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, Ce travail de thèse contribueà l'amélioration et au développement de méthodes utilisant des données transcriptomiques pour l'analyse génétique d'un caractère complexe

. Dans-une-première-partie-;-friguet, )), permet de capter la variabilité indépendante d'un caractère d'intérêt au travers de facteurs appelées facteurs d'hétérogénéité. Nos travaux montrent l'intérêt de travailler sur des données d'expression ajustées par ces facteurs a la fois pour les analyses différentielles, les analyses eQTL et la détection de sous-types phénotypiques pour le caractère d'intérêt. En particulier, sur des données d'expression issues de dispositifs familiaux où les profils d'expression entre individus peuventêtre peu variablesétant donné leur degré d'apparenté, notre approche peut se révéler très fructueuse comparéeà des approches classiques, on améliore les procédures classiques pour la recherche de gènes ou de régions du génome impliqués dans la variabilité du caractère d'intérêt. L'originalité de notre approche est de prendre en compte l'hétérogénéité d'expression dans les données transcriptomiques. Tout d'abord, on prend en compte l'hétérogénéité du signal du niveau d'expressionà l'aide de la méthode FAMT, 2009.

, Suiteà ces travaux, une fonctionnalitéàété incorporée au package FAMT, 2009.

, permettant d'interpréter les facteurs d'hétérogénéité détectésà l'aide d'informations extérieures fournies par l'utilisateur

, On Discussion -Perspectives peut tout d'abord remarquer que ces deux grandes approches reposent sur des mesures de dépendances linéaires entre gènes. C'est pourquoi certains auteurs utilisent une autre mesure appelée Mutual Information permettant de mettre enévidence des dépendance non-linéaires. Cette mesure requiert la discrétisation des profils d'expression et est calculée ainsi entre deux gènes i et j : M I i, Dans une deuxième partie, on propose de nouvelles méthodes pour l'inférence de réseaux géniques dans le contexte des relevance networks et celui des modèles graphiques Gaussiens

K. Butte, l'ont utilisé en premier dans le cadre des relevance networks en choisissant, tout comme avec les corrélations, un certain seuil pour considérer une dépendance effective, 2000.

. Margolin, D'autres approches utilisant cette mesure ontété développées par la suite et ontété rassemblées sous le nom de Information theory based methods, 2006.

, puis elle considère en plus les triplets de gènes afin d'éliminer des liens indirects avec comme hypothèse que si le gène X 1 interagit avec le gène X 3 via le gène X 2 , alors : M I(X 1 ; X 3 ) ? min(M I(X 1 ; X 2 ), M I(X 2 ; X 3 )). Ainsi, la méthodeélimine dans certains cas, l'arête correspondantà la mesure M I la plus faible au sein d'un, 2000.

. Steuer, )), ces méthodes donnent globalement des résultats similaires. Dans l'étude comparative récemment réalisée par Allen et al. (2012), les résultats sont même meilleurs pour les méthodes basées sur des dépendances linéaires en termes de spécificité de détection des arêtes ainsi que d'identification des hub gènes dans le réseau, Desétudes ont comparé des méthodes basées sur des mesures de dépendances linéaires comme les méthodes WGCNA, GeneNet ou SPACE,à d'autres méthodes basées sur la mesure de dépendances non linéaires M I comme la méthode ARACNE. Aussi bien sur données réelles, 2002.

, L'ensemble de ces observations peut s'expliquer finalement par le peu d'information contenu dans les données : l'expression d'un gène est mesurée sur peu d'individus. Ces résultats nous confortent dans l'idée que les hypothèses de linéarité des dépendances et de normalité des profils ne sont pas déraisonnables

. Dans-un-contexte-de-;-le-mignon, ont utilisé une démarche similaire consistantà calculer une variable synthétique pour un ensemble de gènes ayant un eQTL co-localisant avec une région QTL détectée au préalable : leur approche permet alors d'augmenter la puissance de détection du QTL et de gagner en précision sur sa localisation. Par ailleurs, il est intéressant de remarquer que desétudes récentes proposent de combiner la construction de réseaux de gènes avec des informations de cartographie, génétique génomique" nous avons pu voir que la modélisation de réseaux géniques permet d'identifier des modules de gènes qui peuventêtre associésà des processus biologiques apportant de nouvelles informations quant aux mécanismes de régulations sous-jacentsà un caractère complexe, 2006.

. Enfin, commeévoqué en section 2.1, un réseau de régulation génique est une simplification du réseau biologique global impliquant différents niveaux de régulation (gènes/protéines/métabolites)

. De-plus and . Schwanhäusser, ARNm explique seulement 40 % de la variabilité du niveau de protéines ce qui suggère l'existence de nombreux processus de régulation lors de l'étape de la traduction, Ces résultats incitentà intégrer différentes fenêtres d'observation pour réellement comprendre les mécanismes biologiques sous-jacentsà la variabilité de caractères complexes, 2011.

