20 research outputs found

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

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    <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

    Etude des bases (épi) génétiques de l'adaptation dans une expérience de sélection divergente pour la précocité de floraison chez le maïs

    No full text
    Quantitative variation results from the combined action of multiple genes and their environment. Two approaches are currently employed to gain insights into the link between genotype and phenotype and to dissect the genetic architecture of complex traits. On one hand, experimental evolution allows quantifying the number of mutations and their effect on the evolution of a phenotype subject to artificial selection. On the other hand, QTL (Quantitative Trait Locus) and association mapping are used to identify loci responsible for phenotypic variation. In this work, we have combined all 3 approaches in order to (1) evaluate the role of new mutations and standing genetic variation to the response to selection ; (2) to identify the genetic determinants underlying this response ; (3) to dissect at one candidate locus the genetic mechanisms of its contribution to phenotypic variation. We have used the material produced by a divergent selection experiment for flowering time conducted for over 10 years in the field. This experiment was conducted in parallel from two commercial maize inbred line, F252 and MBS847. From each initial seed lot, two populations, an early population and a late population, were created by selecting and selfing the earliest/latest individuals at each generation. We characterized the response to selection after 7 generations. The response was fast, asymmetric between populations and significant in 3 out of 4 populations. It was linear through time indicating that new mutations have generated new additive genetic variance at each generation. We identified a major locus contributing to 35% of the variation for flowering time in the late F252 population. At this locus, two alleles were present as residual heterozygocity in the initial seed lot. The two alleles exhibited haplotypes extending on a region around the eIF-4A (Eukaryotic Initiation Translation Factor 4A) that diverged drastically both at the nucleotide (5.7%) and structural level. We were able to confirm the association of the candidate locus to flowering time variation and other traits such as height and leaf number, first using an association panel containing 317 maize lines, second through the developmental characterization of early and late genotypes. In addition, to its pleiotropic effect, we have shown by developing a specific statistical framework that this locus exhibit pervasive epistatic interactions with other loci segregating in the population. Hence, its effect largely depended on the genetic background. We have finally applied methyl-sensitive AFLP (Amplified Frgament length Polymorphisms) to screen all genotypes in order to identify the polymorphisms potentially involved in the response to selection during the first 7 generations Our preliminary results indicate both a genetic and epigenetic differentiation between early and late populations. This differentiation seems however to be mainly driven by standing genetic variation.La variation quantitative rĂ©sulte de l’action combinĂ©e des gĂšnes et de leur environnement. Pour comprendre la relation gĂ©notype-phĂ©notype et dissĂ©quer l’architecture des caractĂšres complexes, deux approches sont couramment employĂ©es. D’une part l’évolution expĂ©rimentale qui permet de quantifier le nombre et l’effet des mutations dans la construction d’un phĂ©notype soumis Ă  une pression de sĂ©lection, d’autre part la cartographie de QTL (Quantitative Trait Loci) et/ou la gĂ©nĂ©tique d’association qui permettent d’identifier les locus responsables de la variation phĂ©notypique. Au cours de cette thĂšse, nous avons combinĂ© l’ensemble de ces approches pour (1) Ă©valuer le rĂŽle relatif des nouvelles mutations et de la variabilitĂ© rĂ©siduelle dans la rĂ©ponse Ă  la sĂ©lection ; (2) identifier les dĂ©terminants gĂ©nĂ©tiques sous tendant cette rĂ©ponse ; (3) dissĂ©quer, pour un locus candidat, les mĂ©canismes gĂ©nĂ©tiques de sa contribution Ă  la variation phĂ©notypique. Pour cela, nous disposons d’un matĂ©riel gĂ©nĂ©tique rĂ©sultant d’une expĂ©rience de sĂ©lection divergente pour la date de floraison menĂ©e depuis plus de dix ans. Cette expĂ©rience a Ă©tĂ© conduite en parallĂšle Ă  partir de deux lots de semences de lignĂ©es commerciales de maĂŻs (F252 et MBS847). Pour chaque lignĂ©e de dĂ©part, deux populations ont Ă©tĂ© constituĂ©es, une population prĂ©coce et une population tardive produites en sĂ©lectionnant et autofĂ©condant les gĂ©notypes les plus prĂ©coces/tardifs Ă  chaque gĂ©nĂ©ration. Nous avons caractĂ©risĂ© la rĂ©ponse Ă  la sĂ©lection aprĂšs 7 gĂ©nĂ©rations. Cette rĂ©ponse est rapide, asymĂ©trique entre populations et significative dans 3 des 4 populations. Elle est linĂ©aire avec le temps ce qui indique que des nouvelles mutations contribuent Ă  crĂ©er de la variance gĂ©nĂ©tique Ă  chaque gĂ©nĂ©ration. Nous avons identifiĂ© un locus majeur contribuant Ă  35% de la variation pour la date de floraison dans la population F252 tardive et pour lequel les deux allĂšles Ă©taient prĂ©sents dans le lot de semence initial sous forme d’hĂ©tĂ©rozygotie rĂ©siduelle. Les deux allĂšles prĂ©sentent des haplotypes trĂšs divergents autant au niveau de leur variation nuclĂ©otidique (5.7%) que d’un point de vue structural (16 indels) sur une rĂ©gion proche du gĂšne eIF-4A (Eukaryotic Initiation Translation Factor 4A). L’association de ce locus avec la date de floraison et d’autres caractĂšres corrĂ©lĂ©s tels que la hauteur et le nombre de feuilles a Ă©tĂ© confirmĂ©e par une caractĂ©risation dĂ©veloppementale fine de gĂ©notypes prĂ©coces et tardifs et Ă©galement dans un panel d’association comprenant 317 lignĂ©es de maĂŻs cultivĂ©. En plus d’un effet plĂ©iotrope, nous avons montrĂ© grĂące au dĂ©veloppement de mĂ©thodes statistiques que ce locus prĂ©sente des interactions Ă©pistatique fortes avec d’autres locus en sĂ©grĂ©gation puisque son effet dĂ©pend largement du fond gĂ©nĂ©tique. Nous avons finalement utilisĂ© des AFLP (Amplified Fragment Length Polymorphisms) sur tous les gĂ©notypes issus des 7 premiĂšres gĂ©nĂ©rations de sĂ©lection afin d’identifier d’autres polymorphismes potentiellement impliquĂ©s dans la rĂ©ponse Ă  la sĂ©lection. Nos rĂ©sultats prĂ©liminaires montrent une diffĂ©renciation gĂ©nĂ©tique et Ă©pigĂ©nĂ©tique entre les populations sĂ©lectionnĂ©es qui semble ĂȘtre prĂ©fĂ©rentiellement due Ă  de l’hĂ©tĂ©rozygotie rĂ©siduelle

