Standing variation and new mutations both contribute to a fast response to selection for flowering time in maize inbreds

Table 4 Monte-Carlo simulations of the response to selection under the null hypothesis of absence of de novo mutations

n _P	n _H	estimates	other^a	% simulations with linear response	Average response^b	P value
100	100	Late MBS	add	0.08	0.67 (0.43-0.99)	0.019*
100	60	Late MBS	add	0.12	0.46 (0.25-0.74)	0.320^ns
100	60	Late MBS	dom	0.28	0.49 (0.23-0.76)	0.317^ns
100	60	Early F252	add	0.24	-0.35 (-0.57 - -0.16)	0.068^ns
100	20	Late MBS	add	0.33	0.22 (0.08-0.38)	0.046*
100	10	Late MBS	add	0.51	0.13 (-0.02-0.28)	0.006**
20	20	Late MBS	add	0.06	0.49 (0.27-0.78)	0.288^ns
20	10	Late MBS	add	0.18	0.31 (0.13-0.55)	0.220^ns
20	5	Late MBS	add	0.32	0.18 (0.04-0.36)	0.030*

^aadd = additivity within and between loci. dom = dominance of the most favourable allele. ^b95% confidence interval is given between brackets.
Each simulation is defined by an initial number of polymorphic loci n_P, an initial number of heterozygous loci n_Hand the experimental population from which the initial genetic variance was estimated. Corresponding values of initial heritabilities are given in (Table 3). % simulations with linear response is the fraction of the 500 runs for which the model that minimizes AICc in the segmented regression is the one with a breakpoint at G7 or after, indicating a linear response to selection. Average response is the average response to selection computed as in (3) for each run and averaged over the 500 runs. P value contains the percentage of simulations for which the average response to selection is lower than the one observed in the corresponding experimental population. When n_His too high, the simulated response to selection is always higher than the observed one. When n_His too low, the simulated response to selection is always lower than the observed one.

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