Phenotype Capture in Genetic Variant Databases

Phenotype Capture in Genetic Variant Databases

Peng Chen

School of Computer and Information Science

[email protected]

Supervisor: Dr Jan Stanek

Research Fields: Health Informatics

Health Computer Science

Health Information System

mailto:[email protected]

Outline

Motivation Research Question Literature Methodology Phenotype Data Review Result The openEHR Archetypes Review Result Phenotype Capture Experiment Result Conclusion

Motivation

1950s health computer science, EHR (Electronic Health Record)

Slow development

Bio-medical research & EHR systems

Genotype – Phenotype correlation

Research Question

Can the existing standard openEHR be used to capture and store phenotype data/clinical data?

Hypothesis one: most of the phenotype data in genetic variant databases is not coded, has little clinical details, not stored in a consistent manner.

Hypothesis two: openEHR is potentially suitable to store phenotype data as a standard.

Literature

Claustres et al. (2002) ‘Time for a Unified System of Mutation Description and Reporting: A Review of Locus-Specific Mutation Databases’

Mitropoulou et al. (2010) ‘Locus-specific database domain and data content analysis: evolution and content maturation toward clinical use’

Spath & Grimson (2011) ‘Applying the archetype approach to the database of a biobank information management system’

Chen et al. (2009) ‘Archetype-based conversion of EHR content models: pilot experience with a regional EHR system’

Methodology

Criteria form for phenotype review 　 1. Storage 4. Granualrity Collect phenotypes Overall granularity level Internal storage Partial fine-grained phenotypesProprietary external storage Foreign external storage 5. Curation Curated 2. Terminology Formal terminology 6. Multiple phenotypes Proprietary terms (mapped to Single phenotype a recognised terminology) Multiple phenotype External terminology used directly Recognised terminology 7. Case level Variant-level phenotypes 3. Coding standard Case-level phenotypes Formal coding standard Proprietary codes (mapped to 8. Database a recognised coding standard) Database family External coding standard used directly Flatform Recognised coding standard 　　　　　　　

Methodology

The openEHR phenotype capture model

Methodology

Data integration workflow towards a proposed health care EHR integration architecture

Phenotype Data Review Result

Reviewed 1224 databases, 978 collect phenotype, all stored in internal storages.

40 (4.1%) has formal terminology, 30 (3.1%) has formal coding. 959 (98%) store low-granularity phenotype data. 604 (62%) were curated by experts. 534 (54.6%) store single phenotype data, 444 (45.5%) store multiple

phenotype data. 757 (77.4%) store phenotypes on case basis, 221 (22.6%) on

variant basis. Database:

Database family Number Platform

LOVD 614 MySQL

UMD 13 4D SQL DB

63% of databases are LOVD

Platform Number

MySQL DB 617

Web page table form 209

Web page free text 132

4D SQL DB 13

PDF table form 4

Excel table form 2

Web page bar chart 1

Phenotype Data Review Result

Phenotype samples:

Sample 1: ‘MRX’, ‘ARRP’, ‘AMD’, ‘arCRD’, ‘CIPA or HSN IV (H406Y + G613V are polymorphisms)’, ‘Type I, type II, non syndromic recessive’

Sample 2: ‘Failure to thrive; Pneumocystis carinii pneumonia; Diarrhea; Marked lymphopenia’

Sample 3: Symptoms Other bacterial infections:

Symptoms Pseudomonas aeruginosa;

Symptoms Escherichia coli;

Symptoms Stenotrophomonas maltophilia;

Symptoms Other; Enterobacter cloacae

Symptoms Other symptoms: perirectal abscess and failure of the

Symptoms umbilical stump to involute, recurrent perirectal

Symptoms abscesses, an infected urachal cyst, a failure to heal

Symptoms surgical wounds, and the absence of pus in infected areas,

Symptoms leucocytosis, neutrophilia, hypochromic anemia

Treatment Bone marrow transplantation: Yes

Treatment Donor: matched sibling

Treatment Outcome: alive and well

Comment D57N mutant behaves in a dominant-negative fashion at the

Comment cellular level

The openEHR Archetypes Review Result

Reviewed 283 existing openEHR archetypes

Multilingual translation mechanism Term binding mechanism

Criteria Result

Number of terms 7361

Number of term bindings 94

Coding system SNOMED-CT, LOINC

Has term binding 7 (0.24% archetypes)

Has multilingual translations 83 (29.3% archetypes)

Languages English, German, Arabic, Portuguese, Japanese, Russian, Dutch, Chinese, Spanish, Farsi

Compile failure 14

Multilingual translation mechanism - example

ontology

terminologies_available = <"SNOMED-CT", ...>

term_definitions = <

…

["zh-cn"] = <

items = <

...

["at0004"] = <

text = <" 收缩压 ">

description = <" 一个血液循环周期中，系统性动脉血压高峰值。收缩期血压 ">

…

["de"] = <

items = <

...

["at0004"] = <

text = <"Systolisch">

description = <"Der höchste arterielle Blutdruck eines Zyklus - gemessen in der systolischen oder

Kontraktionsphase des Herzens.">

…

["en"] = <

items = <

...

["at0004"] = <

text = <"Systolic">

description = <"Peak systemic arterial blood pressure - measured in systolic or contraction phase of the heart cycle.">

>

(ADL display)



Multilingual translation mechanism - compare

Term binding mechanism

term_bindings = <

["SNOMED-CT"] = <

items = <

["at0000"] = <[SNOMED-CT(2003)::163020007]>

["at0004"] = <[SNOMED-CT(2003)::163030003]>

["at0005"] = <[SNOMED-CT(2003)::163031004]>

["at0013"] = <[SNOMED-CT(2003)::246153002]>

>

>

(ADL display)


Phenotype Capture Experiment Result

The chosen sample:

The mapping of concepts:

Diagnosis Wiskott Aldrich syndrome

Symptoms Platelets

Symptoms At date of diagnosis: Count: 28,000/µL

Treatment Bone marrow transplatation: Yes

Treatment Donor: mismatched family donor


The openEHR archetypes mapping:

Evaluation DiagnosisObservation SymptomAction Treatment

NO. Archetypes Entry items

1 openEHR-EHR-EVALUATION.problem-diagnosis.v1.adl Diagnosis

2 openEHR-EHR-OBSERVATION.lab_test-full_blood_count.v1.adl Platelet count

3 openEHR-EHR-ACTION.procedure.v1.adl Procedure, Comments


Phenotype capture snapshots:







A conceptual patient-centric EHR data warehouse schema

Conclusion

The research results have justified the hypotheses and have matched the expected outcomes

The openEHR standard is potentially suitable for storing clinical data, even for integrating health information systems.

The multilingual language mechanism and term binding mechanism are two strong evidences for semantic interoperability between heterogeneous systems.

We need international cooperation on managing the archetypes and completing a full set of archetypes for health concepts.

We need international agreement on choosing terminologies and enhancing the terminologies for resolving semantic conflicts.

Conclusion

The philosophy and the future

A health care EHR integration architecture

Archetype-ontology

Cognitive IS

Human friendly

Robust, scalable, integrated

Semantic interoperability

Syntactic consistency

Data modelling neutral

Start from learning terms and concepts

IS essentially for communication

Ubiquitous information computing

Documents

Phenotype Capture in Genetic Variant Databases