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dc.contributor.authorLam, S.
dc.contributor.authorMiglior, F.
dc.contributor.authorFonseca, P.A.S.
dc.contributor.authorGómez-Redondo, I.
dc.contributor.authorZeidan, J.
dc.contributor.authorSuárez-Vega, A.
dc.contributor.authorSchenkel, F.
dc.contributor.authorGuan, L.L.
dc.contributor.authorWaters, Sinéad M
dc.contributor.authorStothard, P.
dc.contributor.authorCánovas, A.
dc.date.accessioned2022-08-23T14:05:39Z
dc.date.available2022-08-23T14:05:39Z
dc.date.issued2021-02-28
dc.identifier.citationS. Lam, F. Miglior, P.A.S. Fonseca, I. Gómez-Redondo, J. Zeidan, A. Suárez-Vega, F. Schenkel, L.L. Guan, S. Waters, P. Stothard, A. Cánovas, Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing, Journal of Dairy Science, 2021, 104(2), 1928-1950, DOI: https://doi.org/10.3168/jds.2020-18241en_US
dc.identifier.issn00220302
dc.identifier.urihttp://hdl.handle.net/11019/2830
dc.descriptionpeer-revieweden_US
dc.description.abstractThe identification of functional genetic variants and associated candidate genes linked to feed efficiency may help improve selection for feed efficiency in dairy cattle, providing economic and environmental benefits for the dairy industry. This study used RNA-sequencing data obtained from liver tissue from 9 Holstein cows [n = 5 low residual feed intake (RFI), n = 4 high RFI] and 10 Jersey cows (n = 5 low RFI, n = 5 high RFI), which were selected from a single population of 200 animals. Using RNA-sequencing, 3 analyses were performed to identify: (1) variants within low or high RFI Holstein cattle; (2) variants within low or high RFI Jersey cattle; and (3) variants within low or high RFI groups, which are common across both Holstein and Jersey cattle breeds. From each analysis, all variants were filtered for moderate, modifier, or high functional effect, and co-localized quantitative trait loci (QTL) classes, enriched biological processes, and co-localized genes related to these variants, were identified. The overlapping of the resulting genes co-localized with functional SNP from each analysis in both breeds for low or high RFI groups were compared. For the first two analyses, the total number of candidate genes associated with moderate, modifier, or high functional effect variants fixed within low or high RFI groups were 2,810 and 3,390 for Holstein and Jersey breeds, respectively. The major QTL classes co-localized with these variants included milk and reproduction QTL for the Holstein breed, and milk, production, and reproduction QTL for the Jersey breed. For the third analysis, the common variants across both Holstein and Jersey breeds, uniquely fixed within low or high RFI groups were identified, revealing a total of 86,209 and 111,126 functional variants in low and high RFI groups, respectively. Across all 3 analyses for low and high RFI cattle, 12 and 31 co-localized genes were overlapping, respectively. Among the overlapping genes across breeds, 9 were commonly detected in both the low and high RFI groups (INSRR, CSK, DYNC1H1, GAB1, KAT2B, RXRA, SHC1, TRRAP, PIK3CB), which are known to play a key role in the regulation of biological processes that have high metabolic demand and are related to cell growth and regeneration, metabolism, and immune function. The genes identified and their associated functional variants may serve as candidate genetic markers and can be implemented into breeding programs to help improve the selection for feed efficiency in dairy cattle.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesJournal of Dairy Science;
dc.rights© 2020 American Dairy Science Association®.
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectfeed efficiencyen_US
dc.subjectHolsteinen_US
dc.subjectJerseyen_US
dc.subjectRNA-sequencingen_US
dc.titleIdentification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencingen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3168/jds.2020-18241
dc.identifier.eid1-s2.0-S0022030220310444
dc.identifier.piiS0022-0302(20)31044-4
dc.contributor.sponsorOntario Ministry of Agriculture, Food, and Rural Affairsen_US
dc.contributor.sponsorOntario Ministry of Research and Innovation, Agriculture and Agri-Food Canadaen_US
dc.contributor.sponsorGenome Canadaen_US
dc.contributor.sponsorGenome Albertaen_US
dc.contributor.sponsorOntario Genomicsen_US
dc.contributor.sponsorAlberta Ministry of Agricultureen_US
dc.contributor.sponsorOntario Ministry of Research and Innovationen_US
dc.contributor.sponsorOntario Ministry of Agriculture, Food and Rural Affairsen_US
dc.contributor.sponsorCanadian Dairy Networken_US
dc.contributor.sponsorGrowSafe Systemsen_US
dc.contributor.sponsorAlberta Milken_US
dc.contributor.sponsorSustainable Beef and Forage Science Clusteren_US
dc.source.volume104
dc.source.issue2
dc.source.beginpage1928
dc.source.endpage1950
refterms.dateFOA2022-08-23T14:05:40Z
dc.source.journaltitleJournal of Dairy Science
dc.identifier.journalJournal of Dairy Science


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