O. Dans-cette, Lesétapes d'acquisitions expérimentales et de pré-traitements des données sont résumées en Annexe. L'objectif des travaux futurs est d'intégrer les différentes données biologiquesà la fois pour les analyses de cartographie et pour la modélisation de réseaux de régulation, afin de mieux comprendre les mécanismes biologiques contrôlant la variabilité d'adiposité chez le poulet de chair (figure 2.6). Comme illustré en figure 2.6, une première stratégie sera d'identifier des régions QTL/eQTL/mQTL (pour métabolite-QTL) contrôlantà la fois le caractère complexe d'intérêt, des gènes et des métabolites (lipides ou acides gras dans notreétude), j'ai participéà la génération de nouvelles données transcriptomiques et métabolites (lipidomiques) sur le même dispositif d'animaux décrit dans les travaux en sections 1.2.1 et 1.3.1, mais sur unéchantillon de taille 4 fois plus importante (?200 individus)

. Workman, Bien que différentes approches d'intégration génomique ont déjàété proposées dans la littérature, 2006.

N. Mach, Y. Blum, A. Bannink, D. Causeur, M. Houée et al., Pleiotropic effects of polymorphism of the gene diacylglycerol-o-transferase 1 (DGAT1) in the mammary gland tissue of dairy cows, Journal of Dairy Science, vol.95, pp.1-12, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00841015

Y. Blum, L. Mignon, G. Causeur, D. Filangi, O. Désert et al., Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken, BMC Genomics, vol.12, p.567, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00730144

F. Lecerf, A. Bretaudeau, C. Desert, Y. Blum, S. Lagarrigue et al., AnnotQTL: a new tool to gather functional and comparative information of a genomic region, Nucleic Acids Research, 2011.

Y. Blum, L. Mignon, G. Lagarrigue, S. , C. D. Le-mignon et al., Contribution of functional genomics to the fine mapping of QTL, BMC Bioinformatics, vol.11, issue.4, pp.343-358, 2010.

, Articles en préparation

Y. Blum, M. Cadoret, M. Houée-bigot, and D. Causeur, Sparse factor models for high dimensional relevance networks

Y. Blum, C. Friguet, M. Houée-bigot, L. S. Causeur, and D. , Inferring gene networks using a sparse factor Gaussian graphical model

Y. Blum and M. Cadoret, Houée M & Causeur D-Inférence sur réseaux d'interaction parcimonieux par modèlesà facteurs, SFdS Journées de Statistique, pp.21-25, 2012.

Y. Blum, M. Houée, C. Friguet, S. Lagarrigue, and D. Causeur, Inferring gene networks using a sparse factor model approach, Statistical Learning and Data Science, pp.7-9, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00841017

Y. Blum, M. Houée, and C. Friguet, Lagarrigue S & Causeur D-Inférence de réseaux de gènesà partir de données transcriptomiques, Séminaire de Biologie Intégrative et, pp.7-9, 2011.

Y. Blum, O. Demeure, C. Désert, H. Guillou, J. Bertrand-michel et al., Lagarrigue S -Integrating QTL controlling fatness, lipid metabolites and gene expressions to genetically dissect the adiposity complex trait in a meat chicken cross, 62nd Annual Meeting EAAP, 2011.

N. Mach, Y. Blum, M. Smits, and D. Causeur, Lagarrigue S -Effect of the gene diacylglycerol-Otransferase 1 (DGAT1) polymorphism on the global expression pattern of genes in the mammary gland tissue of dairy cows, 62nd Annual Meeting EAAP, 2011.

M. Houée, C. Friguet, S. Lagarrigue, and Y. Blum, Causeur D -Large-scale significance testing of high-thoroughput Data with FAMT, ASMDA 2011 Conference, pp.7-10, 2011.

Y. Blum, L. Mignon, G. Causeur, D. Pitel, F. Demeure et al., Transcriptome profiling reveals interaction between two QTL for fatness in chicken, The 15th QTL-MAS workshop, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00841016

Y. Blum, C. Friguet, S. Lagarrigue, and D. Causeur, Inferring gene networks using a sparse factor model approach: application in a lipid metabolism study, International Biometric Society Channel Network, pp.11-13, 2011.

Y. Blum, S. Lagarrigue, and D. Causeur, Genetic analysis of a complex trait using transcriptomic data: contribution of gene regulatory network modeling, Séminaire des Thésards du Département de Génétique Animale INRA, Pornichet, pp.5-6, 2011.
URL : https://hal.archives-ouvertes.fr/hal-02806842

Y. Blum, S. Lagarrigue, and D. Causeur, , p.140

G. Fonctionnelle, . Agronomie, and R. Santé, Discussion -Perspectives Blum Y, Lagarrigue S & Causeur D -A factor model to analyze heterogeneity in gene expression in a context of QTL characterization, International Society for Animal Genetics, pp.26-30, 2010.

Y. Blum and S. Lagarrigue, Causeur D -Caractérisation fonctionnelle d'un QTL par prise en compte de la dépendance génique, Journée des Doctorants de Rennes & Brest en Biologie-Santé, 2010.

