T. Mackay, S. Ea, and J. F. Ayroles, The genetics of quantitative traits: challenges and prospects, Nat Rev Genet, vol.10, issue.8, pp.565-77, 2009.

Y. Han, S. Gao, K. Muegge, W. Zhang, and B. Zhou, Advanced Applications of RNA Sequencing and Challenges, Bioinforma Biol Insights, vol.9, issue.1, p.28991, 2015.

E. B. Josephs, S. I. Wright, J. R. Stinchcombe, and D. J. Schoen, The Relationship between Selection, Network Connectivity, and Regulatory Variation within a Population of Capsella grandiflora, Genome Biol Evol, vol.9, issue.4, pp.1099-109, 2017.

N. Mähler, J. Wang, B. K. Terebieniec, P. K. Ingvarsson, N. R. Street et al., Gene co-expression network connectivity is an important determinant of selective constraint, PLOS Genet, vol.13, issue.4, p.1006402, 2017.

P. Langfelder and S. Horvath, WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics, vol.9, issue.1, p.559, 2008.

S. H. Williamson, R. Hernandez, A. Fledel-alon, L. Zhu, R. Nielsen et al., Simultaneous inference of selection and population growth from patterns of variation in the human genome, Proc Natl Acad Sci, vol.102, issue.22, pp.7882-7889, 2005.

E. Josephs, Y. W. Lee, J. R. Stinchcombe, and S. I. Wright, Association mapping reveals the role of purifying selection in the maintenance of genomic variation in gene expression, PNAS, vol.112, issue.50, pp.1-6, 2015.

A. Sicard, C. Kappel, E. B. Josephs, Y. W. Lee, C. Marona et al., Divergent sorting of a balanced ancestral polymorphism underlies the establishment of gene-flow barriers in Capsella, Nat Commun, vol.6, issue.1, p.7960, 2015.

M. Han, S. Qin, X. Song, Y. Li, J. P. Chen et al., Evolutionary rate patterns of genes involved in the Drosophila Toll and Imd signaling pathway, BMC Evol Biol, vol.13, issue.1, p.245, 2013.

Y. Lu, Evolutionary Rate Variation in Anthocyanin Pathway Genes, Mol Biol Evol, vol.20, issue.11, pp.1844-53, 2003.

M. D. Rausher, Y. Lu, and K. Meyer, Variation in Constraint Versus Positive Selection as an Explanation for Evolutionary Rate Variation Among Anthocyanin Genes, J Mol Evol, vol.67, issue.2, pp.137-181, 2008.

M. D. Rausher, R. E. Miller, and P. Tiffin, Patterns of evolutionary rate variation among genes of the anthocyanin biosynthetic pathway, Mol Biol Evol, vol.16, issue.2, pp.266-74, 1999.

R. M. Riley, J. W. Gibson, and G. , Contrasting selection pressures on components of the Ras-mediated signal transduction pathway in Drosophila, Mol Ecol, vol.12, issue.5, pp.1315-1338, 2003.

H. Yu, Y. Shen, G. Yuan, Y. Hu, H. Xu et al., Evidence of Selection at Melanin Synthesis Pathway Loci during Silkworm Domestication, Mol Biol Evol, vol.28, issue.6, pp.1785-99, 2011.

R. Jovelin and P. C. Phillips, Expression Level Drives the Pattern of Selective Constraints along the Insulin/Tor Signal Transduction Pathway in Caenorhabditis, Genome Biol Evol, vol.3, pp.715-737, 2011.

X. Song, J. P. Qin, S. Chen, L. Ma, and F. , The Evolution and Origin of Animal Toll-Like Receptor Signaling Pathway Revealed by Network-Level Molecular Evolutionary Analyses, PLoS ONE, vol.7, issue.12, p.51657, 2012.

X. Wu, X. Chi, P. Wang, D. Zheng, R. Ding et al., The evolutionary rate variation among genes of HOG-signaling pathway in yeast genomes, Biol Direct, vol.5, issue.1, p.46, 2010.

D. A. Drummond, J. D. Bloom, C. Adami, C. O. Wilke, and F. H. Arnold, Why highly expressed proteins evolve slowly, Proc Natl Acad Sci, vol.102, issue.40, pp.14338-14381, 2005.

