Title |
Evaluation of approaches for estimating the accuracy of genomic prediction in plant breeding
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Published in |
BMC Genomics, December 2013
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DOI | 10.1186/1471-2164-14-860 |
Pubmed ID | |
Authors |
Sidi Boubacar Ould Estaghvirou, Joseph O Ogutu, Torben Schulz-Streeck, Carsten Knaak, Milena Ouzunova, Andres Gordillo, Hans-Peter Piepho |
Abstract |
In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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France | 2 | 2% |
Netherlands | 1 | <1% |
Indonesia | 1 | <1% |
Brazil | 1 | <1% |
Finland | 1 | <1% |
India | 1 | <1% |
United Kingdom | 1 | <1% |
Mexico | 1 | <1% |
Belgium | 1 | <1% |
Other | 1 | <1% |
Unknown | 120 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 39 | 30% |
Student > Ph. D. Student | 25 | 19% |
Student > Master | 14 | 11% |
Student > Doctoral Student | 12 | 9% |
Professor | 6 | 5% |
Other | 20 | 15% |
Unknown | 15 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 97 | 74% |
Biochemistry, Genetics and Molecular Biology | 6 | 5% |
Mathematics | 5 | 4% |
Computer Science | 2 | 2% |
Physics and Astronomy | 1 | <1% |
Other | 0 | 0% |
Unknown | 20 | 15% |