Haendel clingenetics.3.14.14


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  • Note: these searches don’t seem to work in OMIM anymore, they may have gotten rid of the ability to search for quoted strings.
  • Different terminology is used to describe clinical manifestations than is used to describe model system biological features.
  • Distribution of human annotations from GWAS catalog and OMIM Morbidmap are largely disjoint and touch only 38% of protein-coding genes. Combining together human and ortholog data, nearly 80% of humanprotein-coding genes have phenotype annotations in at least one organism, with more than half only present in animal models.Note that human "phenotypes" are those things liked via GWAS catalog and OMIM. it means that some of the inferences might be artificially low because we aren't yet mapping CNVs to their constituent genes. Note that this also does not include the ClinVar data stats that we recently ingested, and only the model organisms: mouse, zebrafish, fly, yeast, rat. We have a lot more phenotype data now coming from other databases and organisms. These statistics will be available soon.
  • Also point out the functional classification axis
  • Things like finding models of sirenomelia due to disruption of the lateral plate mesoderm . Helping to find models and gene candidates based on the relationships in the development
  • Without additional knowledge and linking, computers can’t make the connections. These links take us from the molecular to the protein, to the cellular and anatomical, to the disease level of phenotypes
  • OWLsim computes semantic similarity between sets of phenotypes within and across species using the bridging semantics. Phenotypes in common from the bridging ontologies relate human clinical phenotypes with model organism phenotypes.Examples include motor systems, olfaction, and digestion. In this case, data encoded using the human phenotype ontology has been made interoperable with mouse, zebrafish and other model system ontologies. This also enables the use of more complex algorithms to detect similarity – not bases solely on mapping or string matching; e.g. constipation and decreased gut peristalsis are both subtypes of abnormal digestive system physiology.
  • The norm in exome analysis is to run either single exomes or to do trio analysis. These methods generally use some combination of quality filter, frequency filter, a form of predicted deleteriousness and often a candidate gene method. This is followed by a some basic Mendalian filtration, and then the remain variants are ranked by allele frequency, correlation to phenotype according to an annotation like HGMD, apparent pathogenicity.single exomes or to do trio analysisCANDIDATE GENE LIST
  • The procedure used at the Undiagnosed Disease Program puts more emphasis on the Mendelian inheritance models. Normally we use SNP chip data coupled with Mendelian filters for the exome data. A script in this case or the program Varsifter is used to filter out all variants that do not meet a homozygous rec, compound het, de novo dominant, or X-linked. Then after using the BAM files to check the quality of the variants, a final, very labor intensive step is done where these variants are currated and annotated by hand based on allele frequecy, predicted deleteriousness and PubMed articles. It is not uncommon that it ends up there is no way to distinguish between the last few variant for which is causing disease.emphasis on the Mendelian inheritance modelsSNPshomozygous rec, compound het, de novo dominant, or X-linkedFinally step done by hand labour intensive
  • PhenIXusese human data and predicted deleteriousness HGMD ClinVAR OMIM Orphanet
  • Exomiser Mouse Pheno and deleteriousness
  • The analysis that we have been experimenting with has been the use of the UDP standard operating procedure script being run on a families’ exome data, then the output of those filters was then put through phenotypic and variant analysis using either Mouse phenotype data via Exomiser or Human data via PhenIX.UDP standard operating procedure script Homo rec, comp het, denovo, X-linked Frequencyunneled into Exomizer or PhenIX for ranking
  • Run through pipeline: Exomiser LOT is a version of exomiser that is less restrictive as far as what transcripts it recognizes (not at worried about off target reads because of the Mendalian filters and the ability to look at the BAMS)ExomiserExomiser LOT Pheno only
  • The goal of the following computational analysis is to specifically understand the minimum human phenotype annotation that will enable useful identification of candidate genes and additional related phenotypes for UDP patients based on the current corpus available in Monarch (covering a large set of annotated human diseases from OMIM, Decipher, and Orphanet, as well as phenotype data from mouse, zebrafish and many other species).
  • Shown is a survey of the human annotations currently in the Monarch system. IC is information content, and higher numbers are a graph measure of specificity. sumIC is a combined indicator of depth of annotation. For the UDP set, each patient id is considered a distinct disease.