    (Epi)-genetic basis of adaptation in a divergent selection experiment for flowering time in maize inbred lines

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    La variation quantitative rĂ©sulte de l’action combinĂ©e des gĂšnes et de leur environnement. Pour comprendre la relation gĂ©notype-phĂ©notype et dissĂ©quer l’architecture des caractĂšres complexes, deux approches sont couramment employĂ©es. D’une part l’évolution expĂ©rimentale qui permet de quantifier le nombre et l’effet des mutations dans la construction d’un phĂ©notype soumis Ă  une pression de sĂ©lection, d’autre part la cartographie de QTL (Quantitative Trait Loci) et/ou la gĂ©nĂ©tique d’association qui permettent d’identifier les locus responsables de la variation phĂ©notypique. Au cours de cette thĂšse, nous avons combinĂ© l’ensemble de ces approches pour (1) Ă©valuer le rĂŽle relatif des nouvelles mutations et de la variabilitĂ© rĂ©siduelle dans la rĂ©ponse Ă  la sĂ©lection ; (2) identifier les dĂ©terminants gĂ©nĂ©tiques sous tendant cette rĂ©ponse ; (3) dissĂ©quer, pour un locus candidat, les mĂ©canismes gĂ©nĂ©tiques de sa contribution Ă  la variation phĂ©notypique. Pour cela, nous disposons d’un matĂ©riel gĂ©nĂ©tique rĂ©sultant d’une expĂ©rience de sĂ©lection divergente pour la date de floraison menĂ©e depuis plus de dix ans. Cette expĂ©rience a Ă©tĂ© conduite en parallĂšle Ă  partir de deux lots de semences de lignĂ©es commerciales de maĂŻs (F252 et MBS847). Pour chaque lignĂ©e de dĂ©part, deux populations ont Ă©tĂ© constituĂ©es, une population prĂ©coce et une population tardive produites en sĂ©lectionnant et autofĂ©condant les gĂ©notypes les plus prĂ©coces/tardifs Ă  chaque gĂ©nĂ©ration. Nous avons caractĂ©risĂ© la rĂ©ponse Ă  la sĂ©lection aprĂšs 7 gĂ©nĂ©rations. Cette rĂ©ponse est rapide, asymĂ©trique entre populations et significative dans 3 des 4 populations. Elle est linĂ©aire avec le temps ce qui indique que des nouvelles mutations contribuent Ă  crĂ©er de la variance gĂ©nĂ©tique Ă  chaque gĂ©nĂ©ration. Nous avons identifiĂ© un locus majeur contribuant Ă  35% de la variation pour la date de floraison dans la population F252 tardive et pour lequel les deux allĂšles Ă©taient prĂ©sents dans le lot de semence initial sous forme d’hĂ©tĂ©rozygotie rĂ©siduelle. Les deux allĂšles prĂ©sentent des haplotypes trĂšs divergents autant au niveau de leur variation nuclĂ©otidique (5.7%) que d’un point de vue structural (16 indels) sur une rĂ©gion proche du gĂšne eIF-4A (Eukaryotic Initiation Translation Factor 4A). L’association de ce locus avec la date de floraison et d’autres caractĂšres corrĂ©lĂ©s tels que la hauteur et le nombre de feuilles a Ă©tĂ© confirmĂ©e par une caractĂ©risation dĂ©veloppementale fine de gĂ©notypes prĂ©coces et tardifs et Ă©galement dans un panel d’association comprenant 317 lignĂ©es de maĂŻs cultivĂ©. En plus d’un effet plĂ©iotrope, nous avons montrĂ© grĂące au dĂ©veloppement de mĂ©thodes statistiques que ce locus prĂ©sente des interactions Ă©pistatique fortes avec d’autres locus en sĂ©grĂ©gation puisque son effet dĂ©pend largement du fond gĂ©nĂ©tique. Nous avons finalement utilisĂ© des AFLP (Amplified Fragment Length Polymorphisms) sur tous les gĂ©notypes issus des 7 premiĂšres gĂ©nĂ©rations de sĂ©lection afin d’identifier d’autres polymorphismes potentiellement impliquĂ©s dans la rĂ©ponse Ă  la sĂ©lection. Nos rĂ©sultats prĂ©liminaires montrent une diffĂ©renciation gĂ©nĂ©tique et Ă©pigĂ©nĂ©tique entre les populations sĂ©lectionnĂ©es qui semble ĂȘtre prĂ©fĂ©rentiellement due Ă  de l’hĂ©tĂ©rozygotie rĂ©siduelle.Quantitative variation results from the combined action of multiple genes and their environment. Two approaches are currently employed to gain insights into the link between genotype and phenotype and to dissect the genetic architecture of complex traits. On one hand, experimental evolution allows quantifying the number of mutations and their effect on the evolution of a phenotype subject to artificial selection. On the other hand, QTL (Quantitative Trait Locus) and association mapping are used to identify loci responsible for phenotypic variation. In this work, we have combined all 3 approaches in order to (1) evaluate the role of new mutations and standing genetic variation to the response to selection ; (2) to identify the genetic determinants underlying this response ; (3) to dissect at one candidate locus the genetic mechanisms of its contribution to phenotypic variation. We have used the material produced by a divergent selection experiment for flowering time conducted for over 10 years in the field. This experiment was conducted in parallel from two commercial maize inbred line, F252 and MBS847. From each initial seed lot, two populations, an early population and a late population, were created by selecting and selfing the earliest/latest individuals at each generation. We characterized the response to selection after 7 generations. The response was fast, asymmetric between populations and significant in 3 out of 4 populations. It was linear through time indicating that new mutations have generated new additive genetic variance at each generation. We identified a major locus contributing to 35% of the variation for flowering time in the late F252 population. At this locus, two alleles were present as residual heterozygocity in the initial seed lot. The two alleles exhibited haplotypes extending on a region around the eIF-4A (Eukaryotic Initiation Translation Factor 4A) that diverged drastically both at the nucleotide (5.7%) and structural level. We were able to confirm the association of the candidate locus to flowering time variation and other traits such as height and leaf number, first using an association panel containing 317 maize lines, second through the developmental characterization of early and late genotypes. In addition, to its pleiotropic effect, we have shown by developing a specific statistical framework that this locus exhibit pervasive epistatic interactions with other loci segregating in the population. Hence, its effect largely depended on the genetic background. We have finally applied methyl-sensitive AFLP (Amplified Frgament length Polymorphisms) to screen all genotypes in order to identify the polymorphisms potentially involved in the response to selection during the first 7 generations Our preliminary results indicate both a genetic and epigenetic differentiation between early and late populations. This differentiation seems however to be mainly driven by standing genetic variation

