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Melissa Haendel Febrary 20, 2014 Phenotype Research Coordination Network Annual meeting. The integrated cross-species phenome as a tool for exploring disease. Candidate gene prioritization. Models recapitulate various phenotypic aspects. GENOTYPE. ALSM1(NM_015120.4)
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Melissa Haendel Febrary 20, 2014 Phenotype Research Coordination Network Annual meeting The integrated cross-species phenome as a tool for exploring disease
Models recapitulate various phenotypic aspects GENOTYPE ALSM1(NM_015120.4) [c.10775delC] + [-] kcnj11c14/c14; insrt143/+(AB) B6.Cg-Alms1foz/fox/J obesity, diabetes mellitus, insulin resistance increased weight, adipose tissue volume, glucose homeostasis altered increased food intake, hyperglycemia, insulin resistance PHENOTYPE
“Expanding” the phenotypic coverage of the human genome Five model organisms provide almost 80% phenotypic coverage of the human genome
Problem: Clinical and model phenotypes are described differently
How then can we take advantage the model organism phenotype data?
Solution: bridging semantics is_a (SubClassOf) anatomical structure part_of develops_from capable_of endoderm is_a (taxon equivalent) only_in_taxon organ part foregut swim bladder organ endoderm of forgut NCBITaxon: Actinopterygii respiration organ respiratory primordium GO: respiratory gaseous exchange pulmonary acinus alveolus lung lung primordium NCBITaxon: Mammalia alveolus of lung alveolar sac lung bud FMA: pulmonary alveolus FMA:lung MA:lung alveolus MA:lung EHDAA: lung bud Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012). Uberon, an integrative multi-species anatomy ontology. Genome Biology, 13(1), R5. doi:10.1186/gb-2012-13-1-r5 Köhleret al. (2014) Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research F1000Research 2014, 2:30
OWLsim: Phenotype similarity across patients or organisms https://code.google.com/p/owltools/wiki/OwlSim
PHenotypicInterpretation of Variants in Exomes (PHIVE) Whole exome Remove off-target and common variants Variant score from allele freq and pathogenicity Phenotype score from phenotypic similarity PHIVE score to give final candidates
Defining a minimum phenotype annotation standard: • Is the annotation specificity of the profile similar to or better than the corpus of available phenotype data? • Is the number of annotations/organism similar or better? • How does the ontology and annotation set differ across anatomical systems in terms of granularity? • How does use of NOT annotations help further specify the uniqueness of a organism profile? • How do onset, temporal ordering, and severity affect specificity?
Monarch phenotype data Also in the system: Rat; IMPC; GO annotations; Coriell cell lines; OMIA; MPD; Yeast; CTD; GWAS; Panther, Homologeneorthologs; BioGrid interactions; Drugbank;AutDB; Allen Brain …157 sources to date Coming soon: Animal QTLs for pig, cattle, chicken, sheep, trout, dog, horse
The environment continuum Experimental perturbations Gene targets Exposure Habitat Acid rain Morpholinos AGACTACTACGTACGTCCTCC Acid treatment shhbMO1-shhb Rice paddy MeSH_D015258 EFO_0004416 ENVO_00000296 short fin phenotype?
Acknowledgments • Sanger • AnikaOehlrich • Jules Jacobson • Damian Smedley • Toronto • Marta Girdea • SergiuDumitriu • Mike Brudno • JAX • Cynthia Smith • Charité Sebastian Kohler Sandra Doelken Sebastian Bauer Peter Robinson NIH-UDP Murat Sincan David Adams William Bone Amanda Links David Draper Neal Boerkoel Cyndi Tifft Bill Gahl OHSU Nicole Vasilesky Matt Brush Bryan Laraway Lawrence Berkeley Nicole Washington Suzanna Lewis Chris Mungall UCSD Amarnath Gupta Jeff Grethe Anita Bandrowski Maryann Martone U of Pitt Chuck Boromeo Jeremy Espino Harry Hochheiser • Funding: • NIH Office of Director: 1R24OD011883 • NIH-UDP: HHSN268201300036C