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1.
The current study evaluates reliability of genomic predictions in selection candidates using multi‐trait random regression model, which accounts for interactions between marker effects and breed of origin in the Nordic Red dairy cattle (RDC). The population structure of the RDC is admixed. Data consisted of individual animal breed proportions calculated from the full pedigree, deregressed proofs (DRP) of published estimated breeding values (EBV) for yield traits and genotypic data for 37 595 single nucleotide polymorphic markers. The analysed data included 3330 bulls in the reference population and 812 bulls that were used for validation. Direct genomic breeding values (DGV) were estimated using the model under study, which accounts for breed effects and also with GBLUP, which assume uniform population. Validation reliability was calculated as a coefficient of determination from weighted regression of DRP on DGV (), scaled by the mean reliability of DRP. Using the breed‐specific model increased the reliability of DGV by 2 and 3% for milk and protein, respectively, when compared to homogeneous population GBLUP. The exception was for fat, where there was no gain in reliability. Estimated validation reliabilities were low for milk (0.32) and protein (0.32) and slightly higher (0.42) for fat.  相似文献   

2.
Using a combined multi‐breed reference population, this study explored the influence of model specification and the effect of including a polygenic effect on the reliability of genomic breeding values (DGV and GEBV). The combined reference population consisted of 2986 Swedish Red Breed (SRB) and Finnish Ayrshire (FAY) dairy cattle. Bayesian methodology (common prior and mixture models with different prior distribution settings for the marker effects) as well as a best linear unbiased prediction with a genomic relationship matrix [genomic best linear unbiased predictor (GBLUP)] was used in the prediction of DGV. Mixture models including a polygenic effect were used to predict GEBV. In total, five traits with low, high and medium heritability were analysed. For the models using a mixture prior distribution, reliabilities of DGV tended to decrease with an increasing proportion of markers with small effects. The influence of the inclusion of a polygenic effect on the reliability of DGV varied across traits and model specifications. Average correlation between DGV with the Mendelian sampling term, across traits, was highest (R2 = 0.25) for the GBLUP model and decreased with increasing proportion of markers with large effects. Reliabilities increased when DGV and parent average information were combined in an index. The GBLUP model with the largest gain across traits in the reliability of the index achieved the highest DGV mean reliability. However, the polygenic models showed to be less biased and more consistent in the estimation of DGV regardless of the model specifications compared with the mixture models without the polygenic effect.  相似文献   

3.
Breeding animals can be accurately evaluated using appropriate genomic prediction models, based on marker data and phenotype information. In this study, direct genomic values (DGV) were estimated for 16 traits of Nordic Total Merit (NTM) Index in Nordic Red cattle population using three models and two different response variables. The three models were as follows: a linear mixed model (GBLUP), a Bayesian variable selection model similar to BayesA (BayesA*) and a Bayesian least absolute shrinkage and selection operator model (Bayesian Lasso). The response variables were deregressed proofs (DRP) and conventional estimated breeding values (EBV). The reliability of genomic predictions was measured on bulls in the validation data set as the squared correlation between DGV and DRP divided by the reliability of DRP. Using DRP as response variable, the reliabilities of DGV among the 16 traits ranged from 0.151 to 0.569 (average 0.317) for GBLUP, from 0.152 to 0.576 (average 0.318) for BayesA* and from 0.150 to 0.570 (average 0.320) for Bayesian Lasso. Using EBV as response variable, the reliabilities ranged from 0.159 to 0.580 (average 0.322) for GBLUP, from 0.157 to 0.578 (average 0.319) for BayesA* and from 0.159 to 0.582 (average 0.325) for Bayesian Lasso. In summary, Bayesian Lasso performed slightly better than the other two models, and EBV performed slightly better than DRP as response variable, with regard to prediction reliability of DGV. However, these differences were not statistically significant. Moreover, using EBV as response variable would result in problems with the scale of the resulting DGV and potential problem due to double counting.  相似文献   

