BLUP Method Research Articles

Development and optimization of the equations of the mixed BLUP model for the evaluation of the breed value of bulls-producers of the golstin black-motioned breed of the Republic of Kazakhstan

The relevance of research. Currently the methodological base for assessing the breeding qualities of bulls-producers of dairy and milk-meat breeds by the quality of offspring is carried out in accordance with the Instruction approved by the MA of the Republic of Kazakhstan in 2007. The principle of assessment, set out in the Instruction, is to compare the phenotypic indicators of the offspring with each other according to the principle of “peer daughter”. Although this document was approved already in the XXI century, nevertheless the basic approaches, which were put the basis, were developed in the first half of the last century and currently do not correspond to modern scientific principles. At the same time the world leaders in the field of breeding in dairy cattle breeding have been successfully using the BLUP method in breeding practice to assess the breeding qualities of animals for decades. This principle of assessing the breeding value of bulls is the most theoretically grounded and allows you to obtain results comparable to each other. Therefore the development and optimization of the equations of mixed BLUP models is extremely relevant for the conditions of the Republic of Kazakhstan from both scientific and practical points of view.Material and research methods. The material of the research was the data on the phenotypic indicators of the signs of milk productivity of first-calf cows of the Holstein black-and-white breed obtained from the republican database of the Republic of Kazakhstan for 2016-2017. As a criterion for choosing the best equation the residual variance values of each model under study were used.Results: when improving the method for assessing bulls-sires of the Holstein black-and-white breed according to the quality of offspring, out of the four studied equations of the mixed BLUP model one equation was optimized to assess the breeding qualities of the sires. In principle, to assess the breeding qualities of producers by the quality of offspring it is possible to use any of the models under consideration, since the established differences for all analyzed characteristics of milk productivity are insignificant (no more than 6%)

ОТБОР СВИНОМАТОК ПО ГЕНЕТИЧЕСКИМ МАРКЕРАМ И ИНДЕКСУ BEST LINEAR UNBIASED PREDICTION (BLUP)

The authors conducted studies on the effect of the estrogen receptor (ESR), prolactin receptor (PRLR), and ryanodine receptor (RYR-1) genotypes on the breeding value of sows. Using the BLUP method, they evaluated the indicators of the large white, landrace, and Duroc breeds to develop a regional hybridization system in the pig industry of the Belgorod region. The research determined a significant influence of the “desirable” BB and AB genotypes of the ESR gene in large white sows and the “desirable” BB genotype of the RPLR gene in Landrace and Duroc sows on the maternal BLUP index.

Improving the assessment of the breeding value of pigs based on the use of BLUP methods and genomic analysis

The present paper presents the preliminary results of experiments to improve the evaluation of the breeding value of sows by such qualities as multiple births in live-born piglets, the number of business piglets at weaning at 30 days of age, and the safety of the litter. The evaluation of the breeding value of sows was carried out based on the use of BLUP methods and genomic analysis. It was found that the reliability of assessing the breeding value of sows increases significantly when both factors are taken into account: the use of genotypes of candidate genes ESR and PRLR and BLUP evaluation.

Метод племенной оценки быков-производителей мясных пород на основе BLUP

Метод племенной оценки быков-производителей мясных пород на основе BLUP

Comparative accuracies of genetic values predicted for economically important milk traits, genome-wide association, and linkage disequilibrium patterns of Canadian Holstein cows

Genomic selection methodologies and genome-wide association studies use powerful statistical procedures that correlate large amounts of high-density SNP genotypes and phenotypic data. Actual 305-d milk (MY), fat (FY), and protein (PY) yield data on 695 cows and 76,355 genotyping-by-sequencing-generated SNP marker genotypes from Canadian Holstein dairy cows were used to characterize linkage disequilibrium (LD) structure of Canadian Holstein cows. Also, the comparison of pedigree-based BLUP, genomic BLUP (GBLUP), and Bayesian (BayesB) statistical methods in the genomic selection methodologies and the comparison of Bayesian ridge regression and BayesB statistical methods in the genome-wide association studies were carried out for MY, FY, and PY. Results from LD analysis revealed that as marker distance decreases, LD increases through chromosomes. However, unexpected high peaks in LD were observed between marker pairs with larger marker distances on all chromosomes. The GBLUP and BayesB models resulted in similar heritability estimates through 10-fold cross-validation for MY and PY; however, the GBLUP model resulted in higher heritability estimates than BayesB model for FY. The predictive ability of GBLUP model was significantly lower than that of BayesB for MY, FY, and PY. Association analyses indicated that 28 high-effect markers and markers on Bos taurus autosome 14 located within 6 genes (DOP1B, TONSL, CPSF1, ADCK5, PARP10, and GRINA) associated significantly with FY.

Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd.

Simple SummaryThe objective of this study was to quantify the benefit from the inclusion of genomic information in the estimation of breeding values for lactation yields of milk, fat, and protein or somatic cell score in a New Zealand dairy goat herd. The dataset included lactation yields of milk, fat, and protein and average somatic cell score of 839 does and genotypes from 388 does. A prototype single-step BayesC model was developed to predict genomic breeding values and demonstrated that including genomic information into the evaluation can increase the accuracy of predictions compared with the traditional best linear unbiased prediction methods based on pedigrees alone, which is currently implemented in the New Zealand dairy goat industry.Selection on genomic breeding values (GBVs) is now readily available for ranking candidates in improvement schemes. Our objective was to quantify benefits in terms of accuracy of prediction from including genomic information in the single-trait estimation of breeding values (BVs) for a New Zealand mixed breed dairy goat herd. The dataset comprised phenotypic and pedigree records of 839 does. The phenotypes comprised estimates of 305-day lactation yields of milk, fat, and protein and average somatic cell score from the 2016 production season. Only 388 of the goats were genotyped with a Caprine 50K SNP chip and 41,981 of the single nucleotide polymorphisms (SNPs) passed quality control. Pedigree-based best linear unbiased prediction (PBLUP) was used to obtain across-breed breeding values (EBVs), whereas a single-step BayesC model (ssBC) was used to estimate across-breed GBVs. The average prediction accuracies ranged from 0.20 to 0.22 for EBVs and 0.34 to 0.43 for GBVs. Accuracies of GBVs were up to 103% greater than EBVs. Breed effects were more reliably estimated in the ssBC model compared with the PBLUP model. The greatest benefit of genomic prediction was for individuals with no pedigree or phenotypic records. Including genomic information improved the prediction accuracy of BVs compared with the current pedigree-based BLUP method currently implemented in the New Zealand dairy goat population.

Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle

BackgroundRecently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations.ResultsThe accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method.ConclusionsOur findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.

17 Investigating pig survival in different productive stages using genomic models

Abstract Pig survival is an economically important trait with animal welfare implications. Although survival is highly affected by the environment, previous studies reported genetic variability of this trait, indicating the opportunity for improvement through selection. Genomic information is currently included in the major commercial pig breeding programs, which can be incorporated by single-step genomic BLUP (ssGBLUP). The objectives of this study were to: 1) estimate (co)variance components for farrowing, lactation, nursery, and finishing survival; and 2) compare the individual breeding value accuracies obtained using traditional pedigree-based BLUP (BLUP) and ssGBLUP methods. Individual survival records were collected for a crossbred pig population, and two-trait threshold models, which included maternal effects, were used for (co)variance components estimation. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods, and individual accuracies were obtained based on posterior standard deviation. Heritabilities for the four survival phases were low, ranging from 0.04 to 0.12. Pre-weaning survival was controlled by dam and piglet additive gene effects. The additive direct and maternal components were equally important at farrowing, whereas the piglet’s own genetic merit was the most expressive during lactation. Common environment estimates were higher than maternal genetic effects, indicating early life experiences related to the sow, but independent of the maternal genetic component. Nursery and finishing survival showed the same or higher heritabilities compared to pre-weaning stages. The genetic correlation between the pre-weaning phases was high (0.68), whereas the correlation between the post-weaning measurements was moderate (0.42). The incorporation of genomic information through ssGBLUP increased the individual accuracy, on average, from 0.36 to 0.41 for direct and from 0.29 to 0.37 for maternal effects compared to BLUP. Although the heritabilities for survival in different productive stages are low, genetic gains can be obtained, given that breeding values benefit from the inclusion of genomic information.

