Machine learning algorithms identified relevant SNPs for milk fat content in cattle

Autores
Ríos, Pablo J.; Raschia, Maria Agustina; Maizon, Daniel Omar; Demitrio, Daniel Arturo; Poli, Mario Andres
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.
Instituto de Genética
Fil: Ríos, Pablo J. Universidad de Buenos Aires; Argentina
Fil: Ríos, Pablo J. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina
Fil: Raschia, Maria Agustina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Genética; Argentina
Fil: Raschia, Maria Agustina. Universidad Nacional de La Plata. Facultad de Ciencias Médicas; Argentina
Fil: Maizon, Daniel Omar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; Argentina
Fil: Maizon, Daniel Omar. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina
Fil: Demitrio, Daniel Arturo. Instituto Nacional de Tecnología Agropecuaria (INTA). Dirección General de Sistemas de Información, Comunicación y Procesos. Gerencia de Informática y Gestión de la Información; Argentina
Fil: Demitrio, Daniel Arturo. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina
Fil: Poli, Mario Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Genética; Argentina
Fil: Poli, Mario Andres. Universidad del Salvador. Facultad de Ciencias Agrarias y Veterinaria; Argentina
Fuente
50 Jornadas Argentinas de Informática (50 JAIIO), 13 Congreso Argentino de AgroInformática (CAI 2021), 18 al 29 de octubre de 2021 (virtual)
Materia
Single Nucleotide Polymorphism
Dairy Cattle
Algorithms
Milk Fat
Polimorfismo de un Solo Nucleótido
Ganado de Leche
Algoritmos
Grasa de la Leche
Machine Learning Methods
Métodos de Aprendizaje Automático
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/11706
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oai_identifier_str oai:localhost:20.500.12123/11706
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network_name_str INTA Digital (INTA)
spelling Machine learning algorithms identified relevant SNPs for milk fat content in cattleRíos, Pablo J.Raschia, Maria AgustinaMaizon, Daniel OmarDemitrio, Daniel ArturoPoli, Mario AndresSingle Nucleotide PolymorphismDairy CattleAlgorithmsMilk FatPolimorfismo de un Solo NucleótidoGanado de LecheAlgoritmosGrasa de la LecheMachine Learning MethodsMétodos de Aprendizaje AutomáticoIn 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.Instituto de GenéticaFil: Ríos, Pablo J. Universidad de Buenos Aires; ArgentinaFil: Ríos, Pablo J. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; ArgentinaFil: Raschia, Maria Agustina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Genética; ArgentinaFil: Raschia, Maria Agustina. Universidad Nacional de La Plata. Facultad de Ciencias Médicas; ArgentinaFil: Maizon, Daniel Omar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; ArgentinaFil: Maizon, Daniel Omar. Universidad Nacional de La Pampa. Facultad de Agronomía; ArgentinaFil: Demitrio, Daniel Arturo. Instituto Nacional de Tecnología Agropecuaria (INTA). Dirección General de Sistemas de Información, Comunicación y Procesos. Gerencia de Informática y Gestión de la Información; ArgentinaFil: Demitrio, Daniel Arturo. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; ArgentinaFil: Poli, Mario Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Genética; ArgentinaFil: Poli, Mario Andres. Universidad del Salvador. Facultad de Ciencias Agrarias y Veterinaria; ArgentinaSociedad Argentina de Informática2022-04-22T11:01:37Z2022-04-22T11:01:37Z2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://hdl.handle.net/20.500.12123/1170650 Jornadas Argentinas de Informática (50 JAIIO), 13 Congreso Argentino de AgroInformática (CAI 2021), 18 al 29 de octubre de 2021 (virtual)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2019-PE-E6-I145-001/2019-PE-E6-I145-001/AR./Mejora genética objetiva para aumentar la eficiencia de los sistemas de producción animal.info:eu-repograntAgreement/INTA/2019-PT-E6-I513-001/2019-PT-E6-I513-001/AR./Plataforma de mejoramiento animalinfo:eu-repograntAgreement/INTA/2019-PT-E9-I180-001/2019-PT-E9-I180-001/AR./TICs y gestión de Big Datainfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-16T09:30:40Zoai:localhost:20.500.12123/11706instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-16 09:30:43.093INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
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 J.
Single Nucleotide Polymorphism
Dairy Cattle
Algorithms
Milk Fat
Polimorfismo de un Solo Nucleótido
Ganado de Leche
Algoritmos
Grasa de la Leche
Machine Learning Methods
Métodos de Aprendizaje Automático
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 J.
Raschia, Maria Agustina
Maizon, Daniel Omar
Demitrio, Daniel Arturo
Poli, Mario Andres
author Ríos, Pablo J.
author_facet Ríos, Pablo J.
Raschia, Maria Agustina
Maizon, Daniel Omar
Demitrio, Daniel Arturo
Poli, Mario Andres
author_role author
author2 Raschia, Maria Agustina
Maizon, Daniel Omar
Demitrio, Daniel Arturo
Poli, Mario Andres
author2_role author
author
author
author
dc.subject.none.fl_str_mv Single Nucleotide Polymorphism
Dairy Cattle
Algorithms
Milk Fat
Polimorfismo de un Solo Nucleótido
Ganado de Leche
Algoritmos
Grasa de la Leche
Machine Learning Methods
Métodos de Aprendizaje Automático
topic Single Nucleotide Polymorphism
Dairy Cattle
Algorithms
Milk Fat
Polimorfismo de un Solo Nucleótido
Ganado de Leche
Algoritmos
Grasa de la Leche
Machine Learning Methods
Métodos de Aprendizaje Automático
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.
Instituto de Genética
Fil: Ríos, Pablo J. Universidad de Buenos Aires; Argentina
Fil: Ríos, Pablo J. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina
Fil: Raschia, Maria Agustina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Genética; Argentina
Fil: Raschia, Maria Agustina. Universidad Nacional de La Plata. Facultad de Ciencias Médicas; Argentina
Fil: Maizon, Daniel Omar. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; Argentina
Fil: Maizon, Daniel Omar. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina
Fil: Demitrio, Daniel Arturo. Instituto Nacional de Tecnología Agropecuaria (INTA). Dirección General de Sistemas de Información, Comunicación y Procesos. Gerencia de Informática y Gestión de la Información; Argentina
Fil: Demitrio, Daniel Arturo. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina
Fil: Poli, Mario Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Genética; Argentina
Fil: Poli, Mario Andres. Universidad del Salvador. Facultad de Ciencias Agrarias y Veterinaria; Argentina
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
2022-04-22T11:01:37Z
2022-04-22T11:01:37Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/11706
url http://hdl.handle.net/20.500.12123/11706
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2019-PE-E6-I145-001/2019-PE-E6-I145-001/AR./Mejora genética objetiva para aumentar la eficiencia de los sistemas de producción animal.
info:eu-repograntAgreement/INTA/2019-PT-E6-I513-001/2019-PT-E6-I513-001/AR./Plataforma de mejoramiento animal
info:eu-repograntAgreement/INTA/2019-PT-E9-I180-001/2019-PT-E9-I180-001/AR./TICs y gestión de Big Data
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Sociedad Argentina de Informática
publisher.none.fl_str_mv Sociedad Argentina de Informática
dc.source.none.fl_str_mv 50 Jornadas Argentinas de Informática (50 JAIIO), 13 Congreso Argentino de AgroInformática (CAI 2021), 18 al 29 de octubre de 2021 (virtual)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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