Genetics Selection Evolution - Latest Articles

The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes

Samuel Clark — 2012-02-09T00:00:00Z

Background: The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values. Methods: Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated. Results: The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy. Conclusions: An animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.

Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures

John Bastiaansen — 2012-01-24T00:00:00Z

Background: Genomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects. Methods: Three methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations. Results: Differences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure. Conclusions: The ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was achieved. The reference population structure had a limited effect on long-term accuracy and response. Use of a shallow reference population, most closely related to the selection candidates, gave early benefits while in later generations, when marker effects were not updated, the estimation of marker effects based on a deeper reference population did not pay off.

The genetic parameters of feed efficiency and its component traits in the turkey (Meleagris gallopavo)

Lindsay Case — 2012-01-23T00:00:00Z

Residual feed intake (RFI) and feed conversion ratio (FCR) can be incorporated into a breeding program as traits to select for feed efficiency. Alternatively, the direct measures used to calculate RFI and FCR can be analyzed to determine the underlying variation in the traits that impact overall efficiency. These constituent traits can then be appropriately weighted in an index to achieve genetic gain. To investigate feed efficiency in the turkey, feed intake and weight gain were measured on male primary breeder line turkeys housed in individual feeding cages from 15 to 19 weeks of age. The FCR and RFI showed moderate heritability values of 0.16 and 0.21, respectively. Feed intake, body weight, and weight gain were also moderately heritable (0.25, 0.35, and 0.18, respectively). Weight gain was negatively correlated to feed conversion ratio and was not genetically correlated to RFI. Body weight had a small and positive genetic correlation to RFI (0.09) and FCR (0.12). Feed intake was positively genetically correlated to RFI (0.62); however, there was no genetic correlation between feed intake and FCR. These estimates of heritability and the genetic correlations can be used in the development of an index to improve feed efficiency and reduce the cost of production.

Optimising multistage dairy cattle breeding schemes including genomic selection using decorrelated or optimum selection indices

Vinzent Borner — 2012-01-17T00:00:00Z

Background: The prediction of the outcomes from multistage breeding schemes is especiallyimportant for the introduction of genomic selection in dairy cattle. Decorrelated selection indices can beused for the optimisation of such breeding schemes. However, they decrease the accuracy of estimatedbreeding values and, therefore, the genetic gain to an unforeseeable extent and have not been applied tobreeding schemes with different generation intervals and selection intensities in each selection path. Methods: A grid search was applied in order to identify optimum breeding plans to maximise thegenetic gain per year in a multistage, multipath dairy cattle breeding program. In this program,different values of the accuracy of estimated genomic breeding values and of their costs per individualwere applied, whereby the total breeding costs were restricted. Both decorrelated indices and optimumselection indices were used together with fast multidimensional integration algorithms to produce results. Results: In comparison to optimum indices, the genetic gain with decorrelated indices was up to 40%less and the proportion of individuals undergoing genomic selection was different. Additionally, theinteraction between selection paths was counter-intuitive and diffcult to interpret. Independent of usingdecorrelated or optimum selection indices, genomic selection replaced traditional progeny testing whenmaximising the genetic gain per year, as long as the accuracy of estimated genomic breeding values was>0.45%. Overall breeding costs were mainly generated in the path dam-sire. Selecting males was stillthe main source of genetic gain per year. Conclusion: Decorrelated selection indices should not be used because of misleading results and theavailability of accurate and fast algorithms for exact multidimensional integration. Genomic selection isthe method of choice when maximising the genetic gain per year but genotyping females may not allowfor a reduction in overall breeding costs. Furthermore, the economic justifcation of genotyping femalesremains questionable.