Comparison of BLUP and Bayesian methods for different sizes of training population in genomic selection

This study aims to compare the accuracy of pedigree-based and genomic-based breeding value prediction for different training population sizes. In this study, Bayes (A, B, C, Cpi) and GBLUP methods for genomic selection and BLUP method for pedigree-based selection were used. Genomic and pedigree-based breeding values were estimated for partial milk yield (158 days) of Holstein cows (400 individuals) from a private enterprise in the USA. For this aim, populations were created for indirect breeding value estimates as training (322–360) and test (78–40) populations. In animals genotyped with a 54k SNP, the marker file was encoded as –10, 0, and 10 for AA, AB, and BB marker genotypes, respectively. Bayes and GBLUP methods were performed using GenSel 4.55 software. A total of 50,000 iterations were used, with the first 5000 excluded as the burn-in. Pedigree-based breeding values were estimated by REML using MTDFREML software employing an animal model. Correlations between partial milk yield and estimated breeding values were used to assess the predictive ability for methods. Bayes B method gave the highest accuracy for the indirect estimate of breeding value.

Genetic evaluation of sheep for resistance to gastrointestinal nematodes and body size including genomic information.

ObjectiveThe genetic evaluation of Santa Inês sheep was performed for resistance to gastrointestinal nematode infection (RGNI) and body size using different relationship matrices to assess the efficiency of including genomic information in the analyses.MethodsThere were 1,637 animals in the pedigree and 500, 980, and 980 records of RGNI, thoracic depth (TD), and rump height (RH), respectively. The genomic data consisted of 42,748 SNPs and 388 samples genotyped with the OvineSNP50 BeadChip. The (co)variance components were estimated in single- and multi-trait analyses using the numerator relationship matrix (A) and the hybrid matrix H, which blends A with the genomic relationship matrix (G). The BLUP and single-step genomic BLUP methods were used. The accuracies of estimated breeding values and Spearman rank correlation were also used to assess the feasibility of incorporating genomic information in the analyses.ResultsThe heritability estimates ranged from 0.11±0.07, for TD (in single-trait analysis using the A matrix), to 0.38±0.08, for RH (using the H matrix in multi-trait analysis). The estimates of genetic correlation ranged from −0.65±0.31 to 0.59±0.19, using A, and from −0.42±0.30 to 0.57±0.16 using H. The gains in accuracy of estimated breeding values ranged from 2.22% to 75.00% with the inclusion of genomic information in the analyses.ConclusionThe inclusion of genomic information will benefit the direct selection for the traits in this study, especially RGNI and TD. More information is necessary to improve the understanding on the genetic relationship between resistance to nematode infection and body size in Santa Inês sheep. The genetic evaluation for the evaluated traits was more efficient when genomic information was included in the analyses.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ РЕЗУЛЬТАТОВ ОЦЕНКИ БЫКОВ-ПРОИЗВОДИТЕЛЕЙ ГОЛШТИНСКОЙ ЧЕРНО-ПЕСТРОЙ ПОРОДЫ ПО ПРОДУКТИВНОСТИ ДОЧЕРЕЙ НА ОСНОВЕ ОФИЦИАЛЬНОЙ ИНСТРУКЦИИ И МОДЕЛИ BLUP

