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1.
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   

2.
The amount of variance captured in genetic estimations may depend on whether a pedigree‐based or genomic relationship matrix is used. The purpose of this study was to investigate the genetic variance as well as the variance of predicted genetic merits (PGM) using pedigree‐based or genomic relationship matrices in Brown Swiss cattle. We examined a range of traits in six populations amounting to 173 population‐trait combinations. A main aim was to determine how using different relationship matrices affect variance estimation. We calculated ratios between different types of estimates and analysed the impact of trait heritability and population size. The genetic variances estimated by REML using a genomic relationship matrix were always smaller than the variances that were similarly estimated using a pedigree‐based relationship matrix. The variances from the genomic relationship matrix became closer to estimates from a pedigree relationship matrix as heritability and population size increased. In contrast, variances of predicted genetic merits obtained using a genomic relationship matrix were mostly larger than variances of genetic merit predicted using pedigree‐based relationship matrix. The ratio of the genomic to pedigree‐based PGM variances decreased as heritability and population size rose. The increased variance among predicted genetic merits is important for animal breeding because this is one of the factors influencing genetic progress.  相似文献   

3.
Information about genetic parameters is essential for selection decisions and genetic evaluation. These estimates are population specific; however, there are few studies with dairy cattle populations reared under tropical and sub‐tropical conditions. Thus, the aim was to obtain estimates of heritability and genetic correlations for milk yield and quality traits using pedigree and genomic information from a Holstein population maintained in a tropical environment. Phenotypic records (n = 36 457) of 4203 cows as well as the genotypes for 57 368 single nucleotide polymorphisms from 755 of these cows were used. Covariance components were estimated using the restricted maximum likelihood method under a mixed animal model, considering a pedigree‐based relationship matrix or a combined pedigree‐genomic matrix. High heritabilities (around 0.30) were estimated for lactose and protein content in milk whereas moderate values (between 0.19 and 0.26) were obtained for percentages of fat, saturated fatty acids and palmitic acid in milk. Genetic correlations ranging from −0.38 to −0.13 were determined between milk yield and composition traits. The smaller estimates compared to other similar studies can be due to poor environmental conditions, which may reduce genetic variability. These results highlight the importance in using genetic parameters estimated in the population under evaluation for selection decisions.  相似文献   

4.
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   

5.
旨在设计利用不同信息来源的模型估计荷斯坦后备牛不同月龄体重性状的遗传参数。本研究于2014—2020年测定并收集了7 122头荷斯坦牛32 338条0~12月龄体重数据，分别利用系谱信息（linear mixed model with pedigree relationship matrix，LM_A）和系谱-基因组信息构建亲缘关系矩阵（linear mixed model with genotype-pedigree joint relationship matrix，LM_H），基于母体效应动物模型估计初生重，基于是否考虑初生重作为协变量的单性状动物模型估计2~12月龄各月龄体重遗传力，并利用双性状动物模型估计初生重与其它月龄体重的遗传相关。结果显示，对于初生重，根据赤池信息量准则（Akaike information criterion，AIC），LM_H方法的拟合程度显著优于LM_A方法，但两种方法估计的遗传参数相差不大：直接遗传力分别为0.30和0.32，母体遗传力分别为0.08和0.09，个体直接遗传效应和母体遗传效应遗传相关系数分别为-0.65和-0.64；对于2~12月龄体重，LM_A和LM_H两种方法估计的校正初生重后的各月龄体重遗传力分别为0.15~0.55和0.28~0.49，未校正初生重的各月龄体重遗传力分别为0.16~0.54和0.28~0.51。初生重与2、5月龄体重之间为高遗传相关（相关系数>0.6）。5月龄后，各月龄体重与初生重的遗传相关系数随着时间间隔的增加而减小。相较于LM_A，LM_H方法更稳定，AIC值较小（即拟合优度较大），遗传参数标准误较小。综上，采用LM_H方法估计目标性状可获得更准确、更稳定的遗传参数。本研究为建立中国荷斯坦牛生长性状基因组选择体系提供了理论依据。  相似文献   

