Abstract

In interval mapping pairs or groups of marker loci are used for the identification of quantitative trait loci (QTLs) lying between those markers. With data from a segregating population from an inbred line cross the markers flanking each interval can be considered a pair at a time. The method can be applied by regression or maximum likelihood (ML), with the two approaches giving similar results. Regression is simpler and hence allows the fitting of more realistic models. Interval mapping gives some extra power to detect QTLs and better separation of the estimates of the size and location of the QTLs compared to using markers one at a time. Groups of linked QTLs can mislead the experimenter if a model with only a single QTL is fined to the data, but models fitting multiple QTLs or a single QTL and additional markers as cofactors can ameliorate this problem. In outbred populations marker loci may not be fully informative and this will reduce the power to detect a QTL and may lead to biased estimates of its position. For both the analysis of crosses between outbred lines and the analysis of half-sib structured outbred populations there are simple ways to combine information from a number of markers in a linkage group to increase the power to detect QTLs and reduce biases in estimates of QTL position and effect. The benefits of using multiple, as opposed to single, markers are greater in the analysis of outbred populations than they are in the analysis of inbred line crosses. Future experimental studies of QTLs in livestock populations should attempt to use multiple marker methods in order to reap these benefits.
 

Chris S Haley, Sara A Knott

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume 21. Gene mapping; polymorphisms; disease genetic markers; marker assisted selection; gene expression; transgenes; non-convention, , 25–32, 1994
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