According to the current “Instruction” used in dairy cattle selection and breeding in the Republic of Kazakhstan, bulls-producers of dairy breeds are assessed according to the their offspring quality based on the principle of “peer daughter”. This means that the phenotypic indicators of the daughters of the tested bulls are compared with the corresponding indicators of their peers. In European countries with developed dairy cattle breeding, as well as in Canada, the USA, etc., to ensure a reliable forecast of the genetic value of individuals (primarily, bulls-producers), use is made of the best linear unbiased forecast method (BLUP method). This method implies that the breeding value of producers is determined by the deviation values of the development of traits of the examined animal from its average values in the population. Especially urgent area is the research aimed at improving breeding programs, including assessing the breeding value of bulls-producers of dairy breeds using BLUP methods based on the productive qualities of the mass of dairy cattle in the Republic of Kazakhstan. The research material included the data on the phenotypic indicators of the milk productivity of first-calf cows (the amount of milk yield, the content of fat and protein in milk, the yield of milk fat and protein) of the Holstein black-motley dairy cattle breed, obtained from the information and analytical database of the Republic of Kazakhstan for 2016–2017. It was found that when evaluating according to the official “Instruction”, 16 sires out of 256 bulls (6.2%) got the stud category in 2016, 14 sires (9.2%) out of 152 bulls in 2017, and – 30 sires of 249 bulls (12.0%) over the cumulative period. The results of the conducted research prove that the use of the classic “Instructions” in dairy cattle breeding has lower efficiency (by 42.8–90.0%) as compared with the assessment of the breeding value of bulls based on the BLUP method.The selection of sire bulls into breeding groups based on the “peer daughter” methodology is not reliable enough and rather ineffective. Comparing the results of assessing the breeding qualities of sire bulls, obtianed using two methods in all compared periods (2016, 2017, 2016–2017), the authors established a clear superiority of the BLUP method over the current Instruction used in the Republic of Kazakhstan.

Accuracies of direct genomic breeding values for birth and weaning weights of registered Charolais cattle in Mexico

Context Genomic prediction is now routinely used in many livestock species to rank individuals based on genomic breeding values (GEBV). Aims This study reports the first assessment aimed to evaluate the accuracy of direct GEBV for birth (BW) and weaning (WW) weights of registered Charolais cattle in Mexico. Methods The population assessed included 823 animals genotyped with an array of 77000 single nucleotide polymorphisms. Genomic prediction used genomic best linear unbiased prediction (GBLUP), Bayes C (BC), and single-step Bayesian regression (SSBR) methods in comparison with a pedigree-based BLUP method. Key results Our results show that the genomic prediction methods provided low and similar accuracies to BLUP. The prediction accuracy of GBLUP and BC were identical at 0.31 for BW and 0.29 for WW, similar to BLUP. Prediction accuracies of SSBR for BW and WW were up to 4% higher than those by BLUP. Conclusions Genomic prediction is feasible under current conditions, and provides a slight improvement using SSBR. Implications Some limitations on reference population size and structure were identified and need to be addressed to obtain more accurate predictions in liveweight traits under the prevalent cattle breeding conditions of Mexico.

Удосконалення алгоритму критеріїв добору свиней батьківської форми за використання методу BLUP

The purpose. To improve algorithm of criteria of selection of pigs of male parental form by results of assessment of animals of 4 generations with the help of BLUP method. Methods. Theoretical and methodological basis of research were works by Ch.R. Henderson, R.A. Mrode, V.M. Kuznetsov, N.P. Yudina, and other scientists on the investigated problem. During researches they applied general trial-and-error method: measurement, comparison, simulation; and special — BLUP, zoo-technical, instrumental, etc. Biometrical analysis of data was carried out with the help of statistical and mathematical methods, realized in package Statistica 6. Results. Researches testify to the following: selection of pigs in herd with the use of BLUP-index assessment is necessary for realizing in some stages, proceeding from the reached level of productivity, and also the purposes and problems of selection. For preliminary determination of the best alternatives of selection of young boars and pigs they simulated 5 levels of selection pression on terminal BLUP-index. With increase of selection pression they traced dynamics of values of attributes of specific breeding value of animals which treated to different pleiads, for the purpose of stabilization of their reproductive qualities. At the final stage the selection of young boars and pigs was spent on minimum acceptable values: terminal BLUP-index, specific breeding value of daily average increase, width of fat, length of a trunk and amount of papillae. Simulation of results of selection at the improved algorithm of criteria of selection in herd testifies to the following: selection effect for one generation on specific breeding value of width of fat and daily average increase makes–0,53 mm and +4,66 g accordingly. Conclusions. By results of researches it is established that development of algorithm of selection of animals of male parental form promoted increase of genotypic consolidation of young boars on specific breeding value of daily average increase and width of fat on 50,8 and 65,6%, and young pigs — on 23,1 and 32,6% accordingly.