    Etude des bases (épi) génétiques de l'adaptation dans une expérience de sélection divergente pour la précocité de floraison chez le maßs

    No full text
    La variation quantitative rĂ©sulte de l action combinĂ©e des gĂšnes et de leur environnement. Pour comprendre la relation gĂ©notype-phĂ©notype et dissĂ©quer l architecture des caractĂšres complexes, deux approches sont couramment employĂ©es. D une part l Ă©volution expĂ©rimentale qui permet de quantifier le nombre et l effet des mutations dans la construction d un phĂ©notype soumis Ă  une pression de sĂ©lection, d autre part la cartographie de QTL (Quantitative Trait Loci) et/ou la gĂ©nĂ©tique d association qui permettent d identifier les locus responsables de la variation phĂ©notypique. Au cours de cette thĂšse, nous avons combinĂ© l ensemble de ces approches pour (1) Ă©valuer le rĂŽle relatif des nouvelles mutations et de la variabilitĂ© rĂ©siduelle dans la rĂ©ponse Ă  la sĂ©lection ; (2) identifier les dĂ©terminants gĂ©nĂ©tiques sous tendant cette rĂ©ponse ; (3) dissĂ©quer, pour un locus candidat, les mĂ©canismes gĂ©nĂ©tiques de sa contribution Ă  la variation phĂ©notypique. Pour cela, nous disposons d un matĂ©riel gĂ©nĂ©tique rĂ©sultant d une expĂ©rience de sĂ©lection divergente pour la date de floraison menĂ©e depuis plus de dix ans. Cette expĂ©rience a Ă©tĂ© conduite en parallĂšle Ă  partir de deux lots de semences de lignĂ©es commerciales de maĂŻs (F252 et MBS847). Pour chaque lignĂ©e de dĂ©part, deux populations ont Ă©tĂ© constituĂ©es, une population prĂ©coce et une population tardive produites en sĂ©lectionnant et autofĂ©condant les gĂ©notypes les plus prĂ©coces/tardifs Ă  chaque gĂ©nĂ©ration. Nous avons caractĂ©risĂ© la rĂ©ponse Ă  la sĂ©lection aprĂšs 7 gĂ©nĂ©rations. Cette rĂ©ponse est rapide, asymĂ©trique entre populations et significative dans 3 des 4 populations. Elle est linĂ©aire avec le temps ce qui indique que des nouvelles mutations contribuent Ă  crĂ©er de la variance gĂ©nĂ©tique Ă  chaque gĂ©nĂ©ration. Nous avons identifiĂ© un locus majeur contribuant Ă  35% de la variation pour la date de floraison dans la population F252 tardive et pour lequel les deux allĂšles Ă©taient prĂ©sents dans le lot de semence initial sous forme d hĂ©tĂ©rozygotie rĂ©siduelle. Les deux allĂšles prĂ©sentent des haplotypes trĂšs divergents autant au niveau de leur variation nuclĂ©otidique (5.7%) que d un point de vue structural (16 indels) sur une rĂ©gion proche du gĂšne eIF-4A (Eukaryotic Initiation Translation Factor 4A). L association de ce locus avec la date de floraison et d autres caractĂšres corrĂ©lĂ©s tels que la hauteur et le nombre de feuilles a Ă©tĂ© confirmĂ©e par une caractĂ©risation dĂ©veloppementale fine de gĂ©notypes prĂ©coces et tardifs et Ă©galement dans un panel d association comprenant 317 lignĂ©es de maĂŻs cultivĂ©. En plus d un effet plĂ©iotrope, nous avons montrĂ© grĂące au dĂ©veloppement de mĂ©thodes statistiques que ce locus prĂ©sente des interactions Ă©pistatique fortes avec d autres locus en sĂ©grĂ©gation puisque son effet dĂ©pend largement du fond gĂ©nĂ©tique. Nous avons finalement utilisĂ© des AFLP (Amplified Fragment Length Polymorphisms) sur tous les gĂ©notypes issus des 7 premiĂšres gĂ©nĂ©rations de sĂ©lection afin d identifier d autres polymorphismes