CNRS Researcher, HDR
Member of : Pole Genome
Tel. +33(0)1 69 15 58 94
My research project aims at improving our understanding of evolutionary mechanisms through a theoretical approach. Such an approach consists in setting up models, based on current knowledge in evolutionary biology, in order to formalise and test hypotheses of interest. These models can also be at the origin of new statistical tools that make it possible to estimate, from empirical data, key parameters for the understanding of species’ evolutionary properties.
Theoretical approaches of genome evolution
Technical progress during the last decades lead to a situation in which the accumulation of genome sequence data is increasingly fast and cheap. In parallel, with the development of computer sciences and bioinformatics, it is now possible to compare the whole DNA content of individuals from the same species, or from different species with various degrees of divergence. The general principles of genome evolution fit well with the frame known as the “theory of evolution”: some DNA sequences are conserved over long periods of time because of natural selection that eliminates defective variants, others evolve rapidly because they are involved in species adaptation to their environment, and the rest seems not to be affected by selection. However, the respective impact of selection (oriented evolution) vs. genetic drift (neutral evolution) remains a matter of debate in the scientific community. For instance, the genome size is known to vary across species, while the real role of selection in such differences remains poorly known. The number of genes also varies, as does the complexity of interaction networks between genes. Finally, a significant part of genome complexity is due to repeated sequences and parasitic DNA, such as transposable elements, which evolutionary properties are still not well known.
Modelling genome evolution aims at understanding, through mathematical and numerical models, the way by which species DNA changes in the course of time. The goal of this work is not only to describe the major mechanisms involved in the evolution of such a complex system, but also to interpret empirical data in the light of population genetics and the theory of evolution.
Evolutionary quantitative genetics
The genetic architecture of quantitative traits may be extremely complex. Characters related to the size of an organism, its morphology, or its behavior can be influenced by dozens of genes as well as by the environment, and these multiple factors may interact in a way that is difficult to predict. Nevertheless, it is of major importance to understand and define the general properties of such traits in order to predict the evolutionary features. As the accumulation of precise data is tedious and expensive, although necessary to dissect finely the genetic basis of a trait, it is common to describe the general properties of the genetic achitecture of quantitative traits by their statistical properties, through the tools provided by quantitative genetics. Such tools, at the cost of some approximations, make it possible to get powerful predictions about the evolutionary properties of characters. However, perhaps because no alternative framework can be used in practice, the impact of the details of the genetic architecture (such as the number of genes, the existence of significant interactions between genetic or environmental factors), remains poorly known.
My research work aims at providing statistical and mathematical tools devoted to the understanding the evolution of quantitative characters on different time scales, and to assess the prediction power of such tools by confronting them to empirical or simulated data.
The package ‘noia’ is an implementation for R of the Natural and Orthogonal InterAction model (NOIA), a statistical framework aiming at estimating and manipulating genetic effects of quantitative characters. This page is an informal tutorial describing the practical use of the software, as well as some basic concepts in quantitative genetics modeling: noia-tutorial.
The package sra for R provides a set of tools to analyse artificial-selection response datasets. This page is an informal tutorial describing the practical use of the software: tutorial.
LE ROUZIC, A. 2022. Gene network robustness as a multivariate character. Peer Community Journal 2, e26
CHEVIN, L.M., LEUNG, C., LE ROUZIC, A., ULLER, T. 2022. Using phenotypic plasticity to understand the structure and evolution of the genotype–phenotype map. Genetica, 1-13
BURBAN, E., TENAILLON, M.I., LE ROUZIC, A. 2022. Gene network simulations provide testable predictions for the molecular domestication syndrome. Genetics 220 (2), iyab214
DESBIEZ-PIAT, A., LE ROUZIC, A., TENAILLON, M.I., DILLMANN, C. Interplay between extreme drift and selection intensities favors the fixation of beneficial mutations in selfing maize populations. Genetics 219 (2), iyab123
PÉLABON, C., ALBERTSEN, E., LE ROUZIC, A., FIRMAT, C., BOLSTAD, G.H., ARMBRUSTER, W.S., HANSEN, T.F. 2021. Quantitative assessment of observed vs. predicted responses to selection. Evolution.
DAVID, O., LE ROUZIC, A., & DILLMANN, C. 2021. Optimization of sampling designs for pedigrees and association studies. Biometrics.
DEVILLIERS, M., GARRIDO, D., POIDEVIN, M., RUBUN, T., LE ROUZIC, A., & MONTAGNE, J. 2021. Differential metabolic sensitivity of insulin-like-response-and TORC1-dependent overgrowth in Drosophila fat cells. Genetics, 217(1), 1-12.
LE ROUZIC, A., RENNEVILLE, C., MILLOT, A., AGOSTINI, S., CARMIGNAC, D., & ÉDELINE, É. 2020. Unidirectional response to bidirectional selection on body size II. Quantitative genetics. Ecology and Evolution, 10(20), 11453-11466.
RENNEVILLE, C., MILLOT, A., AGOSTINI, S., CARMIGNAC, D., MAUGARS, G., DUFOUR, S., LE ROUZIC A, & ÉDELINE, E. 2020. Unidirectional response to bidirectional selection on body size. I. Phenotypic, life‐history, and endocrine responses. Ecology and Evolution, 10(19), 10571-10592.