Genome-wide association studies for growth traits in buffaloes using the single step genomic BLUP.

Growth traits are important for the profitability of buffalo breeding systems, since in general, these animals are raised both for meat and milk. In this study, the single-step genomic BLUP method was employed to prospect the genomic regions' associated with weight at standard ages of 100, 210, 365, and 550days in a buffalo population, aiming to identify genes with stronger expression for those characteristics. We found 6, 1, 2, and 5 SNPs significantly associated (p value < 10-5) with weight at 100, 210, 365, and 550days of age, respectively, where those SNPs respectively explained 0.164, 0.040, 0.044, and 0.213% of the additive variance of each trait. SNP AX-85099682 (BBU24) was significant for weight at 100, 210, and 365days, indicating the existence of a possible QTL affecting the initial growth rate of buffaloes. All told, eight genes (CBLB, TRNAG-UCC, GADD45B, LOC112583811, MGAT4C, KCNMA1, SLC5A2, and TGFB1I1) were identified as candidates for the growth traits of buffaloes. However, molecular and gene expression studies are necessary to validate these genes for subsequent use in programs for genetic improvement of the species.

Alternative methods improve the accuracy of genomic prediction using information from a causal point mutation in a dairy sheep model

BackgroundGenomic evaluation is usually based on a set of markers assumed to be linked with causal mutations. Selection and precise management of major genes and the remaining polygenic component might be improved by including causal polymorphisms in the evaluation models. In this study, various methods involving a known mutation were used to estimate prediction accuracy. The SOCS2 gene, which influences body growth, milk production and somatic cell scores, a proxy for mastitis, was studied as an example in dairy sheep.MethodsThe data comprised 1,503,148 phenotypes and 9844 54K SNPs genotypes. The SOCS2 SNP was genotyped for 4297 animals and imputed in the above 9844 animals. Breeding values and their accuracies were estimated for each of nine traits by using single-step approaches. Pedigree-based BLUP, single-step genomic BLUP (ssGBLUP) involving the 54K ovine SNPs chip, and four weighted ssGBLUP (WssGBLUP) methods were compared. In WssGBLUP methods, weights are assigned to SNPs depending on their effect on the trait. The ssGBLUP and WssGBLUP methods were again tested after including the SOCS2 causal mutation as a SNP. Finally, the Gene Content approach was tested, which uses a multiple-trait model that considers the SOCS2 genotype as a trait.ResultsEBV accuracies were increased by 14.03% between the pedigree-based BLUP and ssGBLUP methods and by 3.99% between ssGBLUP and WssGBLUP. Adding the SOCS2 SNP to ssGBLUP methods led to an average gain of 0.26%. Construction of the kinship matrix and estimation of breeding values was generally improved by placing emphasis on SNPs in regions with a strong effect on traits. In the absence of chip data, the Gene Content method, compared to pedigree-based BLUP, efficiently accounted for partial genotyping information on SOCS2 as accuracy was increased by 6.25%. This method also allowed dissociation of the genetic component due to the major gene from the remaining polygenic component.ConclusionsCausal mutations with a moderate to strong effect can be captured with conventional SNP chips by applying appropriate genomic evaluation methods. The Gene Content method provides an efficient way to account for causal mutations in populations lacking genome-wide genotyping.

