BLUP Method Research Articles

WPSIII-8 The effectiveness of the using bulls evaluated by different methods.

The aim of the study was to show the efficiency of using bulls (n=18) evaluated by various methods for improvement of herds with average cow milk yield of more than 8000 kg. The material was animal databases. We used statistical data processing. Results of simultaneous calculation of breeding value in bulls, which were genomically evaluated in the USA and Canada, demonstrated that sires evaluated in Canada by their own comparison base exceeded milk breeding value of bulls evaluated in the USA by the national comparison base. Using genomic evaluations and progeny tests in Russian animal breeding one should take into account a difference of bull breeding values in the USA and Canada. Use of semen of the same genomic bulls during period of 2014–2017 demonstrated that among 25 sires with genomic evaluation in the USA one sire maintained its superiority by 95.7% and three sires improved milk yield breeding value by 4.4–35.9%. Among Canadian genomic bulls evaluated in 2017, 6 sires maintained 80.7–97.6% of the breeding value in comparison with that of 2014. Use of genomic bulls demonstrated that best progeny tested sires were also among the best of genomic evaluation. In sire selection one should take into account the superiority by productive traits of daughters of given bulls, change in breeding values which took place during years after 1st genomic scoring. Comparison of results for the same 18 sires genomically evaluated in USA,which were progeny tested in the USA and Leningrad region, resulted in correlation coefficients between EBV BLUP score and “Daughter-contemporaries” score by milk yield about 0.55, by fat content – 0.72, by protein – 0.72. Loss of efficiency of cow breeding for milk yield because of ignoring BLUP method may reach 45%. Thus,the genetic evaluation by BLUP method should be introduce in breeding in dairy cattle.

Efficient and accurate computation of base generation allele frequencies

Allele frequencies are used for several aspects of genomic prediction, with the assumption that these are equal to the allele frequency in the base generation of the pedigree. The current standard method, however, calculates allele frequencies from the current genotyped population. We compared the current standard method with BLUP and general least squares (GLS) methods explicitly targeting the base population to determine whether there is a more accurate and still efficient method of calculating allele frequencies that better represents the base generation. A data set based on a typical dairy population was simulated for 325,266 animals; the last 100,078 animals in generations 9 to 12 of the population were genotyped, with 1,670 SNP markers. For the BLUP method, several SNP genotypes were analyzed with a multitrait model by assuming a heritability of 0.99 and no genetic correlation among them. This method was limited by the time required for each BLUP to converge (approximately 6 min per BLUP run of 15 SNP). The GLS method had 2 implementations. The first implementation, using imputation on the fly and multiplication of sparse matrices, was very efficient and required just 49 s and 1.3 GB of random access memory. The second implementation, using a dense full A22-1 matrix, was very inefficient and required more than 1 d of wall clock time and more than 118.2 GB of random access memory. When no selection was considered in the simulations, all methods predicted equally well. When selection was introduced, higher correlations between the estimated allele frequency and known base generation allele frequency were observed for BLUP (0.96 ± 0.01) and GLS (0.97 ± 0.01) compared with the current standard method (0.87 ± 0.01). The GLS method decreased in accuracy when introducing incomplete pedigree, with 25% of sires in the first 5 generations randomly replaced as unknown to erroneously identify founder animals (0.93 ± 0.01) and a further decrease for 8 generations (0.91 ± 0.01). There was no change in accuracy when introducing 5% genotyping errors (0.97 ± 0.01), 5% missing genotypes (0.97 ± 0.01), or both 5% genotyping errors and missing genotypes (0.97 ± 0.01). The GLS method provided the most accurate estimates of base generation allele frequency and was only slightly slower compared with the current method. The efficient implementation of the GLS method, therefore, is very well suited for practical application and is recommended for implementation.

Phenotypic Selection in Ornamental Breeding: It's Better to Have the BLUPs Than to Have the BLUEs.

