Literature DB >> 22785165

Genomic-polygenic evaluation of Angus-Brahman multibreed cattle for feed efficiency and postweaning growth using the Illumina 3K chip.

M A Elzo1, G C Lamb, D D Johnson, M G Thomas, I Misztal, D O Rae, C A Martinez, J G Wasdin, J D Driver.   

Abstract

The objectives of this study were to determine the fraction of additive genetic variance explained by the SNP from the Illumina Bovine3K chip; to compare the ranking of animals evaluated with genomic-polygenic, genomic, and polygenic models; and to assess trends in predicted values from these 3 models for residual feed intake (RFI), daily feed intake (DFI), feed conversion ratio (FCR), and postweaning BW gain (PWG) in a multibreed Angus-Brahman cattle population under subtropical conditions. Data consisted of phenotypes and genotypes from 620 bulls, steers, and heifers ranging from 100% Angus to 100% Brahman. Phenotypes were collected in a GrowSafe automated feeding facility (GrowSafe Systems, Ltd., Airdrie, Alberta, Canada) from 2006 to 2010. Variance components were estimated using single-trait genomic-polygenic mixed models with option VCE (Markov chain Monte Carlo) of the program GS3. Fixed effects were contemporary group (year-pen), age of dam, sex of calf, age of calf, Brahman fraction of calf, and heterozygosity of calf. Random effects were additive SNP, animal polygenic, and residual effects. Genomic predictions were computed using a model without polygenic effects and polygenic predictions with a model that excluded additive SNP effects. Heritabilities were 0.20 for RFI, 0.31 for DFI, 0.21 for FCR, and 0.36 for PWG. The fraction of the additive genetic variance explained by SNP in the Illumina 3K chip was 15% for RFI, 11% for DFI, 25% for FCR, and 15% for PWG. These fractions will likely differ in other multibreed populations. Rank correlations between genomic-polygenic and polygenic predictions were high (0.95 to 0.99; P < 0.0001), whereas those between genomic-polygenic and genomic predictions were low (0.65 to 0.74; P < 0.0001). Genomic-polygenic, genomic, and polygenic predictions for all traits tended to decrease as Brahman fraction increased, indicating that calves with greater Brahman fraction were more efficient but grew more slowly than calves with greater Angus fraction. Predicted SNP values were small for all traits, and those above and below 0.2 SNP SD were in multiple chromosomes, supporting the contention that quantitative traits are determined by large numbers of alleles with small effects located throughout the genome.

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Year:  2012        PMID: 22785165     DOI: 10.2527/jas.2011-4730

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  8 in total

1.  The effect of Brahman genes on body temperature plasticity of heifers on pasture under heat stress.

Authors:  Raluca G Mateescu; Kaitlyn M Sarlo-Davila; Serdal Dikmen; Eduardo Rodriguez; Pascal A Oltenacu
Journal:  J Anim Sci       Date:  2020-05-01       Impact factor: 3.159

2.  Thermoregulatory response of Brangus heifers to naturally occurring heat exposure on pasture.

Authors:  Heather Hamblen; Peter J Hansen; Adriana M Zolini; Pascal A Oltenacu; Raluca G Mateescu
Journal:  J Anim Sci       Date:  2018-07-28       Impact factor: 3.159

3.  Determination of the optimum contribution of Brahman genetics in an Angus-Brahman multibreed herd for regulation of body temperature during hot weather.

Authors:  Serdal Dikmen; Raluca G Mateescu; Mauricio A Elzo; Peter J Hansen
Journal:  J Anim Sci       Date:  2018-06-04       Impact factor: 3.159

4.  Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle.

Authors:  Megan M Rolf; Dorian J Garrick; Tara Fountain; Holly R Ramey; Robert L Weaber; Jared E Decker; E John Pollak; Robert D Schnabel; Jeremy F Taylor
Journal:  Genet Sel Evol       Date:  2015-04-01       Impact factor: 4.297

5.  Identification of genomic regions associated with feed efficiency in Nelore cattle.

Authors:  Priscila S N de Oliveira; Aline S M Cesar; Michele L do Nascimento; Amália S Chaves; Polyana C Tizioto; Rymer R Tullio; Dante P D Lanna; Antonio N Rosa; Tad S Sonstegard; Gerson B Mourao; James M Reecy; Dorian J Garrick; Maurício A Mudadu; Luiz L Coutinho; Luciana C A Regitano
Journal:  BMC Genet       Date:  2014-09-26       Impact factor: 2.797

6.  Combining information from genome-wide association and multi-tissue gene expression studies to elucidate factors underlying genetic variation for residual feed intake in Australian Angus cattle.

Authors:  Sara de Las Heras-Saldana; Samuel A Clark; Naomi Duijvesteijn; Cedric Gondro; Julius H J van der Werf; Yizhou Chen
Journal:  BMC Genomics       Date:  2019-12-06       Impact factor: 3.969

Review 7.  Connecting Heat Tolerance and Tenderness in Bos indicus Influenced Cattle.

Authors:  Tracy L Scheffler
Journal:  Animals (Basel)       Date:  2022-01-18       Impact factor: 2.752

8.  Accuracy of genomic predictions in Bos indicus (Nellore) cattle.

Authors:  Haroldo H R Neves; Roberto Carvalheiro; Ana M Pérez O'Brien; Yuri T Utsunomiya; Adriana S do Carmo; Flávio S Schenkel; Johann Sölkner; John C McEwan; Curtis P Van Tassell; John B Cole; Marcos V G B da Silva; Sandra A Queiroz; Tad S Sonstegard; José Fernando Garcia
Journal:  Genet Sel Evol       Date:  2014-02-27       Impact factor: 4.297

  8 in total

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