  • Each anatomical system is indicated in a color that is inversely listed in the legend to the graph (e.g. Skeletal System is at the top of the graph). Data are combined from Orphanet, OMIM, and Decipher. The graph shows that the systems with the largest proportion of annotations are the skeletal system and the nervous system. Note that the data is not disjoint - some annotations may fall into multiple categories according to the structure of the ontology.
  • First implementation. User guidelines written by Monarch will be implemented in PhenoTips in the next few days as a help menu.
  • Monarch is curating and assisting clinicians to create quality annotation profiles and the clinicians are helping to improve the ontology and therefore the corpus against which the similarity algorithms run.
  • Large scale data integration of genotypes, phenotypes and many other dataBased on NIF, contains large number of integrated databases (157 to date, more added every week).Building innovative visualization tools to explore model system phenotype data in context of other biomedical data. Widgets and services publicly availableWhy an initiative? Because it is a partnership to promote standardization and integration across model systems and clinical applications and all are welcome.
  • Using the phenotypes associated with the patient, one can query all model systems to find the ones that have the most related sets of phenotypes. Choosing the right model for a co-clinical trial, or for further analysis must involve an understanding of how well the model recapitulates the full spectrum (or not) of phenotypes. One would want to choose the model with the phenotype that one is most interested in understanding, assaying, or treating. This also have the benefit of providing collaborator suggestions, since the person who phenotyped each model is related to the model. They could be tasked to help perform the co-clinical trial or further phenotyping.These people are best at phenotyping the model, can inform human phenotyping, and conversely be trained to perform additional clinical assays in the modelsThis visualization is under active development and will be available in PhenoTips in the next few weeks.Also, one can drill down on the right side to see more specific annotations as to how, for example, the cerebrum is abnormal.
  • Lewy bodies, a hallmark of this disease, seems to only manifest phenotypes from a few of the genes, resulting in cerebral abnormalities and other CNS morphological changes in the mice. Lewy Bodies maps to these LCS (genes):Abnormality of the cerebrum (Snca, Slc6a3, Mfn2, Cox8a)Morphological abnormality of the central nervous system (Uchl1, Uchl3)
  • If we take a closer look at Bradykinesia, and the double-mutant mouse in Uchl1 and Uchl3 (Uchl1<gad>/Uchl1<gad>; Uchl3<tm1Tilg>/Uchl3<tm1Tilg>), Here, we examine the mouse phenotypes in our model that are related to Bradykinesia. There are three recorded phenotypes for this mouse that show some similarity.
  • Each model organism has a different suite of phenotypes that are examined, because different models are used to explore different types of biological function and malfunction. By using a diversity of model systems, we have the potential to identify candidates based on partial overlaps with the patient phenotype profile by looking at different models with mutations in potential candidates or related via interactions, co-expression, genomic regulatory region, etc.
  • Bare Lymphocyte Syndrome Type 1 Protein-Interaction NetworkThe protein-interaction network associated with bare lymphocyte syndrome type 1, which comprises the genes TAP1, TAP2, and TAPBP. Each of these genes is shown in red. The DI and SP methods additionally identified the unrelated genes PSMB8 and PSMB9 (shown in yellow) as potential disease genes because they each have an interaction with one of the true disease genes. The RWR method ranks the true disease genes higher because each true disease gene has interactions with two other family members and because there is a dense net of proteins that connect the disease genes via paths with two interactions.
  • Phenoviz is a new graphviz plugin that can be used as a standalone app for Windows, Mac, or Linux. The user uploads a list of CNVs detected by Array CGH (SNP Chips, or even genome sequence data would also work as a starting point, but the program expects a simple list). You also enter a list of the HPO terms observed in the patient. The application then tries to find “matches” based on the single gene disorders (human – HPO annotations) or the mouse models (mainly knockouts, MP annotations from MGI) or fish models (ZFIN E/Q annotations). This is being in the Charite Array CGH diagnostics service to help with interpretation of CNVs. Subjectively, the tool helps you to quickly find good candidates in order to write reports. The program also picks out the best matching CNV in case the user enters several (a typical array CGH finding in our lab has up to 50 CNVs, of which 2-5 are not found in databases of common variants like DGV).
  • There are a lot of people who have contributed to this work over many years.