Plant breeders always face the challenge to select the best individuals. Selection methods are required that maximize selection gain based on available data. When several crosses have been made, the BLUP procedure achieves this by combining phenotypic data with information on pedigree relationships via an index, known as family-index selection. The index, estimated based on the intra-class correlation coefficient, exploits the relationship among individuals within a family relative to other families in the population. An intra-class correlation coefficient of one indicates that the individual performance can be fully explained based on the family background, whereas an intra-class correlation coefficient of zero indicates the performance of individuals is independent of the family background. In the case the intra-class correlation coefficient is one, family-index selection is considered. In the case the intra-class correlation coefficient is zero, individual selection is considered. The main difference between individual and family-index selection lies in the adjustment in estimating the individual's effect depending on the intra-class correlation coefficient afforded by the latter. Two examples serve to illustrate the application of the BLUP method. The efficiency of individual and family-index selection was evaluated in terms of the heritability obtained from linear mixed models implementing the selection methods by suitably defining the treatment factor as the sum of individual and family effect. Family-index selection was found to be at least as efficient as individual selection in Dianthus caryophyllus L., except for flower size in standard carnation and vase life in mini carnation for which traits family-index selection outperformed individual selection. Family-index selection was superior to individual selection in Pelargonium zonale in cases when the heritability was low. Hence, the pedigree-based BLUP procedure can enhance selection efficiency in production-related traits in P. zonale or shelf-life related in D. caryophyllus L.

Short communication: Single-step genomic evaluation of milk production traits using multiple-trait random regression model in Chinese Holsteins

The objectives of this study were to evaluate the prediction performance of the single-step genomic BLUP method using a multi-trait random regression model in genomic evaluation for milk production traits of Chinese Holsteins, and investigate how parameters w, τ, and ω used in the construction of the combined relationship matrix (H) affected prediction accuracy and bias. A total of 2.8 million test-day records from 0.2 million cows were available for milk, protein, and fat yields. Pedigree information included 0.3 million animals and 7,577 of them were genotyped with medium-density single nucleotide polymorphism marker panels. Genotypes were imputed into Geneseek Genomic Profiler HD (GeneSeek, Lincoln, NE) including 77K markers. A reduced data set for evaluating models was extracted from the full data set by removing test-day records from the last 4 yr. Bull and cow validation populations were constructed for each trait. We evaluated the prediction performance of the multiple-trait multiple-lactation random regression single-step genomic BLUP (RR-ssGBLUP) models with different values of parameters w, τ, and ω in the H matrix, taking consideration of inbreeding. We compared RR-ssGBLUP with the multiple-trait multiple-lactation random regression model based on pedigree and genomic BLUP. De-regressed proofs for 305-d milk, protein, and fat yields averaged over 3 lactations, which were calculated from the full data set, were used for posteriori validations. The results showed that RR-ssGBLUP was feasible for implementation in breeding practice, and its prediction performance was superior to the other 2 methods in the comparison, including prediction accuracy and unbiasedness. For bulls, RR-ssGBLUP models with w0.05τ2.0ω1.0,w0.05τ2.5ω1.0, and w0.1τ1.6ω0.4 achieved the best performance for milk, protein, and fat yields, respectively. For cows, the RR-ssGBLUP with w0.2τ1.6ω0.4 performed the best for all 3 traits. The H matrix constructed with larger τ and smaller ω gave better convergence in solving mixed model equations. Among different RR-ssGBLUP models, the differences in validation accuracy were small. However, the regression coefficient indicating prediction bias varied substantially. The increase of w and τ, and decrease of ω, led to an increase in the regression coefficient. The results demonstrated RR-ssGBLUP is a good alternative to the multi-step approach, but the optimal choice of parameters should be found via preliminary validation study to achieve the best performance.

GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait.