4.
This study investigated the effect of including Nordic Holsteins in the reference population on the imputation accuracy and prediction accuracy for Chinese Holsteins. The data used in this study include 85 Chinese Holstein bulls genotyped with both 54K chip and 777K (HD) chip, 2862 Chinese cows genotyped with 54K chip, 510 Nordic Holstein bulls genotyped with HD chip, and 4398 Nordic Holstein bulls genotyped with 54K chip and with deregressed proofs for five milk production traits. Based on these data, the accuracy of imputation from 54K to HD marker data and the accuracy of genomic predictions in Chinese Holstein were assessed. The allele correct rate increased around 2.7 and 1.7% in imputation from the 54K to the HD marker data for Chinese Holstein bulls and cows, respectively, when the Nordic HD‐genotyped bulls were included in the reference data for imputation. However, the prediction accuracy was improved slightly when using the marker data imputed based on the combined HD reference data, compared with using the marker data imputed based on the Chinese HD reference data only. On the other hand, when using the combined reference population including 4398 Nordic Holstein bulls, the accuracy of genomic predictions increased 6.5 percentage points together with a reduction of prediction bias. The HD markers did not outperform the 54K markers in genomic prediction based on the present data. The results indicate that for Chinese Holsteins, it is necessary to genotype more individuals with 54K chip to increase reference population rather than increasing marker density.  相似文献   

5.
The degree of linkage disequilibrium (LD) between markers differs depending on the location of the genome; this difference biases genetic evaluation by genomic best linear unbiased prediction (GBLUP). To correct this bias, we used three GBLUP methods reflecting the degree of LD (GBLUP‐LD). In the three GBLUP‐LD methods, genomic relationship matrices were conducted from single nucleotide polymorphism markers weighted according to local LD levels. The predictive abilities of GBLUP‐LD were investigated by estimating variance components and assessing the accuracies of estimated breeding values using simulation data. When quantitative trait loci (QTL) were located at weak LD regions, the predictive abilities of the three GBLUP‐LD methods were superior to those of GBLUP and Bayesian lasso except when the number of QTL was small. In particular, the superiority of GBLUP‐LD increased with decreasing trait heritability. The rates of QTL at weak LD regions would increase when selection by GBLUP continues; this consequently decreases the predictive ability of GBLUP. Thus, the GBLUP‐LD could be applicable for populations selected by GBLUP for a long time. However, if QTL were located at strong LD regions, the accuracies of three GBLUP‐LD methods were lower than GBLUP and Bayesian lasso.  相似文献   

6.
Genomic selection is a method to predict breeding values using genome‐wide single‐nucleotide polymorphism (SNP) markers. High‐quality marker data are necessary for genomic selection. The aim of this study was to investigate the effect of marker‐editing criteria on the accuracy of genomic predictions in the Nordic Holstein and Jersey populations. Data included 4429 Holstein and 1071 Jersey bulls. In total, 48 222 SNP for Holstein and 44 305 SNP for Jersey were polymorphic. The SNP data were edited based on (i) minor allele frequencies (MAF) with thresholds of no limit, 0.001, 0.01, 0.02, 0.05 and 0.10, (ii) deviations from Hardy–Weinberg proportions (HWP) with thresholds of no limit, chi‐squared p‐values of 0.001, 0.02, 0.05 and 0.10, and (iii) GenCall (GC) scores with thresholds of 0.15, 0.55, 0.60, 0.65 and 0.70. The marker data sets edited with different criteria were used for genomic prediction of protein yield, fertility and mastitis using a Bayesian variable selection and a GBLUP model. De‐regressed EBV were used as response variables. The result showed little difference between prediction accuracies based on marker data sets edited with MAF and deviation from HWP. However, accuracy decreased with more stringent thresholds of GC score. According to the results of this study, it would be appropriate to edit data with restriction of MAF being between 0.01 and 0.02, a p‐value of deviation from HWP being 0.05, and keeping all individual SNP genotypes having a GC score over 0.15.  相似文献   

7.
We studied the effect of including GWAS results on the accuracy of single‐ and multipopulation genomic predictions. Phenotypes (backfat thickness) and genotypes of animals from two sire lines (SL1, n = 1146 and SL3, n = 1264) were used in the analyses. First, GWAS were conducted for each line and for a combined data set (both lines together) to estimate the genetic variance explained by each SNP. These estimates were used to build matrices of weights (D), which was incorporated into a GBLUP method. Single population evaluated with traditional GBLUP had accuracies of 0.30 for SL1 and 0.31 for SL3. When weights were employed in GBLUP, the accuracies for both lines increased (0.32 for SL1 and 0.34 for SL3). When a multipopulation reference set was used in GBLUP, the accuracies were higher (0.36 for SL1 and 0.32 for SL3) than in single‐population prediction. In addition, putting together the multipopulation reference set and the weights from the combined GWAS provided even higher accuracies (0.37 for SL1, and 0.34 for SL3). The use of multipopulation predictions and weights estimated from a combined GWAS increased the accuracy of genomic predictions.  相似文献   

