Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

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    We will mail you new results for this query: keywords==Bayesian variable selection
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Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection
Calus, M.P.L. ; Goddard, M.E. ; Wientjes, Y.C.J. ; Bowman, P.J. ; Hayes, B.J. - \ 2018
Journal of Dairy Science 101 (2018)5. - ISSN 0022-0302 - p. 4279 - 4294.
Bayesian variable selection - genomic prediction - multibreed
Genomic prediction is applicable to individuals of different breeds. Empirical results to date, however, show limited benefits in using information on multiple breeds in the context of genomic prediction. We investigated a multitask Bayesian model, presented previously by others, implemented in a Bayesian stochastic search variable selection (BSSVS) model. This model allowed for evidence of quantitative trait loci (QTL) to be accumulated across breeds or for both QTL that segregate across breeds and breed-specific QTL. In both cases, single nucleotide polymorphism effects were estimated with information from a single breed. Other models considered were a single-trait and multitrait genomic residual maximum likelihood (GREML) model, with breeds considered as different traits, and a single-trait BSSVS model. All single-trait models were applied to each of the 2 breeds separately and to the pooled data of both breeds. The data used included a training data set of 6,278 Holstein and 722 Jersey bulls, as well as 374 Jersey validation bulls. All animals had genotypes for 474,773 single nucleotide polymorphisms after editing and phenotypes for milk, fat, and protein yields. Using the same training data, BSSVS consistently outperformed GREML. The multitask BSSVS, however, did not outperform single-trait BSSVS, which used pooled Holstein and Jersey data for training. Thus, the rigorous assumption that the traits are the same in both breeds yielded a slightly better prediction than a model that had to estimate the correlation between the breeds from the data. Adding the Holstein data significantly increased the accuracy of the single-trait GREML and BSSVS in predicting the Jerseys for milk and protein, in line with estimated correlations between the breeds of 0.66 and 0.47 for milk and protein yields, whereas only the BSSVS model significantly improved the accuracy for fat yield with an estimated correlation between breeds of only 0.05. The relatively high genetic correlations for milk and protein yields, and the superiority of the pooling strategy, is likely the result of the observed admixture between both breeds in our data. The Bayesian model was able to detect several QTL in Holsteins, which likely enabled it to outperform GREML. The inability of the multitask Bayesian models to outperform a simple pooling strategy may be explained by the fact that the pooling strategy assumes equal effects in both breeds; furthermore, this assumption may be valid for moderate- to large-sized QTL, which are important for multibreed genomic prediction.
Using markers with large effect in genetic and genomic predictions
Soares Lopes, Marcos ; Bovenhuis, H. ; Son, M. van; Nordbø, ; Grindflek, E.H. ; Knol, Edward F. ; Bastiaansen, J.W.M. - \ 2017
Journal of Animal Science 95 (2017)1. - ISSN 0021-8812 - p. 59 - 71.
Bayesian variable selection - Genome-wide association study - Genomic selection - Marker-assisted selection
The first attempts of applying marker-assisted selection (MAS) in animal breeding were not very successful because the identification of markers closely linked to QTL using low-density microsat-ellite panels was difficult. More recently, the use of high-density SNP panels in genome-wide association studies (GWAS) have increased the power and precision of identifying markers linked to QTL, which offer new possibilities for MAS. However, when GWAS started to be performed, the focus of many breeders had already shifted from the use of MAS to the application of genomic selection (using all available markers without any preselection of markers linked to QTL). In this study, we aimed to evaluate the prediction accuracy of a MAS approach that accounts for GWAS findings in the prediction models by including the most significant SNP from GWAS as a fixed effect in the marker-assisted BLUP (MA-BLUP) and marker-assisted genomic BLUP (MA-GBLUP) prediction models. A second aim was to compare the prediction accuracies from the marker-assisted models with those obtained from a Bayesian variable selection (BVS) model. To compare the prediction accuracies of traditional BLUP, MA-BLUP, genomic BLUP (GBLUP), MA-GBLUP, and BVS, we applied these models to the trait “number of teats” in 4 distinct pig populations, for validation of the results. The most significant SNP in each population was located at approximately 103.50 Mb on chromosome 7. Applying MAS by accounting for the most significant SNP in the prediction models resulted in improved prediction accuracy for number of teats in all evaluated populations compared with BLUP and GBLUP. Using MA-BLUP instead of BLUP, the increase in prediction accuracy ranged from 0.021 to 0.124, whereas using MA-GBLUP instead of GBLUP, the increase in prediction accuracy ranged from 0.003 to 0.043. The BVS model resulted in similar or higher prediction accuracies than MA-GBLUP. For the trait number of teats, BLUP resulted in the lowest prediction accuracies whereas the highest were observed when applying MA-GBLUP or BVS. In the same data set, MA-BLUP can yield similar or superior accuracies compared with GBLUP. The superiority of MA-GBLUP over traditional GBLUP is more pronounced when training populations are smaller and when relationships between training and validation populations are smaller. Marker-assisted GBLUP did not outperform BVS but does have implementation advantages in large-scale evaluations.
Data from: Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP
Berg, S. van den; Calus, M.P.L. ; Meuwissen, T.H.E. ; Wientjes, Y.C.J. - \ 2015
genomic prediction - across population - Bayesian variable selection - GBLUP - accuracy - number of independent chromosome segments
Background: The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different populations, are currently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models when the number of QTL underlying the trait is low. Therefore, our objective was to identify across population genomic prediction scenarios in which Bayesian variable selection models outperform GBLUP in terms of prediction accuracy. In this study, high density genotype information of 1033 Holstein Friesian, 105 Groningen White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were simulated using two changing variables: (1) the number of QTL underlying the trait (3000, 300, 30, 3), and (2) the correlation between allele substitution effects of QTL across populations, i.e. the genetic correlation of the simulated trait between the populations (1.0, 0.8, 0.4). Results: The accuracy obtained by the Bayesian variable selection model was depending on the number of QTL underlying the trait, with a higher accuracy when the number of QTL was lower. This trend was more pronounced for across population genomic prediction than for within population genomic prediction. It was shown that Bayesian variable selection models have an advantage over GBLUP when the number of QTL underlying the simulated trait was small. This advantage disappeared when the number of QTL underlying the simulated trait was large. The point where the accuracy of Bayesian variable selection and GBLUP became similar was approximately the point where the number of QTL was equal to the number of independent chromosome segments (M e ) across the populations. Conclusion: Bayesian variable selection models outperform GBLUP when the number of QTL underlying the trait is smaller than M e . Across populations, M e is considerably larger than within populations. So, it is more likely to find a number of QTL underlying a trait smaller than M e across populations than within population. Therefore Bayesian variable selection models can help to improve the accuracy of across population genomic prediction.
Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP
Berg, S. van den; Calus, M.P.L. ; Meuwissen, T.H.E. ; Wientjes, Y.C.J. - \ 2015
BMC Genetics 16 (2015)1. - ISSN 1471-2156 - 12 p.
Accuracy - Across population - Bayesian variable selection - GBLUP - Genomic prediction - Number of independent chromosome segments