Bayesian models for genomic prediction and association mapping are being increasingly used in genetics analysis of quantitative traits. Given a point estimate of variance components, the popular methods SNP-BLUP and GBLUP result in joint estimates of the effect of all markers on the analyzed trait; single and multiple marker frequentist tests (EMMAX) can be constructed from these estimates. Indeed, BLUP methods can be seen simultaneously as Bayesian or frequentist methods. So far there is no formal method to produce Bayesian statistics from GBLUP. Here we show that the Bayes Factor, a commonly admitted statistical procedure, can be computed as the ratio of two normal densities: the first, of the estimate of the marker effect over its posterior standard deviation; the second of the null hypothesis (a value of 0 over the prior standard deviation). We extend the BF to pool evidence from several markers and of several traits. A real data set that we analyze, with ours and existing methods, analyzes 630 horses genotyped for 41711 polymorphic SNPs for the trait “outcome of the qualification test” (which addresses gait, or ambling, of horses) for which a known major gene exists. In the horse data, single marker EMMAX shows a significant effect at the right place at Bonferroni level. The BF points to the same location although with low numerical values. The strength of evidence combining information from several consecutive markers increases using the BF and decreases using EMMAX, which comes from a fundamental difference in the Bayesian and frequentist schools of hypothesis testing. We conclude that our BF method complements frequentist EMMAX analyses because it provides a better pooling of evidence across markers, although its use for primary detection is unclear due to the lack of defined rejection thresholds.

The identification of novel regions for reproduction trait in Landrace and Large White pigs using a single step genome-wide association study.

ObjectiveThe purpose of this study was to investigate a single step genome-wide association study (ssGWAS) for identifying genomic regions affecting reproductive traits in Landrace and Large White pigs.MethodsThe traits included the number of pigs weaned per sow per year (PWSY), the number of litters per sow per year (LSY), pigs weaned per litters (PWL), born alive per litters (BAL), non-productive day (NPD) and wean to conception interval per litters (W2CL). A total of 321 animals (140 Landrace and 181 Large White pigs) were genotyped with the Illumina Porcine SNP 60k BeadChip, containing 61,177 single nucleotide polymorphisms (SNPs), while multiple traits single-step genomic BLUP method was used to calculate variances of 5 SNP windows for 11,048 Landrace and 13,985 Large White data records.ResultsThe outcome of ssGWAS on the reproductive traits identified twenty-five and twenty-two SNPs associated with reproductive traits in Landrace and Large White, respectively. Three known genes were identified to be candidate genes in Landrace pigs including retinol binding protein 7, and ubiquitination factor E4B genes for PWL, BAL, W2CL, and PWSY and one gene, solute carrier organic anion transporter family member 6A1, for LSY and NPD. Meanwhile, five genes were identified to be candidate genes in Large White, two of which, aldehyde dehydrogenase 1 family member A3 and leucine rich repeat kinase 1, associated with all of six reproduction traits and three genes; retrotransposon Gag like 4, transient receptor potential cation channel subfamily C member 5, and LHFPL tetraspan subfamily member 1 for five traits except W2CL.ConclusionThe genomic regions identified in this study provided a start-up point for marker assisted selection and estimating genomic breeding values for improving reproductive traits in commercial pig populations.

Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

Improvement of statistical methods is crucial for realizing the potential of increasingly dense genetic markers. Bayesian methods treat all markers as random effects, exhibit an advantage on dense markers, and offer the flexibility of using different priors. In contrast, genomic best linear unbiased prediction (gBLUP) is superior in computing speed, but only superior in prediction accuracy for extremely complex traits. Currently, the existing variety in the BLUP method is insufficient for adapting to new sequencing technologies and traits with different genetic architectures. In this study, we found two ways to change the kinship derivation in the BLUP method that improve prediction accuracy while maintaining the computational advantage. First, using the settlement under progressively exclusive relationship (SUPER) algorithm, we substituted all available markers with estimated quantitative trait nucleotides (QTNs) to derive kinship. Second, we compressed individuals into groups based on kinship, and then used the groups as random effects instead of individuals. The two methods were named as SUPER BLUP (sBLUP) and compressed BLUP (cBLUP). Analyses on both simulated and real data demonstrated that these two methods offer flexibility for evaluating a variety of traits, covering a broadened realm of genetic architectures. For traits controlled by small numbers of genes, sBLUP outperforms Bayesian LASSO (least absolute shrinkage and selection operator). For traits with low heritability, cBLUP outperforms both gBLUP and Bayesian LASSO methods. We implemented these new BLUP alphabet series methods in an R package, Genome Association and Prediction Integrated Tool (GAPIT), available at http://zzlab.net/GAPIT.