  • Haendel clingenetics.3.14.14

    1. 1. Expanding the Clinical Phenotype Space with Semantics and Model Systems Melissa Haendel March 14th, 2014 Updates in Clinical Genetics 2014
    2. 2. Outline  Issues in candidate prioritization  Computational techniques for comparing phenotypes  Undiagnosed Disease Program semantic phenotyping  Minimum phenotype requirements  Tools leveraging phenotypes
    3. 3. The Challenge: Interpretation of Disease Candidates ?  What’s in the box?  How are candidates identified?  How do they compare? Prioritized Candidates, Models, functional validation M1 M2 M3 M4 ... Phenotypes P1 P2 P3 … Genotype info G1 G2 G3 G4 … Pathogenicity, frequency, p rotein interactions, gene expression, gene networks, epigenomics, m etabolomics….
    4. 4. Candidate gene prioritization Phenot ypic inf or mat ionGenet ic inf or mat ion gene/ gene pr oduct Inf o Phenotypes collected for individual patients Sequences from an individual,family,or related group Candidate interpretation Human sequence reference sequences (e.g.reference sequence,1K genome data, genomic location) Community phenotype data (e.g. literature MODS,KOMP2,OMIM, EHRs,GWAS,ClinVar,disease specific repositories,etc.) Pathway Functional (GO) Gene expression, OMICS data Protein-Protein Interactions Enrichment analysis (e.g.GATACA,Galaxy) Combined variant + phenotype candidate reporting(e.g.Exomizer) BiomedicalKnowledgeIndividual'sInformation Phenotypic comparison methods Variant calling (e.g.GATK) Pathogenicity /Impact calling (e.g. VAAST,SIFT) Orthologs Network module analysis
    5. 5. B6.Cg-Alms1foz/fox/J increased weight, adipose tissue volume, glucose homeostasis altered ALSM1(NM_015120.4) [c.10775delC] + [-] GENOTYPE PHENOTYPE obesity, diabetes mellitus, insulin resistance increased food intake, hyperglycemia, insulin resistance kcnj11c14/c14; insrt143/+(AB) Models recapitulate various phenotypic aspects of disease ?
    6. 6. OMIM Query # Records “large bone” 785 “enlarged bone” 156 “big bone” 16 “huge bones” 4 “massive bones” 28 “hyperplastic bones” 12 “hyperplastic bone” 40 “bone hyperplasia” 134 “increased bone growth” 612 Searching for phenotypes using text alone is insufficient
    7. 7. Problem: Clinical and model phenotypes are described differently
    8. 8. “Expanding” the phenotypic coverage of the human genome 0% 20% 40% 60% 80% 100% %humancodinggenes OMIM OMIM+GWA S Ortholog only Human+Ortholog Human only Five model organisms (mouse, zebrafish, fly, yeast, rat) provide almost 80% phenotypic coverage of the human genome
    9. 9. How can we take advantage this model organism phenotype data?
    10. 10. Outline  Issues in candidate prioritization  Computational techniques for comparing phenotypes  Undiagnosed Disease Program semantic phenotyping  Minimum phenotype requirements  Tools leveraging phenotypes
    11. 11. Using ontologies to compare phenotypes across species Washington, N. L., Haendel, M. A., Mungall, C. J., Ashburner, M., Westerfield, M., & Lewis, S. E. (2009). Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation. PLoS Biol, 7(11). doi:10.1371/journal.pbio.1000247
    12. 12. What is an ontology? A set of logically defined, inter-related terms used to annotate data Use of common or logically related terms across databases enables integration Relationships between terms allow annotations to be grouped in scientifically meaningful ways Reasoning software enables computation of inferred knowledge Groups of annotations can be compared using semantic similarity algorithms
    13. 13. An ontology provides the logical basis of classification Any sense organ that functions in the detection of smell is an olfactory sense organ sense organ capable_of some detection of smell olfactory sense organ
    14. 14. nose sense organ nose capable_of some detection of smell sense organ capable_of some detection of smell olfactory sense organ nose => These are necessary and sufficient conditions Classifying
    15. 15. Representating phenotypes
    16. 16. Human Phenotype Ontology Used to annotate: • Patients • Disorders • Genotypes • Genes • Sequence variants In human Reduced pancreatic beta cells Abnormality of pancreatic islet cells Abnormality of endocrine pancreas physiology Pancreatic islet cell adenoma Pancreatic islet cell adenoma Insulinoma Multiple pancreatic beta-cell adenomas Abnormality of exocrine pancreas physiology Köhler et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74.