A common reference population from four European Holstein populations increases reliability of genomic predictions

Mogens Lund — 2011-12-12T00:00:00Z

Background: Size of the reference population and reliability of phenotypes are crucial factors influencing the reliability of genomic predictions. It is therefore useful to combine closely related populations. Increased accuracies of genomic predictions depend on the number of individuals added to the reference population, the reliability of their phenotypes, and the relatedness of the populations that are combined. Methods: This paper assesses the increase in reliability achieved when combining four Holstein reference populations of 4000 bulls each, from European breeding organizations, i.e. UNCEIA (France), VikingGenetics (Denmark, Sweden, Finland), DHV-VIT (Germany) and CRV (The Netherlands, Flanders). Each partner validated its own bulls using their national reference data and the combined data, respectively. Results: Combining the data significantly increased the reliability of genomic predictions for bulls in all four populations. Reliabilities increased by 10%, compared to reliabilities obtained with national reference populations alone, when they were averaged over countries and the traits evaluated. For different traits and countries, the increase in reliability ranged from 2% to 19%. Conclusions: Genomic selection programs benefit greatly from combining data from several closely related populations into a single large reference population.

Whole-genome SNP association analysis of reproduction traits in the Finnish Landrace pig breed

Pekka Uimari — 2011-12-01T00:00:00Z

Background: Good genetic progress for pig reproduction traits has been achieved using a quantitative genetics-based multi-trait BLUP evaluation system. At present, whole-genome single nucleotide polymorphisms (SNP) panels provide a new tool for pig selection. The purpose of this study was to identify SNP associated with reproduction traits in the Finnish Landrace pig breed using the Illumina PorcineSNP60 BeadChip. Methods: Association of each SNP with different traits was tested with a weighted linear model, using SNP genotype as a covariate and animal as a random variable. Deregressed estimated breeding values of the progeny tested boars were used as the dependent variable and weights were based on their reliabilities. Statistical significance of the associations was based on Bonferroni-corrected P-values. Results: Deregressed estimated breeding values were available for 328 genotyped boars. Of the 62 163 SNP in the chip, 57 868 SNP had a call rate > 0.9 and 7 632 SNP were monomorphic. Statistically significant results (P-value < 2.0E-06) were obtained for total number of piglets born in first and later parities and piglet mortality between birth and weaning in later parity, and suggestive associations (P-value < 4.0E-06) for piglet mortality between birth and weaning in first parity, number of stillborn piglets in later parity, first farrowing interval and second farrowing interval. Two of the statistically significant regions for total number of piglets born in first and later parities are located on chromosome 9 around 95 and 79 Mb. The estimated SNP effect in these regions was approximately one piglet between the two homozygote classes. By combining the two most significant SNP in these regions, favourable double homozygote animals are expected to have 1.3 piglets (P-value = 1.69E-08) more than unfavourable double homozygote animals. A region on chromosome 9 (66 Mb) was statistically significant for piglet mortality between birth and weaning in later parity (0.44 piglets between homozygotes, P-value = 6.94E-08). Conclusions: Three separate regions on chromosome 9 gave significant results for litter size and pig mortality. The frequencies of favourable alleles of the significant SNP are moderate in the Finnish Landrace population and these SNP are thus valuable candidates for possible marker-assisted selection.

Simulation study for analysis of binary responses in the presence of extreme case problems

Romdhane Rekaya — 2011-11-30T00:00:00Z

Background: Estimates of variance components for binary responses in presence of extreme case problems tend to be biased due to an under-identified likelihood. The bias persists even when a normal prior is used for the fixed effects. Methods: A simulation study was carried out to investigate methods for the analysis of binary responses with extreme case problems. A linear mixed model that included a fixed effect and random effects of sire and residual on the liability scale was used to generate binary data. Five simulation scenarios were conducted based on varying percentages of extreme case problems, with true values of heritability equal to 0.07 and 0.17. Five replicates of each dataset were generated and analyzed with a generalized prior (g-prior) of varying weight. Results: Point estimates of sire variance using a normal prior were severely biased when the percentage of extreme case problems was greater than 30%. Depending on the percentage of extreme case problems, the sire variance was overestimated when a normal prior was used by 36 to 102% and 25 to 105% for a heritability of 0.17 and 0.07, respectively. When a g-prior was used, the bias was reduced and even eliminated, depending on the percentage of extreme case problems and the weight assigned to the g-prior. The lowest Pearson correlations between true and estimated fixed effects were obtained when a normal prior was used. When a 15% g-prior was used instead of a normal prior with a heritability equal to 0.17, Pearson correlations between true and fixed effects increased by 11, 20, 23, 27, and 60% for 5, 10, 20, 30 and 75% of extreme case problems, respectively. Conversely, Pearson correlations between true and estimated fixed effects were similar, within datasets of varying percentages of extreme case problems, when a 5, 10, or 15% g-prior was included. Therefore this indicates that a model with a g-prior provides a more adequate estimation of fixed effects. Conclusions: The results suggest that when analyzing binary data with extreme case problems, bias in the estimation of variance components could be eliminated, or at least significantly reduced by using a g-prior.