PIG PRODUCTIVITY UNDER PURE BREEDING AND CROSSBREEDING

For the research the groups were formed by method of analogues based on origin, age, physiological state, live weight. Studying sperm productivity, the number of ejaculates obtained during the year, average volume, concentration, and the total number of spermatozoa in the ejaculate were taken into account and conducted according to conventional techniques using a digital photometer “SDM-30” produced by "Minitüb”. Indicators of economical useful traits of the test animals were calculated according to primary zoo-technical accounting by standard methods of biometric analysis. To determine breeding value of animals by the BLUP method, software developed at the Institute of Pig Breeding "System determining the breeding value of pigs" was used. In carrying out the work, population genetics and mathematical methods were applied for studying of patterns of variability, repeatability, assessment of the genotype of animals.
 Reproductive quality of the sows was assessed by multiparity (total number of piglets at farrowing), prolificacy (piglet’s weight at birth, kg), milk ability (total weight of litter at 21 days, kg) piglet’s weight at weaning and survival of offspring to weaning. Survival of piglets, homogeneity and uniformity of litter were determined by modified M. D. Berezovsky – D. V. Lomako index and V. P. Kovalenko homogeneity index. Growth and development were controlled by change of individual live weight via weighing. Average daily gain was determined based on the initial and final live weight and number of days between weightings.
 Dynamics of indicators of growth and development, maternal qualities of sows and sperm productivity and fertility of boars at the purebred animal of initial breeds and live weight and age at 100 kg in the hybrids of first generation were studied. The youngsters obtained at crossbreeding Large White breed of domestic and foreign selection with Landrace had the best indicators of growth and development than the purebred counterparts in all age periods. Higher live weight at weaning at age of 60 days was typical for combination ♀LWE x ♂LE and ♀LE x ♂LWE; it is quite natural, because they had lower average number of piglets at weaning. For maturing volumes of back of the carcass and a well-developed layer of fat should be adjusted to Landrace sows and Large White boars. Fattening of commercial hybrids obtained by this scheme under Ukrainian conditions is advisable to check out at animal’s live weight of 115 ... 120 kg.
 Significant correlations were proven on the basis of: BLUP index – back fat thickness at the level of the 6-7 thoracic vertebra – -0,221 ± 0,0938 (tr = 2,35), BLUP index – back fat thickness on sacrum – -0,298 ± 0,0898 (tr = 3,31), BLUP index – back fat thickness at the midpoint of the back between the withers and sacrum – -0,239 ± 0,0929 (tr = 2,57), BLUP index – body length – -0,338 ± 0,0873 (tr = 3, 86), BLUP index – multiparity – -0,294 ± 0,0900 (tr = 3,26), BLUP index – total weight of the litter at the date of weaning – -0,233 ± 0,0932 (tr = 2,49). Keeping the selection process towards selection and combination of parental pairs by BLUP method is one of the ways to increase the productive capacity of the animals.
 According to the research it was found that asymmetry of testes sizes was observed together with individual peculiarities of testes form at the same breeding boars. Testes have physiological asymmetry. Ratio of areas of the left testes to right was Sl/Sr = 1.04, and volumes respectively Vl/Vr = 1.20, on average, left testes by their areas were 2.7% more than the right, and by volumes – 14.6% respectively.
 Comparing the live weight of boars with weight of their testes does not have a logical pattern. The average weight of testes was 0.14% of the live weight of breeding boars. Total volume of ejaculate on a group of boars was 355,3 ± 16,9 ml. The concentration of sperm in the ejaculate was 64,2 ± 4,6 billion, activity – 8,7 ± 0,2 points. The studies found that boars with greater weight of testes produce sperm with more sperm concentration and that's why more sperms in the ejaculate were obtained from them.
 Combination of Landrace and Large White breeds is apposite for obtaining precocious pigs with well-developed layer of fat at the 6-7 thoracic vertebra and high level of prolificacy (1,8-1,9 kg).

