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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/11706
Ver los metadatos del registro completo
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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)2025-09-29T13:45:32Zoai: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:l2025-09-29 13:45:32.636INTA 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 |
format |
conferenceObject |
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 |
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INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
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tripaldi.nicolas@inta.gob.ar |
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