Use of a Bayesian model including QTL markers increases prediction reliability when test animals are distant from the reference population.

Relatedness between reference and test animals has an important effect on the reliability of genomic prediction for test animals. Because genomic prediction has been widely applied in practical cattle breeding and bulls have been selected according to genomic breeding value without progeny testing, the sires or grandsires of candidates might not have phenotypic information and might not be in the reference population when the candidates are selected. The objective of this study was to investigate the decreasing trend of the reliability of genomic prediction given distant reference populations, using genomic best linear unbiased prediction (GBLUP) and Bayesian variable selection models with or without including the quantitative trait locus (QTL) markers detected from sequencing data. The data used in this study consisted of 22,242 bulls genotyped using the 54K SNP array from EuroGenomics. Among them, 1,444 Danish bulls born from 2006 to 2010 were selected as test animals. Different reference populations with varying relationships to test animals were created according to pedigree-based relationships. The reference individuals having a relationship with one or more test animals higher than 0.4 (scenario ρ < 0.4), 0.2 (ρ < 0.2), or 0.1 (ρ < 0.1, where ρ = relationship coefficient) were removed from reference sets; these represented the distance between reference and test animals being 2 generations, 3 generations, and 4 generations, respectively. Imputed whole-genome sequencing data of bulls from Denmark were used to conduct a genome-wide association study (GWAS). A small number of significant variants (QTL markers) from the GWAS were added to the array data. To compare the effects of different models, the basic GBLUP model, a Bayesian selection variable model, a GBLUP model with 2 components of genetic effects, and a Bayesian model with pooled array data and QTL markers were used for estimating genomic estimated breeding values (GEBV) of test animals. The reliability of genomic prediction decreased when the test animals were more generations away from the reference population. The reliability of genomic prediction was 0.461 for 1 generation away and 0.396 for 3 generations away, with the same number of individuals in the reference set, using a GBLUP model with chip markers only. The results showed that using the Bayesian method and QTL markers improved the reliability of genomic prediction in all scenarios of relationship between test and reference animals, in a range of 1.3% and 65.1% (4 generations away with only 841 individuals in the reference set). However, most gains were for predictions of milk yield and fat yield. There was little improvement for predictions of protein yield and mastitis, and no improvement for prediction of fertility, except for scenario ρ < 0.1, in which there was a large improvement for predictions of all traits. On the other hand, models including more than 10% polygenic effect decreased prediction reliability when the relationship between test and reference animals was distant.

Ma P ，Lund MS ，Aamand GP ，Su G ... -  《-》

Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances.

Various models have been used for genomic prediction. Bayesian variable selection models often predict more accurate genomic breeding values than genomic BLUP (GBLUP), but GBLUP is generally preferred for routine genomic evaluations because of low computational demand. The objective of this study was to achieve the benefits of both models using results from Bayesian models and genome-wide association studies as weights on single nucleotide polymorphism (SNP) markers when constructing the genomic matrix (G-matrix) for genomic prediction. The data comprised 5,221 progeny-tested bulls from the Nordic Holstein population. The animals were genotyped using the Illumina Bovine SNP50 BeadChip (Illumina Inc., San Diego, CA). Weighting factors in this investigation were the posterior SNP variance, the square of the posterior SNP effect, and the corresponding minus base-10 logarithm of the marker association P-value [-log10(P)] of a t-test obtained from the analysis using a Bayesian mixture model with 4 normal distributions, the square of the estimated SNP effect, and the corresponding -log10(P) of a t-test obtained from the analysis using a classical genome-wide association study model (linear regression model). The weights were derived from the analysis based on data sets that were 0, 1, 3, or 5 yr before performing genomic prediction. In building a G-matrix, the weights were assigned either to each marker (single-marker weighting) or to each group of approximately 5 to 150 markers (group-marker weighting). The analysis was carried out for milk yield, fat yield, protein yield, fertility, and mastitis. Deregressed proofs (DRP) were used as response variables to predict genomic estimated breeding values (GEBV). Averaging over the 5 traits, the Bayesian model led to 2.0% higher reliability of GEBV than the GBLUP model with an original unweighted G-matrix. The superiority of using a GBLUP with weighted G-matrix over GBLUP with an original unweighted G-matrix was the largest when using a weighting factor of posterior variance, resulting in 1.7 percentage points higher reliability. The second best weighting factors were -log10 (P-value) of a t-test corresponding to the square of the posterior SNP effect from the Bayesian model and -log10 (P-value) of a t-test corresponding to the square of the estimated SNP effect from the linear regression model, followed by the square of estimated SNP effect and the square of the posterior SNP effect. In addition, group-marker weighting performed better than single-marker weighting in terms of reducing bias of GEBV, and also slightly increased prediction reliability. The differences between weighting factors and scenarios were larger in prediction bias than in prediction accuracy. Finally, weights derived from a data set having a lag up to 3 yr did not reduce reliability of GEBV. The results indicate that posterior SNP variance estimated from a Bayesian mixture model is a good alternative weighting factor, and common weights on group markers with a size of 30 markers is a good strategy when using markers of the 50,000-marker (50K) chip. In a population with gradually increasing reference data, the weights can be updated once every 3 yr.

Su G ，Christensen OF ，Janss L ，Lund MS ... -  《-》

Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population.

