Problem of comparing clinical and basic science phenotypes

The integrated cross-species phenome as a tool for exploring diseaseMelissa HaendelFebrary 20, 2014Phenotype Research Coordination Network Annual meeting

Candidate gene prioritization2 B6.Cg-Alms1foz/fox/Jincreased weight,adipose tissue volume, glucose homeostasis alteredALSM1(NM_015120.4)[c.10775delC] + [-]GENOTYPEPHENOTYPEobesity,diabetes mellitus, insulin resistance

increased food intake, hyperglycemia,insulin resistancekcnj11c14/c14; insrt143/+(AB)

Models recapitulate various phenotypic aspects3Expanding the phenotypic coverage of the human genomeFive model organisms provide almost 80% phenotypic coverage of the human genome 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. 4

Problem: Clinical and model phenotypes are described differentlyDifferent terminology is used to describe clinical manifestations than is used to describe model system biological features.5How then can we take advantage the model organism phenotype data?Solution: bridging semanticsMungall, 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-r5anatomical structureendoderm of forgutlung budlungrespiration organorganforegutalveolusalveolus of lungorgan partFMA:lungMA:lungendodermGO: respiratory gaseous exchangeMA:lung alveolusFMA: pulmonary alveolusis_a (taxon equivalent)develops_frompart_ofis_a (SubClassOf)capable_ofNCBITaxon: MammaliaEHDAA:lung budonly_in_taxonpulmonary acinusalveolar saclung primordiumswim bladderrespiratory primordiumNCBITaxon:ActinopterygiiKhler et al. (2014) Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research F1000Research 2014, 2:30OWLsim: Phenotype similarity across patients or organisms

https://code.google.com/p/owltools/wiki/OwlSim

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.8PHenotypic Interpretation of Variants in Exomes (PHIVE)

Whole exomeRemove off-target and common variantsVariant score from allele freq and pathogenicity

Phenotype score from phenotypic similarityPHIVE score to give final candidates9What constitutes an adequate phenotype profile?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?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).

11Monarch phenotype dataSpeciesSourceUnique genotypes/variantsDisease/phenotype associationsMouseMGI53,573406,618ZebrafishZFIN14,70375,698C. elegansWormbase116,106411,154Fruit flyFlybase98,596265,329HumanOMIM26,37227,798Human Orphanet2,8725,095HumanClinVar62,437178,424Also 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, horse12Phenotypic context

Phenotypic context is temporal

The environment continuum

AGACTACTACGTACGTCCTCCshort fin phenotype?MorpholinosshhbMO1-shhbGene targetsExperimental perturbationsExposureHabitatRice paddyENVO_00000296

Acid rainMeSH_D015258

EFO_0004416

Acid treatment15NIH-UDPMurat SincanDavid AdamsWilliam BoneAmanda LinksDavid DraperNeal BoerkoelCyndi TifftBill GahlOHSUNicole VasileskyMatt BrushBryan Laraway

Lawrence BerkeleyNicole WashingtonSuzanna LewisChris MungallUCSDAmarnath GuptaJeff GretheAnita BandrowskiMaryann MartoneU of PittChuck BoromeoJeremy EspinoHarry Hochheiser

AcknowledgmentsSangerAnika OehlrichJules JacobsonDamian SmedleyTorontoMarta GirdeaSergiu Dumitriu Mike BrudnoJAXCynthia SmithCharitSebastian KohlerSandra DoelkenSebastian BauerPeter Robinson

Funding:NIH Office of Director: 1R24OD011883NIH-UDP: HHSN268201300036CThere are a lot of people who have contributed to this work over many years. 16