title: Identification of Gene Networks for Residual Feed Intake in Angus Cattle Using Genomic Prediction and RNA-seq.
abstract: Improvement in feed conversion efficiency can improve the sustainability of beef cattle production, but genomic selection for feed efficiency affects many underlying molecular networks and physiological traits. This study describes the differences between steer progeny of two influential Angus bulls with divergent genomic predictions for residual feed intake (RFI). Eight steer progeny of each sire were phenotyped for growth and feed intake from 8 mo. of age (average BW 254 kg, with a mean difference between sire groups of 4.8 kg) until slaughter at 14-16 mo. of age (average BW 534 kg, sire group difference of 28.8 kg). Terminal samples from pituitary gland, skeletal muscle, liver, adipose, and duodenum were collected from each steer for transcriptome sequencing. Gene expression networks were derived using partial correlation and information theory (PCIT), including differentially expressed (DE) genes, tissue specific (TS) genes, transcription factors (TF), and genes associated with RFI from a genome-wide association study (GWAS). Relative to progeny of the high RFI sire, progeny of the low RFI sire had -0.56 kg/d finishing period RFI (P = 0.05), -1.08 finishing period feed conversion ratio (P = 0.01), +3.3 kg^0.75 finishing period metabolic mid-weight (MMW; P = 0.04), +28.8 kg final body weight (P = 0.01), -12.9 feed bunk visits per day (P = 0.02) with +0.60 min/visit duration (P = 0.01), and +0.0045 carcass specific gravity (weight in air/weight in air-weight in water, a predictor of carcass fat content; P = 0.03). RNA-seq identified 633 DE genes between sire groups among 17,016 expressed genes. PCIT analysis identified >115,000 significant co-expression correlations between genes and 25 TF hubs, i.e. controllers of clusters of DE, TS, and GWAS SNP genes. Pathway analysis suggests low RFI bull progeny possess heightened gut inflammation and reduced fat deposition. This multi-omics analysis shows how differences in RFI genomic breeding values can impact other traits and gene co-expression networks.
authors.affiliation: VMRD Genetics R&D, Zoetis Inc., Kalamazoo, MI, United States of America.
authors.forename: Kristina L
authors.initials: KL
authors.lastname: Weber
authors.affiliation: Department of Animal Science, University of California Davis, Davis, CA, United States of America.
authors.forename: Bryan T
authors.initials: BT
authors.lastname: Welly
authors.affiliation: Department of Animal Science, University of California Davis, Davis, CA, United States of America.
authors.forename: Alison L
authors.initials: AL
authors.lastname: Van Eenennaam
authors.affiliation: Department of Animal Science, University of California Davis, Davis, CA, United States of America.
authors.forename: Amy E
authors.initials: AE
authors.lastname: Young
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, QLD, Australia.
authors.forename: Laercio R
authors.initials: LR
authors.lastname: Porto-Neto
authors.affiliation: CSIRO Agriculture, Queensland Bioscience Precinct, St. Lucia, QLD, Australia.
authors.forename: Antonio
authors.initials: A
authors.lastname: Reverter
authors.affiliation: VMRD Genetics R&D, Zoetis Inc., Kalamazoo, MI, United States of America.
authors.forename: Gonzalo
authors.initials: G
authors.lastname: Rincon
mesh_terms: D000818:Animals; D001835:Body Weight; D002417:Cattle; D004435:Eating / Q000235:genetics*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D055106:Genome-Wide Association Study; D008954:Models, Biological; D010641:Phenotype; D012313:RNA / Q000737:chemistry*; D017423:Sequence Analysis, RNA; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D014157:Transcription Factors; D012313:RNA