MATER PhD (Hungarian University of Agriculture and Life Sciences)

Hegyi, Ádám István

A szőlő nemesrothadásának transzkriptomikai elemzése

Visy, Anna

Aktív ultrahang alkalmazása húsok pácolási technológiájában

Kodak, Oleksandr

 
 
THESES OF THE DOCTORAL (PhD) 
DISSERTATION 
 
 
 
 
 
OLEKSANDR KODAK 
 
 
 
 
 
 
 
KAPOSVAR  
2023 
2 
 
THESES OF THE DOCTORAL (PhD) 
DISSERTATION 
 
Institute of Animal Sciences 
 
Head of Doctoral School 
Dr. ANDRÁS SZABÓ DSc 
 
Supervisors 
Dr.  ISTVÁN NAGY DSc 
Full Professor 
Dr. KÖVÉR GYÖRGY 
Associate Professor 
 
DEVELOPMENT OF SELECTION INDICES IN THE 
HUNGARIAN PIG BREEDS 
 
Written by 
OLEKSANDR KODAK 
 
 
 
Kaposvár 
2023 
 
DOI: 10.54598/002760
3 
 
1. Research backgrounds and objective 
 
Different methods of selection have been used in the pig industry 
throughout the history. The general methods of selection are: random 
selection, tandem selection, independent culling levels, total score method 
(index selection), selection index, estimated breeding value (EBV), 
expected progeny difference (EPD), best linear unbiased prediction 
(BLUP). Selection based on a selection index is the most commonly used 
method in genetic improvement programs for pigs (Stas, 2017). In pig 
industry measuring the important traits are accomplished through two 
types of performance tests which are station test and field test, 
respectively (Csató et al., 2002). Station test is more precise and accurate 
but currently field test have been becoming more and more frequent. The 
advantage of such an assessment, compared with the use of stations test, 
is that the assessment is a significantly cheaper. After collecting all 
required information measurements of different traits are combined to one 
score called selection index. Thus breeders can perform selection 
simultaneously for several traits. Structure and form of indices can differ 
among countries (McPhee, 1981; Morris et al., 1982; Visser, 2004; Nagy 
et al., 2008; Csató et al., 2002) based on the varying interest in the 
breeding objective. 
 
In the Hungarian pig breeding sector performance test of pigs is mainly 
based on field tests. Kaposvár campus has been close partner of 
Hungarian Pig Breeders Association for many years, together they are 
responsible for collecting information and developing breeding 
programme for many different farms across Hungary. Based on the 
measurements collected in the course of the field tests the future 
generations‘ performance can be predicted. The nucleus pig farms in 
4 
 
Hungary perform their selection process based on BLUP methodology 
(Best Linear Unbiased Prediction) where the prediction of breeding 
values is taking into account environmental factors and performances of 
relatives. It has to be noted that at present breeding value prediction for 
reproduction traits is limited to the Hungarian: Duroc, Hampshire, 
Pietrain, Large White and Landrace breeds. Because for instance breeds 
as Pietrain, Duroc and Hampshire targeting mainly only average daily 
gain and lean meat percentage. Besides all pig breeds are selected based 
on progeny test for feed conversion ratio and for the weight of the 
valuable cuts (Hungarian Pig Breeders Association). 
 
Special selected breeds or lines for different species are responsible for 
specific productivity traits. In such way breeds conditionally divided into 
two types or groups: parental and maternal forms. First one are 
responsible for carrying production traits such as: lean meat content; 
backfat thickness; dressing percentage; average metabolic weight; 
lightness of the meat of the gluteus superficialis muscle; ultimate pH 
measurement of the semimembranosus muscle; water holding capacity of 
the gluteus superficialis muscle and so on (Saintilan, et al., 2013; Evans, 
et al., 2003). Second one are responsible for so call reproduction traits - 
farrowing after first insemination; number of still born piglets; number of 
piglets born in total or alive; interval from weaning to first insemination; 
high for gestation length and age at first insemination; semen quality; age 
at puberty and so on (Hanenberg, et al., 2001; Zak, et al., 2017). 
 
The economic importance of the international and Hungarian pig breeding 
sectors is both high. The Hungarian pig population size was 2558 million 
(148.000 sows) in 2022 year (KSH, 2023). We began our research and 
5 
 
data collecting in 2017 year and in that time based on our investigation 
population size of pigs was 3.136 million 2015 year (200.000 sows) 
(KSH, 2023). For this period of time pig population dramatically decrease 
in Hungary. The most important pig breeds and breed constructions are 
Hungarian Large White, Hungarian Landrace and their first cross, 
respectively. 
 
