QTL Mapping Model Algorithm

Select a QTL mapping algorithm: the EM Algorithm or Haley-Knott Regression.

Available algorithms are described in the table below:

Algorithm	Definition
EM	The EM Algorithm1 computes LOD scoresfrom maximum likelihood estimates for model parameters that are generated based on modeling the conditional phenotype as a mixture of normal distributions derived from the QTL genotype probabilities at each testing location.
Haley-Knott Regression	The Haley-Knott algorithm2 regresses the genotype probabilities at QTL testing points directly on the trait instead of via a mixture model. LOD scores are estimated via maximum likelihood method. Note: This method is a simpler alternative to EM, but should return similar results.

Algorithm

Definition

The EM Algorithm1 computes LOD scoresfrom maximum likelihood estimates for model parameters that are generated based on modeling the conditional phenotype as a mixture of normal distributions derived from the QTL genotype probabilities at each testing location.

Haley-Knott Regression

The Haley-Knott algorithm2 regresses the genotype probabilities at QTL testing points directly on the trait instead of via a mixture model.

LOD scores are estimated via maximum likelihood method.

Note: This method is a simpler alternative to EM, but should return similar results.

To Specify a QTL Mapping Model Algorithm:

Click on the radio button corresponding to your choice.