EVALUATION OF GENETIC PARAMETERS AND DEVELOPMENT OF AN OPTIMAL, COMMERCIALLY-VIABLE SPF BREEDING STRATEGY FOR Litopenaeus vannamei

Jeffrey Prochaskaa*, Supawadee Poompuanga, Uthairat Na-Nakorna, Skorn Koonawootrittrironb, and Hein van der Steenc
aDepartment of Aquaculture, Faculty of Fisheries, Kasetsart University, Bangkok, 10900, Thailand
 bDepartment of Animal Science, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
c Stonebridge Breeding Ltd, Gate House, Abbotswood, Evesham, WR11 4NS, UK
 
 

Introduction and Objective

Litopenaeus vannamei (Pacific White Shrimp) is a widely-cultured species in Asia and Latin America. In order to improve and adapt production of L. vannamei, numerous public and private organizations have developed domestication or selective breeding programs of varying complexity.  A breeding program is an integration of population management (at the Nucleus Breeding Center), phenotypic or molecular data collection, and the use of available analysis and selection tools. The system needs to be optimized given the defined breeding goals and resource constraints. Balancing these components is not a clear-cut decision. This study involves post hoc or a posteriori analysis of existing breeding data and seeks to determine how a for-profit breeding program can best be optimized regarding the structure, data collection, data analysis, and trait selection to maximize performance gains.

Methods

Multiple generations of pedigree and phenotypic data from over 1,000 full-sib families will be analyzed. Data will be consolidated and organized, and undergo various quality-control measures. Analysis will be conducted to estimate (co)variance components by restricted maximum likelihood (REML) procedures, and breeding value estimates by best linear unbiased prediction (BLUP) or most appropriate method. Linear Mixed Models and data fitting strategies for BLUP (individual data vs. family means) will be evaluated to determine the best or most appropriate methods. Statistical analysis will be conducted using Genstat (18th Edition, VSN Intl., Hemel Hempstead, UK). Data analysis will begin with the most basic animal model including a single trait and single random (animal) effect, as follows:

y = Xb + Za + e

More complex models will then be utilized to confirm appropriate effects and the best model will be chosen to continue analysis and estimations of genetic parameters. Based on results of analysis, recommendations will be made regarding routine statistical analysis procedures and how to best optimize the breeding program to achieve maximum genetic gain, given operational, facility, and budget constraints.

Results

Results regarding estimation of breeding values and trends for growth traits will be presented.