    17. 17. Mammalian Phenotype Ontology Smith et al. (2005). The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol, 6(1). doi:10.1186/gb-2004-6-1-r7 Used to annotate and query: • Genotypes • Alleles • Genes In mice abnormal pancreatic beta cell mass abnormal pancreatic beta cell morphology abnormal pancreatic islet morphology abnormal endocrine pancreas morphology abnormal pancreatic beta cell differentiation abnormal pancreatic alpha cell morphology abnormal pancreatic alpha cell differentiation abnormal pancreatic alpha cell number
    18. 18. Post-composed models of phenotype annotation Entity Anatomy: head Anatomy: heart Anatomy: ventral mandibular arch Gene Ontology: swim bladder inflation Quality Small size Edematous Thick Arrested
    19. 19. A human phenotype example Abnormality of the eye Vitreous hemorrhage Abnormal eye morphology Abnormality of the cardiovascular system Abnormal eye physiology Hemorrhage of the eye Internal hemorrhage Abnormality of the globe Abnormality of blood circulation
    20. 20. lung lung lobular organ parenchymatous organ solid organ pleural sac thoracic cavity organ thoracic cavity abnormal lung morphology abnormal respiratory system morphology Mammalian Phenotype Mouse Anatomy FMA abnormal pulmonary acinus morphology abnormal pulmonary alveolus morphology lung alveolus organ system respiratory system Lower respiratory tract alveolar sac pulmonary acinus organ system respiratory system Human development lung lung bud respiratory primordium pharyngeal region Problem: Data silos develops_from part_of is_a (SubClassOf) surrounded_by
    21. 21. Solution: bridging semantics 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 anatomical structure endoderm of forgut lung bud lung respiration organ organ foregut alveolus alveolus of lung organ part FMA:lung MA:lung endoderm GO: respiratory gaseous exchange MA:lung alveolus FMA: pulmonary alveolus is_a (taxon equivalent) develops_from part_of is_a (SubClassOf) capable_of NCBITaxon: Mammalia EHDAA: lung bud only_in_taxon pulmonary acinus alveolar sac lung primordium swim bladder respiratory primordium NCBITaxon: Actinopterygii Köhler et al. (2014) Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research F1000Research 2014, 2:30
    22. 22. Phenotype representation requires more than “phenotype ontologies” glucose metabolism (GO:0006006 ) Gene/protein function data glucose (CHEBI:172 34) Metabolomics, t oxicogenomics data Disease & phenotype data type II diabetes mellitus (DOID:9352) pyruvate (CHEBI:153 61) Disease Gene Ontology Chemical pancreatic beta cell (CL:0000169) transcriptomic data Cell
    23. 23. OWLsim: Phenotype similarity across patients or organisms Unstable posture Constipation Neuronal loss in Substantia Nigra Shuffling gait Resting tremors REM disorder Hyposmia poor rotarod performance decreased gut peristalsis axon degeneration decreased stride length sterotypic behavior abnormal EEG failure to find food abnormal coordination abnormal digestive physiology CNS neuron degeneration abnormal locomotion abnormal motor function sleep disturbance abnormal olfaction https://code.google.com/p/owltools/wiki/OwlSim
    24. 24. Outline  Issues in candidate prioritization  Computational techniques for comparing phenotypes  Undiagnosed Disease Program semantic phenotyping  Minimum phenotype requirements  Tools leveraging phenotypes
    25. 25. General exome analysis Single Exome Remove off-target and common variants, filter on predicted deleteriousness, candidate gene strategies Prioritize based on known genes, allele frequency, and pathogenicity Homozygous recessive, X- linked, De novo (if trio)
    26. 26. Undiagnosed Disease Program exome analysis Family exome data Prioritize based on alignment quality, allele frequency, predicted deleterious, and PubMed Filter using SNP chip data, Mendelian models of inheritance and Population frequency
    27. 27. exome analysis Recessive, De novo filters Remove off-target, common variants, and variants not in known disease causing genes Zemojtelet al., manuscript submittedhttp://compbio.charite.de/PhenIX/
