Arnaud Le Rouzic

CNRS Researcher, HDR

Tel. +33(0)1 69 15 58 94

arnaud.le-rouzic@universite-paris-saclay.fr
secondary emails: arnaud.le-rouzic@cnrs.fr, arnaud.lerouzic.cnrs@gmail.com

Research themes

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.

Software

noia

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.

sra

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.

External links

Mastodon: @arnaudlerouzic@fediscience.org

Researcher ID: A-4106-2008

ORCID number: /0000-0002-2158-3458

Google Scholar Citation

Publications

Akiki, P., Delamotte, P., Poidevin, M., van Dijk, E. L., Petit, A. J. R., Le Rouzic, A., Mery, F., Marion-Poll, F., & Montagne, J. (2024). Male manipulation impinges on social-dependent tumor suppression in Drosophila melanogaster females. Scientific Reports, 14(1), 6411. https://doi.org/10.1038/s41598-024-57003-3

Le Rouzic, A., Roumet, M., Widmer, A., & Clo, J. (2024). Detecting directional epistasis and dominance from cross-line analyses in alpine populations of Arabidopsis thaliana. Journal of Evolutionary Biology, 37(7), 839–847. https://doi.org/10.1093/jeb/voae056

Tomar, S. S., Hua-Van, A., & Le Rouzic, A. (2023). A population genetics theory for piRNA-regulated transposable elements. Theoretical Population Biology, 150, 1–13. https://doi.org/10.1016/j.tpb.2023.02.001

Petit, A. J. R., Guez, J., & Le Rouzic, A. (2023). Correlated stabilizing selection shapes the topology of gene regulatory networks. Genetics, 224(2), iyad065. https://doi.org/10.1093/genetics/iyad065

Pavlicev, Mihaela, Bourg, S., & Le Rouzic, A. (2023). The genotype-phenotype map structure and its role for evolvability. In T. Hansen, D. Houle, M. Pavlicev, & C. Pélabon (Eds.), Evolvability: A Unifying Concept in Evolutionary Biology? (pp. 147–170). The MIT Press.

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, 150(3–4), 209–221. https://doi.org/10.1007/s10709-021-00135-5

Le Rouzic, A. (2022). Gene network robustness as a multivariate character. Peer Community Journal, 2, e26. https://doi.org/10.24072/pcjournal.125

Burban, E., Tenaillon, M. I., & Le Rouzic, A. (2022). Gene network simulations provide testable predictions for the molecular domestication syndrome. Genetics, 220(2), iyab214. https://doi.org/10.1093/genetics/iyab214

Devilliers, M., Garrido, D., Poidevin, M., Rubin, 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), iyaa010. https://doi.org/10.1093/genetics/iyaa010

David, O., Le Rouzic, A., & Dillmann, C. (2021). Optimization of sampling designs for pedigrees and association studies. Biometrics. https://doi.org/10/gkbgs2

Desbiez-Piat, A., Le Rouzic, A., Tenaillon, M. I., & Dillmann, C. (2021). Interplay between extreme drift and selection intensities favors the fixation of beneficial mutations in selfing maize populations. Genetics, 219(2). https://doi.org/10/gm5w52

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. https://doi.org/10.1002/ece3.6783

Renneville, C., Millot, A., Agostini, S., Carmignac, D., Maugars, G., Dufour, S., Le Rouzic, A., & Edeline, E. (2020). Unidirectional response to bidirectional selection on body size. I. Phenotypic, life‐history, and endocrine responses. Ecology and Evolution, 10(19), 10571–10592. https://doi.org/10.1002/ece3.6713

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, 573829. https://doi.org/10.3389/fmicb.2020.573829

Debat, V., & Le Rouzic, A. (2019). Canalization, a central concept in biology. Seminars in Cell & Developmental Biology, 88, 1–3. https://doi.org/10.1016/j.semcdb.2018.05.012

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. https://doi.org/10.1007/s10519-018-9937-8

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. https://doi.org/10.1111/jeb.13255

Guyeux, C., Couchot, J.-F., Le Rouzic, A., Bahi, J., & Marangio, L. (2018). Theoretical Study of the One Self-Regulating Gene in the Modified Wagner Model. Mathematics, 6(4), 58. https://doi.org/10.3390/math6040058

Denis, B., Claisse, G., Le Rouzic, A., Wicker-Thomas, C., Lepennetier, G., & Joly, D. (2017). Male accessory gland proteins affect differentially female sexual receptivity and remating in closely related Drosophila species. Journal of Insect Physiology, 99, 67–77. https://doi.org/10.1016/j.jinsphys.2017.03.008

Rünneburger, E., & Le Rouzic, A. (2016). Why and how genetic canalization evolves in gene regulatory networks. BMC Evolutionary Biology, 16(1), 239. https://doi.org/10.1186/s12862-016-0801-2

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. https://doi.org/10.1093/molbev/msv341

Nepoux, V., Babin, A., Haag, C., Kawecki, T. J., & Le Rouzic, A. (2015). Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster. Ecology and Evolution, 5(3), 543–556. https://doi.org/10.1002/ece3.1379

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. The American Naturalist, 185(6), E182–E196.

