INFERENCE OF GENOME-WIDE MUTATION-RATES AND DISTRIBUTIONS OF MUTATIONEFFECTS FOR FITNESS TRAITS - A SIMULATION STUDY

The properties and limitations of maximum likelihood (ML) inference of genome-wide mutation rates (U) and parameters of distributions of mut ation effects are investigated. Mutation parameters are estimated from simulated experiments in which mutations randomly accumulate in inbre d lines. ML produces more accurate estimates than the procedure of Bat eman and Mukai and is more robust if the data do not conform to the mo del assumed. Unbiased ML estimates of the mutation effects distributio n parameters can be obtained if a value for U can be assumed, but if U is estimated simultaneously with the distribution parameters, likelih ood may increase monotonically as a function of U. If the distribution of mutation effects is leptokurtic, the number of mutation events per line is large, or if genotypic values are poorly estimated, only a lo wer limit for U,an upper limit for the mean mutation effect, and a low er limit for the kurtosis of the distribution can be given. It is argu ed that such lower (upper) limits are appropriate minima (maxima). Est imates of the mean mutational effect are unbiased but may convey littl e about the properties of the distribution if it is leptokurtic.