6.
Genomic imprinting should be considered in animal breeding systems to avoid lead in bias in genetic parameter estimation. The objective of this study was to clarify the effects of pedigree information on imprinting variances for carcass traits and fatty acid composition in Japanese Black cattle. Carcass records [carcass weight, rib eye area, rib thickness (RT), subcutaneous fat thickness and beef marbling score (BMS)] and fatty acid composition were obtained for 11,855 Japanese Black feedlot cattle. To estimate and compare the imprinting variances for the traits, two imprinting models with different pedigree information [the sire–dam gametic relationship matrix (Model 1) and the sire–maternal grandsire (MGS) numerator relationship matrix (Model 2)] were fitted. The ratio of the imprinting variance to the total additive genetic variance for RT (6.33%) and BMS (19.00%) was significant in Model 1, but only that for BMS (21.09%) was significant in Model 2. This study revealed that fitting the sire–MGS model could be useful in estimating imprinting variance under certain conditions, such as when restricted pedigree information is available. Furthermore, the present result suggested that the maternal gametic effects on BMS should be included in breeding programmes for Japanese Black cattle to avoid selection bias caused by imprinting effects.  相似文献   

7.
旨在提出一种新型基因组关系矩阵并验证其在多品种联合群体中的模拟应用效果。本研究利用QMsim软件模拟牛的表型数据和基因型数据;利用Gmatrix软件构建常规G阵;利用R语言构建新型G阵,新型G阵在常规G阵的基础上,将多品种联合群体的非哈代-温伯格平衡位点考虑在内;利用DMU软件使用“一步”法模型计算基因组估计育种值（estimated genomic breeding value,GEBV）;比较不同情况下使用两种G阵的GEBV预测准确性。结果表明,在不同遗传力及QTL数下,不对新型G阵使用A22阵加权就能达到常规G阵使用A22阵加权时的GEBV预测准确性。在系谱部分缺失时,新型G阵不加权较常规G阵加权时GEBV预测准确性高。证明,在系谱有部分缺失时,新型G阵对多品种GEBV的预测有一定优势。  相似文献   

8.
The aim of this study was to estimate genetic and phenotypic parameters for growth and survival traits of Sahiwal cattle in Kenya and determine their relationship to milk production and fertility. Performance records of 5,681 animals were obtained from the National Sahiwal Stud and the traits considered were: birth weight (kilogrammes), weaning weight (kilogrammes), pre-weaning average daily gain (grammes per day), post-weaning average daily gain (grammes per day), yearling weight (kilogrammes), mature weight at 36 months (kilogrammes), pre-weaning survival rate (SR), post-weaning survival rate (PSR), lactation milk yield (kilogrammes), age at first calving (days), and calving interval (days). The data was analysed using univariate and bivariate animal model based on restricted maximum likelihood methods, incorporating all known pedigree relationship among animals. The additive direct effects were more pronounced than maternal genetic effects in early and in post-yearling growth performance. The additive genetic variance and heritabilities were low for SR and PSR. The correlation between direct additive genetic and maternal genetic effect were negative for pre-yearling traits. Genetic and phenotypic correlations among growth traits and between growth and milk yield were positive, whilst those between growth and fertility were weak and negative. Correlations between survival and growth were generally low and positive. The estimates obtained in this study provide the necessary technical parameters for evaluating alternative breeding programmes and selection schemes for sustainable improvement of Sahiwal cattle.  相似文献   