Álvarez-Castro, J. M., & Le Rouzic, A. (2015). On the Partitioning of Genetic Variance with Epistasis. In J. H. Moore & S. M. Williams (Eds.), Epistasis: Methods and Protocols (pp. 95–114). Springer. https://doi.org/10.1007/978-1-4939-2155-3_6

Le Rouzic, A. (2014). Estimating directional epistasis. Frontiers in Genetics, 5. https://www.frontiersin.org/articles/10.3389/fgene.2014.00198

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. Methods in Ecology and Evolution, 4(6), 595–600. https://doi.org/10.1111/2041-210X.12041

Le Rouzic, A., Payen, T., & Hua-Van, A. (2013). Reconstructing the Evolutionary History of Transposable Elements. Genome Biology and Evolution, 5(1), 77–86. https://doi.org/10.1093/gbe/evs130

Startek, M., Le Rouzic, A., Capy, P., Grzebelus, D., & Gambin, A. (2013). Genomic parasites or symbionts? Modeling the effects of environmental pressure on transposition activity in asexual populations. Theoretical Population Biology, 90, 145–151. https://doi.org/10.1016/j.tpb.2013.07.004

Le Rouzic, A., Álvarez-Castro, J. M., & Hansen, T. F. (2013). The Evolution of Canalization and Evolvability in Stable and Fluctuating Environments. Evolutionary Biology, 40(3), 317–340. https://doi.org/10.1007/s11692-012-9218-z

Egset, C. K., Hansen, T. F., Le Rouzic, A., Bolstad, G. H., Rosenqvist, G., & Pélabon, C. (2012). Artificial selection on allometry: change in elevation but not slope: Artificial selection on allometry. Journal of Evolutionary Biology, 25(5), 938–948. https://doi.org/10.1111/j.1420-9101.2012.02487.x

Boutin, T. S., Le Rouzic, A., & Capy, P. (2012). How does selfing affect the dynamics of selfish transposable elements? Mobile DNA, 3(1), 5. https://doi.org/10.1186/1759-8753-3-5

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: NATURAL SELECTION IN STICKLEBACKS. Molecular Ecology, 20(12), 2483–2493. https://doi.org/10.1111/j.1365-294X.2011.05071.x

Le Rouzic, A., Houle, D., & Hansen, T. F. (2011). A modelling framework for the analysis of artificial-selection time series. Genetics Research, 93(2), 155–173. https://doi.org/10.1017/S0016672311000024

Le Rouzic, A., Skaug, H. J., & Hansen, T. F. (2010). Estimating genetic architectures from artificial-selection responses: A random-effect framework. Theoretical Population Biology, 77(2), 119–130. https://doi.org/10.1016/j.tpb.2009.12.003

Pavlicev, M., Le Rouzic, A., Cheverud, J. M., Wagner, G. P., & Hansen, T. F. (2010). Directionality of Epistasis in a Murine Intercross Population. Genetics, 185(4), 1489–1505. https://doi.org/10.1534/genetics.110.118356

Besnier, F., Le Rouzic, A., & Álvarez-Castro, J. M. (2010). Applying QTL analysis to conservation genetics. Conservation Genetics, 11(2), 399–408. https://doi.org/10.1007/s10592-009-0036-5

Le Rouzic, A., & Capy, P. (2009). Theoretical Approaches to the Dynamics of Transposable Elements in Genomes, Populations,and Species. In D.-H. Lankenau & J.-N. Volff (Eds.), Transposons and the Dynamic Genome (pp. 1–19). Springer. https://doi.org/10.1007/7050_017

Édeline, E., Le Rouzic, A., Winfield, I. J., Fletcher, J. M., James, J. B., Stenseth, N. Chr., & Vøllestad, L. A. (2009). Harvest-induced disruptive selection increases variance in fitness-related traits. Proceedings of the Royal Society B: Biological Sciences, 276(1676), 4163–4171. https://doi.org/10.1098/rspb.2009.1106

Le Rouzic, A., & Álvarez-Castro, J. M. (2008). Estimation of Genetic Effects and Genotype-Phenotype Maps. Evolutionary Bioinformatics, 4, EBO.S756. https://doi.org/10.4137/EBO.S756

Le Rouzic, A., Álvarez-Castro, J. M., & Carlborg, Ö. (2008). Dissection of the Genetic Architecture of Body Weight in Chicken Reveals the Impact of Epistasis on Domestication Traits. Genetics, 179(3), 1591–1599. https://doi.org/10.1534/genetics.108.089300

Le Rouzic, A., Dupas, S., & Capy, P. (2007). Genome ecosystem and transposable elements species. Gene, 390(1), 214–220. https://doi.org/10.1016/j.gene.2006.09.023

Le Rouzic, A., Boutin, T. S., & Capy, P. (2007). Long-term evolution of transposable elements. Proceedings of the National Academy of Sciences, 104(49), 19375–19380. https://doi.org/10.1073/pnas.0705238104

Le Rouzic, A., Siegel, P. B., & Carlborg, Ö. (2007). Phenotypic evolution from genetic polymorphisms in a radial network architecture. BMC Biology, 5(1), 50. https://doi.org/10.1186/1741-7007-5-50

Le Rouzic, A., & Capy, P. (2006). Reversible introduction of transgenes in natural populations of insects. Insect Molecular Biology, 15(2), 227–234. https://doi.org/10.1111/j.1365-2583.2006.00631.x

Le Rouzic, A., & Capy, P. (2006). Population Genetics Models of Competition Between Transposable Element Subfamilies. Genetics, 174(2), 785–793. https://doi.org/10.1534/genetics.105.052241

Le Rouzic, A., & Deceliere, G. (2005). Models of the population genetics of transposable elements. Genetics Research, 85(3), 171–181. https://doi.org/10.1017/S0016672305007585

Le Rouzic, A., & Capy, P. (2005). The First Steps of Transposable Elements Invasion. Genetics, 169(2), 1033–1043. https://doi.org/10.1534/genetics.104.031211

Hua-Van, A., Le Rouzic, A., Maisonhaute, C., & Capy, P. (2005). Abundance, distribution and dynamics of retrotransposable elements and transposons: similarities and differences. Cytogenetic and Genome Research, 110(1–4), 426–440. https://doi.org/10.1159/000084975