This study compared genomic predictions based on imputed high-density markers (~777,000) in the Nordic Holstein population using a genomic BLUP (GBLUP) model, 4 Bayesian exponential power models with different shape parameters (0.3, 0.5, 0.8, and 1.0) for the exponential power distribution, and a Bayesian mixture model (a mixture of 4 normal distributions). Direct genomic values (DGV) were estimated for milk yield, fat yield, protein yield, fertility, and mastitis, using deregressed proofs (DRP) as response variable. The validation animals were split into 4 groups according to their genetic relationship with the training population. Groupsmgs had both the sire and the maternal grandsire (MGS), Groupsire only had the sire, Groupmgs only had the MGS, and Groupnon had neither the sire nor the MGS in the training population. Reliability of DGV was measured as the squared correlation between DGV and DRP divided by the reliability of DRP for the bulls in validation data set. Unbiasedness of DGV was measured as the regression of DRP on DGV. The results indicated that DGV were more accurate and less biased for animals that were more related to the training population. In general, the Bayesian mixture model and the exponential power model with shape parameter of 0.30 led to higher reliability of DGV than did the other models. The differences between reliabilities of DGV from the Bayesian models and the GBLUP model were statistically significant for some traits. We observed a tendency that the superiority of the Bayesian models over the GBLUP model was more profound for the groups having weaker relationships with training population. Averaged over the 5 traits, the Bayesian mixture model improved the reliability of DGV by 2.0 percentage points for Groupsmgs, 2.7 percentage points for Groupsire, 3.3 percentage points for Groupmgs, and 4.3 percentage points for Groupnon compared with GBLUP. The results showed that a Bayesian model with intense shrinkage of the explanatory variable, such as the Bayesian mixture model and the Bayesian exponential power model with shape parameter of 0.30, can improve genomic predictions using high-density markers.

Gao H ，Su G ，Janss L ，Zhang Y ，Lund MS ... -  《-》

Single-step genomic model improved reliability and reduced the bias of genomic predictions in Danish Jersey.

A bias in the trend of genomic estimated breeding values (GEBV) was observed in the Danish Jersey population where the trend of GEBV was smaller than the deregressed proofs for individuals in the validation population. This study attempted to improve the prediction reliability and reduce the bias of predicted genetic trend in Danish Jersey. The data consisted of 1,238 Danish Jersey bulls and 611,695 cows. All bulls were genotyped with the 54K chip, and 1,744 cows were genotyped with either 7K chips (1,157 individuals) or 54K chips (587 individuals). The trait used in the analysis was protein yield. All cows with EBV were used in a single-step approach. Deregressed proofs were used as the response variable. Four alternative approaches were compared with genomic best linear unbiased prediction (GBLUP) model with bulls in the reference data (GBLUPBull): (1) GBLUP with both bulls and genotyped cows in the reference data; (2) GBLUP including a year of birth effect; (3) GEBV from a GBLUP model that accounted for the difference of EBV between dams and maternal grandsires; and (4) using a single-step approach. The results indicated all 4 alternatives could reduce the bias of predicted genetic trend and that the single-step approach performed best. However, not all these approaches improved reliability or reduced inflation of GEBV. The reliability was 0.30 and regression coefficients of deregressed proofs on GEBV were 0.69 in the scenario GBLUPBull. When genotyped cows were included in the reference population, the regression coefficients decreased to 0.59 but the reliability increased to 0.35. If a year effect was included in the model, the prediction reliability decreased to 0.29 and the regression coefficient improved to 0.75. The method in which GEBV were adjusted for the difference between dam EBV and maternal grandsire EBV led to much lower regression coefficients though the reliability increased to 0.4. The single-step approach improved both the reliability, to 0.38 and regression coefficient to 0.78. Therefore, the bias in genetic trend was reduced. The results suggest that implementing the single-step approach is an effective way to improve genomic prediction in Danish Jersey cattle.

Ma P ，Lund MS ，Nielsen US ，Aamand GP ，Su G ... -  《-》

Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction.

This study investigated the effect on the reliability of genomic prediction when a small number of significant variants from single marker analysis based on whole genome sequence data were added to the regular 54k single nucleotide polymorphism (SNP) array data. The extra markers were selected with the aim of augmenting the custom low-density Illumina BovineLD SNP chip (San Diego, CA) used in the Nordic countries. The single-marker analysis was done breed-wise on all 16 index traits included in the breeding goals for Nordic Holstein, Danish Jersey, and Nordic Red cattle plus the total merit index itself. Depending on the trait's economic weight, 15, 10, or 5 quantitative trait loci (QTL) were selected per trait per breed and 3 to 5 markers were selected to tag each QTL. After removing duplicate markers (same marker selected for more than one trait or breed) and filtering for high pairwise linkage disequilibrium and assaying performance on the array, a total of 1,623 QTL markers were selected for inclusion on the custom chip. Genomic prediction analyses were performed for Nordic and French Holstein and Nordic Red animals using either a genomic BLUP or a Bayesian variable selection model. When using the genomic BLUP model including the QTL markers in the analysis, reliability was increased by up to 4 percentage points for production traits in Nordic Holstein animals, up to 3 percentage points for Nordic Reds, and up to 5 percentage points for French Holstein. Smaller gains of up to 1 percentage point was observed for mastitis, but only a 0.5 percentage point increase was seen for fertility. When using a Bayesian model accuracies were generally higher with only 54k data compared with the genomic BLUP approach, but increases in reliability were relatively smaller when QTL markers were included. Results from this study indicate that the reliability of genomic prediction can be increased by including markers significant in genome-wide association studies on whole genome sequence data alongside the 54k SNP set.

Brøndum RF ，Su G ，Janss L ，Sahana G ，Guldbrandtsen B ，Boichard D ，Lund MS ... -  《-》