Robust QTL effect estimation using the Minimum Distance method
Robustness has received little attention in QTL studies. We compare Maximum Likelihood (ML) and the Minimum Distance (MD) methods when there exists data contamination caused by outliers A backcross population of size (N) 200 and 500 and 0, 5 or 25 outliers was simulated. The mean and standard deviation of the first QTL genotype were set to 1. Four cases were considered (i)μ2 = 1, σ2 = 1; (ii) μ2 = 1, σ2 = 1.25 (iii) μ2 = 1.252, σ2 = 1; (iv) μ2 = 1.282, σ2 = 1.25 where μ2 and σ2 are the mean and standard deviation of the second genotype. Either full or selective genotyping was considered. A Monte-Carlo MD method is proposed to deal with missing genotypes. MD estimates were much more robust than ML estimates, especially with respect to scale parameter estimates, and with selective genotyping.
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| Formato: | journal article biblioteca |
| Idioma: | English |
| Publicado em: |
Springer Nature
1999
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| Assuntos: | Maximum likelihood, Minimum distance, Outliers, Quantitative trait loci, |
| Acesso em linha: | http://hdl.handle.net/20.500.12792/3445 http://hdl.handle.net/10261/294336 |
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dig-inia-es-10261-2943362023-02-20T10:37:43Z Robust QTL effect estimation using the Minimum Distance method Pérez-Enciso, M. Toro, M. A. Maximum likelihood Minimum distance Outliers Quantitative trait loci Robustness has received little attention in QTL studies. We compare Maximum Likelihood (ML) and the Minimum Distance (MD) methods when there exists data contamination caused by outliers A backcross population of size (N) 200 and 500 and 0, 5 or 25 outliers was simulated. The mean and standard deviation of the first QTL genotype were set to 1. Four cases were considered (i)μ2 = 1, σ2 = 1; (ii) μ2 = 1, σ2 = 1.25 (iii) μ2 = 1.252, σ2 = 1; (iv) μ2 = 1.282, σ2 = 1.25 where μ2 and σ2 are the mean and standard deviation of the second genotype. Either full or selective genotyping was considered. A Monte-Carlo MD method is proposed to deal with missing genotypes. MD estimates were much more robust than ML estimates, especially with respect to scale parameter estimates, and with selective genotyping. 2023-02-20T10:37:43Z 2023-02-20T10:37:43Z 1999 journal article Heredity 83: 347-353 (1999) 0018-067X http://hdl.handle.net/20.500.12792/3445 http://hdl.handle.net/10261/294336 10.1038/sj.hdy.6885800 1365-2540 en none Springer Nature |
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| topic |
Maximum likelihood Minimum distance Outliers Quantitative trait loci Maximum likelihood Minimum distance Outliers Quantitative trait loci |
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Maximum likelihood Minimum distance Outliers Quantitative trait loci Maximum likelihood Minimum distance Outliers Quantitative trait loci Pérez-Enciso, M. Toro, M. A. Robust QTL effect estimation using the Minimum Distance method |
| description |
Robustness has received little attention in QTL studies. We compare Maximum Likelihood (ML) and the Minimum Distance (MD) methods when there exists data contamination caused by outliers A backcross population of size (N) 200 and 500 and 0, 5 or 25 outliers was simulated. The mean and standard deviation of the first QTL genotype were set to 1. Four cases were considered (i)μ2 = 1, σ2 = 1; (ii) μ2 = 1, σ2 = 1.25 (iii) μ2 = 1.252, σ2 = 1; (iv) μ2 = 1.282, σ2 = 1.25 where μ2 and σ2 are the mean and standard deviation of the second genotype. Either full or selective genotyping was considered. A Monte-Carlo MD method is proposed to deal with missing genotypes. MD estimates were much more robust than ML estimates, especially with respect to scale parameter estimates, and with selective genotyping. |
| format |
journal article |
| topic_facet |
Maximum likelihood Minimum distance Outliers Quantitative trait loci |
| author |
Pérez-Enciso, M. Toro, M. A. |
| author_facet |
Pérez-Enciso, M. Toro, M. A. |
| author_sort |
Pérez-Enciso, M. |
| title |
Robust QTL effect estimation using the Minimum Distance method |
| title_short |
Robust QTL effect estimation using the Minimum Distance method |
| title_full |
Robust QTL effect estimation using the Minimum Distance method |
| title_fullStr |
Robust QTL effect estimation using the Minimum Distance method |
| title_full_unstemmed |
Robust QTL effect estimation using the Minimum Distance method |
| title_sort |
robust qtl effect estimation using the minimum distance method |
| publisher |
Springer Nature |
| publishDate |
1999 |
| url |
http://hdl.handle.net/20.500.12792/3445 http://hdl.handle.net/10261/294336 |
| work_keys_str_mv |
AT perezencisom robustqtleffectestimationusingtheminimumdistancemethod AT toroma robustqtleffectestimationusingtheminimumdistancemethod |
| _version_ |
1767603600461135872 |