In a typical composite interval mapping experiment,
the probability of obtaining false QTL is likely to be at least an order of
magnitude greater than the nominal experiment-wise Type I error rate, as set by
permutation test. F2 mapping crosses were simulated with three
different genetic maps. Each map contained ten QTL on either three, six or
twelve linkage groups. QTL effects were additive only, and heritability was
50%. Each linkage group had 11 evenly-spaced (10 cM) markers. Selective genotyping was used.
Simulated data were analyzed by composite interval mapping with the Zmapqtl program
of QTL Cartographer. False positives were minimized by using the largest
feasible number of markers to control genetic background effects. Bootstrapping
was then used to recover mapping power lost to the large number of conditioning
markers. Bootstrapping is shown to be a useful tool for QTL discovery, although
it can also produce false positives. Quantitative bootstrap support—the
proportion of bootstrap replicates in which a significant likelihood maximum occurred
in a given marker interval—was positively correlated with the probability that
the likelihood maxima revealed a true QTL. X-linked QTL were detected with much
lower power than autosomal QTL. It is suggested that QTL mapping experiments
should be supported by accompanying simulations that replicate the marker map,
crossing design, sample size, and method of analysis used for the actual experiment.
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