Literature DB >> 33358171

Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing.

S Lam1, F Miglior1, P A S Fonseca1, I Gómez-Redondo1, J Zeidan1, A Suárez-Vega1, F Schenkel1, L L Guan2, S Waters3, P Stothard2, A Cánovas4.   

Abstract

The 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. The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Entities:  

Keywords:  Holstein; Jersey; RNA-sequencing; feed efficiency

Mesh:

Substances:

Year:  2020        PMID: 33358171     DOI: 10.3168/jds.2020-18241

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  4 in total

1.  Transcriptome Analysis of Breast Muscle Reveals Pathways Related to Protein Deposition in High Feed Efficiency of Native Turkeys.

Authors:  Zahra Pezeshkian; Seyed Ziaeddin Mirhoseini; Shahrokh Ghovvati; Esmaeil Ebrahimie
Journal:  Animals (Basel)       Date:  2022-05-11       Impact factor: 3.231

2.  Genes Involved in Feed Efficiency Identified in a Meta-Analysis of Rumen Tissue from Two Populations of Beef Steers.

Authors:  Amanda K Lindholm-Perry; Allison M Meyer; Rebecca J Kern-Lunbery; Hannah C Cunningham-Hollinger; Taran H Funk; Brittney N Keel
Journal:  Animals (Basel)       Date:  2022-06-10       Impact factor: 3.231

3.  Identification of novel alternative splicing associated with mastitis disease in Holstein dairy cows using large gap read mapping.

Authors:  V Asselstine; J F Medrano; A Cánovas
Journal:  BMC Genomics       Date:  2022-03-19       Impact factor: 3.969

4.  Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits.

Authors:  Babatunde S Olasege; Laercio R Porto-Neto; Muhammad S Tahir; Gabriela C Gouveia; Angela Cánovas; Ben J Hayes; Marina R S Fortes
Journal:  BMC Genomics       Date:  2022-10-05       Impact factor: 4.547

  4 in total

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