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

dc.creatorRíos, Pablo
dc.creatorRaschia, María Agustina
dc.creatorMaizon, Daniel O.
dc.creatorDemitrio, Daniel
dc.creatorPoli, Mario A.
dc.date2021-10
dc.date2021
dc.date2022-08-17T15:49:38Z
dc.date.accessioned2023-07-15T07:39:49Z
dc.date.available2023-07-15T07:39:49Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/140595
dc.identifierhttp://50jaiio.sadio.org.ar/pdfs/cai/CAI-14.pdf
dc.identifierissn:2525-0949
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7481355
dc.descriptionIn 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.
dc.descriptionSociedad Argentina de Informática e Investigación Operativa
dc.formatapplication/pdf
dc.format94-103
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/3.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
dc.subjectCiencias Informáticas
dc.subjectMachine learning methods
dc.subjectSingle nucleotide polymorphisms
dc.subjectEstimated breeding values
dc.subjectDairy cattle
dc.titleMachine learning algorithms identified relevant SNPs for milk fat content in cattle
dc.typeObjeto de conferencia
dc.typeObjeto de conferencia


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