L. Duret and D. Mouchiroud, Determinants of Substitution Rates in Mammalian Genes: Expression Pattern Affects Selection Intensity but Not Mutation Rate, Mol Biol Evol, vol.17, issue.1, pp.68-070, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00427068

C. Pál, B. Papp, and L. D. Hurst, Highly expressed genes in yeast evolve slowly, Genetics, vol.158, issue.2, pp.927-958, 2001.

L. Montanucci, H. Laayouni, G. M. Dall'olio, and J. Bertranpetit, Molecular Evolution and Network-Level Analysis of the N-Glycosylation Metabolic Pathway Across Primates, Mol Biol Evol, vol.28, issue.1, pp.813-836, 2011.

J. D. Bloom and C. Adami, Evolutionary rate depends on number of proteinprotein interactions independently of gene expression level: response

, BMC Evol Biol, vol.4, issue.1, p.14, 2004.

H. B. Fraser and A. E. Hirsh, Evolutionary rate depends on number of protein-protein interactions independently of gene expression level

, BMC Evol Biol, vol.4, issue.1, p.13, 2004.

M. N. Gebreselassie, K. Ader, N. Boizot, F. Millier, J. Charpentier et al., Near-infrared spectroscopy enables the genetic analysis of chemical properties in a large set of wood samples from Populus nigra (L.) natural populations, Ind Crops Prod, vol.107, pp.159-71, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01608814

P. Faivre-rampant, G. Zaina, J. V. Giacomello, S. Segura, V. Scalabrin et al., New resources for genetic studies in Populus nigra: genome-wide SNP discovery and development of a 12k Infinium array, Mol Ecol Resour, vol.16, issue.4, pp.1023-1059, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02636258

. R-core-team, R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing, 2020.

F. Muñoz, L. Sanchez, and . Breedr, Statistical Methods for Forest Genetic Resources Analysts, pp.12-14, 2017.

K. Luu, E. Bazin, and M. Blum, pcadapt: an R package to perform genome scans for selection based on principal component analysis, Mol Ecol Res, vol.17, issue.1, pp.67-77, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01430346

M. B. Kursa and W. R. Rudnicki, Feature Selection with the Boruta Package, J Stat Softw, vol.36, issue.11, pp.1-13, 2010.

O. González-recio, G. Rosa, and D. Gianola, Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits, Livest Sci, vol.166, pp.217-248, 2014.

A. N. Barbeira, S. P. Dickinson, R. Bonazzola, J. Zheng, H. E. Wheeler et al., Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics, Nat Commun, vol.9, issue.1, p.1825, 2018.

A. Tong, Global Mapping of the Yeast Genetic Interaction Network, Science, vol.303, issue.5659, pp.808-821, 2004.

P. De-villemereuil, O. E. Gaggiotti, and M. Mouterde, Till-Bottraud I. Common garden experiments in the genomic era: new perspectives and opportunities, Heredity, vol.116, issue.3, pp.249-54, 2016.

B. A. Mckinney, D. M. Reif, M. D. Ritchie, and J. H. Moore, Machine learning for detecting gene-gene interactions: a review, Appl Bioinforma, vol.5, issue.2, pp.77-88, 2006.

X. Chen, C. T. Liu, M. Zhang, and H. Zhang, A forest-based approach to identifying gene and gene-gene interactions, Proc Natl Acad Sci, vol.104, issue.49, pp.19199-203, 2007.

R. Jiang, W. Tang, X. Wu, and W. Fu, A random forest approach to the detection of epistatic interactions in case-control studies, BMC Bioinformatics, vol.10, p.65, 2009.

,

T. Meuwissen, B. J. Hayes, and M. E. Goddard, Prediction of total genetic value using genome-wide dense marker maps, Genetics, vol.157, issue.4, pp.1819-1848, 2001.

G. De-los-campos, J. M. Hickey, R. Pong-wong, H. D. Daetwyler, and M. Calus, Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding, Genetics, vol.193, issue.2, pp.327-372, 2013.

