Interval Mapping by Maximum Likelihood Approach

Part of the Statistics for Biology and Health book series (SBH)

As discussed in Chapter 10, interval mapping is powerful for the separation of QTL effects and QTL-marker linkage. Interval mapping based on a regression approach is computationally faster than a maximum likelihood (ML) approach, with comparable results between the two approaches in some particular cases. Kao (2000) discussed analytically and through simulation studies the conditions under which the regressionand ML-based approaches generate different results. Further comparisons by Mayer (2005) between the two approaches were made in terms of power, accuracy of position and effect estimates, and estimation of the residual variance when multiple linked QTLs are involved in the genetic control of a quantitative trait.


Mapping Population Double Haploid Interval Mapping Marker Interval Recombination Fraction 
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© Springer Science + Business Media, LLC 2007

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