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.
| Main Authors: | , |
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| Format: | journal article biblioteca |
| Language: | eng |
| Published: |
1999
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| Online Access: | http://hdl.handle.net/20.500.12792/3445 |
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| Summary: | 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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