Machine learning algorithms identified relevant SNPs for milk fat content in cattle
- Autores
- Ríos, Pablo; Raschia, María Agustina; Maizon, Daniel O.; Demitrio, Daniel; Poli, Mario A.
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- In recent years, machine learning methods have been shown to be efficient in identifying a subset of single nucleotide polymorphisms (SNP) underlying a trait of interest. The aim of this study was the construction of predictive models using machine learning algorithms, for the identification of loci that best explain the variance in milk fat production of dairy cattle. Further objectives involve determining the genes flanking relevant SNPs and retrieving the pathways, biological processes, or molecular functions overrepresented by them. Fat production values adjusted for fixed effects (FPadj) and estimated breeding values for milk fat production (EBVFP) were used as phenotypes and SNPs as predictor variables. The models constructed for EBVFP performed better and yield considerably less relevant SNPs than models for FPadj. Among the genes flanking relevant SNPs, signaling transduction pathways and gated channel activities were detected as overrepresented. The loci obtained for EBVFP matched better with previously reported relevant loci for milk fat content than those obtained for FPadj. Based on the better performance showed by the models trained for EBVFP and their agreement with previous reported results for the trait studied, we conclude that the relationship among individuals should be accounted for in the phenotype used.
Sociedad Argentina de Informática e Investigación Operativa - Materia
-
Ciencias Informáticas
Machine learning methods
Single nucleotide polymorphisms
Estimated breeding values
Dairy cattle - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/3.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/140595
Ver los metadatos del registro completo
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Machine learning algorithms identified relevant SNPs for milk fat content in cattleRíos, PabloRaschia, María AgustinaMaizon, Daniel O.Demitrio, DanielPoli, Mario A.Ciencias InformáticasMachine learning methodsSingle nucleotide polymorphismsEstimated breeding valuesDairy cattleIn recent years, machine learning methods have been shown to be efficient in identifying a subset of single nucleotide polymorphisms (SNP) underlying a trait of interest. The aim of this study was the construction of predictive models using machine learning algorithms, for the identification of loci that best explain the variance in milk fat production of dairy cattle. Further objectives involve determining the genes flanking relevant SNPs and retrieving the pathways, biological processes, or molecular functions overrepresented by them. Fat production values adjusted for fixed effects (FPadj) and estimated breeding values for milk fat production (EBVFP) were used as phenotypes and SNPs as predictor variables. The models constructed for EBVFP performed better and yield considerably less relevant SNPs than models for FPadj. Among the genes flanking relevant SNPs, signaling transduction pathways and gated channel activities were detected as overrepresented. The loci obtained for EBVFP matched better with previously reported relevant loci for milk fat content than those obtained for FPadj. Based on the better performance showed by the models trained for EBVFP and their agreement with previous reported results for the trait studied, we conclude that the relationship among individuals should be accounted for in the phenotype used.Sociedad Argentina de Informática e Investigación Operativa2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf94-103http://sedici.unlp.edu.ar/handle/10915/140595enginfo:eu-repo/semantics/altIdentifier/url/http://50jaiio.sadio.org.ar/pdfs/cai/CAI-14.pdfinfo:eu-repo/semantics/altIdentifier/issn/2525-0949info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:35:39Zoai:sedici.unlp.edu.ar:10915/140595Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:35:40.256SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Machine learning algorithms identified relevant SNPs for milk fat content in cattle |
title |
Machine learning algorithms identified relevant SNPs for milk fat content in cattle |
spellingShingle |
Machine learning algorithms identified relevant SNPs for milk fat content in cattle Ríos, Pablo Ciencias Informáticas Machine learning methods Single nucleotide polymorphisms Estimated breeding values Dairy cattle |
title_short |
Machine learning algorithms identified relevant SNPs for milk fat content in cattle |
title_full |
Machine learning algorithms identified relevant SNPs for milk fat content in cattle |
title_fullStr |
Machine learning algorithms identified relevant SNPs for milk fat content in cattle |
title_full_unstemmed |
Machine learning algorithms identified relevant SNPs for milk fat content in cattle |
title_sort |
Machine learning algorithms identified relevant SNPs for milk fat content in cattle |
dc.creator.none.fl_str_mv |
Ríos, Pablo Raschia, María Agustina Maizon, Daniel O. Demitrio, Daniel Poli, Mario A. |
author |
Ríos, Pablo |
author_facet |
Ríos, Pablo Raschia, María Agustina Maizon, Daniel O. Demitrio, Daniel Poli, Mario A. |
author_role |
author |
author2 |
Raschia, María Agustina Maizon, Daniel O. Demitrio, Daniel Poli, Mario A. |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Machine learning methods Single nucleotide polymorphisms Estimated breeding values Dairy cattle |
topic |
Ciencias Informáticas Machine learning methods Single nucleotide polymorphisms Estimated breeding values Dairy cattle |
dc.description.none.fl_txt_mv |
In recent years, machine learning methods have been shown to be efficient in identifying a subset of single nucleotide polymorphisms (SNP) underlying a trait of interest. The aim of this study was the construction of predictive models using machine learning algorithms, for the identification of loci that best explain the variance in milk fat production of dairy cattle. Further objectives involve determining the genes flanking relevant SNPs and retrieving the pathways, biological processes, or molecular functions overrepresented by them. Fat production values adjusted for fixed effects (FPadj) and estimated breeding values for milk fat production (EBVFP) were used as phenotypes and SNPs as predictor variables. The models constructed for EBVFP performed better and yield considerably less relevant SNPs than models for FPadj. Among the genes flanking relevant SNPs, signaling transduction pathways and gated channel activities were detected as overrepresented. The loci obtained for EBVFP matched better with previously reported relevant loci for milk fat content than those obtained for FPadj. Based on the better performance showed by the models trained for EBVFP and their agreement with previous reported results for the trait studied, we conclude that the relationship among individuals should be accounted for in the phenotype used. Sociedad Argentina de Informática e Investigación Operativa |
description |
In recent years, machine learning methods have been shown to be efficient in identifying a subset of single nucleotide polymorphisms (SNP) underlying a trait of interest. The aim of this study was the construction of predictive models using machine learning algorithms, for the identification of loci that best explain the variance in milk fat production of dairy cattle. Further objectives involve determining the genes flanking relevant SNPs and retrieving the pathways, biological processes, or molecular functions overrepresented by them. Fat production values adjusted for fixed effects (FPadj) and estimated breeding values for milk fat production (EBVFP) were used as phenotypes and SNPs as predictor variables. The models constructed for EBVFP performed better and yield considerably less relevant SNPs than models for FPadj. Among the genes flanking relevant SNPs, signaling transduction pathways and gated channel activities were detected as overrepresented. The loci obtained for EBVFP matched better with previously reported relevant loci for milk fat content than those obtained for FPadj. Based on the better performance showed by the models trained for EBVFP and their agreement with previous reported results for the trait studied, we conclude that the relationship among individuals should be accounted for in the phenotype used. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/140595 |
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http://sedici.unlp.edu.ar/handle/10915/140595 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/3.0/ Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/3.0/ Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) |
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