INTEGRATED STRATEGIES AND METHODOLOGIES FOR THE GENETIC-IMPROVEMENT OF ANIMALS

被引:1
作者
MCLAREN, DG
FERNANDO, RL
LEWIN, HA
SCHOOK, LB
机构
[1] Department of Animal Sciences, University of Illinois, Urbana
关键词
genetic improvement; molecular genetics; quantitative genetics;
D O I
10.3168/jds.S0022-0302(90)78950-3
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
The current emphasis of biotechnology in animal agriculture stresses the need for identification of major genes affecting growth and development, reproductive performance, lactation, and disease resistance characteristics. Identification of allelic variants of genes that affect quantitative traits, either positively or negatively, may be used to increase genetic potential for production of livestock products. The long-term objectives of our integrated quantitative and molecular approach are to develop precise genomic maps of livestock species and to understand how allelic genes affect quantitative phenotypes. Work is underway to identify new polymorphic genetic markers and to perform linkage analysis of such markers with important economic traits. The relevance of this research is specifically to permit an accelerated rate of genetic improvement via marker-assisted selection, selecting animals based upon their genotype in addition to using phenotypic data. Other important ramifications of this endeavor include the identification of genes that may have commercial application through construction of transgenic animals and the development of methods and reagents to further gene mapping in livestock species. This paper outlines the nature of the collaborative approach to animal breeding research developed by animal scientists at the University of Illinois and discusses strategies for integrating traditional molecular and quantitative genetics disciplines. © 1990, American Dairy Science Association. All rights reserved.
引用
收藏
页码:2647 / 2656
页数:10
相关论文
共 50 条
  • [21] Genetic models of hypertension in experimental animals
    Yagil, Y
    Yagil, C
    EXPERIMENTAL NEPHROLOGY, 2001, 9 (01): : 1 - 9
  • [22] Artificial Selection Program for the Improvement of Genetics in Bovine Animals
    Reyna-Gonzalez, Julissa Elizabeth
    Cardenas, Jorge Ruben Hilario
    Cornejo, Janeth Leynig Tello
    Pozo, Marco Alberto Suarez
    Tolentino, Ines Eusebia Jesus
    JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (04) : 781 - 793
  • [23] Guiding Unconstrained Genetic Improvement
    White, David R.
    PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION), 2016, : 1133 - 1134
  • [24] On Adaptive Specialisation in Genetic Improvement
    Blot, Aymeric
    Petke, Justyna
    PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION), 2019, : 1703 - 1704
  • [25] Genetic Improvement for DNN Security
    Baxter, Hunter
    Huang, Yu
    Leach, Kevin
    PROCEEDINGS OF THE 2024 IEEE/ACM INTERNATIONAL WORKSHOP ON GENETIC IMPROVEMENT, GI@ICSE 2024, 2024, : 11 - 12
  • [26] Genetic Improvement of GPU Code
    Liou, Jhe-Yu
    Forrest, Stephanie
    Wu, Carole-Jean
    2019 IEEE/ACM 6TH INTERNATIONAL WORKSHOP ON GENETIC IMPROVEMENT (GI@ICSE 2019), 2019, : 20 - 27
  • [27] Genetic Improvement for Code Obfuscation
    Petke, Justyna
    PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION), 2016, : 1135 - 1136
  • [28] INCLUDING GENETIC-RELATIONSHIPS IN SELECTION DECISIONS - ALTERNATIVE METHODOLOGIES
    BRISBANE, JR
    GIBSON, JP
    THEORETICAL AND APPLIED GENETICS, 1995, 91 (05) : 769 - 775
  • [29] Integrated phenotypes: understanding trait covariation in plants and animals
    Armbruster, W. Scott
    Pelabon, Christophe
    Bolstad, Geir H.
    Hansen, Thomas F.
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2014, 369 (1649)
  • [30] Applying genetic improvement to a genetic programming library in C plus
    Lopez-Lopez, Victor R.
    Trujillo, Leonardo
    Legrand, Pierrick
    SOFT COMPUTING, 2019, 23 (22) : 11593 - 11609