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Part II

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  1. Part II The make of QTLdb calls for a trait ontology (Development of a trait ontology prototype) Zhiliang Hu and James Reecy Iowa State University

  2. by methods by time by locations Animal Trait Ontology • Trait measurement variations, e.g. Backfat • Backfat Thickness (Average Backfat) by ruler • Backfat Thickness (Average Backfat) by ultrasound • Backfat Thickness (Average Backfat) by Fat-O-Meater • Backfat at First Rib (First Rib Backfat) • Backfat at First Rib (Measured at 14 Weeks of Age) • Backfat at First Rib (Measured at 26 Weeks of Age) • Backfat at Last Rib (Measured at 14 Weeks of Age) • Backfat at Last Rib (Measured at 26 Weeks of Age) • Backfat Thickness at Last Rib • Backfat at Shoulder • Backfat at Tenth Rib • Backfat between 3th and 4th Rib • Backfat between 6th and 7th Rib • Backfat Depth at Max. Mus. Depth • Backfat at Last Lumbar • Backfat weight (dissected total weight) • ……….

  3. Animal Trait Ontology • Trait definitions ? • Trait types ? • Trait categories ?

  4. Animal Trait Ontology • Trait names are defined with actual terms that closely represent the concept of a trait. • Trait description and measurement criteria are information expanded from trait names to describe the detailed criteria in which a trait is recorded. • Trait type (“super trait”) describes physical or a chemical property of the pork products, or feature that can influence the process in which a pork product is made, for example, backfat, ADG, muscle pH, feed intake, etc. • Trait category describes very general aspects of pork products, or processes in which the product is made, for example, meat quality, health, growth, reproduction, etc.

  5. Animal Trait Ontology Revisit the Pig traits in the QTLdb • Controlled vocabulary • Trait classification hierarchy • 5 categories • 25 trait types (super-traits) • 236 traits Zhiliang Hu, Svetlana Dracheva, Wonhee Jang, Donna Maglott, John Bastiaansen, Max F. Rothschild and James M. Reecy (2005). A QTL resource and comparison tool for pigs: PigQTLDB. Mammalian Genome. Volume 16(10):792-800.

  6. Animal Trait Ontology New challenges: • What’s the best way to classify the trait data? • What’s the best way to organize the trait data? • What’s the best way to manage the trait data? • For example, is “backfat” • A growth trait ? • A meat quality trait ? • A health trait ? • Or does it matter anyway ?

  7. Animal Trait Ontology • Ontology: • A description of concepts and relationships • For the purpose of enabling knowledge sharing and re-use • Ontological commitment is an agreement to use a vocabulary • Data Management • Ways to house the data (Word processor; Spread sheet; Database; etc.) • Ways to modify the data (Definitions, Descriptions, Relationships, etc.) in a coorperative way

  8. Animal Trait Ontology Editor Project web site and software download: http://www.animalgenome.org/bioinfo/software/ATO/ • Collaborative Ontology Building (COB) editor • For more details and discussions • For further development of ATO • From “Traits” to “Phenotypes” A community effort is needed

  9. Acknowledgement Max Rothschild(Iowa State University) John Bastiaansen (former PIC) Jie Bao(Dept of Computer Sci., Iowa State University) LaRon Hughes (Dept of Animal Sci., Iowa State University)