A generalized statistical framework to assess mixing ability from incomplete mixing designs using binary or higher order variety mixtures and application to wheat

There has been recently a renewed interest for variety mixtures due to their potential capacity to stabilize production through buffering abiotic and biotic stresses. Part of this results from complementarity and/or compensation between varieties which can be assessed under mixed stands only. Mixing ability of varieties can be partitioned into General and Specific Mixing Abilities (GMA and SMA) that have been estimated so far through the evaluation of binary mixtures in complete diallel designs. However, the number of mixtures increases exponentially with the number of studied varieties, and the only feasible devices are incomplete designs. Despite the long history of statistical analysis of variety mixtures, such incomplete design analysis has rarely been addressed so far. To fill the gap, we proposed a generalized statistical framework to assess mixing abilities based on mixed models and BLUP method, with an original modeling of plant-plant interactions. The approach has been applied to a panel of 25 winter wheat genotypes observed in two contrasted experimental designs: (i) an incomplete diallel of 75 binary mixtures, and (ii) a trial including higher order mixtures (four and eight components). The use of mixing ability models improved prediction accuracy (of modeled values for observed traits) in comparison to predictions from the mean of the pure stand components, especially in the first experiment. Genetic variability was detected for the GMA of yield and its components, whereas variability for SMA was lower. GMA predictions based on the diallel trial were highly correlated with the GMA of the second trial providing accurate inter-trial predictions. A new model has been proposed to jointly account for inter and intra-genotypic interactions for specific mixing ability, thus contributing to a better understanding of mixture functioning. This framework constitutes a step forward to the screening for mixing ability, and could be further integrated into breeding programs for the development of intra- or inter-specific crop mixtures.

The effect of progeny numbers and pedigree depth on the accuracy of the EBV with the BLUP method

The effect of progeny numbers and pedigree depth on the accuracy of the EBV with the BLUP method

Estimating breeding values for juvenile body weight using trait associated SNP assisted BLUP in turbot (Scophthalmus maximus)

Turbot is an important marine aquaculture species with great economic value. Marker-assisted selection is an attractive breeding technique to improve economic traits in turbot. The objective of this study was 1) to validate SNPs associated with juvenile turbot body weight (BW) on a population scale and 2) to evaluate the performance of a marker-assisted BLUP (MA-BLUP) method to estimate breeding values for BW. Based on a previous study, four SNPs associated to candidate QTLs for juvenile BW were genotyped in a breeding population consisting of 926 individuals from 40 full-sib families. General linear models (GLM) and mixed linear models (MLM) were applied to identify associations between SNPs and juvenile BW. Individual breeding values were estimated by MA-BLUP based on associated SNP and traditional BLUP. Thereafter, we compared the accuracy of both estimators by cross validation. Both GLM and MLM obtained similar results, with SNP105 showing significant effect on juvenile BW. Least squares mean of genotypes AA, GA and GG at SNP105 were 10.635 ± 0.454 g, 9.953 ± 0.449 g and 9.897 ± 0.497 g, respectively. Cross validation revealed that the accuracy of estimated breeding values was 0.573 with MA-BLUP and 0.565 with traditional BLUP, and t-test suggested that the accuracy of both methods differed significantly (P < .001), which indicated that accuracy of estimators based on MA-BLUP was better than traditional method. The results suggested MA-BLUP was a feasible approach to apply trait associated markers in turbot breeding.

Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits, udder type traits, and somatic cell scores in French dairy goats

Genomic evaluation of French dairy goats is routinely conducted using the single-step genomic BLUP (ssGBLUP) method. This method has the advantage of simultaneously using all phenotypes, pedigrees, and genotypes. However, ssGBLUP assumes that all SNP explain the same amount of genetic variance, which is unlikely in the case of traits whose major genes or QTL are segregating. In this study, we investigated the effect of weighted ssGBLUP and its alternatives, which give more weight to SNP associated with the trait, on the accuracy of genomic evaluation of milk production, udder type traits, and somatic cell scores. The data set included 2,955 genotyped animals and 2,543,680 pedigree animals. The number of phenotypes varied with the trait. The accuracy of genomic evaluation was assessed on 205 genotyped Alpine and 146 genotyped Saanen goats born between 2009 and 2012. For traits with unknown QTL, weighted ssGBLUP was less accurate than, or as accurate as, ssGBLUP. For traits with identified QTL (i.e., QTL only present in the Saanen breed), weighted ssGBLUP outperformed ssGBLUP by between 2 and 14%.

Estimation of breeding values by BLUP method in Ukrainian Meat sows based on reproductive traits

Estimation of breeding values by BLUP method in Ukrainian Meat sows based on reproductive traits