8.
Joint Nordic (Denmark, Finland, Sweden) genetic evaluation of female fertility is currently based on the multiple trait multilactation animal model (BLUP). Here, single step genomic model (ssGBLUP) was applied for the Nordic Red dairy cattle fertility evaluation. The 11 traits comprised of nonreturn rate and days from first to last insemination in heifers and first three parities, and days from calving to first insemination in the first three parities. Traits had low heritabilities (0.015–0.04), but moderately high genetic correlations between the parities (0.60–0.88). Phenotypic data included 4,226,715 animals with records and pedigree 5,445,392 animals. Unknown parents were assigned into 332 phantom parent groups (PPG). In mixed model equations animals were associated with PPG effects through the pedigree or both the pedigree and genomic information. Genotype information of 46,914 SNPs was available for 33,969 animals in the pedigree. When PPG used pedigree information only, BLUP converged after 2,420 iterations whereas the ssGBLUP evaluation needed over ten thousand iterations. When the PPG effects were solved accounting both the pedigree and the genomic information, the ssGBLUP model converged after 2,406 iterations. Also, with the latter model breeding values by ssGBLUP and BLUP became more consistent and genetic trends followed each other well. Models were validated using forward prediction of the young bulls. Reliabilities and variance inflation of predicted genomic breeding values (values for parent averages in brackets) for the 11 traits ranged 0.22–0.31 (0.10–0.27) and 0.81–0.95 (0.83–1.06), respectively. The ssGBLUP model gave always higher validation reliabilities than BLUP, but largest increases were for the cow fertility traits.  相似文献   

9.
The reliability of genomic evaluations depends on the proportion of genetic variation explained by the DNA markers. In this study, we have estimated the proportion of variance in daughter trait deviations (DTDs) of dairy bulls explained by 45 993 genome wide single‐nucleotide poly‐ morphism (SNP) markers for 29 traits in Australian Holstein‐Friesian dairy cattle. We compare these proportions to the proportion of variance in DTDs explained by the additive relationship matrix derived from the pedigree, as well as the sum of variance explained by both pedigree and marker information when these were fitted simultaneously. The propor‐ tion of genetic variance in DTDs relative to the total genetic variance (the total genetic variance explained by the genomic relationships and pedigree relationships when both were fitted simultaneously) varied from 32% for fertility to approximately 80% for milk yield traits. When fitting genomic and pedigree relationships simultaneously, the variance unexplained (i.e. the residual variance) in DTDs of the total variance for most traits was reduced compared to fitting either individually, suggesting that there is not complete overlap between the effects. The proportion of genetic variance accounted by the genomic relationships can be used to modify the blending equations used to calculate genomic estimated breeding value (GEBV) from direct genomic breeding value (DGV) and parent average. Our results, from a validation population of young dairy bulls with DTD, suggest that this modification can improve the reliability of GEBV by up to 5%.  相似文献   

10.
Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single‐nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic‐based [genomic best linear unbiased prediction (GBLUP)‐REML and BayesC] and pedigree‐based (PBLUP‐REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP‐REML across traits, from 0 to 0.03 with GBLUP‐REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic‐based methods were small (0.01–0.05), with GBLUP‐REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP‐REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.  相似文献   