Bayesian single-step genomic evaluations combining local and foreign information in Walloon Holsteins

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (<0.25) without MACE results but sired by genotyped bulls with MACE results, the average increase of REL for the studied traits was 0.38 points of which 0.08 points could be traced to the inclusion of MACE information. For other categories of genotyped animals, the contribution by MACE information was also high. The Walloon genomic evaluation system passed for the first time the Interbull GEBV tests for several traits in July 2013. Recent experiences reported here refer to its use in April 2016 for the routine genomic evaluations of milk production, udder health and type traits. Results showed that the proposed methodology should also be of interest for other, similar, populations.

Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus

BackgroundThe advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses.ResultsPredictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000–10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study.ConclusionsThis study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.

Genomic analysis of stayability in Nellore cattle.

Stayability, which can be defined as the probability of a cow calving at a certain age when given the opportunity, is an important reproductive trait in beef cattle because it is directly related to herd profitability. The objective of this study was to estimate genetic parameters and to identify possible genomic regions associated with the phenotypic expression of stayability in Nellore cows. The variance components were estimated by Bayesian inference using a threshold animal model that included the systematic effects of contemporary group and sexual precocity and the random effects of animal and residual. The SNP effects were estimated by the single-step genomic BLUP method using information of 2,838 animals (2,020 females and 930 sires) genotyped with the Illumina High-Density BeadChip Array (San Diego, CA, USA). The variance explained by windows formed by 200 consecutive SNPs was used to identify genomic regions of largest effect on the expression of stayability. The heritability was 0.11 ± 0.01 when A matrix (pedigree) was used and 0.14 ± 0.01 when H matrix (relationship matrix that combines pedigree information and SNP data) was used. A total of 147 candidate genes for stayability were identified on chromosomes 1, 2, 5, 6, 9 and 20 and on the X chromosome. New candidate regions for stayability were detected, most of them related to reproductive, immunological and central nervous system functions.

Technical note: Genomic evaluation for crossbred performance in a single-step approach with metafounders1

A single-step genomic BLUP method (ssGBLUP) has been successfully developed and applied for purebred and crossbred performance in pigs. However, it requires phasing the genotypes and inferring the breed origin of alleles in crossbred animals, which is somewhat inconvenient. Recently, a new concept of metafounders that considers the relationship within and across base populations was developed. With this concept of metafounders, regular methods to build and invert the pedigree relationships matrix can be used with only minor modifications and, moreover, genomic relationships and pedigree-based relationships are automatically compatible in the ssGBLUP. In this study, data for the total number of piglets born in Danish Landrace, Yorkshire, and 2-way crossbred pigs and models for purebred and crossbred performance were revisited by use of ssGBLUP with 2 metafounders. Genetic variances and genetic correlations between purebred and crossbred performances were first reestimated. Then, model-based reliabilities of purebred boars for their crossbred performance and predictive abilities for crossbred animals were compared in different scenarios. Results in this study were compared to those in a previous study with identical data but with models that required known breed origin of crossbred genotypes. Results show that relationships for base individuals within Landrace and within Yorkshire are similar and that the ancestor populations for Landrace and Yorkshire are related. In terms of model-based reliabilities and predictive abilities, ssGBLUP with metafounders performs at least as well as the single-step method requiring phasing at a lower complexity.