potentiellement impliquĂ©s dans la rĂ©ponse Ă  la sĂ©lection. Nos rĂ©sultats prĂ©liminaires montrent une diffĂ©renciation gĂ©nĂ©tique et Ă©pigĂ©nĂ©tique entre les populations sĂ©lectionnĂ©es qui semble ĂȘtre prĂ©fĂ©rentiellement due Ă  de l hĂ©tĂ©rozygotie rĂ©siduelle.Quantitative variation results from the combined action of multiple genes and their environment. Two approaches are currently employed to gain insights into the link between genotype and phenotype and to dissect the genetic architecture of complex traits. On one hand, experimental evolution allows quantifying the number of mutations and their effect on the evolution of a phenotype subject to artificial selection. On the other hand, QTL (Quantitative Trait Locus) and association mapping are used to identify loci responsible for phenotypic variation. In this work, we have combined all 3 approaches in order to (1) evaluate the role of new mutations and standing genetic variation to the response to selection ; (2) to identify the genetic determinants underlying this response ; (3) to dissect at one candidate locus the genetic mechanisms of its contribution to phenotypic variation. We have used the material produced by a divergent selection experiment for flowering time conducted for over 10 years in the field. This experiment was conducted in parallel from two commercial maize inbred line, F252 and MBS847. From each initial seed lot, two populations, an early population and a late population, were created by selecting and selfing the earliest/latest individuals at each generation. We characterized the response to selection after 7 generations. The response was fast, asymmetric between populations and significant in 3 out of 4 populations. It was linear through time indicating that new mutations have generated new additive genetic variance at each generation. We identified a major locus contributing to 35% of the variation for flowering time in the late F252 population. At this locus, two alleles were present as residual heterozygocity in the initial seed lot. The two alleles exhibited haplotypes extending on a region around the eIF-4A (Eukaryotic Initiation Translation Factor 4A) that diverged drastically both at the nucleotide (5.7%) and structural level. We were able to confirm the association of the candidate locus to flowering time variation and other traits such as height and leaf number, first using an association panel containing 317 maize lines, second through the developmental characterization of early and late genotypes. In addition, to its pleiotropic effect, we have shown by developing a specific statistical framework that this locus exhibit pervasive epistatic interactions with other loci segregating in the population. Hence, its effect largely depended on the genetic background. We have finally applied methyl-sensitive AFLP (Amplified Frgament length Polymorphisms) to screen all genotypes in order to identify the polymorphisms potentially involved in the response to selection during the first 7 generations Our preliminary results indicate both a genetic and epigenetic differentiation between early and late populations. This differentiation seems however to be mainly driven by standing genetic variation.PARIS11-SCD-Bib. Ă©lectronique (914719901) / SudocSudocFranceF