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

Mahdi Saatchi — 2011-11-28T00:00:00Z

Background: Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Methods: Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Results: Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. Conclusions: These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.

Performance of penalized maximum likelihood in estimation of genetic covariances matrices

Karin Meyer — 2011-11-27T00:00:00Z

Background: Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits. This paper investigates the efficacy of regularized estimation of covariance components in a maximum likelihood framework, imposing a penalty on the likelihood designed to reduce sampling variation. In particular, penalties that ``borrow strength'' from the phenotypic covariance matrix are considered. Methods: An extensive simulation study was carried out to investigate the reduction in average `loss', i.e. the deviation in estimated matrices from the population values, and the accompanying bias for a range of parameter values and sample sizes. A number of penalties are examined, penalizing either the canonical eigenvalues or the genetic covariance or correlation matrices. In addition, several strategies to determine the amount of penalization to be applied, i.e. to estimate the appropriate tuning factor, are explored. Results: It is shown that substantial reductions in loss for estimates of genetic covariance can be achieved for small to moderate sample sizes. While no penalty performed best overall, penalizing the variance among the estimated canonical eigenvalues on the logarithmic scale or shrinking the genetic towards the phenotypic correlation matrix appeared most advantageous. Estimating the tuning factor using cross-validation resulted in a loss reduction 10 to 15% less than that obtained if population values were known. Applying a mild penalty, chosen so that the deviation in likelihood from the maximum was non-significant, performed as well if not better than cross-validation and can be recommended as a pragmatic strategy. Conclusions: Penalized maximum likelihood estimation provides the means to `make the most' of limited and precious data and facilitates more stable estimation for multi-dimensional analyses. It should become part of our everyday toolkit for multivariate estimation in quantitative genetics.

Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs

Tage Ostersen — 2011-11-09T00:00:00Z

Background: Genomic selection can be implemented by a multi-step procedure, which requires a response variable and a statistical method. For pure-bred pigs, it was hypothesised that deregressed estimated breeding values (EBV) with the parent average removed as the response variable generate higher reliabilities of genomic breeding values than EBV, and that the normal, thick-tailed and mixture-distribution models yield similar reliabilities. Methods: Reliabilities of genomic breeding values were estimated with EBV and deregressed EBV as response variables and under the three statistical methods, genomic BLUP, Bayesian Lasso and MIXTURE. The methods were examined by splitting data into a reference data set of 1375 genotyped animals that were performance tested before October 2008, and 536 genotyped validation animals that were performance tested after October 2008. The traits examined were daily gain and feed conversion ratio. Results: Using deregressed EBV as the response variable yielded 18 to 39% higher reliabilities of the genomic breeding values than using EBV as the response variable. For daily gain, the increase in reliability due to deregression was significant and approximately 35%, whereas for feed conversion ratio it ranged between 18 and 39% and was significant only when MIXTURE was used. Genomic BLUP, Bayesian Lasso and MIXTURE had similar reliabilities. Conclusions: Deregressed EBV is the preferred response variable, whereas the choice of statistical method is less critical for pure-bred pigs. The increase of 18 to 39% in reliability is worthwhile, since the reliabilities of the genomic breeding values directly affect the returns from genomic selection.