JALLET, A. J., LE ROUZIC, A., & GENISSEL, A. 2020. Evolution and plasticity of the transcriptome under temperature fluctuations in the fungal plant pathogen Zymoseptoria tritici. Frontiers in Microbiology, 11, 2180.
BENOIST, R., CAPDEVIELLE-DULAC, C., CHANTRE, C., JEANETTE, R., CALATAYUD, P. A., DREZEN, J. M., DUPAS, S., LE ROUZIC, A., LE RU, B., MOREAU, L., VAN DIJK, E., KAISER, L., & MOUGEL, F. 2020. Quantitative trait loci involved in the reproductive success of a parasitoid wasp. Molecular Ecology, 29 (18), 3476-3493.
DEBAT, V., & LE ROUZIC, A. 2019. Canalization, a central concept in biology. In Seminars in Cell & Developmental Biology (Vol. 88, pp. 1-3). Academic Press.
SCHNEIDER, D. I., EHRMAN, L., ENGL, T., KALTENPOTH, M., HUA-VAN, A., LE ROUZIC, A., & MILLER, W. J. 2019. Symbiont-driven male mating success in the neotropical Drosophila paulistorum superspecies. Behavior Genetics, 49(1), 83-98.
GUYEUX C, COUCHOT J.F., LE ROUZIC A., BAHI J.M., MARANGIO L. 2018. Theoretical study of the one self-regulating gene in the modified Wagner model. Mathematics 6(4):58.
ODORICO A., RÜNNEBURGER E., LE ROUZIC A. 2018. Modelling the influence of parental effects on gene‐network evolution. Journal of Evolutionary Biology 31 (5), 687-700.
DENIS B., LE ROUZIC A., WICKER-THOMAS C. 2017. Hydrocarbon patterns and mating behaviour in populations of Drosophila yakuba. Insects 6(4) 897-911.
ROBILLARD É., LE ROUZIC A., ZHANG Z., CAPY P., HUA-VAN A. 2016. Experimental evolution reveals hyperparasitic interactions among transposable elements. PNAS 113(5): 14763:14768.
RÜNNEBURGER E., LE ROUZIC A. 2016. Why and how genetic canalization evolves in gene regulatory networks. BMC Evolutionary Biology, 16(1): 329.
LE ROUZIC A., ALVAREZ-CASTRO J.M. 2016. Epistasis-driven evolutionary plateaus in selection responses. The American Naturalist, 188(6): E134-E150.
WALLAU G.L., CAPY P., LORETO E., LE ROUZIC A., HUA-VAN A. 2016. VHICA, a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila. Molecular Biology and Evolution 33(4) 1094-1109.
RENNEVILLE C., LE ROUZIC A, BAYLAC M., MILLOT A., LOISEL S., EDELINE E. 2016. Morphological drivers of trophic cascades. Oikos 125(8) 1193-1202.
LE ROUZIC A., HANSEN T.F., GOSDEN T.P., SVENSSON E.I. 2015. Evolutionary time-series analysis reveals the signature of frequency-dependent selection on a female mating polymorphism. American Naturalist, 185(6) E182-E196.
GARRIDO D., RUBIN T., POIDEVIN M., MARONI B., LE ROUZIC A., PARVY J.P., MONTAGNE J. 2015. Fatty acid synthase cooperates with glyoxalase 1 to protect against sugar toxicity. PLoS Genetics 11(2): e1004995-e1004995.
NEPOUX V., BABIN A., HAAG C., KAWECKI T., LE ROUZIC A. 2015. Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster. Ecology and Evolution 5(3): 543-556.
ALVAREZ-CASTRO J.M., LE ROUZIC A. 2015. On the partitioning of genetic variance with epistasis, in Epistasis: Methods and Protocols, Eds: Moore, J.H. and Williams, S.M., Springer, Humana Press, pp 95-114.
LE ROUZIC A. 2014 Estimating directional epistasis. Frontiers in Genetics 5:198.
PÉLABON C., FIRMAT C., BOLSTAD G.H., VOJE K.L., HOULE D., CASSARA J., LE ROUZIC A., HANSEN T.F. 2014. Evolution of morphological allometry. Annals of the New York Academy of Sciences 1320(1): 58:75.
GUILLOT G. , VITALIS R. , LE ROUZIC A. , GAUTIER M. 2013 Detecting correlation between allele frequencies and environmental variables as a signature of selection. A fast computational approach for genome-wide studies. Spatial Statistics 8:145-155.
REBAUDO F. , LE ROUZIC A. , DUPAS S. , SILVAIN J. F. , HARRY M. , DANGLES O. 2013 SimAdapt: an individual-based genetic model for simulating landscape management impacts on populations. Molecular Ecology Resources 4(6): 595-600.
STARTEK M. , LE ROUZIC A. , CAPY P. , GRZEBELUS D. , GAMBIN A. 2013 Genomic parasites or symbionts? Modeling the effects of environmental pressure on transposon activity in asexual populations Theoretical Population Biology 90:145-151.
