Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN.

B M Lopez,K S Seo,H S Kang,T H Kim,H S Kim,V S Viterbo,C S Na

doi:10.5713/ajas.15.0842

Abstract

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.

Highlights

Animal breeding schemes need to be well-designed to maximize long-term genetic gains obtained from genomic information (Henryon et al, 2014)
Where, Ne denotes the effective population size, L is the average length of a chromosome in Morgans and k is the Generation intervals and accuracies number of chromosome pairs
Assuming Ne = 100, k = 19 Four parameters influence genetic gain: selection and L = 1.2 Morgans, the value of Me was 1,000. This intensity, accuracy of selection, genetic variance of the assumption was based on a study conducted by Haberland breeding goal and generation interval

Summary

Introduction

Animal breeding schemes need to be well-designed to maximize long-term genetic gains obtained from genomic information (Henryon et al, 2014). With advances in molecular technology or new biological tools, researchers have established methods to incorporate variations at the very basic DNA level into breeding programs. Genomic tools, such as single nucleotide polymorphism (SNP), have led to a new method known as “genomic selection.”. This method, Submitted Oct. 14, 2015; Revised Dec. 3, 2015; Accepted Jan. 7, 2016 which was developed by Meuwissen (2007), utilizes dense SNP genotypes covering the entire genome to predict the breeding value.

Objectives

Methods

Results