    Uncovering strategic assumptions: Understanding managers' ability to build representations

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    Strategic management researchers are increasingly showing interest in the underlying assumptions behind strategic decisions made by managers. This article intends to revisit the conceptual framework currently available to study managers' representations. Many researchers in cognitive and social sciences have indeed suggested that construction may be a better metaphor for cognition than the traditional perception paradigm. We suggest that management research could learn a great deal on this matter from other social sciences. Two main arguments are put forward. First we argue that the cognitive/computational framework is not relevant to address the epistemological challenge of describing managerial cognition. Second we argue that adopting a perspective focused on either the individual or the collective level is not suited for the challenge. We suggest at least two simultaneous moves thus leading to a paradigmatic shift: introducing the social (and emotional) dimensions into the picture and recognizing the need for Interactionism as the core of representation-building processes.

    CinNapht AIE(E)Gens for selective imaging of lipid droplets

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    International audienceThis article describes the synthesis and photophysical properties of Aggregation-Induced Emission (enhancement) luminoGens derivativated from CinNaphts dyes. These fluorophores can be obtained in good yields in a single SNAr step of a fluorinated CinNapht derivative by hindered aromatic amines. They exhibit AIE(E) behavior associated with solid-state fluorescence covering an emission range from 563 to 722 nm. One carbazole derivative demonstrates a remarkable efficiency in imaging lipid droplets in living cells through an original photophysical mechanis
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