Literature DB >> 23825329

Genetic parameters for different measures of feed efficiency and related traits in boars of three pig breeds.

D N Do1, A B Strathe, J Jensen, T Mark, H N Kadarmideen.   

Abstract

Residual feed intake (RFI) is commonly used as a measure of feed efficiency at a given level of production. A total of 16,872 pigs with their pedigree traced back as far as possible was used to estimate genetic parameters for RFI, growth performance, food conversion ratio (FCR), body conformation, and feeding behavior traits in 3 Danish breeds [Duroc (DD), Landrace (LL), and Yorkshire (YY)]. Two measures of RFI were considered: residual feed intake 1 (RFI1) was calculated based on regression of daily feed intake (DFI) from 30 to 100 kg on initial test weight and ADG from 30 to 100 kg (ADG2). Residual feed intake 2 (RFI2) was as RFI1, except it was also regressed with respect to backfat (BF). The estimated heritabilities for RFI1 and RFI2 were 0.34 and 0.38 in DD, 0.34 and 0.36 in LL, and 0.39 and 0.40 in YY, respectively. The heritabilities ranged from 0.32 (DD) to 0.54 (LL) for ADG2, from 0.54 (DD) to 0.67 (LL) for BF, and from 0.13 (DD) to 0.19 (YY) for body conformation. Feeding behavior traits including DFI, number of visits to feeder per day (NVD), total time spent eating per day (TPD), feed intake rate (FR), feed intake per visit (FPV), and time spent eating per visit (TPV) were moderately to highly heritable. Residual feed intake 2 was genetically independent of ADG2 and BF in all breeds, except it had low genetic correlation to ADG2 in YY (0.2). Residual feed intake 1 was also genetically independent of ADG2 in DD and LL. Both RFI traits had strong genetic correlations with DFI (0.85 to 0.96) and FCR (0.76 to 0.99). They had low or no genetic correlations with feeding behavior traits. Unfavorable genetic correlations were found between ADG2 and both BF and DFI. Among feeding behavior traits, DFI had low genetic correlations to other traits in all breeds. High and negative genetic correlations were also found between TPD with FR (-0.79 in YY to -0.88 in DD), NVD, and TPD (-0.91 in DD to -0.94 in YY) and between NVD and FPV (-0.83 in DD to -0.91 in YY) in all breeds. The genetic trend for feed efficiency was favorable in all breeds regardless of the definition of feed efficiency used. In summary, RFI1 and RFI2 were heritable and selection for reduced RFI2 can be performed without adversely affecting ADG and BF and could replace FCR in the selection index for the Danish pig breeds. Selection could also be based on RFI1 for breeds with fewer concerns about a negative effect of BF or for breeds that do not have BF records.

Entities:  

Mesh:

Year:  2013        PMID: 23825329     DOI: 10.2527/jas.2012-6197

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


  43 in total

1.  A Genome-Wide Association Study on Feed Efficiency Related Traits in Landrace Pigs.

Authors:  Lu Fu; Yao Jiang; Chonglong Wang; Mengran Mei; Ziwen Zhou; Yifan Jiang; Hailiang Song; Xiangdong Ding
Journal:  Front Genet       Date:  2020-07-03       Impact factor: 4.599

2.  Genetic structured antedependence and random regression models applied to the longitudinal feed conversion ratio in growing Large White pigs.

Authors:  V H Huynh-Tran; H Gilbert; I David
Journal:  J Anim Sci       Date:  2017-11       Impact factor: 3.159

3.  Genetic and phenotypic parameters for feed efficiency and component traits in American mink.

Authors:  Pourya Davoudi; Duy Do; Stefanie M Colombo; Bruce Rathgeber; Guoyu Hu; Mehdi Sargolzaei; Zhiquan Wang; Graham Plastow; Younes Miar
Journal:  J Anim Sci       Date:  2022-08-01       Impact factor: 3.338

Review 4.  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

5.  Use of Host Feeding Behavior and Gut Microbiome Data in Estimating Variance Components and Predicting Growth and Body Composition Traits in Swine.

Authors:  Yuqing He; Francesco Tiezzi; Jicai Jiang; Jeremy T Howard; Yijian Huang; Kent Gray; Jung-Woo Choi; Christian Maltecca
Journal:  Genes (Basel)       Date:  2022-04-26       Impact factor: 4.141

6.  Genetic analysis of disease resilience in wean-to-finish pigs from a natural disease challenge model.

Authors:  Jian Cheng; Austin M Putz; John C S Harding; Michael K Dyck; Frederic Fortin; Graham S Plastow; PigGen Canada; Jack C M Dekkers
Journal:  J Anim Sci       Date:  2020-08-01       Impact factor: 3.159

7.  Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information.

Authors:  Bjarke G Poulsen; Birgitte Ask; Hanne M Nielsen; Tage Ostersen; Ole F Christensen
Journal:  Genet Sel Evol       Date:  2020-10-07       Impact factor: 4.297

8.  Using nontargeted LC-MS metabolomics to identify the Association of Biomarkers in pig feces with feed efficiency.

Authors:  Jie Wu; Yong Ye; Jianping Quan; Rongrong Ding; Xingwang Wang; Zhanwei Zhuang; Shenping Zhou; Qian Geng; Cineng Xu; Linjun Hong; Zheng Xu; Enqin Zheng; Gengyuan Cai; Zhenfang Wu; Jie Yang
Journal:  Porcine Health Manag       Date:  2021-06-02

9.  Relationship between Fertility Traits and Kinematics in Clusters of Boar Ejaculates.

Authors:  Vinicio Barquero; Eduardo R S Roldan; Carles Soler; Bernardo Vargas-Leitón; Francisco Sevilla; Marlen Camacho; Anthony Valverde
Journal:  Biology (Basel)       Date:  2021-06-28

10.  Transcriptome analysis of mRNA and miRNA in skeletal muscle indicates an important network for differential Residual Feed Intake in pigs.

Authors:  Lu Jing; Ye Hou; Hui Wu; Yuanxin Miao; Xinyun Li; Jianhua Cao; John Michael Brameld; Tim Parr; Shuhong Zhao
Journal:  Sci Rep       Date:  2015-07-07       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.