The various methods of quantitative genetics have been applied by 
breeders for decades in order to improve the performance of domesticated 
animals in some pre-defined traits. In the pig breeding sector, similarly to 
other multiparous species, the crossing is widely used in order to increase 
performance, where generally separate breeds are used to increase 
reproductive (e.g., number of piglets born alive, number of piglets born 
dead, number of piglets weaned, litter weight at weaning) and growth and 
carcass (e.g., average daily gain, feed conversion ratio, lean meat 
percentage, the proportion of valuable cuts) performances (Bidanel, et al., 
2011). Looking at the various traits used in pig breeding number of 
piglets born alive is among the most important trait from the economic 
viewpoint, as this trait has the largest economic weight besides the feed 
conversion ratio (Houska, et al., 2010; Krupa, et al., 2017). Monitoring 
the actual genetic parameters of the economically important traits and 
evaluating the genetic progress of the populations is always an important 
task that helps us to determine the expected efficiency of the breeding 
program.  
 
To characterize and possibly predict the response, we built and used 
linear regressions based on available genetic parameters for attaining the 
genetic trends for five pure breeds and their cross (Tinh, et al., 2021). 
6 
 
Additionally, based on these predictions, the overall genetic merit was 
also determined as an inevitable condition of carrying out efficient 
selection using the procedure of desired gain (Brascamp, et al., 1984; 
Suzuki, et al., 2005). Coefficients of the index for breeding value 
estimation were realized for targeted traits by applying aggregate 
genotype where estimated index-weighted factors were taken into account 
(Brascamp, 1984). The advantage of this is avoiding the necessity of 
calculating economic weights while estimating index-weighing factors 
(Gjedrem, 1972; Pešek and Baker, 1969). 
 
2. Materials and methods 
 
1. Data collection 
Our research was extended and divided on two parts. First one was related 
to examine paternal and second maternal breeds of pigs. Testing was 
based on totally 5 pure breeds (Hungarian: Duroc, Hampshire, Pietrain, 
Large White and Landrace and one cross Hungarian: Large White x 
Landrace and Landrace x Large White).  
 
In Hungary, the selection process and prediction of genetic merit for 
reproduction traits for maternal breeds are focused mainly on the number 
of piglets born alive and litter weight at weaning. Data used in our 
research were collected by Hungarian Pig Breeders’ Association from 21 
herds for breeds Hungarian Duroc (HD), Hungarian Hampshire (HH) and 
Hungarian Pietrain (HP). And from 56 herds for breeds Hungarian Large 
White (HLW), Hungarian Landrace (HL), and F1 generation (Large 
White boars mated with Landrace sows and Landrace boars mated with 
Large White sows) were collected between 2001 and 2018 in the course 
7 
 
of the field test. Number of herds, sows and the number of farrowing 
records were summarized in table 1.  
 
Table 1. Farrowing records 
Breed/Name Herds Sows Number of 
Farrowings 
Total 21 2843 8806 
HD 10 1294 3796 
HH 7 381 1246 
HP 16 1168 3764 
Total 56 27561 73871 
HLW 42 16749 50147 
HL 23 4372 12645 
F1 34 6440 11079 
HD: Hungarian Duroc; HH: Hungarian Hampshire; HP: Hungarian Pietrain; 
HLW: Hungarian Large White; HL: Hungarian Landrace and F1 generation (Large 
White boars mated with Landrace sows and Landrace boars mated with Large White 
sows). 
 
The recorded traits were number of piglets born alive (NBA), 
number of weaned piglets (NWE), and litter weight at weaning (LWWE), 
which directly affect future genetic diversity for any species. 
 
2. Animal Model 
The farrowing data set was used for testing 14 repeatability animal 
models to select the most appropriate model for estimating genetic 
parameters, and the structures of these models are presented in table 2. 
The breeds (HD, HH, HP and HLW, HL and cross F1) were analyzed 
together (was built 7 models for paternal and maternal forms, totally 14) 
as one population constructing their common pedigree. In this way, the 
8 
 
heterosis effect could also be accounted for including the breed 
construction of the animals in every model.  
 