Y. Blum and C. Friguet, Lagarrigue S & Causeur D -Inférence sur réseaux géniques par Analyse en Facteurs, SFdS Journées de Statistique, pp.25-28, 2010.

Y. Blum, S. Lagarrigue, and D. Causeur, A factor model to analyze heterogeneity in gene expression in a context of QTL mapping. 8th workshop « Statistical Methods for Post-Genomic Data, pp.14-15, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00459362

P. Blum, Y. Houée, M. Friguet, C. Lagarrigue, S. Causeur et al., Genetic analysis of a complex trait using transcriptomic data: contribution of gene regulatory network modeling. Session "Genes and pathways, Plant&Animal Genome XX, pp.14-18, 2012.
URL : https://hal.archives-ouvertes.fr/hal-02806842

Y. Blum, Modélisation de réseaux de gènes : apport dans le déterminisme génétique de caractères complexes, Génétique Animale INRA, pp.6-7, 2010.

Y. Blum, S. Lagarrigue, and D. Causeur, A factor model to analyze heterogeneity in gene expression in a context of QTL characterization, International Society for Animal Genetics, pp.26-30, 2010.

Y. Blum, L. Mignon, G. Causeur, D. Pitel, F. Demeure et al., Transcriptome profiling reveals interaction between two QTL for fatness in chicken, International Society for Animal Genetics, pp.26-30, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00841016

, Genetic analysis of a complex trait using transcriptomic data: contribution of gene regulatory network modeling, Prix et bourses INRA-Agrocampus Ouest Grant to attend the PAG XX workshop, 2012.

, Mobility Grant from Rennes Metropole: 4 months in the Jake Lusis Laboratory at UCLA, 2012.

, Yoshi Suzuki Award for the best Abstract and Poster -A factor model to analyze heterogeneity in gene expression in a context of QTL characterization, International Society for Animal Genetics, pp.26-30, 2010.

, A factor model to analyze heterogeneity in gene expression in a context of QTL characterization" and "Transcriptome profiling reveals interaction between two QTL for fatness in chicken, ISAG bursary to attend the International Society for Animal Genetics workshop, pp.26-30, 2010.

, Modélisation de réseaux de gènes : apport dans le déterminisme génétique de caractères complexes, Séminaire des Thésards du Département de Génétique Animale INRA, Pornichet, pp.6-7, 2010.

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O. Filangi, Y. Beausse, A. Assi, L. Legrand, J. M. Larre et al., BioMAJ: a flexible framework for databanks synchronization and processing, Bioinformatics, vol.24, pp.1823-1825, 2008.
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S. Lagarrigue, F. Pitel, W. Carre, B. Abasht, P. Le-roy et al., Mapping quantitative trait loci affecting fatness and breast muscle weight in meat-type chicken lines divergently selected on abdominal fatness, Genet. Sel. Evol, vol.38, pp.85-97, 2006.
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G. Le-mignon, F. Pitel, H. Gilbert, L. Bihan-duval, E. Vignoles et al., A comprehensive analysis of QTL for abdominal fat and breast muscle weights on chicken chromosome 5 using a multivariate approach, Anim. Genet, vol.40, pp.157-164, 2009.
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S. Ponsuksili, E. Jonas, E. Murani, C. Phatsara, T. Srikanchai et al., Trait correlated expression combined with expression QTL analysis reveals biological pathways and candidate genes affecting water holding capacity of muscle, BMC Genomics, vol.9, p.367, 2008.

T. Babak, P. Garrett-engele, C. D. Armour, C. K. Raymond, M. P. Keller et al., Genetic validation of whole-transcriptome sequencing for mapping expression affected by cis-regulatory variation, BMC Genomics, p.473, 2010.

D. R. Drost, C. I. Benedict, A. Berg, E. Novaes, C. R. Novaes et al., Diversification in the genetic architecture of gene expression and transcriptional networks in organ differentiation of Populus, Proc. Natl Acad. Sci. USA, vol.107, pp.8492-8497, 2010.

A. Van-nas, L. Ingram-drake, J. S. Sinsheimer, S. S. Wang, E. E. Schadt et al., Expression quantitative trait loci: replication, tissue-and sex-specificity in mice, Genetics, vol.185, pp.1059-1068, 2010.

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M. Bansal, V. Belcastro, A. Ambesi-impiombato, and D. Di-bernardo, How to infer gene networks from expression profiles, Molecular systems biology, vol.3, issue.1, 2007.

Y. Blum, G. Le-mignon, D. Causeur, O. Filangi, C. Désert et al., Complex trait subtypes identification using transcriptome profiling reveals an interaction between two qtl affecting adiposity in chicken, BMC genomics, vol.12, issue.1, p.567, 2011.
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K. Broman, H. Wu, ?. Sen, and G. Churchill, R/qtl: Qtl mapping in experimental crosses, Bioinformatics, vol.19, issue.7, pp.889-890, 2003.

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