Another look at multiplicative models in quantitative genetics

This paper reviews basic theory and features of the multiplicative model of gene action. A formal decomposition of the mean and of the genotypic variance is presented. Connections between the statistical parameters of this model and those of the factorial decomposition into additive, dominance and epistatic effects are also emphasized. General formulae for the genotypic covariance among inbred relatives are given in the case of linkage equilibrium. It is shown that neglecting the epistatic components of variation makes the multiplicative model a pseudo-additive one, since this approximation does not break the strong dependency between mean and variance effects. Similarities and differences between the classical polygenic ’additive-dominance’ and the multiplicative gene action approaches are outlined and discussed. Numerical examples for the biallelic case are produced to illustrate that comparison.Cet article présente la théorie et les principales caractéristiques du modèle multiplicatif d’action des gènes. Une décomposition formelle de la moyenne et de la variance génotypique permet d’établir les relations entre les paramètres statistiques de ce modèle et ceux issus de la décomposition factorielle de l’effet des gènes en effets additifs, de dominance et d’épistasie. Une formule générale de la covariance entre apparentés dans une population consanguine en équilibre de liaison est proposée. On montre que les composantes épistatiques de la variabilité génétique peuvent être négligées ; le modèle multiplicatif devient alors un modèle pseudo-additif, l’approximation ne supprimant pas la forte liaison entre moyenne et variance. Les similitudes et les différences entre le modèle polygénique « additif-dominance » classique et le modèle multiplicatif d’action des gènes sont discutées et illustrées par des exemples dans le cas biallélique

“Ant” and “Grasshopper” Life-History Strategies in Saccharomyces cerevisiae

From the evolutionary and ecological points of view, it is essential to distinguish between the genetic and environmental components of the variability of life-history traits and of their trade-offs. Among the factors affecting this variability, the resource uptake rate deserves particular attention, because it depends on both the environment and the genetic background of the individuals. In order to unravel the bases of the life-history strategies in yeast, we grew a collection of twelve strains of Saccharomyces cerevisiae from different industrial and geographical origins in three culture media differing for their glucose content. Using a population dynamics model to fit the change of population size over time, we estimated the intrinsic growth rate (r), the carrying capacity (K), the mean cell size and the glucose consumption rate per cell. The life-history traits, as well as the glucose consumption rate, displayed large genetic and plastic variability and genetic-by-environment interactions. Within each medium, growth rate and carrying capacity were not correlated, but a marked trade-off between these traits was observed over the media, with high K and low r in the glucose rich medium and low K and high r in the other media. The cell size was tightly negatively correlated to carrying capacity in all conditions. The resource consumption rate appeared to be a clear-cut determinant of both the carrying capacity and the cell size in all media, since it accounted for 37% to 84% of the variation of those traits. In a given medium, the strains that consume glucose at high rate have large cell size and low carrying capacity, while the strains that consume glucose at low rate have small cell size but high carrying capacity. These two contrasted behaviors may be metaphorically defined as “ant” and “grasshopper” strategies of resource utilization. Interestingly, a strain may be “ant” in one medium and “grasshopper” in another. These life-history strategies are discussed with regards to yeast physiology, and in an evolutionary perspective

Transcriptomic response to divergent selection for flowering times reveals convergence and key players of the underlying gene regulatory network

International audienceArtificial selection experiments are designed to investigate phenotypic evolution of complex traits and its genetic basis. Here we focused on flowering time, a trait of key importance for plant adaptation and life-cycle shifts. We undertook divergent selection experiments from two maize inbred lines. After 13 generations of selection, we obtained a time-lag of roughly two weeks between Early-and Late-populations. We used this material to characterize the genome-wide transcriptomic response to selection in the shoot apical meristem before, during and after floral transition in field conditions during two consecutive years. We validated the reliability of performing RNA-sequencing in uncontrolled conditions. We found that roughly half of maize genes were expressed in the shoot apical meristem, 59.3% of which were differentially expressed. We detected a majority of genes with differential expression between inbreds and across meristem status, and retrieved a subset of 2,451 genes involved in the response to selection. Among these, we found a significant enrichment for genes with known function in maize flowering time. Furthermore, they were more often shared between inbreds than expected by chance, suggesting convergence of gene expression. We discuss new insights into the expression pattern of key players of the underlying gene regulatory network including the Zea mays genes CENTRORADIALIS (ZCN8), RELATED TO AP2.7 (RAP2.7), MADS4 (ZMM4), KNOTTED1 (KN1), GIBBERELLIN2-OXIDASE1 (GA2ox1), as well as alternative scenarios for genetic convergence

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

<p>Abstract</p> <p>Background</p> <p>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, <it>F</it>252 and <it>MBS</it>847, 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.</p> <p>Results</p> <p>All populations, except the Early <it>MBS</it>847, 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 <it>F</it>252 seed lot at a candidate locus for flowering time, which explained 35% of the trait variation within the Late <it>F</it>252 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.</p> <p>Conclusions</p> <p>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.</p

Systemic properties of metabolic networks lead to an epistasis-based model for heterosis

The genetic and molecular approaches to heterosis usually do not rely on any model of the genotype–phenotype relationship. From the generalization of Kacser and Burns’ biochemical model for dominance and epistasis to networks with several variable enzymes, we hypothesized that metabolic heterosis could be observed because the response of the flux towards enzyme activities and/or concentrations follows a multi-dimensional hyperbolic-like relationship. To corroborate this, we used the values of systemic parameters accounting for the kinetic behaviour of four enzymes of the upstream part of glycolysis, and simulated genetic variability by varying in silico enzyme concentrations. Then we “crossed” virtual parents to get 1,000 hybrids, and showed that best-parent heterosis was frequently observed. The decomposition of the flux value into genetic effects, with the help of a novel multilocus epistasis index, revealed that antagonistic additive-by-additive epistasis effects play the major role in this framework of the genotype–phenotype relationship. This result is consistent with various observations in quantitative and evolutionary genetics, and provides a model unifying the genetic effects underlying heterosis

Coulomb dissociation of N 20,21

Neutron-rich light nuclei and their reactions play an important role in the creation of chemical elements. Here, data from a Coulomb dissociation experiment on N20,21 are reported. Relativistic N20,21 ions impinged on a lead target and the Coulomb dissociation cross section was determined in a kinematically complete experiment. Using the detailed balance theorem, the N19(n,γ)N20 and N20(n,γ)N21 excitation functions and thermonuclear reaction rates have been determined. The N19(n,γ)N20 rate is up to a factor of 5 higher at

Flowering time Records in the F252 DSE

Compilation of field notations for flowering time realized between 1997 (generation G1) and 2014 (generation G18) in the F252 DSE. Columns description in the Read.me fil

Flowering time Records in the MBS DSE

Compilation of field notations for flowering time realized between 1997 (generation G1) and 2014 (generation G18) in the MBS DSE. Columns description are in the Read.me fil

Pedigrees from the MBS DSE

Pedigree of the progenitors since generation G0 (1993) of the MBS DSE