9.
A simulation was carried out to investigate the implementation of a genetic evaluation when the additive relationship matrix is not completely known due to the presence of uncertain paternity in the pedigree. Data were simulated and analyzed using a linear mixed model that included a fixed contemporary group effect plus random additive and residual effects. For the univariate scenario, either 1 or 2 records of a single trait with heritabilities of 33, 50, and 67% were used to compute the probability of being the true sire (PTS) of each candidate sire for a given offspring. One record of 3 correlated traits was used to compute PTS in a 3-trait scenario. A Bayesian procedure via Markov Chain Monte Carlo was used to carry out the implementation, in which the PTS was computed without the need to invert the relationship matrix. The average probability of the true sire being identified as such (PSA), as well as the percentage difference (PD) between PSA and an equal prior probability assigned to each candidate sire, were computed for the single and 3-trait scenarios. Using 1 trait, PSA increased with an increase in heritability. When repeated records were considered, the PD was increased by 50 to 386% compared with using just 1 record per animal for the varying heritabilities and number of candidate sires, suggesting that phenotypic information was better able to discriminate among candidate sires when more than 1 record was used to determine PSA. Using 3 correlated traits increased PD by 77 to 98% when compared with using 1 record of a trait with 67% heritability. Similarly, the PD was increased by 105 to 1,021%, when compared with using 1 record of a trait with 33% heritability. These results indicate that the probability of identifying the true sire increased when 3 correlated traits were used to compute PSA. The correlations between true and predicted breeding values of 3 traits were increased by 6 to 7% for all animals and 64 to 89% for animals with unknown paternity in the pedigree when estimated probability of paternity was used as compared with equal prior probability assigned to each candidate sire. For traits such as birth weight and weaning weight, in which only 1 measurement is taken, the 3-trait scenario could result in more animals being assigned the true sire than if birth or weaning weight was used separately. Further research is needed to determine the performance of this methodology in field data as well as the potential implementation of this methodology in conjunction with molecular information.  相似文献   

10.
Genomic evaluations can be calculated using a unified procedure that combines phenotypic, pedigree and genomic information. Implementation of such a procedure requires the inverse of the relationship matrix based on pedigree and genomic relationships. The objective of this study was to investigate efficient computing options to create relationship matrices based on genomic markers and pedigree information as well as their inverses. SNP maker information was simulated for a panel of 40 K SNPs, with the number of genotyped animals up to 30 000. Matrix multiplication in the computation of the genomic relationship was by a simple 'do' loop, by two optimized versions of the loop, and by a specific matrix multiplication subroutine. Inversion was by a generalized inverse algorithm and by a LAPACK subroutine. With the most efficient choices and parallel processing, creation of matrices for 30 000 animals would take a few hours. Matrices required to implement a unified approach can be computed efficiently. Optimizations can be either by modifications of existing code or by the use of efficient automatic optimizations provided by open source or third-party libraries.  相似文献   

11.
Estimates of (co)variance and genetic parameters of birth, weaning (205 days) and yearling (365 days) weight were obtained using single-trait animal models. The data were analysed by restricted maximum likelihood, fitting an animal model that included direct and maternal genetic and permanent environmental effects. The data included records collected between 1976 and 2001. The pedigree information extended as far back as early 1960s. The heritabilities for direct effects of birth, weaning and yearling weights were 0.36, 0.29 and 0.25, respectively. Heritability estimates for maternal effects were 0.13, 0.16 and 0.15 for birth, weaning and yearling weights, respectively. The correlations between direct and maternal additive genetic effects were negative for all traits analysed. The results indicate that both direct and maternal effects should be included in a selection programme for all the traits analysed.  相似文献   

12.
Estimated breeding values (EBVs) using data from genetic markers can be predicted using a genomic relationship matrix, derived from animal's genotypes, and best linear unbiased prediction. However, if the accuracy of the EBVs is calculated in the usual manner (from the inverse element of the coefficient matrix), it is likely to be overestimated owing to sampling errors in elements of the genomic relationship matrix. We show here that the correct accuracy can be obtained by regressing the relationship matrix towards the pedigree relationship matrix so that it is an unbiased estimate of the relationships at the QTL controlling the trait. This method shows how the accuracy increases as the number of markers used increases because the regression coefficient (of genomic relationship towards pedigree relationship) increases. We also present a deterministic method for predicting the accuracy of such genomic EBVs before data on individual animals are collected. This method estimates the proportion of genetic variance explained by the markers, which is equal to the regression coefficient described above, and the accuracy with which marker effects are estimated. The latter depends on the variance in relationship between pairs of animals, which equals the mean linkage disequilibrium over all pairs of loci. The theory was validated using simulated data and data on fat concentration in the milk of Holstein cattle.  相似文献   

