Extension of the bayesian alphabet for genomic selection
<p>Abstract</p> <p>Background</p> <p>Two Bayesian methods, BayesC<it>π </it>and BayesD<it>π</it>, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior...
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| Format: | Article |
| Language: | English |
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BMC
2011-05-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/12/186 |
| _version_ | 1818389409127464960 |
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| author | Kizilkaya Kadir Fernando Rohan L Habier David Garrick Dorian J |
| author_facet | Kizilkaya Kadir Fernando Rohan L Habier David Garrick Dorian J |
| author_sort | Kizilkaya Kadir |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Two Bayesian methods, BayesC<it>π </it>and BayesD<it>π</it>, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability <it>π </it>that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.</p> <p>Results</p> <p>Estimates of <it>π </it>from BayesC<it>π</it>, in contrast to BayesD<it>π</it>, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesC<it>π </it>than for BayesD<it>π</it>, and longest for our implementation of BayesA.</p> <p>Conclusions</p> <p>Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesC<it>π </it>has merit for routine applications.</p> |
| first_indexed | 2024-12-14T04:41:16Z |
| format | Article |
| id | doaj.art-e9b1d24799c14219b0624fce1f23f8dc |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T04:41:16Z |
| publishDate | 2011-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-e9b1d24799c14219b0624fce1f23f8dc2022-12-21T23:16:47ZengBMCBMC Bioinformatics1471-21052011-05-0112118610.1186/1471-2105-12-186Extension of the bayesian alphabet for genomic selectionKizilkaya KadirFernando Rohan LHabier DavidGarrick Dorian J<p>Abstract</p> <p>Background</p> <p>Two Bayesian methods, BayesC<it>π </it>and BayesD<it>π</it>, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability <it>π </it>that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.</p> <p>Results</p> <p>Estimates of <it>π </it>from BayesC<it>π</it>, in contrast to BayesD<it>π</it>, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesC<it>π </it>than for BayesD<it>π</it>, and longest for our implementation of BayesA.</p> <p>Conclusions</p> <p>Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesC<it>π </it>has merit for routine applications.</p>http://www.biomedcentral.com/1471-2105/12/186 |
| spellingShingle | Kizilkaya Kadir Fernando Rohan L Habier David Garrick Dorian J Extension of the bayesian alphabet for genomic selection BMC Bioinformatics |
| title | Extension of the bayesian alphabet for genomic selection |
| title_full | Extension of the bayesian alphabet for genomic selection |
| title_fullStr | Extension of the bayesian alphabet for genomic selection |
| title_full_unstemmed | Extension of the bayesian alphabet for genomic selection |
| title_short | Extension of the bayesian alphabet for genomic selection |
| title_sort | extension of the bayesian alphabet for genomic selection |
| url | http://www.biomedcentral.com/1471-2105/12/186 |
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