# Validation of selection accuracy for the total number of piglets born in landrace pigs using genomic selection.

INTRODUCTIONAs an efficient alternative to accelerate genetic improvement and improve the selection accuracy, genomic selection method using dense single nucleotide polymorphism (SNP) markers covering the whole genome [1,2] has been developed. Genomic selection was first proposed by Meuwissen et al [3]. Through high-density marker mapping, it can predict the genetic ability of individuals. It has been reported that genomic selection is more accurate than conventional breeding value estimation method. Especially when young animals whose phenotypic dada could not be obtained for breeding value estimation, highly accurate genomic selection can be used to make juvenile selection possible [2].

To predict genomic information of individuals without genomic information, Gengler et al [4] and VanRaden [5] have proposed algorithms to calculate genomic relationship matrix and the estimation method for obtaining genomic breeding value. Misztal et al [6] have also reported an algorithm to combine conventional pedigree information with genomic information. Recently, Liu et al [7] have developed SNP single step genomic model (SSSmodel) to estimate SNP effect directly. This model have been applied to animals in Germany and Canada for genetic evaluations.

For average daily gain and feed conversion ratio of Duroc pigs in Denmark, accuracy rates of genetic estimation have been compared among best linear unbiased prediction (BLUP) method (uses pedigree and performance test information for estimation), genomic best linear unbiased prediction (GBLUP) method (uses only genomic information of individual pigs for the estimation), and single step best linear unbiased prediction (ssBLUP, in which all information of individuals with or without genomic information are included in one model for the analysis) [2,8]. It has been reported that the estimation was more accurate when genomic information is used than when only pedigree information is used for animals without genomic information. In addition, ssBLUP was more accurate than BLUP for genetic estimation [2,8].

This experiment was conducted to compare the selection accuracy between genomic breeding value estimation using genomic information and breeding value estimation of BLUP using conventional pedigree information after collecting phenotypic and genomic information for the total number of piglets born in Landrace pigs. In addition, the relationship between estimated breeding value and phenotype information after farrowing was investigated when juvenile selection was made in candidate pigs without phenotype information.

MATERIALS AND METHODS

Single nucleotide polymorphism data and quality control

Using Illumina Porcine SNP60 vl and v2 beadchip genotype analysis was performed for a total of 1,041 sows and boars with the records of total number of piglets born (TNB) and candidate pigs without records. Genotype information on 62,551 SNP markers were collected, and using 48,245 SNP markers quality control was performed after 12,627 SNPs whose position information was not identified and 1,679 SNPs on sex chromosome were excluded.

For quality control, markers with more than 10% of missing rate, markers without polymorphism (all homo or hetero genotype markers), markers with less than 1% of minor allele frequence, and markers with more than 23.93 (p<[10.sup.-6]) of Hardy Weinberg disequilibrium chi square value, and genomic information of animals with more than 20% of SNP missing rate were excluded. A total of 1,038 animals passed quality control and a total of 36,392 SNP markers were used in this experiment (Table 1).

Phenotypic data for validation of selection accuracy

Of the 1,038 pigs with SNP information obtained from Sunjin GGP farm, a total of 916 pigs (836 sows and 80 boars) were used for the selection accuracy test. Data from 122 candidate pigs were not analyzed. Of the 836 sows with SNP information, 673 sows had records of total number of piglets born. The remaining 163 pigs were gilts without records of total number of piglets born (Table 2). Using the records from 916 pigs, the estimated breeding values for total number of piglets born were compared. To determine the relationship of total number of piglets born with phenotype by selection ratio, records for the total number of piglets born from the 163 gilts were collected from 2014 to 2015. Total number of animals in the A matrix and generations were 2,864 heads and 12, respectively.

Statistical models

Linear model for the estimation of genetic parameters and breeding values was as follows:

[y.sub.ijkl] = [mu]+[P.sub.i]+[YS.sub.j]+[a.sub.k]+[pe.sub.k]+[e.sub.ijkl]

Where, [y.sub.ijkl] = observed value of total number of piglets born, overall average, [P.sub.i] = ith fixed effect of parity, [YS.sub.j] = jth fixed effect of farrowing year-season, [a.sub.k] = kth additive genetic effect (breeding value), [pe.sub.k] = kth permanent environmental effect of animals, [e.sub.ijkl] = residual effects.