13.
Non-additive genetic effects are usually ignored in animal breeding programs due to data structure (e.g., incomplete pedigree), computational limitations and over-parameterization of the models. However, non-additive genetic effects may play an important role in the expression of complex traits in livestock species, such as fertility and reproduction traits. In this study, components of genetic variance for additive and non-additive genetic effects were estimated for a variety of fertility and reproduction traits in Holstein cattle using pedigree and genomic relationship matrices. Four linear models were used: (a) an additive genetic model; (b) a model including both additive and epistatic (additive by additive) genetic effects; (c) a model including both additive and dominance effects; and (d) a full model including additive, epistatic and dominance genetic effects. Nine fertility and reproduction traits were analysed, and models were run separately for heifers (N = 5,825) and cows (N = 6,090). For some traits, a larger proportion of phenotypic variance was explained by non-additive genetic effects compared with additive effects, indicating that epistasis, dominance or a combination thereof is of great importance. Epistatic genetic effects contributed more to the total phenotypic variance than dominance genetic effects. Although these models varied considerably in the partitioning of the components of genetic variance, the models including a non-additive genetic effect did not show a clear advantage over the additive model based on the Akaike information criterion. The partitioning of variance components resulted in a re-ranking of cows based solely on the cows’ additive genetic effects between models, indicating that adjusting for non-additive genetic effects could affect selection decisions made in dairy cattle breeding programs. These results suggest that non-additive genetic effects play an important role in some fertility and reproduction traits in Holstein cattle.  相似文献   

14.
Previous proposals for a unified approach for amalgamating information from animals with or without genotypes have combined the numerator relationship matrix A with the genomic relationship G estimated from the markers. These approaches have resulted in biased genomic EBV (GEBV), and methodology was developed to overcome these problems. Firstly, a relationship matrix, G(FG) , based on linkage analysis was derived using the same base population as A, which (i) utilizes the genomic information on the same scale as the pedigree information and (ii) permits the regression coefficients used to propagate the genomic data from the genotyped to ungenotyped individuals to be calculated in the light of the genomic information, rather than ignoring it. Secondly, the elements of G were regressed back towards their expected values in the A matrix to allow for their estimation errors. These developments were combined in a methodology LDLAb and tested on simulated populations where either parents were phenotyped and offspring genotyped or vice versa. The LDLAb method was demonstrated to be a unified approach that maximized accuracy of GEBV compared to previous methodologies and removed the bias in the GEBV. Although LDLAb is computationally much more demanding than MLAC, it demonstrates how to make best use the marker information and also shows the computational problems that need to be solved in the future to make best use of the marker data.  相似文献   

15.
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   

16.
In single‐step genomic evaluation using best linear unbiased prediction (ssGBLUP), genomic predictions are calculated with a relationship matrix that combines pedigree and genomic information. For missing pedigrees, unknown selection processes, or inclusion of several populations, a BLUP model can include unknown‐parent groups (UPG) in the animal effect. For ssGBLUP, UPG equations also involve contributions from genomic relationships. When those contributions are ignored, UPG solutions and genetic predictions can be biased. Options to eliminate or reduce such bias are presented. First, mixed model equations can be modified to include contributions to UPG elements from genomic relationships (greater software complexity). Second, UPG can be implemented as separate effects (higher cost of computing and data processing). Third, contributions can be ignored when they are relatively small, but they may be small only after refinements to UPG definitions. Fourth, contributions may approximately cancel out when genomic and pedigree relationships are constructed for compatibility; however, different construction steps are required for unknown parents from the same or different populations. Finally, an additional polygenic effect that also includes UPG can be added to the model.  相似文献   

17.
The widespread use of the set of multiple-trait derivative-free REML programs for prediction of breeding values and estimation of variance components has led to significant improvement in traits of economic importance. The initial version of this software package, however, was generally limited to pedigree-based relationships. With continued advances in genomic research and the increased availability of genotyping, relationships based on molecular markers are obtainable and desirable. The addition of a new program to the set of multiple-trait derivative-free REML programs is described that allows users the flexibility to calculate relationships using standard pedigree files or an arbitrary relationship matrix based on genetic marker information. The strategy behind this modification and its design is described. An application is illustrated in a QTL association study for canine hip dysplasia.  相似文献   

