Biostatistics & Medical Informatics, University of Wisconsin – Madison

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- Consider each time point individually
- Combine the results from the individual analyses
SLOD(λ) = ave

_{t}LOD_{t}(λ)

MLOD(λ) = max_{t}LOD_{t}(λ) - Fit a model to each individual’s curve;

treat the coefficients as phenotypes

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- Reduce residual variation → Increase power
- Separate linked QTL
- Identify interactions among QTL (epistasis)

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Among additive QTL models, maximize

pLOD(γ) = LOD(γ) – T × |γ|

γ = a multiple-QTL model

|γ| = no. QTL in the model

T = significance threshold

KW Broman, TP Speed (2002) J Roy Stat Soc B 64:641-656

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Among additive QTL models, maximize

pLOD_{t}(γ) = LOD_{t}(γ) – T × |γ|

γ = a multiple-QTL model

|γ| = no. QTL in the model

T = significance threshold

KW Broman, TP Speed (2002) J Roy Stat Soc B 64:641-656

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Among additive QTL models, maximize

pSLOD(γ) = ave_{t} LOD_{t}(γ) – T_{S} × |γ|

pMLOD(γ) = max_{t} LOD_{t}(γ) – T_{M} × |γ|

γ = a multiple-QTL model

|γ| = no. QTL in the model

T = penalties

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- We have focused on the simplest approaches to QTL analysis with a phenotype measured over time.
- Consider each time point individually.
- Combine across time by taking the average or maximum LOD.
- We extended the approach for multiple-QTL models.
- Interactive graphs are useful and fun.
- See the related paper: Moore et al. Genetics 195:1077, 2013

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Edgar Spalding | Botany, UW-Madison | |

Candace Moore | ||

Logan Johnson | ||

Il-youp Kwak | Statistics, UW-Madison | |

NIH: R01 GM074244 |

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