QTL Mapping Model Algorithm

Parameters | Genetics | QTL Mapping Model Algorithm

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 scores from 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 genotypes 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.

1

Lander, E.S. and Botstein, D.(1989) Mapping Mendelian Factors Underlying Quantitative Traits Using RFLP Linkage Maps. Genetics 121: 185-199.

2

Haley, C.S. and Knott, S.A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69: 315–324.

To Specify a QTL Mapping Model Algorithm:

Click on the radio button corresponding to your choice.

Algorithm	Definition
EM	The EM Algorithm¹ computes LOD scores from 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 genotypes 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.