Genetic parameters: Variance components for total number of piglets born were estimated using VCE ver. 6.0 (Institute of Farm Animal Genetics, Friedrich Loeffler Institute, Neustadt, Germany) and heretability {[h.sup.2]) and repeatability (r) calculation formulars were as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where, [[sigma].sup.2.sub.a] = additive genetic variance, [[sigma].sup.2.sub.pe] = permanent environmental variance and [[sigma].sup.2.sub.e] = error variance.

Estimation of genomic breeding values: For the fixed effect, parity and farrowing year-month were included and the analysis model was as follows:

y = Xb + Za + Wp + e

Where, y = n x 1 vector of observation, b = p x 1 vector of fixed effect, a = q x 1 vector of additive genetic random effect, p = q x 1 vector of permanent environmental random effect, X(n x p), Z(n x q), and W(n x q) known incidence matrix corresponding to b, a, and p, e = n x 1 vector of residual effect. Mixed model equation (MME) was as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Where, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], inverse matrix of numerator relationship matrix, [A.sup.-1.sub.22] = inverse matrix of genomic relationship matrix, inverse matrix of numerator relationship matrix of pigs with genomic information. Reliability ([r.sup.2]) of breeding value was calculated using Prediction error variance (PEV) value by the following formular.

[r.sup.2] = 1 - (PEV/[[sigma].sup.2.sub.a])

RESULTS AND DISCUSSION

Genetic parameters

In this experiment, the estimated heritability of total number of piglets born was 0.096, which was higher than that (0.08) of Su et al [9] and Chen et al [10]. It has been reported that the heritability estimates of total number of piglets born in Yorkshire, Duroc, and Berkshire pigs were 0.10, 0.09, and 0.07, respectively [10,11]. The heritability for total number of piglets born could vary due to different genetic characteristics of the population. Generally, it is around 0.1 or lower [12]. The estimated repeatability for total number of piglets born was 0.195. Variance components and genetic parameters are shown in Table 3.

Accuracy of estimated breeding value and genomic estimated breeding value

Since the accuracies of estimated breeding value can vary with different numbers of test records, pedigree information, and heritability, after classifying sows with or without records for the total number of piglets born, the accuracies of breeding values obtained from BLUP and ssGBLUP were estimated. Results are shown in Table 4. When ssBLUP method was used, the accuracy was higher than that when conventional BLUP method was used. The difference in accuracy was bigger in pigs without phenotypic records. This results coincided with those of Forni et al [13]. As a result, there was relatively high accuracy difference in young animals without phenotypic data. When genomic information was used, the accuracy was increased. Such improved accuracy might be due to the fact that numerator relationship matrix used for BLUP was substituted by the genomic relationship matrix derived from the similarity of genotype of all markers with Mendelian sampling.

Correlationw analysis was performed between genomic estimated breeding value (GEBV) estimated from ssBLUP and breeding value estimated from conventional BLUP using only pedigree information (Figure 1). The estimated correlation co-efficient of between GEBV and estimated breeding value (EBV) was 0.83. The estimated regression equation was y = 0.12+0.94X ([r.sup.2] = 0.68).

When there was no phenotypic data in the progeny of pigs with the estimated genomic breeding value, breeding value estimated using BLUP method was the average breeding value of parents. Therefore, pigs with same parents had the same breeding values. On the other hand, since genomic breeding value was estimated using individual genomic information, accurate breeding value with Mendelian sampling could be calculated. Therefore, genomic breeding value seems to be more efficient than breeding value estimated using BLUP for selecting candidate pigs.

Relationship between selection ratio and phenotype

From late 2013, genomic information was collected from 63 candidate pigs. They began to farrow from the year of 2014. Records for total number of piglets born were collected. Of 348 sows farrowed in 2014, genomic information was obtained from 311 sows. The remaining 37 sows had no genomic information. Of the 311 sows with genomic information, 156 were candidate sows with first parity. By 2015, the overall average for total number of piglets born in GGP farms was 11.9. The distributional properties of validation data by year are shown in Table 5.

To determine the relationship between selection ratio and phenotype, 163 candidate pigs were divided into 5 groups by selection ratio (from top 10% to middle 50% candidate pigs). The mean values of total number of piglets born were compared (Table 6).