18.
The objectives of this study were to estimate the additive and dominance variance component of several weight and ultrasound scanned body composition traits in purebred and combined cross‐bred sheep populations based on single nucleotide polymorphism (SNP) marker genotypes and then to investigate the effect of fitting additive and dominance effects on accuracy of genomic evaluation. Additive and dominance variance components were estimated in a mixed model equation based on “average information restricted maximum likelihood” using additive and dominance (co)variances between animals calculated from 48,599 SNP marker genotypes. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of prediction was assessed based on a random 10‐fold cross‐validation. Across different weight and scanned body composition traits, dominance variance ranged from 0.0% to 7.3% of the phenotypic variance in the purebred population and from 7.1% to 19.2% in the combined cross‐bred population. In the combined cross‐bred population, the range of dominance variance decreased to 3.1% and 9.9% after accounting for heterosis effects. Accounting for dominance effects significantly improved the likelihood of the fitting model in the combined cross‐bred population. This study showed a substantial dominance genetic variance for weight and ultrasound scanned body composition traits particularly in cross‐bred population; however, improvement in the accuracy of genomic breeding values was small and statistically not significant. Dominance variance estimates in combined cross‐bred population could be overestimated if heterosis is not fitted in the model.  相似文献   

19.
Genetic parameters for different claw disorders, overall claw health and feet and leg conformation traits were estimated for Finnish Ayrshire cows. The merged data set with records of claw health and feet and leg conformation traits consisted of 105 000 observations from 52 598 Finnish Ayrshire cows between 2000 and 2010. The binary claw health data and the linearly scored conformation data were analysed using an animal model and restricted maximum likelihood method by applying the statistical package ASReml. Binomial logistic models with mixed effects were used to estimate genetic parameters for sole haemorrhages, chronic laminitis, white‐line separation, sole ulcer, interdigital dermatitis, heel horn erosion, digital dermatitis, corkscrew claw and overall claw health. Estimated heritabilities for different claw disorders using a binomial logistic model ranged from 0.01 to 0.20. Estimated heritability for overall claw health using a binomial logistic model was 0.08. Estimated heritabilities for feet and leg conformation traits ranged from 0.07 to 0.39. The genetic correlations between claw health and feet and leg conformation traits ranged from ?0.40 to 0.42. All phenotypic correlations were close to zero. The moderate genetic correlation, together with higher heritability of feet and leg conformation traits, showed that RLSV (rear leg side view) is a useful indicator trait to be used together with claw trimming information to increase the accuracy of breeding values for claw health in genetic evaluation.  相似文献   

20.
This study explored distributions of diagonal elements of genomic relationship matrix (G), evaluated the utility of G as a diagnostic tool to detect mislabelled animals in a genomic dataset and evaluated the effect of mislabelled animals on the accuracy of genomic evaluation. Populations of 10 000 animals were simulated with 60 000 SNP varying in allele frequency at each locus between 0.02 and 0.98. Diagonal elements of G were distributed with a single peak (mean = 1.00 ± 0.03) and ranged from 0.84 through 1.36. Mixed populations were also simulated: 7 000 animals with frequencies of second alleles ranging from 0.02 through 0.98 were combined with 1750 or 7000 animals with frequencies of second alleles ranging from 0.0 through 1.0. The resulting distributions of diagonal elements of G were bimodal. Body weight at 6 weeks was provided by Cobb-Vantress for broiler chickens, of which 3285 were genotyped for 57 636 SNP. Analysis used a combined genomic and pedigree relationship matrix; G was scaled using current allele frequencies. The distribution of diagonal elements was multimodal and ranged from 0.54 to 3.23. Animals with diagonal elements >1.5 were identified as coming from another chicken line or as having low call rates. Removal of mislabelled animals increased accuracy by 0.01. For the studied type of population, diagonal elements of G may be a useful tool to help identify mislabelled animals or secondary populations.  相似文献   

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