Integrative genomic analyses

Tadesse Mahlet G., Denis Marie. 2017. Integrative genomic analyses. In : ENAR 2017 Spring Meeting abstracts. Eastern North American Region International Biometric Society. Washington : Eastern North American Region International Biometric Society, Résumé, 189. ENAR 2017 Spring Meeting, Washington, États-Unis, 12 Mars 2017/15 Mars 2017.

Résumé : Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type provides a snapshot of the molecular processes involved in a particular phenotype. While studies focused on one type of -omic data have led to significant results, an integrative -omic analysis can provide a better understanding of the complex biological mechanisms involved in the etiology or progression of a disease by combining the complementary information from each data type. We investigated flexible modeling approaches under different biological relationship scenarios between the various data sources and evaluated their effects on a clinical outcome using data from the Cancer Genome Atlas project. The integrative models led to improved model fit and predictive performance. However, a systematic integration that allows for all possible links between biological features is not necessarily the best approach.

Classification Agris : U10 - Informatique, mathématiques et statistiques
000 - Autres thèmes
L73 - Maladies des animaux
L10 - Génétique et amélioration des animaux

Auteurs et affiliations

• Tadesse Mahlet G., Georgetown University (USA)
• Denis Marie, CIRAD-BIOS-UMR AGAP (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/586790/)

Métriques

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