11.
旨在比较不同方法对中国荷斯坦牛繁殖性状的基因组预测效果,选择最佳的基因组预测方法及信息矩阵权重组合（τ和ω）用于实际育种。本研究利用北京地区33个牧场1998—2020年荷斯坦牛群繁殖记录,分析了3个重要繁殖性状：产犊至首次配种间隔（ICF）、青年牛配种次数（NSH）和成母牛配种次数（NSC）共98 483~197 764条表型数据。同时收集了8 718头母牛和3 477头公牛的基因芯片数据,根据具有芯片数据的牛群结构划分为公牛验证群和母牛验证群。随后,通过BLUPF90软件的AIREMLF90和BLUPF90模块利用最佳线性无偏预测（BLUP）、基因组最佳线性无偏预测（GBLUP）和一步法（ssGBLUP）对3个性状进行基因组预测,不同方法的预测效果根据准确性和无偏性来评估。结果表明,3个繁殖性状均为低遗传力性状（0.03~0.08）;ssGBLUP方法中,各性状信息矩阵的权重取值能够在一定程度上提升基因组预测的效果;ICF、NSH和NSC在母牛验证群下的最佳权重取值分别为：τ=1.3和ω=0,τ=0.5和ω=0.4以及τ=0.5和ω=0;在公牛验证群下最优权重组合分别为：τ=1.5和ω=0,τ=1.3和ω=0.8以及τ=0.5和ω=0;基于最佳权重的ssGBLUP方法准确性较BLUP和GBLUP方法准确性分别提升了0.10~0.39和0.08~0.15,且无偏性最接近于1。综上,使用最佳权重组合的ssGBLUP时,各性状基因组预测结果具有较高准确性和无偏性,建议作为中国荷斯坦牛繁殖性状基因组选择方法。  相似文献   

12.
Contents: Repeatability and heritability coefficients were estimated for ejaculate volume, mass movement, individual motility, sperm concentration, first and second post-freezing control of motility, number of doses frozen, total number of spermatozoa and total number of motile spermatozoa per ejaculate in the first ten ejaculates of 175 Swiss Holstein and 673 Simmental young bulls (200 pure Simmental, 473 Simmental x Red Holstein crosses). Data were analysed using the univariate individual animal model with effects of interval between consecutive collections, age of bull at collection, season of collection, breed group of Simmental bulls according to percentage of Red Holstein genes, bull (within breed group) and permanent environment. Crossbred bulls had a better performance in all traits. Repeatability coefficients ranged from 0.28 to 0.46. They indicate that the prediction of semen quality in young bulls of the Swiss Holstein and the Simmental breed is possible to some extent. Heritabilities in Swiss Holstein bulls were generally very low and did usually not exceed 0.10, but standard errors of these estimates were high. Relatively high heritabilities (0.18–0.30) were found for Simmental bulls, Differences in heritabilities between these two breed groups were probably a consequence of the low number of Swiss Holstein bulls.  相似文献   

13.
Background: Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci(QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.Methods: Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect.A commonly used method for genome wide association studies is to fit a mixture model such as Bayes B that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a42 K SNP panel.Results: Using data from the first 7 generations, both Bayes B and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with Bayes B and less than 1 % with GBLUP. When predicting phenotypes in generation 8 Bayes B accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with Bayes B in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with Bayes B but only6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using Bayes B but had no predictive power with GBLUP.Conclusions: In conclusion, In the presence of large effect QTL, Bayes B did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.  相似文献   

14.
One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less‐related animals is favourable for reliability of genomic prediction.  相似文献   

15.
Genetic improvement of animals based on artificial selection is leading to changes in the frequency of genes related to desirable production traits. The changes are reflected by the neutral, intergenic single nucleotide polymorphims (SNPs) being in long‐range linkage disequilibrium with functional polymorphisms. Genome‐wide SNP analysis tools designed for cattle, allow for scanning divergences in allelic frequencies between distinct breeds and thus for identification of genomic regions which were divergently selected in breeds' histories. In this study, by using Bovine SNP50 assay, we attempted to identify genomic regions showing the highest differences in allele frequencies between two distinct cattle breeds – preserved, unselected Polish Red breed and highly selected Holstein cattle. Our study revealed 19 genomic regions encompassing 55 protein‐coding genes and numerous quantitative trait loci which potentially may underlie some of the phenotypic traits distinguishing the breeds.  相似文献   

