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Originally published as Genetics Published Articles Ahead of Print on May 15, 2006.

Genetics, Vol. 173, 2269-2282, August 2006, Copyright © 2006
doi:10.1534/genetics.106.058537

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Mapping Quantitative Trait Loci by an Extension of the Haley–Knott Regression Method Using Estimating Equations

Bjarke Feenstra*,1, Ib M. Skovgaard* and Karl W. Broman{dagger}

* Department of Natural Sciences, Royal Veterinary and Agricultural University, DK-1871 Frederiksberg C, Denmark and {dagger} Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205

1 Corresponding author: Department of Natural Sciences, Royal Veterinary and Agricultural University, Thorvaldsensvej 40, DK-1871 Frederiksberg C, Denmark.
E-mail: bjarke{at}dina.kvl.dk

The Haley–Knott (HK) regression method continues to be a popular approximation to standard interval mapping (IM) of quantitative trait loci (QTL) in experimental crosses. The HK method is favored for its dramatic reduction in computation time compared to the IM method, something that is particularly important in simultaneous searches for multiple interacting QTL. While the HK method often approximates the IM method well in estimating QTL effects and in power to detect QTL, it may perform poorly if, for example, there is strong epistasis between QTL or if QTL are linked. Also, it is well known that the estimation of the residual variance by the HK method is biased. Here, we present an extension of the HK method that uses estimating equations based on both means and variances. For normally distributed phenotypes this estimating equation (EE) method is more efficient than the HK method. Furthermore, computer simulations show that the EE method performs well for very different genetic models and data set structures, including nonnormal phenotype distributions, nonrandom missing data patterns, varying degrees of epistasis, and varying degrees of linkage between QTL. The EE method retains key qualities of the HK method such as computational speed and robustness against nonnormal phenotype distributions, while approximating the IM method better in terms of accuracy and precision of parameter estimates and power to detect QTL.




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