Decomposing Phenotype Descriptions for the Human Skeletal Phenome

Groza, Tudor; Hunter, Jane; Zankl, Andreas

doi:10.4137/bii.s10729

Cited by 4 publications

(6 citation statements)

References 26 publications

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“…Since the initiative call phenome based databases were established (e.g. bipolar disorder phenome [ 24 ]; epilepsy phenome/genome project [ 25 ]; mouse phenome [ 26 ]; human pathology centered phenomes on cardiomyopathy [ 27 ], deafness [ 28 ], cardiac conduction characteristics [ 29 ], human skeletal phenome [ 30 ]). Phenotypes forming the basis of the phenome approaches can refer to any characteristic or trait measureable in an organism.…”

Section: Discussionmentioning

confidence: 99%

Biobanking across the phenome - at the center of chronic disease research

Imboden

Probst‐Hensch

2013

BMC Public Health

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BackgroundRecognized public health relevant risk factors such as obesity, physical inactivity, smoking or air pollution are common to many non-communicable diseases (NCDs). NCDs cluster and co-morbidities increase in parallel to age. Pleiotropic genes and genetic variants have been identified by genome-wide association studies (GWAS) linking NCD entities hitherto thought to be distant in etiology. These different lines of evidence suggest that NCD disease mechanisms are in part shared.DiscussionIdentification of common exogenous and endogenous risk patterns may promote efficient prevention, an urgent need in the light of the global NCD epidemic. The prerequisite to investigate causal risk patterns including biologic, genetic and environmental factors across different NCDs are well characterized cohorts with associated biobanks. Prospectively collected data and biospecimen from subjects of various age, sociodemographic, and cultural groups, both healthy and affected by one or more NCD, are essential for exploring biologic mechanisms and susceptibilities interlinking different environmental and lifestyle exposures, co-morbidities, as well as cellular senescence and aging. A paradigm shift in the research activities can currently be observed, moving from focused investigations on the effect of a single risk factor on an isolated health outcome to a more comprehensive assessment of risk patterns and a broader phenome approach. Though important methodological and analytical challenges need to be resolved, the ongoing international efforts to establish large-scale population-based biobank cohorts are a critical basis for moving NCD disease etiology forward.SummaryFuture epidemiologic and public health research should aim at sustaining a comprehensive systems view on health and disease. The political and public discussions about the utilitarian aspect of investing in and contributing to cohort and biobank research are essential and are indirectly linked to the achievement of public health programs effectively addressing the global NCD epidemic.

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Section: Discussionmentioning

confidence: 99%

Biobanking across the phenome - at the center of chronic disease research

Imboden

Probst‐Hensch

2013

BMC Public Health

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“…Similarly, the compositional structure of SNOMED terms has been exploited for assessing the consistency of its hierarchical structure [ 20 ]. Recent work based on the compositionality of phenotype terms investigated skeletal abnormalities [ 21 ] and clinical phenotypes across species [ 22 ]. However in the latter study, the Entity-Quality decomposition strategy yielded better results on the Mammalian Phenotype Ontology than on HPO.…”

Section: Introductionmentioning

confidence: 99%

Interoperability between phenotypes in research and healthcare terminologies—Investigating partial mappings between HPO and SNOMED CT

Dhombres

Bodenreider

2016

J Biomed Semant

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BackgroundIdentifying partial mappings between two terminologies is of special importance when one terminology is finer-grained than the other, as is the case for the Human Phenotype Ontology (HPO), mainly used for research purposes, and SNOMED CT, mainly used in healthcare.ObjectivesTo investigate and contrast lexical and logical approaches to deriving partial mappings between HPO and SNOMED CT.Methods1) Lexical approach—We identify modifiers in HPO terms and attempt to map demodified terms to SNOMED CT through UMLS; 2) Logical approach—We leverage subsumption relations in HPO to infer partial mappings to SNOMED CT; 3) Comparison—We analyze the specific contribution of each approach and evaluate the quality of the partial mappings through manual review.ResultsThere are 7358 HPO concepts with no complete mapping to SNOMED CT. We identified partial mappings lexically for 33 % of them and logically for 82 %. We identified partial mappings both lexically and logically for 27 %. The clinical relevance of the partial mappings (for a cohort selection use case) is 49 % for lexical mappings and 67 % for logical mappings.ConclusionsThrough complete and partial mappings, 92 % of the 10,454 HPO concepts can be mapped to SNOMED CT (30 % complete and 62 % partial). Equivalence mappings between HPO and SNOMED CT allow for interoperability between data described using these two systems. However, due to differences in focus and granularity, equivalence is only possible for 30 % of HPO classes. In the remaining cases, partial mappings provide a next-best approach for traversing between the two systems. Both lexical and logical mapping techniques produce mappings that cannot be generated by the other technique, suggesting that the two techniques are complementary to each other. Finally, this work demonstrates interesting properties (both lexical and logical) of HPO and SNOMED CT and illustrates some limitations of mapping through UMLS.

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“…Several ontological approaches have been proposed to implement EQ statements [ 26 , 27 ] and, subsequently, tools have been developed to manually [ 28 ] or automatically [ 26 , 29 ] construct them. These tools rely on the existence of ontologies that define localization and trait concepts, such as the Foundational Model of Anatomy (FMA) [ 30 ] for human, or the Mouse Adult Gross Anatomy Ontology (MA) [ 31 ] for mouse, and the species-agnostic Phenotype and Trait Ontology (PATO) [ 25 ] for traits.…”

Section: Introductionmentioning

confidence: 99%