 
9 
 
Table 2. The structure of the applied animal models 
Model Traits Factors (Type) 
Animal Rep FSA SWA FYM WYM Breed Herd Parity 
NBA NWE LWWE (A) (R) (C) (C) (F) (F) (F) (F) (F) 
/Breeds 
Hungarian - Duroc, Hampshire and Pietrain 
1 x x x x x   x x x x x 
2 x x  x x   x x x x x 
3 x x  x x x x x x x x  
4 x x x x x x x x x x x  
5 x  x x x   x x x x x 
6 x  x x x x x x x x x  
7  x x x x    x x x x 
/Breeds 
Hungarian - Large White, Landrace and F1 generation 
8 x x  x x   x x x x x 
9 x x  x x x x x x x x  
10 x x x x x   x x x x x 
11 x x x x x x x x x x x  
12 x  x x x   x x x x x 
13 x  x x x x x x x x x  
14  x x x x    x x x x 
NBA: number of piglets born alive; NWE: number of weaned piglets; LWWE: litter weight at weaning; REP: repeatability measurements; FSA: 
age of farrowing sows; SWA: sows age at weaning; FYM: farrowing year-month; WYM: weaning year–month; A: additive genetic effect; R: 
random effect; F: fixed effect. 
10 
 
Traits that were analyzed in models are the number of piglets born alive 
(NBA), number of weaned piglets (NWE), and litter weight at weaning 
(LWWE). Best Linear Unbiased Prediction (BLUP) and Restricted 
maximum likelihood methodology (REML) were used for the estimation 
of breeding values and variance–covariance components. PEST 
(Groeneveld, E., 1990) and VCE 6 (Groeneveld, E., et al., 2008) software 
were used (for data coding) for the estimation of breeding values and 
variance components. 
 
The basic repeatability model was: 
 
y = Xb + Za + Wpe + e 
 
Where: y is the vector of observations; b is the vector of fixed effects; a is 
the vector of random animal effects; pe is the random vector of permanent 
environmental effects (dam identity); e is the vector of random residual 
effects; and X, Z, and W are the incidence matrices relating records to 
fixed, animal, and random maternal permanent effects, respectively. 
 
Expected values of a, c and e were E(a) = E(c) = E(e) = 0. The variance–
covariance structure was assumed to be V(a) = A2a, V(c) = I2c V(e) = I2e 
and V(a) = A2a, 
V(e) = I2e Cov(a,e) = Cov(e,a) = Cov(c,e) = Cov(e,c) = 0 and Cov(a,e) = 
Cov(e,a) = 0, where A is the numerator relationship matrix. Additionally, 
cov(y,a) = ZAI2a. 
 
The suitability of the different models was compared using the log-
likelihood values calculated by the VCE software. The model with the 
11 
 
largest log-likelihood value provides the best fit. SAS 9.4 (SAS., 2013)  
was used for descriptive statistical analysis. In addition, SAS 9.4 was also 
applied to calculating the genetic trend for every trait, which is the linear 
regression coefficient of the average breeding value of animals born in the 
same year (regressed on the successive years of birth). Additionally, Mix 
software was used (Nath, M., et al., 2002) for the calculation of the 
maternal desired index with the purpose of improving all traits by one 
additive standard deviation where the assignment of relative economic 
values of the examined traits is not necessary. The breeding goals are 
defined as the ultimate levels of the traits of interest. The desired gain 
index is constructed to attain the predetermined breeding goals in 
minimum number of generations of selection. A detailed description of 
the index weighing factors’ calculation was given by Yamada et al. 
(1975). The calculated index scores were transformed in order to obtain 
index mean and standard deviation equal to 100 and 20, respectively, as it 
is used in Hungarian pig breeding (Hungarian Pig Breeders Association. 
Pig Performanc., 2017). When constructing the index the objective was to 
improve all traits with one additive standard deviation. 
 
3. Results 
 
1. The results of heritability (HD, HH and HP) of the models analyses 
show that NBA for all models was low and range in diapason 0.10 - 0.11. 
There was also a slight tendency for NWE in all models too and evaluated 
with values between 0.08 - 0.09. Measuring heritability of LWWE for all 
models had a same low tendency and range 0.11 - 0.13.  Standard errors 
for estimated parameters were less than 0.03 in all models. 
 
12 
 
2. The ratios of the permanent environmental variance to the phenotypic 
variance (HD, HH and HP) for NBA had low impact in all models and 
ranged from 0.07 to 0.08. Likewise, NWE showed low magnitude 
between 0.04 - 0.05 for all models. Similarly, LWWE had lowest values 
of environmental impact in comparison with NBA and NWE and was 
assessed at range 0.02 – 0.03 for all models.  Standard errors for 
estimated parameters were less than 0.03. 
 
3. Genetic trends were not significant for NBA and NWE traits in case of 
HD breed. Significant difference observed for LWWE trait with Pr>|t| 
ranging 0.01 - 0.02 with trends values 0.14 - 0.20 kilogram per year. 
Genetic trends of Hampshire breed were not significant for NBA and 
NWE but showed the same tendency for all models. LWWE was 
significant with Pr>|t| 0.03 - 0.04, with negative trends -0.25 - -0.24 
kilogram per year. Estimated genetic trends of Pietrain breed were 
significant for NBA and NWE traits. NBA had positive trends with Pr>|t| 
0.01 - 0.009 with a ranged value 0.01 – 0.02 piglets per year for all 
models. NWE had Pr>|t| 0.001 - 0.0008 with the same genetic trends 0.02 
piglets per year. LWWE was not significant. 
 