When candidate pigs for the next generation were selected by breeding values estimated from the conventional BLUP method, the total numbers of piglets born were severely fluctuated in pigs from Top 10% to 50%. In pigs of top 20% to 30%, the total number of piglets born was lower than the population average, suggesting that the accuracy of the estimated breeding value was very low. On the other hand, when they were selected by GEBV of ssBLUP using genomic information, it was always higher than the population average (Figure 2). These results proved that the accuracy was higher when genomic information was used for selection than that when pedigree information was used for selection.

Genetic gain

To calculate the genetic gain using the formula of ([DELTA]G) = [r.sub.GP] x i X [[sigma].sub.a]/L, selection of sows from the tested sows for the total number of piglets born was based on pedigree information. Of 5,000 tested sows, 550 were selected (recently replacement rate of sows is 100%). Of 500 tested boars, 45 were selected. Therefore, the selection intensity (i) was 1.76. Genetic standard deviation ([[sigma].sub.a] for the total number of piglets born was 0.91. Since there was no data on the total number of piglets born for candidate pigs, the accuracy of the breeding value estimated from pedigree information was 0.080. When genomic information was used, the accuracy was 0.216 (Table 7). Assuming the replacement rate of sows per year at 100% and generation interval of 1 year, genetic gain per year would be 0.346 heads when genomic information was used and 0.128 when BLUP was used. Therefore, genetic gain estimated using ssBLUP method was 2.7 times higher than that estimated using BLUP method, i.e., 270% more efficient in improvement efficiency.

CONCLUSION

The selection accuracy was higher when sows were selected through the GEBV method using many markers than that when sows were selected through the conventional BLUP method using pedigree information. The selection accuracy was especially high for pigs with only genomic information (no phenotypic data). Accurate juvenile selection of them is possible using the GEBV method.

https://doi.org/10.5713/ajas.16.0394

CONFLICT OF INTEREST

We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

ACKNOWLEDGMENTS

This work was supported by a grant (PJO1111502) of the Next Generation BioGreen 21 Program funded by Rural Development Administration, Republic of Korea.

REFERENCES

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[5.] VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23.

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Jae-Don Oh (1),(a), Chong-Sam Na (1),(a), and Kyung-Do Park (1) *

* Corresponding Author: Kyung-Do Park

Tel: +82-63-219-5502, Fax: +82-63-219-5505, E-mail: doobaloOjbnu.ac.kr

(1) Department of Animal Biotechnology, Chonbuk National University, Jeonju 54896, Korea

(a) These authors contributed equally to this work.

Submitted May 19, 2016; Revised Jul 22, 2016; Accepted Aug 10, 2016

Caption: Figure 1. Correlation between estimated breeding value (EBV) and genomic estimated breeding value (GEBV) for the total number of piglets born (TNB).

Caption: Figure 2. Trends on the total number of piglets born (TNB) by selection ratio and the estimation method.

