# Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN+

## Article information

Asian-Australas J Anim Sci.. 2016;29(5):640-645
Publication date (electronic) : 2016 January 18
doi : https://doi.org/10.5713/ajas.15.0842
Department of Animal Science and Technology, Sunchon National University, Suncheon 540-742, Korea
1Department of Animal Biotechnology, Chonbuk National University, Jeonbuk 561-756, Korea
*Corresponding Author: K. S. Seo. Tel: +82-61-750-3231, Fax: +82-61-750-3230, E-mail: sks@sunchon.ac.kr
Received 2015 October 14; Revised 2015 December 03; Accepted 2016 January 07.

## Abstract

### Genomic breeding programs

Three GS strategies were designed to identify the best approach for implementing GS in the swine breeding farm. The background for implementing genomic information in the selection index on this basis was developed by Dekkers (2007) and modified by Haberland et al. (2010) and Daetwyler et al. (2008; 2010). This approach requires correlation of the true breeding value and the GEBV to define every genomic trait rGBV. This was conducted using the approach developed by Erbe et al. (2011) based on an equation derived by Daetwyler et al. (2008):

rGBV=NPr2NPr2+Me

where, N is the size of the reference population, and r2 is the reliability of the GEBV of the animals used in the reference population. In our calculations, we assumed Np = 1,000, which may be considered a minimum for GS in pigs and r2 was assumed to be 0.49 for all traits (Haberland et al., 2013). The proportion of genetic variance explained by markers (q2) was assumed to be 0.8 for all breeding goal traits as suggested by Erbe et al. (2011). Me was the number of independently segregating chromosome segments, which was derived by Goddard et al. (2011) as:

Me=2NeLklog(NeL)

where, Ne denotes the effective population size, L is the average length of a chromosome in Morgans and k is the number of chromosome pairs. Assuming Ne = 100, k = 19 and L = 1.2 Morgans, the value of Me was 1,000. This assumption was based on a study conducted by Haberland et al. (2014). Thus, the accuracy of genomic breeding values was 0.577. Each selection scheme is discussed in detail below.

#### Genomic selection: Scenario 1 (GS1)

This scenario applied a one-step selection. Male candidates were genotyped in the early stage. The selection was based strictly on GEBV of the animal and pedigree. Here, 50 genotyped YB were selected from 1,000 candidates to enter the YB selection group. The top 23 YB comprised the SB group.

#### Genomic selection: Scenario 2 (GS2)

Male candidates were genotyped before they entered the field performance test. The test was conducted after six months. The basis of selection was strictly based on GEBV plus the performance test. Moreover, 50 genotyped YB were selected out of 1,000 candidates to enter the YB selection group. The top 23 YB were the SB group.

#### Genomic selection: Scenario 3 (GS3)

In this strategy, the selection steps were the same as CS except that male piglets were genotyped before they entered the field performance test. The male piglets were genotyped at 21 days of age. Out of 1,000 males genotyped, only 50 YB were selected based on the performance test and GEBV. The 50 YB were mated to breeding sows naturally for the progeny test and to produce the next generation of HS. Based on progenies information and owned GEBV, 23 SB were selected. SB were selected for the selection paths SB>HB and SB>HS.