4. Heritability (HLW, HL and F1) for NBA and NWE was the same for 
all seven models. On the contrary, heritability estimates for LWWE were 
higher in comparison with NBA and NWE.  
 
5. The permanent environmental variance component (HLW, HL and F1) 
was small for all traits. The HLW breed had positive and significant 
genetic trends for all seven models and for all three traits. HL breed had 
13 
 
significant trends for NBA, which was negative and for NWE the results 
were positive.  
 
6. Using the estimated breeding values a desired index was constructed in 
order to improve each trait by one additive genetic standard deviation. 
The constructed indices result in one number (aggregate genetic merit). 
 
4. New scientific results 
 
1. Based on the estimated log likelihood-value models, including parity 
were inferior compared to those models containing age for both type of 
breeds paternal as for maternal. Models 6 and 11 had best fits with data 
with next values 15 808 and 169 370 for paternal and maternal breeds of 
pigs respectively. 
 
2. Next index was constructed for two types of breeds. Here are 
description of indexes from models which best fits data. Paternal breeds 
index - 15.8871*ebv1 + 2.5689*ebv3 and maternal breeds index - 
13.8544*ebv1 + 12.3412*ebv2 + 1.8160*ebv3 (ebv are the estimated 
breeding values for traits: ebv1 - number of piglets born alive, ebv2 - 
number of weaned piglets, and ebv3 - litter weight at weaning). 
 
5. Conclusions and suggestions 
 
It can be seen that index methodology named an aggregate genotype is an 
appropriate tool that we can apply for ranking animals. 
 
The desired gains index technique is a good tool for determining the 
economic importance of traits where the economic values cannot be 
determined. Animals can be selected based on their overall performance. 
14 
 
 
Using the desired gain index method for paternal as for maternal breeds 
of pigs would probably increase the annual genetic trends of the traits 
involved in the index. 
 
The indexes which were construct in frame of my investigations of course 
possible to use in practice. But in general these indexes can be applied 
just inside of this breeds and Hungary. Because out of country will exist 
new environments and some other traits characteristics. Therefore this 
methodology can be used for construction new indexes out of Hungary 
but will require new income data. 
 
6. Publications written in the topic of the dissertation 
 
Kodak, O., & Nagy, I. (2019). Historical overview of the selection indices 
applied in pig breeding. Acta Agraria Kaposváriensis, 23(1), 22-31. 
 
Oleksandr Kodak, Farkas JánosGyörgy Kövér, István Nagy (2020).  
Application of selection indices for Hungarian pig breeds. Scientific and 
Information Bulletin of the Faculty of Biology and Technology. Kherson. 
Vol. 13. С.10-13. 
 
Kodak, O., Kiszlinger, H. N., János, F., Kövér, G., & István, N. (2020). 
Index selection as a key in the selection process for pigs. Hampshire, 
7(381), 1-246. 
 
Kodak, O., Nagyne-Kiszlinger, H., Farkas, J., Köver, G., & Nagy, I. 
(2022). Genetic Parameters of Reproductive Performances in Hungarian 
15 
 
Large White, Landrace, and Their Crossbred F1 Pigs from 2010 to 2018. 
Diversity, 14(12), 1030. 
 
 


Development of selection indices in the Hungarian pig breeds

Bárdos, Boróka

Kisemlősök tartástechnológiájának fejlesztési lehetőségei

Tuan, Le Anh

Effect of Plant Growth Promoting Rhizobacteria (Phylazonit) in tomato (Solanum Lycopersicon L ) production under different irrigation levels.

El Chami, Josepha

Influence of Fusarium head blight on technological quality of wheat

Bindi Omar, Suhana

Study on genetic variability and heterosis of different agronomic characteristics, yield components and grain quality in maize (Zea mays l.)

Cahyadi, Afriyadi

Job satisfaction and performance of employees at small and medium-sized enterprises in Indonesia: The role of going green and technological adaptation after Covid-19

Győri, Tímea

A foglalkoztatáspolitika és a munkanélküliség térszerkezeti összefüggéseinek vizsgálata Európában, különös tekintettel Magyarországra

Földi, Péter

Az élelmiszeripari vállalkozások jövedelmezőségét befolyásoló tényezők hatásának mérhetősége

Not a member yet