Table 1. Frequency of variables after the quality control of raw single nucleotide polymorphisms dataset Variable Frequency Selected animals 1,038 Markers on autosomal chromosomes 48,245 Selected (useful) markers 36,392 Outlier markers 1) 11,853 All missing markers 1,551 All homo genotype markers 6,073 All hetero genotype markers 1 Markers with missing over 10% 3,316 Markers with minor allele frequency less than 0.01 10,398 Markers with HW chisquare over 23.93 817 1) Total number of outlier markers across all criteria (not column sum). Table 2. Distributional properties of data for the total number of piglets born (TNB) Farrowing No. of TNB (head) year records Mean [+ or -] SD 2007 561 11.3 [+ or -] 3.0 2008 536 11.7 [+ or -] 3.0 2009 543 11.6 [+ or -] 3.0 2010 509 11.3 [+ or -] 2.9 2011 552 11.9 [+ or -] 3.0 2012 546 12.2 [+ or -] 2.9 2013 553 12.0 [+ or -] 3.2 Overall 3,800 11.7 [+ or -] 3.0 Farrowing No. of sows year Total With SNPs Without SNPs 2007 284 16 268 2008 293 54 239 2009 296 144 152 2010 291 261 30 2011 321 304 17 2012 309 296 13 2013 316 293 23 Overall 1,282 1) 673 (163) 2) 446 SD, standard deviation; SNPs, single nucleotide polymorphisms. 1) Total number of sows across all farrowing year (not column sum). 2) Without TNB records. Table 3. Additive genetic ([[sigma].sup.2.sub.a]), permanent environmental ([[sigma].sup.2.sub.pe]), residual [[sigma].sup.2.sub.e] variance components, heritabilities ([h.sup.2]) and standard errors (SE), and repeatabilities (r) for the total number of piglets born (TNB). Variance components Trait [[sigma].sup.2.sub.a] [[sigma].sup.2.sub.pe] TNB 0.8284 0.8616 Variance components Trait [[sigma].sup.2.sub.e] [h.sup.2] r [+ or -] SE TNB 6.9664 0.096[+ or -]0.001 0.195 Table 4. Means and standard deviations of estimated breeding value (EBV) and genomic estimated breeding value (GEBV) accuracies in animals with or without records for the total number of piglets born (TNB) by the estimation methods Overall Sows with Sows without Method (836 head) records records (673 head) (163 head) BLUP 0.262 [+ or -] 0.306 [+ or -] 0.080 [+ or -] 0.115 0.080 0.024 ssBLUP 0.356 [+ or -] 0.390 [+ or -] 0.216 [+ or -] 0.094 0.065 0.055 BLUP, best linear unbiased prediction; ssBLUP, single step best linear unbiased prediction. Table 5. Distributional properties of validation data of the phenotypic total number of piglets born (TNB) by best linear unbiased prediction (BLUP) and single step best linear unbiased prediction (ssBLUP) methods Farrow No. of TNB (head) year records Mean [+ or -] SD 2007 561 11.3 [+ or -] 3.0 2008 536 11.7 [+ or -] 3.0 2009 543 11.6 [+ or -] 3.0 2010 509 11.3 [+ or -] 2.9 2011 552 11.9 [+ or -] 3.0 2012 546 12.2 [+ or -] 2.9 2013 553 12.0 [+ or -] 3.2 2014 576 12.4 [+ or -] 3.3 2015 398 12.9 [+ or -] 3.1 Overall 4,774 11.9 [+ or -] 3.1 Farrow No. of sows year Total With SNPs Without SNPs 2007 284 16 268 2008 293 54 239 2009 296 144 152 2010 291 261 30 2011 321 304 17 2012 309 296 13 2013 316 293 23 2014 348 311 (156) 2) 37 2015 262 157 (7) 2) 105 Overall 1,394 1) 836 558 SD, standard deviation; SNPs, single nucleotide polymorphisms. 1) Total number of sows across all farrowing year (not column sum). 2) Number of candidate sows that had the first TNB records with SNPs information. Table 6. Means and standard deviations for the total number of piglets born (TNB) by selection ratio and the estimation method No. Total number of piglets born (TNB) Top % of No. of sows records BLUP ssBLUP 10 16 42 11.90 [+ or -] 4.24 12.50 [+ or -] 3.71 20 16 41 12.54 [+ or -] 3.33 12.30 [+ or -] 3.93 30 16 37 11.70 [+ or -] 3.45 12.07 [+ or -] 3.48 40 17 47 12.87 [+ or -] 2.97 12.50 [+ or -] 3.42 50 16 40 12.05 [+ or -] 3.03 12.20 [+ or -] 3.64 Overall 163 386 12.12 [+ or -] 3.89 12.12 [+ or -] 3.89 BLUP best linear unbiased prediction; ssBLUP single step best linear unbiased prediction. Table 7. Genetic gains of the total number of piglets born (TNB) per year by the selection method of candidate sows Method [[sigma].sub.a] i [r.sub.GP] L [DELTA]G BLUP 0.91 1.76 0.080 1 0.128 ssBLUP 0.91 1.76 0.216 1 0.346 BLUP, best linear unbiased prediction; ssBLUP single step best linear unbiased prediction. [[sigma].sub.a] genetic standard deviation; i, selection intensity, [r.sub.GP] accuracy; L, generation interval.

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Author: | Oh, Jae-Don; Na, Chong-Sam; Park, Kyung-Do |
---|---|

Publication: | Asian - Australasian Journal of Animal Sciences |

Article Type: | Report |

Date: | Feb 1, 2017 |

Words: | 3251 |

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