Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction

Liu, Zengting; Seefried, Franz R.; Reinhardt, F; Rensing, S; Thaller, Georg; Reents, R.

doi:10.1186/1297-9686-43-19

Cited by 102 publications

(145 citation statements)

References 20 publications

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“…Our observation of higher correlations for predictions using more information sources is in agreement with Liu et al (2011). In indexes used for all animals, constant overall weights were 80% for DGV and 20% for pedigree value, which corresponds to combining the pedigree and genomic relationship matrices into H in a single-step procedure.…”

Section: Resultssupporting

confidence: 86%

“…To obtain high reliability, these input parameters should be known without errors, which is difficult to achieve. Genetic markers also do not explain all genetic variability of an analysed trait (Liu et al, 2011). Therefore, a residual polygenic effect is added to the direct genetic values (DGV) that are calculated from regression coefficients or from genomic relationship to produce GEBV.…”

Section: Introductionmentioning

confidence: 99%

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Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Přibyl

Bauer

Čermák

et al. 2015

Animal

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Estimated breeding values (EBVs) and genomic enhanced breeding values (GEBVs) for milk production of young genotyped Holstein bulls were predicted using a conventional BLUP -Animal Model, a method fitting regression coefficients for loci (RRBLUP), a method utilizing the realized genomic relationship matrix (GBLUP), by a single-step procedure (ssGBLUP) and by a one-step blending procedure. Information sources for prediction were the nation-wide database of domestic Czech production records in the first lactation combined with deregressed proofs (DRP) from Interbull files (August 2013) and domestic test-day (TD) records for the first three lactations. Data from 2627 genotyped bulls were used, of which 2189 were already proven under domestic conditions. Analyses were run that used Interbull values for genotyped bulls only or that used Interbull values for all available sires. Resultant predictions were compared with GEBV of 96 young foreign bulls evaluated abroad and whose proofs were from Interbull method GMACE (August 2013) on the Czech scale. Correlations of predictions with GMACE values of foreign bulls ranged from 0.33 to 0.75. Combining domestic data with Interbull EBVs improved prediction of both EBV and GEBV. Predictions by Animal Model (traditional EBV) using only domestic first lactation records and GMACE values were correlated by only 0.33. Combining the nation-wide domestic database with all available DRP for genotyped and un-genotyped sires from Interbull resulted in an EBV correlation of 0.60, compared with 0.47 when only Interbull data were used. In all cases, GEBVs had higher correlations than traditional EBVs, and the highest correlations were for predictions from the ssGBLUP procedure using combined data (0.75), or with all available DRP from Interbull records only (one-step blending approach, 0.69). The ssGBLUP predictions using the first three domestic lactation records in the TD model were correlated with GMACE predictions by 0.69, 0.64 and 0.61 for milk yield, protein yield and fat yield, respectively.

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Section: Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Přibyl

Bauer

Čermák

et al. 2015

Animal

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“…In 2010, the European reference population comprised .17,000 bulls representing .20 million daughters (Lund et al 2010;Liu et al 2011). In addition to their utilization in genomic prediction, these data are extensively used in genome-wide association studies to unravel the genetic factors affecting performance and functional traits.…”

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confidence: 99%

Novel Use of Derived Genotype Probabilities to Discover Significant Dominance Effects for Milk Production Traits in Dairy Cattle

et al. 2013

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The estimation of dominance effects requires the availability of direct phenotypes, i.e., genotypes and phenotypes in the same individuals. In dairy cattle, classical QTL mapping approaches are, however, relying on genotyped sires and daughter-based phenotypes like breeding values. Thus, dominance effects cannot be estimated. The number of dairy bulls genotyped for dense genome-wide marker panels is steadily increasing in the context of genomic selection schemes. The availability of genotyped cows is, however, limited. Within the current study, the genotypes of male ancestors were applied to the calculation of genotype probabilities in cows. Together with the cows' phenotypes, these probabilities were used to estimate dominance effects on a genome-wide scale. The impact of sample size, the depth of pedigree used in deriving genotype probabilities, the linkage disequilibrium between QTL and marker, the fraction of variance explained by the QTL, and the degree of dominance on the power to detect dominance were analyzed in simulation studies. The effect of relatedness among animals on the specificity of detection was addressed. Furthermore, the approach was applied to a real data set comprising 470,000 Holstein cows. To account for relatedness between animals a mixedmodel two-step approach was used to adjust phenotypes based on an additive genetic relationship matrix. Thereby, considerable dominance effects were identified for important milk production traits. The approach might serve as a powerful tool to dissect the genetic architecture of performance and functional traits in dairy cattle. IN the context of genomic selection in dairy cattle, an abundance of bulls has been genotyped by applying genomewide dense marker panels. In 2010, the European reference population comprised .17,000 bulls representing .20 million daughters (Lund et al. 2010;Liu et al. 2011). In addition to their utilization in genomic prediction, these data are extensively used in genome-wide association studies to unravel the genetic factors affecting performance and functional traits. The expression of these traits is naturally limited to female individuals and thus, the phenotypes used in association studies are usually breeding values of sires based on performance data of many daughters. Such a structure of data allows only the estimation of allele substitution effects. There is no direct possibility to distinguish between additive and dominance effects. For the detection of these allelic interactions, genotypes and phenotypes had to be known in the same individuals. Compared to the bulls, the availability of genotype data for cows is limited. With the increasing number of genotyped bulls, genotypes of male ancestors become available for many cows, enabling the derivation of genotype probabilities. Within the current study, these probabilities were converted to additive and dominance coefficients suitable for regression analysis analogous to the procedures commonly applied to QTL mapping in resource populations (Haley and Knott 199...

