An extension of interval mapping is presented
that incorporates all intervals on the linkage map simultaneously.
The approach uses a working model in which
the sizes of putative QTL for all intervals across the
genome are random effects. An outlier detection method
is used to screen for possible QTL. Selected QTL are
subsequently fitted as fixed effects. This screening and
selection approach is repeated until the variance component
for QTL sizes is not statistically significant.
A comprehensive simulation study is conducted in which
map uncertainty is included. The proposed method is
shown to be superior to composite interval mapping in
terms of power of detection of QTL. There is an increase
in the rate of false positive QTL detected when using the
new approach, but this rate decreases as the population
size increases. The new approach is much simpler computationally.
The analysis of flour milling yield in a
doubled haploid population illustrates the improved power
of detection of QTL using the approach, and also shows
how vital it is to allow for sources of non-genetic variation
in the analysis.