralska, Magdalena
Bińkowski, Jan
Lenarczyk, Natalia
Bienias, Anna
Grądzielewska, Agnieszka
Czyczyło-Mysza, Ilona
Kapłoniak, Kamila
Stojałowski, Stefan
Myśków, Beata
author_sort Góralska, Magdalena
collection PubMed
description The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F(2) population were genotyped with the DArTseq (sequencing-based diversity array technology). A maximum likelihood (MLH) algorithm (JoinMap 5.0) and three ML algorithms: logistic regression (LR), random forest and extreme gradient boosted trees (XGBoost), were used to select markers closely linked to the gene encoding wax layer. The allele conditioning the nonglaucous appearance of plants, derived from the cultivar Karlikovaja Zelenostebelnaja, was mapped at the chromosome 2R, which is the first report on this localization. The DNA sequence of DArT-Silico 3585843, closely linked to wax segregation detected by using ML methods, was indicated as one of the candidates controlling the studied trait. The putative gene encodes the ABCG11 transporter.
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spelling pubmed-75939582020-10-30 How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data Góralska, Magdalena Bińkowski, Jan Lenarczyk, Natalia Bienias, Anna Grądzielewska, Agnieszka Czyczyło-Mysza, Ilona Kapłoniak, Kamila Stojałowski, Stefan Myśków, Beata Int J Mol Sci Article The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F(2) population were genotyped with the DArTseq (sequencing-based diversity array technology). A maximum likelihood (MLH) algorithm (JoinMap 5.0) and three ML algorithms: logistic regression (LR), random forest and extreme gradient boosted trees (XGBoost), were used to select markers closely linked to the gene encoding wax layer. The allele conditioning the nonglaucous appearance of plants, derived from the cultivar Karlikovaja Zelenostebelnaja, was mapped at the chromosome 2R, which is the first report on this localization. The DNA sequence of DArT-Silico 3585843, closely linked to wax segregation detected by using ML methods, was indicated as one of the candidates controlling the studied trait. The putative gene encodes the ABCG11 transporter. MDPI 2020-10-12 /pmc/articles/PMC7593958/ /pubmed/33053706 http://dx.doi.org/10.3390/ijms21207501 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Góralska, Magdalena
Bińkowski, Jan
Lenarczyk, Natalia
Bienias, Anna
Grądzielewska, Agnieszka
Czyczyło-Mysza, Ilona
Kapłoniak, Kamila
Stojałowski, Stefan
Myśków, Beata
How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data
title How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data
title_full How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data
title_fullStr How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data
title_full_unstemmed How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data
title_short How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data
title_sort how machine learning methods helped find putative rye wax genes among gbs data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593958/
https://www.ncbi.nlm.nih.gov/pubmed/33053706
http://dx.doi.org/10.3390/ijms21207501
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