T. A. Schrag, M. Westhues, W. Schipprack, F. Seifert, A. Thiemann et al., Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize, Genetics, vol.208, issue.4, pp.1373-85, 2018.

H. J. Cordell, Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans, Hum Mol Genet, vol.11, issue.20, pp.2463-68, 2002.

E. A. Boyle, Y. I. Li, and J. K. Pritchard, An Expanded View of Complex Traits: From Polygenic to Omnigenic, Cell, vol.169, issue.7, pp.1177-86, 2017.

X. Liu, Y. I. Li, and J. K. Pritchard, Trans Effects on Gene Expression Can Drive Omnigenic Inheritance, Cell, vol.177, issue.4, pp.1022-346, 2019.

J. Guet, F. Fabbrini, R. Fichot, M. Sabatti, C. Bastien et al., Genetic variation for leaf morphology, leaf structure and leaf carbon isotope discrimination in European populations of black poplar
URL : https://hal.archives-ouvertes.fr/hal-02634397

, Tree Physiol, vol.35, issue.8, pp.850-63, 2015.

D. Steinbach, M. Alaux, J. Amselem, N. Choisne, S. Durand et al., GnpIS: an information system to integrate genetic and genomic data from plants and fungi, Database, vol.2013, issue.0, p.58, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02648084

S. Y. Dillen, N. Marron, M. Sabatti, R. Ceulemans, and C. Bastien, Relationships among productivity determinants in two hybrid poplar families grown during three years at two contrasting sites, Tree Physiol, vol.29, issue.8, pp.975-87, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02662444

B. Langmead and S. L. Salzberg, Fast gapped-read alignment with Bowtie 2, Nat Methods, vol.9, issue.4, pp.357-59, 2012.

M. D. Robinson and A. Oshlack, A scaling normalization method for differential expression analysis of RNA-seq data, Genome Biol, vol.11, issue.3, p.25, 2010.

C. W. Law, Y. Chen, W. Shi, and G. K. Smyth, voom: precision weights unlock linear model analysis tools for RNA-seq read counts, Genome Biol, vol.15, issue.2, p.29, 2014.

D. Speed, G. Hemani, M. R. Johnson, and D. J. Balding, Improved heritability estimation from genome-wide SNPs, Am J Hum Genet, vol.91, issue.6, pp.1011-1032, 2012.

O. Rogier, A. Chateigner, S. Amanzougarene, M. Lesage-descauses, S. Balzergue et al., Accuracy of RNAseq based SNP discovery and genotyping in Populusnigra, BMC Genomics, vol.19, issue.1, p.909, 2018.

M. Sargolzaei, J. P. Chesnais, and F. S. Schenkel, A new approach for efficient genotype imputation using information from relatives, BMC Genomics, vol.15, issue.1, 2014.

K. Wang, M. Li, and H. Hakonarson, ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data, Nucleic Acids Res, vol.38, issue.16, 2010.

J. Goudet and T. Jombart, Hierfstat: Estimation and Tests of Hierarchical F-Statistics, pp.4-22, 2015.

R. Suzuki and H. Shimodaira, Pvclust: Hierarchical Clustering with p-Values Via Multiscale Bootstrap Resampling, 2015.

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artif Intell, vol.97, issue.1-2, pp.273-324, 1997.

R. Nilsson, J. M. Peñape, P. Jmp, J. Björkegren, and J. J. Tegnér, Consistent Feature Selection for Pattern Recognition in Polynomial Time, 2007.

E. Ledell, N. Gill, S. Aiello, A. Fu, A. Candel et al., H2o: R Interface for 'H2O', Tolosana-Delgado R, Bren M. Compositions: Compositional Data Analysis, pp.40-42, 2018.

G. Cybenko, Approximation by superpositions of a sigmoidal function, Math Control Signals Syst, vol.2, issue.4, pp.303-317, 1989.

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Netw, vol.2, issue.5, pp.359-66, 1989.

S. Gagnot, J. Tamby, M. Bitton, F. Taconnat, L. Balzergue et al., CATdb: a public access to Arabidopsis transcriptome data from the URGV-CATMA platform, Nucleic Acids Res, vol.36, pp.986-90, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01203869

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