Genetic, Phenotypic and Environmental Trend for Milk Yield and Production Efficiency Traits in Gir Cattle

The first lactation records of 1450 Gir cattle progeny of 290 dams and 41 sires maintained over the period from 1987 to 2010 at Cattle Breeding Farm, Junagadh Agricultural University, Junagadh, Gujarat were used to estimate the phenotypic, genetic and environmental trends of traits viz. first lactation total milk yield (FLTMY), first lactation total milk yield per day of first calving interval (FLTMY/FCI), first lactation total milk yield per day of first lactation length (FLTMY/FLL). The phenotypic trends were estimated as: 90.70 ± 6.44 kg, 0.24 ± 0.01 kg and 0.32 ± 0.02 kg for FLTMY, FLTMY/FCI and FLTMY/FLL respectively. The genetic trends were compared by five methods as Smith method1 (SM1), Smith method 2(SM2), Powell and Freeman method 1 (PM1), Powell and Freeman method 2 (PM2) and BLUP. The annual genetic trends for FLTMY were found to be positive by SM1 (2.88 ± 39.58 kg), PM1 (2.21 ± 37.34 kg) and BLUP (9.17 ± 1.94 kg). The annual genetic trends for FLMY/FCI were positive by SM1 (0.43 ± 0.03 kg), SM2 (2.69 ± 0.03 kg), PM1 (0.38 ± 0.02 kg) and PM2 (2.06 ± 0.02kg) while negative by BLUP (-0.05 ± 0.01 kg). The annual genetic trends for FLMY/FLL were found to be positive by SM1 (0.51 ± 0.06 kg) and PM1 (0.45 ± 0.03 kg) methods while negative by SM2 (-2.37 ± 0.05 kg), PM2 (-1.79 ± 0.03 kg) and BLUP (-0.06 ± 0.01 kg). However, the estimates obtained by BLUP method had the lowest standard errors and were more reliable compared to other methods.

Sire evaluation using animal model versus differentconventional methods in red sindhi cattle

The effectiveness of sire evaluation is the backbone of any breed improvement programme as much as 61% of genetic gain in dairy cattle results from selection of sires through “bulls to breed cows” and “bulls to breed bulls” path. The application of latest and simplex method of sire evaluation like BLUPF90 in Indian breeds is scanty. The present investigation was planned to evaluate the Red Sindhi sires by this method and to compare the effectiveness of BLUPF90 method with other conventional methods of sire evaluation in Red Sindhi cattle. The records of production and reproduction performances of first lactation traits of 717 Red Sindhi cows, progeny of 58 sires, spread over a period of 40 years (1966-2005) from CCBF, Chiplima, Orissa under the control of Department of Animal Husbandry, Dairying and Fisheries, Ministry of Agriculture, Government of India and CBF, Kalsi, Deheradun, Uttarakhand were analyzed. The present study was single and multi-trait evaluation of breeding values of Red Sindhi sires. The overall least squares population mean for first lactation milk yield (FLMY) was 1536.35±54.87 kg. The average breeding value of sires of single trait from different methods ranged from 1536.35 kg (LSM, DFREML and BLUPF90-Dairy Pack) to 1588.50 kg (simple daughter average) but in multi-trait method the breeding value does not vary with each other. The comparison of different method of sire evaluation based on single trait for FLMY only shows that the DFREML model for single trait should be preferred over the BLUP, LSM and simple daughter average methods for evaluating the sire breeding value. However, if a sire-breeding value is to be computed from multi traits then BLUPF90 model may be preferred over DFREML model.

Optimizing the gain of social genetic effect under the control of inbreeding using genetic algorithm

Social interactions among animals are widely existed in livestock population. However, some studies showed that the selection of social genetic effect leaded to extra increase of inbreeding. In this study, two optimization methods (SBLUP+GA1, SBLUP+GA2) based on genetic algorithm were used to obtain the optimal genetic contributions of seedstocks and maximize the average genetic gains of direct and social genetic component while minimizing the inbreeding. In SBLUP+GA1, only the contributions of sires were optimized. In SBLUP+GA2, the contributions of sires and dams were optimized together. The results showed SBLUP+GA1 and SBLUP+GA2 resulted in 18.52% and 25.93% lower inbreeding rate than common social genetic effect selection based on BLUP method (SBLUP) under base parameters, respectively. Under that situation, the average gains for direct, social and total genetic effect component in SBLUP+GA1 were actually improved 3.59%, 10.02% and 4.32% relative to SBLUP, respectively. In SBLUP+GA2, they were 1.28%, 10.00% and 2.02%, respectively. SBLUP+GA2 resulted in lower inbreeding rate, but, obtained slightly less genetic gain than SBLUP+GA1.

