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

Abstract

Background: In order to investigate the rate and limits of the response to selection from highly inbred genetic material and evaluate the respective contribution of standing variation and new mutations, we conducted a divergent selection experiment from maize inbred lines in open-field conditions during 7 years. Two maize commercial seed lots considered as inbred lines, F252 and MBS847, constituted two biological replicates of the experiment. In each replicate, we derived an Early and a Late population by selecting and selfing the earliest and the latest individuals, respectively, to produce the next generation.

Results: All populations, except the Early MBS847, responded to selection despite a short number of generations and a small effective population size. Part of the response can be attributed to standing genetic variation in the initial seed lot. Indeed, we identified one polymorphism initially segregating in the F252 seed lot at a candidate locus for flowering time, which explained 35% of the trait variation within the Late F252 population. However, the model that best explained our data takes into account both residual polymorphism in the initial seed lots and a constant input of heritable genetic variation by new (epi)mutations. Under this model, values of mutational heritability range from 0.013 to 0.025, and stand as an upper bound compare to what is reported in other species.

Conclusions: Our study reports a long-term divergent selection experiment for a complex trait, flowering time, conducted on maize in open-field conditions. Starting from a highly inbred material, we created within a few generations populations that strikingly differ from the initial seed lot for flowering time while preserving most of the phenotypic characteristics of the initial inbred. Such material is unique for studying the dynamics of the response to selection and its determinants. In addition to the fixation of a standing beneficial mutation associated with a large phenotypic effect, a constant input of genetic variance by new mutations has likely contributed to the response. We discuss our results in the context of the evolution and mutational dynamics of populations characterized by a small effective population size.

Figure 1

Description of the Selection Scheme. Selection was conducted independently for individuals derived from F252 and MBS in order to obtain an Early and a Late population from each original seed lot. At each generation, S1 families produced by the selfing of the 10 most early-flowering individuals (rectangles with light gray patterns) and the 10 most late-flowering individuals (rectangles with black patterns) of the previous generation are phenotypically evaluated. Hundred individuals per family were sown in a 4 randomized block design. Each block therefore encompasses 25 individuals of each family and contains individuals from the Early and the Late population, as well as a control plot (open rectangles with a dashed line). The same protocol was repeated over generations.

Figure 2

Genealogies of selected individuals in the four populations. From the initial seed lots (G0), individuals were selected for 7 generations (black dots). An Early and a Late population were derived from F252 (a) and MBS (b). Genealogies of the individuals of the last generation (G7) are indicated in black (individuals connected by black lines). Grey lines indicate the genealogy of individuals that were retained during the selection experiment but that did not contribute to the last generation (G7).

Figure 3

Genealogy of the genotypes at the QCK5e06 locus in the Late F252 population. Generations are numbered from G0 to G7. Circles represent the individuals numbered from 1 to 61. They are coloured according to their genotype at locus QCK5e06: light grey = heterozygotes, dark grey = homozygotes for the late allele, white = homozygotes for the other allele. Residual heterozygosity in the initial seed lot is represented by a grey/white pattern. Out of 31 individuals genotypes from the initial seed lot, only one was heterozygote, and all the others were homozygotes for the white allele. Dashed lines around circles indicate missing genotypic data. The genotypes of the corresponding individuals were inferred from their progenies as described in the material and methods (except for individuals 18,31,33,34 and 62 which were treated as missing data). Phenotypic information for individuals 27, 28 and 40 was missing. They were attributed a genotypic value for flowering time by averaging the genotypic values of the individuals of the same sub-family at the same generation (23, 24, 25, 26 for individual 27; 29, 30, 31, 32 for individual 28; and 39, 41, 42 for individual 40).