16.
The aim of this study was to estimate the non‐additive genetic effects of the dominance component of heterosis as well as epistatic loss on semen traits in admixed Swiss Fleckvieh, a composite of Simmental (SI) and Red Holstein Friesian (RHF) cattle. Heterosis is the additional gain in productivity or fitness of cross‐bred progeny over the mid‐purebred parental populations. Intralocus gene interaction usually has a positive effect, while epistatic loss generally reduces productivity or fitness due to lack of evolutionarily established interactions of genes from different breeds. Genotypic data on 38,205 SNP of 818 admixed, as well as 148 RHF and 213 SI bulls as the parental breeds were used to predict breed origin of alleles. The genomewide locus‐specific breed ancestries of individuals were used to calculate effects of breed difference as well as the dominance component of heterosis, while proxies for two definitions of epistatic loss were derived from 100,000 random pairs of loci. The average Holstein Friesian ancestry in admixed bulls was estimated 0.82. Results of fitting different linear mixed models showed including the dominance component of heterosis considerably improved the model adequacy for three of the four traits. Inclusion of epistatic loss increased the accuracy of the models only for our new definition of the epistatic effect for two traits, while the other definition was so highly correlated with the dominance component that statistical separation was impossible.  相似文献   

17.

The aim of this investigation was to establish genetic correlations between foot and leg traits in future potential AI bulls and body conformation traits in their daughters. Data consisted of records of 2394, 5002 and 1098 bulls of the breeds Danish Red, Danish Friesian and Jersey, respectively, which were correlated according to breed with 11846, 62875 and 10954 daughters sand their contemporaries, respectively. The bull traits included hoof measurements, horn characteristics and claw disease frequencies recorded at hoof trimming, while the data for the dairy cows contained the scores for rear leg side view, rear leg rear view, bone structure, quality of hocks and foot angle. The correlations between the claw diseases or horn characteristics on one side and the daughters' conformation on the other side were either negligible or inconclusive, while those between the bull's hoof measurements and the two conformation traits bone structure and hock quality were of statistical significance for all three breeds. Moreover, the stance of the rear leg showed a significant relationship to claw measurements in Danish Red and Jersey cattle. Small feet in bulls were correlated with a favourable expression of feet and legs in daughters. An improvement in foot and leg conformation traits in dairy cattle is possible by including claw measurements in the selection of future AI bulls.  相似文献   

18.
Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k single nucleotide polymorphism (SNP) panels. After imputation and quality control, 61,666 SNPs were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent single nucleotide polymorphisms (SNPs) across all chromosomes were 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help us to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.  相似文献   

19.
The number of genotyped animals has increased rapidly creating computational challenges for genomic evaluation. In animal model BLUP, candidate animals without progeny and phenotype do not contribute information to the evaluation and can be discarded. In theory, genotyped candidate animal without progeny can bring information into single‐step BLUP (ssGBLUP) and affect the estimation of other breeding values. We studied the effect of including or excluding genomic information of culled bull calves on genomic breeding values (GEBV) from ssGBLUP. In particular, GEBVs of genotyped bulls with daughters and GEBVs of young bulls selected into AI to be progeny tested (test bulls) were studied. The ssGBLUP evaluation was computed using Nordic test day (TD) model and TD data for the Nordic Red Dairy Cattle. The results indicate that genomic information of culled bull calves does not affect the GEBVs of progeny tested reference animals, but if genotypes of the culled bulls are used in the TD ssGBLUP, the genetic trend in the test bulls is considerably higher compared to the situation when genomic information of the culled bull calves is excluded. It seems that by discarding genomic information of culled bull calves without progeny, upward bias of GEBVs of test bulls is reduced.  相似文献   

20.
We present here the first genome‐wide characterization of linkage disequilibrium (LD) in the French Blonde d'Aquitaine (BLA) breed, a well‐muscled breed renowned for producing high‐yielding beef carcasses. To assess the pattern and extent of LD, we used a sample of 30 unrelated bulls and 36 923 single nucleotide polymorphisms (SNPs) covering all cattle autosomes. The squared correlation of the alleles at two loci (r2) was used as a measure of LD. The analysis of adjacent marker pairs revealed that the level of LD decreases rapidly with physical distance between SNPs. Overall mean r2 was 0.205 (±0.262). Strong LD (r2 > 0.8) and useful LD (measured as r2 > 0.2) were observed within genomic regions of up to 720 and 724 kb, respectively. We analysed the genetic structure of the BLA population and found stratification. The observed genetic sub‐structuring is consistent with the known recent demographic history that occurred during BLA breed formation. Our results indicate that LD mapping of phenotypic traits in the BLA population is feasible; however, because of this sub‐structuring, special care is needed to reduce the likelihood of false‐positive associations between marker loci and traits of interest.  相似文献   

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