Application of single-step genomic evaluation for crossbred performance in pig.

Crossbreding is predominant and intensively used in commercial meat production systems, especially in poultry and swine. Genomic evaluation has been successfully applied for breeding within purebreds but also offers opportunities of selecting purebreds for crossbred performance by combining information from purebreds with information from crossbreds. However, it generally requires that all relevant animals are genotyped, which is costly and presently does not seem to be feasible in practice. Recently, a novel single-step BLUP method for genomic evaluation of both purebred and crossbred performance has been developed that can incorporate marker genotypes into a traditional animal model. This new method has not been validated in real data sets. In this study, we applied this single-step method to analyze data for the maternal trait of total number of piglets born in Danish Landrace, Yorkshire, and two-way crossbred pigs in different scenarios. The genetic correlation between purebred and crossbred performances was investigated first, and then the impact of (crossbred) genomic information on prediction reliability for crossbred performance was explored. The results confirm the existence of a moderate genetic correlation, and it was seen that the standard errors on the estimates were reduced when including genomic information. Models with marker information, especially crossbred genomic information, improved model-based reliabilities for crossbred performance of purebred boars and also improved the predictive ability for crossbred animals and, to some extent, reduced the bias of prediction. We conclude that the new single-step BLUP method is a good tool in the genetic evaluation for crossbred performance in purebred animals.

A Comparison of Different Methods of Sire Evaluation for Production and Reproduction Traits of Murrah Graded Buffaloes

First lactation monthly test day records of 2329 buffaloes sired by 79 sires distributed in 47 villages under field progeny testing programme in Anand milk shed were used to evaluate sires for age at first calving (AFC), first lactation predicted milk yield (FLY305) and milk fat percent (fat%) traits. The objective of the study was to estimate the sire breeding value and to compare various methods of sire evaluation viz. Simple daughters average, Contemporary comparison, Least squares, Best linear unbiased prediction and Average information restricted maximum likelihood in terms of accuracy and efficiency. Heritability estimates by LS-ANOVA method were 0.252 ± 0.044, 0.065 ± 0.032and 0.073 ± 0.035, respectively for AFC, FMY305 and Fat%. The genetic correlation of AFC with FLY305 and Fat% were -0.782 ± 0.192 and -0.451, respectively while the same between FLY305 and fat percent was 1.044 ± 0.39. The phenotypic correlations of FLY305 with Fat % and AFC were -0.042 and 0.109, respectively while same between Fat % and AFC was -0.099. For AFC, the AIREML had the lowest error variance than LS and BLUP, thus was the most efficient method. For FMY305 and Fat%, the BLUP was found to have lowest error variance and therefore, was the most efficient method. The relative accuracies in terms of rank correlations of D, CC, LS and BLUP methods with most efficient AIREML method were 0.35, 0.90, 0.37 and 0.97, respectively for AFC. These estimates of rank correlation were highly significant (P < 0.01). For FLY305, the value of rank correlations of D, CC, LS and AIREML with most efficient BLUP method were 0.71, 0.71, 0.72 and 0.98, respectively. These estimates of rank correlation were highly significant (P < 0.01). The value of rank correlations of D, CC, LS and AIREML with most efficient BLUP method were 0.89, 0.89, 0.95 and 0.99, respectively and these estimates of rank correlation were highly significant (P < 0.01).

SNP annotation-based whole genomic prediction and selection: an application to feed efficiency and its component traits in pigs.

