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doi:10.1534/genetics.106.058537
A more recent version of this article appeared on August 1, 2006.
REGULAR RESEARCH PAPERS |
Mapping Quantitative Trait Loci by an Extension of the Haley-Knott Regression Method Using Estimating Equations
Bjarke Feenstra 1*, Ib M Skovgaard 1 and Karl W Broman 2
1 Royal Veterinary and Agricultural University
2 Johns Hopkins University
* To whom correspondence should be addressed. E-mail: bjarke{at}dina.kvl.dk.
Submitted on March 24, 2006
Revised on April 19, 2006
Accepted on 12 May 2006
The Haley-Knott regression (HK) method continues to be a popular approximation to standard interval mapping (IM) of quantitative trait loci (QTLs) in experimental crosses. The HK method is favored for its dramatic reduction in computation time compared to the IM method, something which is particularly important in simultaneous searches for multiple interacting QTLs. While the HK method often approximates the IM method well in estimating QTL effects and in power to detect QTLs, it may perform poorly if, for example, there is strong epistasis between QTLs or if QTLs 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 non-normal phenotype distributions, non-random missing data patterns, varying degrees of epistasis, and varying degrees of linkage between QTLs. The EE method retains key qualities of the HK method such as computational speed and robustness against non-normal phenotype distributions, while approximating the IM method better in terms of accuracy and precision of parameter estimates and power to detect QTLs.
Key Words: Haley-Knott regression, QTL, estimating equations, interval mapping, quantitative trait loci
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