Figure 4

Response to selection from generations G1 to G7 in the F252 (a) and MBS (b) populations. Flowering time was measured on S2 families for each genotype of the genealogies in a two years evaluation trial (2004 and 2005). For each population, the average genotypic values (circles) and the interval between the extremes genotypic values (dashed bars) are plotted against the number of generations. Black and Grey colors represent the Early and the Late population respectively. In the Late F252 population, we estimated genotypic values separately for the Late-NVL F252 (dark-grey) and the Late-VL F252 (light-grey). The response to selection is significant in all populations except the Early MBS (see Table 2). For example in G7, the Late MBS flowers on average 3 days later than both the initial seed lot (not shown) and the Early MBS population. Note that the Late-VL F252 genotypes at generation G6 and G7, all descend from a single individual at generation G4 (Figure 3).

Figure 5

Association between the polymorphism at the QCK5e06 locus and flowering time variation in the Late F252. (a) Phenotypic effect associated with the frequency of the Late allele. Datapoints represent the flowering time deviation from the average genotypic value for each genotype in the genealogy as a function of the frequency of the Late allele at the QCK5e06 locus. The straight line represents the slope of the regression. (b) The simulated distribution of estimated additive effect for flowering time, a, was obtained by simulating a matrix of genotypes by gene dropping and performing an association test with the corresponding observed phenotypic matrix (see Material and Methods). Resulting a values from 20,000 simulations are plotted. The position of the triangle indicates the observed additive effect associated with the polymorphism at locus QCK5e06.

Figure 6

Segmented regression model with one breakpoint that best fit observed and simulated data. Segmented regression on observed data from the Late MBS population (a) and the Early MBS population (b). (a) Late MBS population: the best model is a single line (breakpoint occurs at the generation 7 or later). (b) Early MBS population: the best model is provided by a breakpoint at generation 2 leading to a point at this generation and a line between the generations 3 to 7. Examples of segmented regression on simulations of the selection experiment (c to f). The selection experiment was simulated with the average initial heritability = 0.02841 estimated from the observed data of the Late MBS population with Model 2 (no input of de novo mutation see text for the details), and n_P = 100 and n_H = 60. The four examples were chosen among those that display the same average response to selection over the seven generations. (c) The best model is provided by a breakpoint at generation 4 leading to two segments, the first between generations 2 to 4 and the second between generations 5 to 7. (d to f) The best models are provided by a breakpoint at generation 3 leading to two segments, the first between generations 2 to 3 and the second between generations 4 to 7. In (f), the second segment is horizontal and corresponds to a plateau.

Figure 7

Distribution of the AICc weights of segmented regression models with one breakpoint in simulated and observed data from the four populations. The selection experiment was simulated with the same parameters ans in Figure 6. For each simulation, segmented regression with a single breakpoint at each of the seven generations were fitted. The red bars represent the proportion of simulations in which the best fitted model corresponds to a breakpoint at the given generation. Similar results were obtained across simulations with the initial conditions indicated in Table 4. The grey bars represent the AICc weights computed by fitting segmented regression with a single breakpoint at each of the seven generations on the observed data of the two F252 (Early and Late Not Very Late) and MBS (Early and Late) experimental populations. These weights give the probability that the change in the rate of the response to selection occurred at generation G_i (2 ≤ i ≤ 7). Note that while in the simulations a higher probability is associated with a breakpoint occuring at G1, 3 out of the 4 experimental populations are consistent with a linear response to selection through time, i.e. breakpoint at G7.

Figure 8

Experimental procedure with a special emphasize on the evolutionary processes that operate through generations. Bars represent the diploid genotypes present in a virtual population (Early or Late population derived from either F252 or MBS). White and black boxes indicate homozygous regions that are fixed for one of the allele (Early or Late). Heterozygosity can result either from standing variation present at G0 (grey box) or from new mutations occurring in the subsequent generations (white box with a star). Mutations from both sources can become fixed during the course of the experiment (G1 to G7) because of genetic drift and/or selection. At each generation, molecular polymorphism (as revealed by RFLP on 10 candidate regions) and phenotypic variation were evaluated in order to detect association between RFLP and flowering time variation.