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
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/140595

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network_name_str SEDICI (UNLP)
spelling 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
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/
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eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
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