1. health and fitness
2. disease

Use of the Disease Ontology to Screen Genomic Sequencing
Study Participants for Condition-specific Phenotypes: Application
to Population Health Management
Casey Overby1,2*, Elizabeth Cutting1, Priti Kumari3, Simon Guo1, Lynn Schriml3
1
3
Program for Personalized and Genomic Medicine; 2Center for Health-related Informatics and Bioimaging,
Institute for Genome Sciences; University of Maryland School of Medicine, Baltimore, MD
ABSTRACT
There is a growing number of whole genome and exome
sequencing (WGS/WES) studies for which incidental findings
for study participants may be uncovered. Incidental findings
are defined by the American College of Medical Genetics
and Genomics as secondary findings that are unrelated to
the indication for ordering the sequencing, but potentially of
clinical value for patient care. We proposed an approach to
leverage the Disease Ontology to identify condition-specific
phenotypes for incidental findings and conducted a feasibility
study of our approach to provide customized recommendations for population health management. Overall, we validated that our screening approach has potential to support population health management with WGS/WES study participants by demonstrating our ability to identify a personal genome project participant with a known reportable genetic
finding and subsequently recommend a notification action
that was consistent with the true action taken for a that participant. We also highlight several areas for improving upon
biomedical resources and reporting infrastructure in order to
provide a scalable approach to screening for population
health management. These include improved representation
of phenotypes associated with medically actionable incidental findings within ontologies, along with improved data
integration and mapping of existing disease and phenotype
ontologies. Moreover, the discussion regarding reporting and
disclosing incidental findings spans beyond known medically
actionable results therefore more complex scenarios for returning non-medically actionable results should also be considered for population health management.
1
INTRODUCTION
Rapid advancements in next-generation sequencing technologies have led to the increased use of whole exome sequencing (WES) and whole genome sequencing (WGS) in
both the clinical and research realms of medical genetics.
As a consequence, identifying incidental findings is not an
uncommon occurrence. Incidental findings are defined by
the American College of Medical Genetics and Genomics
*
To whom correspondence should be addressed.
(ACMG) as secondary findings that are unrelated to the
indication for ordering the sequencing but that are potentially of clinical value for patient care.[1] In 2013, the ACMG
Work Group on Incidental Findings in Clinical Exome and
Genomic Sequencing (ACMG Workgroup) recommended
evaluating and reporting results from all clinical WGS and
WES and generated an initial list of 56 genes and categories
of variants to be reported as incidental findings. While
ACMG recommendations were developed for clinical WES
and WGS tests, we choose to leverage them as one of many
potential criteria for reporting incidental findings from WGS
in research studies.
Current efforts exploring processes for reporting incidental
findings in large-scale sequencing studies include those of
NIH-funded Clinical Sequencing and Exploratory Research
(CSER)[2] and electronic Medical Records and Genomics
(eMERGE) [3] Networks. Mechanisms for reporting incidental findings within these networks vary, for example
some indicate return by a single clinical specialist, while
others indicate the return should happen only after review
and agreement by a multidisciplinary team, local IRB, or
formal oversight board. For some there were no plans to
return incidental findings. Some sites report plans to place
incidental findings in the electronic health record (EHR),
however, there are few efforts to explore technical approaches for reporting and disclosing incidental findings.
We propose an approach to leverage the Disease
Ontology[4, 5] to identify condition-specific phenotypes for
incidental findings and we conducted a feasibility study of
our approach to provide customized recommendations for
population health management.
This paper is structured as follows. In section 2 we provide
brief overviews of reporting incidental findings for genomic
sequencing study participants, and of some related research
efforts. Section 3, we introduce a framework for leveraging
condition-specific phenotypes to provide customized recommendations for population health management activities.
Section 4 provides an overview of methods for screening a
population for reportable variants in ACMG genes, for
screening a population for condition-specific phenotypes
1
C. Overby et al.
needed to assess a potential incidental finding, and for
providing customized recommendations for notifying the
study team to contact participants. Section 5, provides results from a feasibility study with a publically available dataset of participants in the Personal Genome Project (PGP).
Section 6 provides some discussion of the significance and
generalization of this work as well as areas for future work.
2
2.1
BACKGROUND
Reporting and disclosing incidental finding
for genomic sequencing study participants
For both research and clinical WGS/WES results, the scientific community is in agreement that returning all results is
not feasible as it is laborious and time-consuming. The types
of results from research studies to consider for reporting in
particular remains a matter of debate.[6, 7] When returning
results from a research setting, most genetic researchers
agree that incidental findings for highly penetrant disorders
with immediate clinical utility should be returned, but there
is no consensus regarding conditions with varying penetrance and actionability [8]. This work therefore considers
only recommendations made by the ACMG Workgroup for
clinical genomic testing.[1]
There are important differences, however, between research
and clinical care. Under circumstances where genomic research is being integrated with clinical care, communication
regarding diagnosis and treatment should be informational
so as not to cross the line into clinical care from the researchers perspective.[9] We therefore focus our feasibility
study on (a) approaches to screen a study population for
individuals with reportable incidental findings, and (b) a
mechanism for notifying the study team to contact those
individuals.