Background: The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different populations, are currently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models when the number of QTL underlying the trait is low. Therefore, our objective was to identify across population genomic prediction scenarios in which Bayesian variable selection models outperform GBLUP in terms of prediction accuracy. In this study, high density genotype information of 1033 Holstein Friesian, 105 Groningen White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were simulated using two changing variables: (1) the number of QTL underlying the trait (3000, 300, 30, 3), and (2) the correlation between allele substitution effects of QTL across populations, i.e. the genetic correlation of the simulated trait between the populations (1.0, 0.8, 0.4). Results: The accuracy obtained by the Bayesian variable selection model was depending on the number of QTL underlying the trait, with a higher accuracy when the number of QTL was lower. This trend was more pronounced for across population genomic prediction than for within population genomic prediction. It was shown that Bayesian variable selection models have an advantage over GBLUP when the number of QTL underlying the simulated trait was small. This advantage disappeared when the number of QTL underlying the simulated trait was large. The point where the accuracy of Bayesian variable selection and GBLUP became similar was approximately the point where the number of QTL was equal to the number of independent chromosome segments (M e ) across the populations. Conclusion: Bayesian variable selection models outperform GBLUP when the number of QTL underlying the trait is smaller than M e . Across populations, M e is considerably larger than within populations. So, it is more likely to find a number of QTL underlying a trait smaller than M e across populations than within population. Therefore Bayesian variable selection models can help to improve the accuracy of across population genomic prediction.

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