The study investigated genetic architecture and predictive ability using genomic annotation of residual feed intake (RFI) and its component traits (daily feed intake [DFI], ADG, and back fat [BF]). A total of 1,272 Duroc pigs had both genotypic and phenotypic records, and the records were split into a training (968 pigs) and a validation dataset (304 pigs) by assigning records as before and after January 1, 2012, respectively. SNP were annotated by 14 different classes using Ensembl variant effect prediction. Predictive accuracy and prediction bias were calculated using Bayesian Power LASSO, Bayesian A, B, and Cπ, and genomic BLUP (GBLUP) methods. Predictive accuracy ranged from 0.508 to 0.531, 0.506 to 0.532, 0.276 to 0.357, and 0.308 to 0.362 for DFI, RFI, ADG, and BF, respectively. BayesCπ100.1 increased accuracy slightly compared to the GBLUP model and other methods. The contribution per SNP to total genomic variance was similar among annotated classes across different traits. Predictive performance of SNP classes did not significantly differ from randomized SNP groups. Genomic prediction has accuracy comparable to observed phenotype, and use of genomic prediction can be cost effective by replacing feed intake measurement. Genomic annotation had less impact on predictive accuracy traits considered here but may be different for other traits. It is the first study to provide useful insights into biological classes of SNP driving the whole genomic prediction for complex traits in pigs.

Efficiency of selection within sugarcane families via simulated individual BLUP

The aim of this study was to assess the efficiency of the simulated individual BLUP (BLUPIS) method in selecting genotypes within full-sib families of sugarcane in ratoon stage, through comparison with selection using the individual BLUP method. The optimal number of genotypes to be selected in the best families were established for mean stems mass, total soluble solids assay (BRIX), ton of stalks per hectare, and BRIX tons per hectare traits. Seventeen full-sib families were assessed in the Centre for Experimentation in Sugarcane, located in Oratórios, MG, Brazil. Mixed model methodology was used to predict the genotypic effects of each family and the genotypic values of each individual within family. BLUPIS method is efficient for individual selection. The optimal number of genotypes to be selected in the best family for obtaining higher efficiency of the BLUPIS method is 100 in the majority of cases.

Optimizing selection strategies of genomic selection in swine breeding program based on a dataset simulated

The conventional BLUP uses the phenotype and pedigree information to predict the estimated breeding values (EBV) of individuals in genetic evaluation. However, to obtain the phenotypes of interest in swine breeding program requires relatively a long time until the completion of performance testing, which produces an extra breeding cost for the culled pigs in swine industry. An alternative solution is to pre-select a predefined proportion of young replacement piglets for future performance testing through genomic selection, which could reduce the number of testing animals entering into performance testing program and hence reduce the breeding costs associated with the tests. In this study, four strategies of genomic selection applications in swine breeding program were compared through simulation to investigate the potential benefits in the different strategies in swine breeding program. For comparison purpose, the conventional BLUP selection was simulated as strategy 1 to form the benchmark basis for comparisons. Strategy 2 was an extreme case for applying genomic selection, where newborn piglets were selected directly based on their genomic enhanced breeding value (GEBV) only without further performance testing. Strategy 3 was to pre-select piglets, based on their GEBV, for entry to performance testing in early stage, and then the breeding stock were selected ultimately based on their EBVs predicted by BLUP method when the phenotypic records were available. Similar to strategy 3, strategies 4 and 5 also used the GEBVs to pre-select replacement piglets in an early stage; however, the breeding stocks in strategies 4 and 5 were selected based on the breeding values obtained using the bi-variable model and the conventional index method to combine GEBV and EBV information, respectively, when individuals had both GEBV and phenotypes available after the performance testing. Comparing these strategies, strategy 4 resulted in the highest accuracy in first three generations and achieved the best cumulative selection response in the last generation, followed by strategies 1, 3, 5, and 2. The proportion of pre-selection of boars and sows in the early stage affected the efficiency of genomic selection substantially.