LE ROUZIC A. , ALVAREZ-CASTRO J.M. , HANSEN T.F. 2013 The evolution of canalization and evolvability in stable and fluctuating environments. Evolutionary Biology 40: 317-340.
LE ROUZIC A. , PAYEN T. , HUA-VAN A. 2013 Reconstructing the evolutionary history of transposable elements Genome, Biology and Evolution 5(1): 77-86.
ALVAREZ-CASTRO J.M., LE ROUZIC A., ANDERSSON L., SIEGEL P.B., CARLBORG Ö 2012 Modelling of genetic interactions improves prediction of hybrid patterns: a case study in domestic fowl. Genetics Research 94(5): 255-266.
EGSET C.K., HANSEN T.F., LE ROUZIC A., BOLSTAD G.H., ROSENQVIST G., PELABON C. 2012 Artificial selection on allometry: change in elevation but not slope. Journal of Evolutionary Biology 25(5): 938-948.
BOUTIN T.S., LE ROUZIC A., CAPY P. 2012 How does selfing affect the dynamics of selfish transposable elements? Mobile DNA 3(1): 5.
LE ROUZIC A., HOULE D., HANSEN T.F. 2011 A modelling framework for the analysis of artificial-selection time series. Genetics Research 93: 155.
LE ROUZIC A., ØSTBYE K., KLEPAKER T.O., HANSEN T.F., BERNATCHEZ L., SCHLUTER D., VøLLESTAD L.A. 2011 Strong and consistent natural selection associated with armour reduction in sticklebacks. Molecular Ecology 20: 2483.
HUA-VAN A., LE ROUZIC A., BOUTIN T.S., FILEE J., CAPY P. 2011 The struggle for life of the genome’s selfish architects. Biology Direct 6: 19.
BESNIER, F., LE ROUZIC, A. and ALVAREZ-CASTRO, J.M. 2010 Applying QTL analysis to conservation genetics. Conservation Genetics 11(2): 399.
LE ROUZIC, A., SKAUG, H.J. and HANSEN, T.F. 2010 Estimating genetic architectures from artificial-selection responses: a random-effect framework. Theoretical Population Biology 77(2): 119.
PAVLICEV, M., LE ROUZIC, A., CHEVERUD, J.M., WAGNER, G.P. and HANSEN, T.F. 2010 Directionality of epistasis in a murine intercross population. Genetics 185(4): 1489.
EDELINE, É, LE ROUZIC, A., WINFIELD, I.J., FLETCHER, J.M., JAMES, J.B., STENSETH, N.C. and VøLLESTAD, L.A. 2009 Harvest-induced disruptive selection increases variance in fitness-related traits. Proceedings of the Biological Society of London B. 276(1676): 4163.
LE ROUZIC, A. and CAPY, P. 2009 Theoretical approaches to the dynamics of transposable elements in genomes, populations, and species. Genome dynamics and stability. Transposons and the dynamic genome, Springer. 4: 1.
ALVAREZ-CASTRO, J.M., LE ROUZIC, A. and CARLBORG, Ö. 2008 How to perform meaningful estimates of genetic effects. PLoS Genetics 4(5): 1000062.
LE ROUZIC, A. and ALVAREZ-CASTRO, J.M. 2008 Estimation of genetic effects and genotype-phenotype maps. Evolutionary Bioinformatics 4: 225.
LE ROUZIC, A., ALVAREZ-CASTRO, J.M. and CARLBORG, Ö. 2008 Dissection of the genetic architecture of body weight in chicken reveals the impact of epistasis on domestication traits. Genetics 179(3): 1591.
LE ROUZIC, A. and CARLBORG, Ö. 2008 Evolutionary potential of hidden genetic variation. Trends in Ecology and Evolution 23(1): 33.
LE ROUZIC, A., SIEGEL, P.B. and CARLBORG, Ö. 2007 Phenotypic evolution from genetic polymorphisms in a radial network architecture. BMC Biology 5: 50.
LE ROUZIC, A., BOUTIN, T. S. and CAPY, P. 2007 Long-term evolution of transposable elements. PNAS 104: 19375.
LE ROUZIC, A., DUPAS, S. and CAPY, P. 2007 Genome ecosystem and transposable elements species. Gene 390: 214.
LE ROUZIC, A. and CAPY, P. 2006 Reversible introduction of transgenes in natural populations of insects. Insect Molecular Biology 15: 227.
LE ROUZIC, A. and CAPY, P. 2006 Population genetics models of competition between transposable element subfamilies. Genetics 174: 785.
LE ROUZIC, A. and CAPY, P. 2005 The first steps of transposable elements invasion: parasitic strategy vs. genetic drift. Genetics 169: 1033.
HUA-VAN A., LE ROUZIC A., MAISONHAUTE C. and CAPY P. 2005 Abundance, distribution and dynamics of retrotransposable elements and transposons: similarities and differences. Cytogenetics and Genome Research 110: 426.
LE ROUZIC, A. and DECELIERE, G. 2005 Models of the population genetics of transposable elements. Genetical Research 85: 171.