The investment period carried out in this study was 10 years. The costs, returns and profit with relative percentage in each selection scheme are shown in Table 6. The fixed costs were not considered for every selection scheme in this study; thus, profits were higher than expected. As projected, the discounted costs of GS strategies were higher than those of CS. Specifically, the costs of GS1, GS2, and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. Conducting CS may result in a discounted return per animal of$38.50. This return increases by 14% (GS1), 17% (GS2), or 13% (GS3) when adopting the genomic selection schemes, assuming 1,000 genotyped male candidates and a reference population size of 1,000. However, the relative profit of GS1 was 8% higher than that of CS, while it was only 2% higher under GS2 and 6% lower for GS3. In this study, as much as 8% extra income was possible when implementing GS. Furthermore, this extra income may further increase by reducing the genotyping cost. Several studies have been conducted to reduce the costs associated with genotyping (Ibañez-Escriche and Gonzalez-Recio, 2011). Abell et al. (2014) estimated that the genotyping cost per animal was $115 and$55 for high- and low-density panels, respectively. In Korea, the current genotyping cost per animal is around $300 (Seo, personal communication, 2015). Costs, returns and profit in breeding programs The possible use of high density (HD) and low density (LD) panels to reduce genotyping cost was also investigated in this study. Here, HD and LD were fitted in a two-stage selection. The selection steps were the same as GS3 but LD genotyping was carried out in first stage of selection of YB and HD genotyping was used in selecting SB. The cost of HD and LD genotyping were based on the study of Abell et al. (2014) but adjusted to be able to compare on the existing selection strategies. The genotyping cost per animal using LD is$60 while $120 for HD panels. The results showed that the use of HD and LD panels reduced the over-all cost by 21% and it gives 11% increase in profit compared to GS3. However, further study is needed on the difference of estimation of accuracy of GEBV between HD and LD panels and the possible effect on the over-all genetic gain. ## CONCLUSION GS1 was found to be the most profitable selection strategy, but had a lower accuracy than CS and GS3. GS2 produced the same results of GS1, but with lower profit. GS3 had much better accuracy of selection index, but was not profitable relative to CS. When GS2 is used, the traits to be tested must have high economic value and less cost so that commercial breeders will have greater confidence in the value of the selection index. The use of HD and LD panels reduced the genotyping cost. Overall, the results presented herein suggest that GS can optimize swine breeding programs, especially when the genotyping cost is reduced. However, implementation of GS in pig breeding remains a business decision to be made by individual breeding companies. Any possible extra income for the breeding company depends on whether customers are willing to pay more for improved genetic quality. ## ACKNOWLEDGMENTS This research was supported by Golden Seed Project, Ministry of Agriculture, Food and Rural Affairs (MAFRA), Ministry of Oceans and Fisheries (MOF), Rural Development Administration (RDA) and Korea Forest Service (KFS). ## Notes CONFLICT OF INTEREST We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript. ## References Abell C, Dekkers J, Rothschild M, Mabry J, Stalder K. 2014;Total cost estimation for implementing genome-enabled selection in a multi-level swine production system. Genet Sel Evol 46:32. 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Genet Sel Evol 47:14. ## Article information Continued ### Figure 1 Trends in accuracy of selection index for senior boars depending on reference population size. CS, conventional system; GS, genomic selection. ### Table 1 Selection schemes modeled in ZPLAN+ Scenario Information sources CS Pedigree+Own performance+Progeny GS1 Pedigree+GEBV GS2 Pedigree+GEBV+Own performance GS3 Pedigree+GEBV+Own performance+Progeny CS, conventional system; GS, genomic selection; GEBV, genomic enhanced breeding value. ### Table 2 Heritability (h2), phenotypic standard deviation (σP) and economic weights (w) per unit of considered traits Trait w ($) σP h2
BF (mm) −15.0 5.08 0.52
FCR (kg/kg) −40.0 0.113 0.30
pH 20 0.25 0.38
L* - 2.65 0.29
IMF (%) 9.25 1.0 0.47

ADG, average daily gain; BF, back fat thickness; FCR, feed conversion rate; L*, meat color; IMF, intra-muscular fat.

### Table 3

Phenotypic (above diagonal) and genotypic (below diagonal) correlations between considered traits

Trait ADG BF FCR pH L* IMF
ADG 1 0.20 −0.65 −0.08 0.09 0.07
BF 0.14 1 0.25 0.08 0.08 0.30
FCR −0.70 0.34 1 0.00 0.00 0.00
pH −0.11 0.03 0.00 1 −0.54 0.01
L* 0.11 0.09 0.00 −0.66 1 0.12
IMF 0.06 0.30 0.00 0.01 0.15 1

ADG, average daily gain; BF, back fat thickness; FCR, feed conversion rate; L*, meat color; IMF, intra-muscular fat.

### Table 4

Accuracy of selection index and mean generation intervals

Scenario CS GS1 GS2 GS3
Young boar 0.54 0.68 0.70 0.70
Senior boar 0.91 0.68 0.70 0.92
Mean generation interval 1.88 1.67 1.67 1.88

CS, conventional system; GS, genomic selection.

### Table 5

Discounted monetary genetic gain per year with relative percentage

Parameter CS GS1 GS2 GS3
Overall AMGG ($) 5.804 6.365 6.503 6.415 ADG (g/d) 19.868 21.774 23.378 21.539 BF (mm) −0.36 −0.33 −0.41 −0.36 FCR (kg/kg) −0.032 −0.034 −0.037 −0.034 pH 0.028 0.032 0.031 0.032 L* −0.095 −0.130 −0.126 −0.116 IMF (%) 0.301 0.334 0.325 0.337 AMGG, annual monetary genetic gain; ADG, average daily gain; BF, back fat thickness; FCR, feed conversion rate; L*, meat color; IMF, intramuscular fat. ### Table 6 Costs, returns and profit in breeding programs Parameter CS GS1 GS2 GS3 Discounted return ($) 38.50 43.85 44.90 43.69
Discounted costs ($) 7.75 10.49 13.41 14.65 Profit ($) 30.75 33.36 31.49 29.04

CS, conventional system; GS, genomic selection.