    28. 28. Remove off-target and common variants Recessive, De novo filters https://www.sanger.ac.uk/resourc es/databases/exomiser/ Robinson et al. http://genome.cshlp.org/content/early/2013/10/2 Exomiser exome analysis
    29. 29. Current UDP analysis with semantic phenotyping Family Exome Data Combined Score Phenotype Data Filter using SNP chip data, Mendelian models of inheritance, and population frequency
    30. 30. Benchmarking 1092 unaffected exomes 28,516 disease associated variants 100,000 simulated exomes  Annotate variants  Remove off-target, syn and common(>1% MAF) variants (plus optional inheritance model filtering)  Prioritize based on combined score
    31. 31. 0 10 20 30 40 50 60 70 80 90 100 All diseases Autosomal Dominant Autosomal Recessive (hom) Autosomal Recessive (compound het) %exomeswithdiseasegeneas tophit Variant Phenotypic relevance PHIVE Phenotype and variant data synergistically improve exome interpretation
    32. 32. Results  Correct gene as top scoring hit in 68.3% of exomes out of an average of 272 post-filtering candidate genes  Improvement of between 1.8 and 5.1 fold in the percentage of candidate genes correctly ranked in first place compared to just using pathogenicity and frequency data  Shows utility of structured phenotype data for computational analysis
    33. 33. UDP Experiment UDP Diploid Aligned Cohort VCF file 18 families Phenotype profiles Mendelian filtered files (per family) Mendelian Filters Exomiser PhenIX Phenotype only VCF files with phenotype and variant scores (per family)
    34. 34. Top de novo candidates for patient 2543 Patient Exomiser Phenotype only PhenIX UDP2543 STIM1, CYP2D6, MUC5B ITGA7, PLEC, STIM1, PTGS1, TTN STIM1, RB1, DLEC1, CHRNB4, MUC5B, REPIN1, NBPF8, GPRIN3, TMEFF1, FLT3LG, OSM, FZD10, MUC12 Gene Variant MAF(ESP or 1000g) Consequence Predicted pathogenicity: SIFT, PolyPhen, MutTaster (0-1) STIM1 chr11:g.4045175A>T [0/1] 0% p.I115T 1
    35. 35. UDP2543: phenotypic similarity Patient Stim1 het mouse OMIM:612783 (IMMUNODEFICIENCY 10) - hom STIM1 mutations OMIM:160565 (MYOPATHY, TUBULAR AGGREGATE) - het STIM1 mutations Impaired platelet aggregation abnormal platelet activation Thrombocytopenia Thrombocytopenia decreased platelet cell number Thrombocytopenia Myopathy Myopathy Myopathy Generalized hypotonia Muscular hypotonia Proximal muscle weakness Petechiae increased bleeding time Autoimmune hemolytic anemia Delayed gross motor development Epistaxis increased bleeding time Gower sign
    36. 36. STIM1
    37. 37. Suspected genetic disease DRG sequencing Deep phenotyping Top ranked candidates Clinical rounds Exclude candidate gene Sanger validation Cosegregation studies Diagnosis Fails Passes Inconsistent Consistent Reconsider short list Choose best candidate Variant Analysis HGMD MAF (dbSNP, ESP) ClinVar Annotation sources: Predicted pathogenicity Variant class Location in DRG target region Prediction criteria: Computational Phenotype Analysis HPO Semantic similarity Mode of inheritance OMIM, Orphanet, MGI... Annotation sources: Ontology: Prediction criteria: MP Proposed workflow for undiagnosed diseases
    38. 38. What constitutes an adequate phenotype annotation for an undiagnosed patient?
    39. 39. Defining minimum phenotype standard: 1. Is the annotation specificity similar to or better than the corpus of available phenotype data? 2. Is the number of annotations/patient similar or better? 3. How does the ontology and annotation set differ across anatomical systems in terms of granularity? Does this change specificity requirements for phenotypic profiles? 4. How does use of NOT annotations help further specify the uniqueness of an undiagnosed patient? 5. How do onset, temporal ordering, and severity affect specificity?
    40. 40. UDP phenotype annotation metrics UDP annotations have a similar Information content (IC) and a larger number of average annotations per disease/patient
    41. 41. Anatomical annotation distribution in the corpus Nervous system, skeletal system, and immune system is highest => these categories require greater specificity and numbers of annotations
    42. 42. Annotation specificity meter What about common traits, like blue eyes or acne?
