Integrative genomic analyses
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.
| Main Authors: | , |
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| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
Eastern North American Region International Biometric Society
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| Subjects: | U10 - Informatique, mathématiques et statistiques, 000 - Autres thèmes, L73 - Maladies des animaux, L10 - Génétique et amélioration des animaux, |
| Online Access: | http://agritrop.cirad.fr/586790/ http://agritrop.cirad.fr/586790/1/ID586790.pdf |
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| Summary: | 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. |
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