Literature DB >> 22436149

Genetic parameters between slaughter pig efficiency and growth rate of different body tissues estimated by computed tomography in live boars of Landrace and Duroc.

E Gjerlaug-Enger1, J Kongsro, J Odegård, L Aass, O Vangen.   

Abstract

In this study, computed tomography (CT) technology was used to measure body composition on live pigs for breeding purposes. Norwegian Landrace (L; n = 3835) and Duroc (D; n = 3139) boars, selection candidates to be elite boars in a breeding programme, were CT-scanned between August 2008 and August 2010 as part of an ongoing testing programme at Norsvin's boar test station. Genetic parameters in the growth rate of muscle (MG), carcass fat (FG), bone (BG) and non-carcass tissue (NCG), from birth to ∼100 kg live weight, were calculated from CT data. Genetic correlations between growth of different body tissues scanned using CT, lean meat percentage (LMP) calculated from CT and more traditional production traits such as the average daily gain (ADG) from birth to 25 kg (ADG1), the ADG from 25 kg to 100 kg (ADG2) and the feed conversion ratio (FCR) from 25 kg to 100 kg were also estimated from data on the same boars. Genetic parameters were estimated based on multi-trait animal models using the average information-restricted maximum likelihood (AI-REML) methodology. The heritability estimates (s.e. = 0.04 to 0.05) for the various traits for Landrace and Duroc were as follows: MG (0.19 and 0.43), FG (0.53 and 0.59), BG (0.37 and 0.58), NCG (0.38 and 0.50), LMP (0.50 and 0.57), ADG1 (0.25 and 0.48), ADG2 (0.41 and 0.42) and FCR (0.29 and 0.42). Genetic correlations for MG with LMP were 0.55 and 0.68, and genetic correlations between MG and ADG2 were -0.06 and 0.07 for Landrace and Duroc, respectively. LMP and ADG2 were clearly unfavourably genetically correlated (L: -0.75 and D: -0.54). These results showed the difficulty in jointly improving LMP and ADG2. ADG2 was unfavourably correlated with FG (L: 0.84 and D: 0.72), thus indicating to a large extent that selection for increased growth implies selection for fatness under an ad libitum feeding regime. Selection for MG is not expected to increase ADG2, but will yield faster growth of the desired tissues and a better carcass quality. Hence, we consider MG to be a better biological trait in selection for improved productivity and carcass quality. CT is a powerful instrument in conjunction with breeding, as it combines the high accuracy of CT data with measurements taken from the selection candidates. CT also allows the selection of new traits such as real body composition, and in particular, the actual MG on living animals.

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Year:  2012        PMID: 22436149     DOI: 10.1017/S1751731111001455

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  12 in total

1.  The genetic correlation between scapula shape and shoulder lesions in sows.

Authors:  Ø Nordbø; L E Gangsei; T Aasmundstad; E Grindflek; J Kongsro
Journal:  J Anim Sci       Date:  2018-04-14       Impact factor: 3.159

Review 2.  Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency.

Authors:  Pourya Davoudi; Duy Ngoc Do; Stefanie M Colombo; Bruce Rathgeber; Younes Miar
Journal:  Front Genet       Date:  2022-06-09       Impact factor: 4.772

3.  Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review.

Authors:  A M Scholz; L Bünger; J Kongsro; U Baulain; A D Mitchell
Journal:  Animal       Date:  2015-03-06       Impact factor: 3.240

4.  A co-association network analysis of the genetic determination of pig conformation, growth and fatness.

Authors:  Anna Puig-Oliveras; Maria Ballester; Jordi Corominas; Manuel Revilla; Jordi Estellé; Ana I Fernández; Yuliaxis Ramayo-Caldas; Josep M Folch
Journal:  PLoS One       Date:  2014-12-11       Impact factor: 3.240

5.  Genetic determinism of bone and mineral metabolism in meat-type chickens: A QTL mapping study.

Authors:  Sandrine Mignon-Grasteau; Céline Chantry-Darmon; Marie-Yvonne Boscher; Nadine Sellier; Marie Chabault-Dhuit; Elisabeth Le Bihan-Duval; Agnès Narcy
Journal:  Bone Rep       Date:  2016-02-26

6.  Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs.

Authors:  Pernille Sarup; Just Jensen; Tage Ostersen; Mark Henryon; Peter Sørensen
Journal:  BMC Genet       Date:  2016-01-05       Impact factor: 2.797

7.  EQUIFAT: A novel scoring system for the semi-quantitative evaluation of regional adipose tissues in Equidae.

Authors:  Philippa K Morrison; Patricia A Harris; Charlotte A Maltin; Dai Grove-White; Caroline McG Argo
Journal:  PLoS One       Date:  2017-03-15       Impact factor: 3.240

8.  Modelling the shape of the pig scapula.

Authors:  Øyvind Nordbø
Journal:  Genet Sel Evol       Date:  2020-07-01       Impact factor: 4.297

9.  Quantitative Genetics of Growth Rate and Filet Quality Traits in Atlantic Salmon Inferred From a Longitudinal Bayesian Model for the Left-Censored Gaussian Trait Growth Rate.

Authors:  Ólafur H Kristjánsson; Bjarne Gjerde; Jørgen Ødegård; Marie Lillehammer
Journal:  Front Genet       Date:  2020-11-30       Impact factor: 4.599

10.  Osteochondrosis and other lesions in all intervertebral, articular process and rib joints from occiput to sacrum in pigs with poor back conformation, and relationship to juvenile kyphosis.

Authors:  Kristin Olstad; Torunn Aasmundstad; Jørgen Kongsro; Eli Grindflek
Journal:  BMC Vet Res       Date:  2022-01-18       Impact factor: 2.741

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