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“…A low density SNP panel allows for reasonably good prediction of future breeding values. Even though correlations between direct and conventional breeding values were moderate, for young selection candidates a low density panel is a better predictor than a commonly used average of parental breeding values.Keywords: 3K chip; genomic selection; prediction; single nucleotide polymorphism Since Meuwissen et al (2001) proposed the application of genetic values predicted from a large number of single nucleotide polymorphisms (SNPs) for selection, many studies related to development of methodology and practical application of genomic selection have been conducted (for recent reviews see Hayes et al, 2009;Calus, 2010;Liu et al, 2011). Challenges remain, however: (i) in view of rapidly growing sizes of training data sets, how to deal with the large dimensions of the statistical model used for estimation of SNP effects; and (ii) in view of widespread genotyping of all members of active populations and all incoming selection candidates, how to reduce genotyping costs.…”

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confidence: 99%

Testing different single nucleotide polymorphism selection strategies for prediction of genomic breeding values in dairy cattle based on low density panels

et al. 2013

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ABSTRACT:In human and animal genetics dense single nucleotide polymorphism (SNP) panels are widely used to describe genetic variation. In particular genomic selection in dairy cattle has become a routinely applied tool for prediction of additive genetic values of animals, especially of young selection candidates. The aim of the study was to investigate how well an additive genetic value can be predicted using various sets of approximately 3000 SNPs selected out of the 54 001 SNPs in an Illumina BovineSNP50 BeadChip high density panel. Effects of SNPs from the nine subsets of the 54 001 panel were estimated using a model with a random uncorrelated SNPs effect based on a training data set of 1216 Polish Holstein-Friesian bulls whose phenotypic records were approximated by deregressed estimated breeding values for milk, protein, and fat yields. Predictive ability of the low density panels was assessed using a validation data set of 622 bulls. Correlations between direct and conventional breeding values routinely estimated for the Polish population were similar across traits and clearly across sets of SNPs. For the training data set correlations varied between 0.94 and 0.98, for the validation data set between 0.25 and 0.46. The corresponding correlations estimated using the 54 001 panel were: 0.98 for the three traits (training), 0.98 (milk and fat yields, validation), and 0.97 (protein yield, validation). The optimal subset consisted of SNPs selected based on their highest effects for milk yield obtained from the evaluation of all 54 001 SNPs. A low density SNP panel allows for reasonably good prediction of future breeding values. Even though correlations between direct and conventional breeding values were moderate, for young selection candidates a low density panel is a better predictor than a commonly used average of parental breeding values.Keywords: 3K chip; genomic selection; prediction; single nucleotide polymorphism Since Meuwissen et al. (2001) proposed the application of genetic values predicted from a large number of single nucleotide polymorphisms (SNPs) for selection, many studies related to development of methodology and practical application of genomic selection have been conducted (for recent reviews see Hayes et al., 2009;Calus, 2010;Liu et al., 2011). Challenges remain, however: (i) in view of rapidly growing sizes of training data sets, how to deal with the large dimensions of the statistical model used for estimation of SNP effects; and (ii) in view of widespread genotyping of all members of active populations and all incoming selection candidates, how to reduce genotyping costs. While dimension reduction can be realized by the choice of an appropriate statistical model, a primary way of cost reduction is the application of cheaper, low density SNP panels. A currently widespread procedure in genomic evaluations is to use sparse commercially available 3K or 6K SNP panels geno-

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Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction

Cited by 102 publications

References 20 publications

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Novel Use of Derived Genotype Probabilities to Discover Significant Dominance Effects for Milk Production Traits in Dairy Cattle

Testing different single nucleotide polymorphism selection strategies for prediction of genomic breeding values in dairy cattle based on low density panels

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