    43. 43. Making the patient phenotype profiles as good as can be Total requests from UDP 614 Examples Number of requests assigned to HPO terms 423 Chronic limb pain -> limb pain Number of terms that need consideration by UDP 145 Expressive language -> delay? Increase? Abnormal? Number of requests that belong in other parts of the patient record 68 Abnormal aCGH 12q21.1- 12q.2 (662 kb duplication) paternal origin -> move to genotype information portion of the record It is a community effort to contribute requests to the ontologies and quality profiling helps make our tools work better for everyone
    44. 44. Limitations and ongoing work  Adding negation to the algorithm  Temporal ordering of phenotypes  Leveraging severity, expressivity, and penetrance data
    45. 45. Additional tools leveraging structured phenotype data
    46. 46. The Monarch system http://monarchinitiative.org
    47. 47. Monarch phenotype data Species Source Unique genotypes/va riants Disease/phen otype associations Mouse MGI 53,573 406,618 Zebrafish ZFIN 14,703 75,698 C. elegans Wormbase 116,106 411,154 Fruit fly Flybase 98,596 265,329 Human OMIM 26,372 27,798 Human Orphanet 2,872 5,095 Human ClinVar 62,437 178,424 Also in the system: Rat; IMPC; GO annotations; Coriell cell lines; OMIA; MPD; Yeast; CTD; GWAS; Panther, Homologene orthologs; BioGrid interactions; Drugbank; AutDB; Allen Brain …157 sources to date Coming soon: Animal QTLs for pig, cattle, chicken, sheep, trout, dog, horse
    48. 48. ModelCompare: How do the models recapitulate the disease? Late-onset Parkinson’s Phenotypes Mouse Phenotypes
    49. 49. Slc6a3 Dbh Tyrosine metabolism Slc6a3 Slc18a2 Uchl1 Uchl3 Snca Mfn2 Cx IV Cox8a Th Late-onset Parkinson’s Phenotypes (subset) Bradykinesia Depression Dysphagia Lewy bodies Network phenotype distribution
    50. 50. Slc6a3 Dbh Tyrosine metabolism Slc6a3 Slc18a2 Uchl1 Uchl3 Snca Mfn2 Cx IV Cox8a Th Late-onset Parkinson’s Phenotypes (subset) Bradykinesia Depression Dysphagia Lewy bodies Abnormal gait ataxia paralysis Bradykinesia Abnormal locomotion Abnormality of central motor function Phenotypes in common
    51. 51. Finding collaborators for functional validation Patient Phenotype profile Phenotyping experts
    52. 52. Exome Walker: Network based exploration of phenotypically similar diseases http://compbio.charite.de/ExomeWalker/ Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet. 2008 Apr;82(4):949-58. doi: 10.1016/j.ajhg.2008.02.013. Bare Lymphocyte Syndrome Type 1 Protein-Interaction Network  Exploits vicinity in the protein interaction network between phenotypically related diseases and uses this to rank exome candidates  Large boost in rankings of candidate genes using 250 disease gene-families  Prototype version online, manuscript in preparation
    53. 53. PhenoViz: Integrate all human, mouse, and fish data to understand CNVs Desktop application for differential diagnostics in CNVs  Explain manifestations of CNV diseases based on genes contained in CNV E.g., Supravalcular aortic stenosis in Williams syndrome can be explained by haploinsufficiency for elastin  Double the number of explanations using model data Doelken, Köhler, et al. (2013) Dis Model Mech 6:358-72
    54. 54. Conclusions  Cross-species phenotype data can be used to perform semantic similarity  Structured phenotype data for rare and undiagnosed disease patients can aid candidate evaluation  We are experimenting with these methods for UDP patient phenotypes to aid candidate prioritization, identify models, explore mechanisms, and find collaborators
    55. 55. NIH-UDP William Bone Murat Sincan David Adams Amanda Links David Draper Neal Boerkoel Cyndi Tifft Bill Gahl OHSU Nicole Vasilesky Matt Brush 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 Acknowledgments Sanger Anika Oehlrich Jules Jacobson Damian Smedley Toronto Marta Girdea Sergiu Dumitriu Mike Brudno JAX Cynthia Smith Charité Sebastian Kohler Sandra Doelken Sebastian Bauer Peter Robinson Funding: NIH Office of Director: 1R24OD011883 NIH-UDP: HHSN268201300036C