2.2
Screening population genomic datasets
To date, there are few efforts exploring electronic screening
of populations with clinical WGS or WES data for incidental findings reporting and disclosure. One relevant effort
is the Global Alliance for Genomics and Health (GAGH)
that is developing technologies for sharing population genomics data.[10] The GAGH Beacon Project specifically
provides a technical interface for implementing a web service for limited sharing and querying of genetic data. For
example, a query can be conducted at a participating ‘beacon’ to determine whether a human genome with a certain
allele at a certain position exists at that institution. This
population-based approach is being explored for the purpose
of supporting the identification of rare variants across beacons. Currently the service is limited to a simple ‘yes’ or
‘no’ In this feasibility study, we explore a screening approach for a more complex scenario for reporting incidental
findings with a population dataset. The service provided by
2
Beacon, however, could potentially be expanded and applied in the context of population health management.
2.3
Genomic analysis tools with mappings to disease ontologies
Many efforts that leverage mappings between disease ontologies and the Human Phenotype Ontology (HP)[11-13], to
perform genomic variant analyses have focused on predicting possible diagnoses. These include PhenoVar[14], Phenomizer[15], and PhenoTips[16] software. Another application has been in the discovery of new disease-gene associations with software such as Exomiser.[17] Our approach is
different in our exploration of a scenario for population
management.
In addition, given the focus of those efforts on hard-todiagnose conditions, leveraged disease ontologies are primarily Online Mendelian Inheritance in Man (OMIM)[18]
for genetic disorders and Orphanet[19] for rare diseases.
While this paper discusses only the hard-to-diagnose conditions relevant to ACMG recommendations, the approaches
we explore may also apply to diseases thought of as common diseases that also have Monogenic forms (e.g., Diabetes Mellitus). For this reason we believe it is valuable to
explore use of the Disease Ontology, that includes common
diseases, as a resource for population management.
3
3.1
MATERIALS
Personal genome project dataset
The Personal Genome Project (PGP)[20, 21] has been recruiting individuals who share genomic, medical and other
information online (http://www.personalgenomes.org).
Unique from other studies, the PGP project participants
consent to having all data made open and publically available without restrictions. This project provides a good use
case for exploring an approach to screening populations
with clinical WGS data given several PGP participants share
their clinical and WGS results. In this feasibility study, we
use a sample of 13 PGP study participants available to
download from a public bucket on Google Cloud Storage
(gs://pgp-harvard-data-public ). Prior to initiating this study,
we were aware of one PGP study participant with a false
positive Long QT Syndrome incidental finding (hu034DB1)
from a recently published study[22], we therefore ensured
that this patient was included in our sample in order to facilitate validating our approach.
3.2
Resources for interpreting and linking genomic variations with incidental findings
We utilize the ClinVar knowledge base[23, 24] and a genedisease mapping table derived from ACMG recommendations[25]. The ClinVar knowledge base includes information about genomic variation and its relationship to human health. While there exist other annotations for assign-
Use of the Disease Ontology to Screen Genomic Sequencing Study Participants for Condition-specific Phenotypes: Application to
Population Health Management
ing variants to pathogenic status (e.g., The Human Gene
Mutation Database, [HGMD®][26]) and methods for evaluating the likelihood pathogenicity[27], we did not explore
those in this feasibility study. Our gene-disease mapping
table includes for each gene, an associated disease and associated Disease Ontology Identifier (DOID).
3.3
Population surveillance database
MongoDB, a document oriented database system, was used
to create two databases: a public catalogs database with one
collection (ClinVar data) and a population surveillance database with three collections (Population genomic data,
population ACMG reportable data, and population clinical
data). ClinVar data contains a local copy of ClinVar, population genomic data includes VCF (variant call format) files
of our PGP sample, population ACMG reportable data contains records for each reportable gene for each PGP participant, and population clinical data contains one medical record for each PGP participant.
3.4
Disease Ontology (DO)
The Disease Ontology (http://www.disease-ontology.org ) is
a community driven ontology representing common and rare
diseases.[4, 5].. Some groups have published DO RDFs with
SPARQL endpoints for specifying queries. These include
Ontobee[28] (www.ontobee.org ) and Bioportal[29]
(http://bioportal.bioontology.org ). In this feasibility study,
we use Ontobee to query the DO for condition-specific phenotypes needed to disclose a potential incidental finding.
Figure 1. Framework for a population health management scenario
the DO, and locally developed python scripts and databases
to provide customized guidance for a population health
management scenario (see Figure 1). For our sample we
implement rules for (a) classifying pathogenic variants, (b)
reporting pathogenic variants associated with incidental
findings, (c) determining condition-specific phenotypes associated with potential incidental findings, and (d) recommendations for notifying the study team to call participants.
4
4.1
METHODS
Screening population genomic data files for
reportable ACMG genes
Genomic data from 13 PGP participants were stored as VCF
files in the population genomic data collection. We identify
for these participants any likely pathogenic variants in any
of the ACMG genes (according to ClinVar). We also apply
rules for reporting incidental findings according to the
ACMG gene-disease mapping table. Reportable findings are
stored in the population ACMG reportable data collection.
(Figure 1, steps 1 and 2).
4.2
Querying the Disease Ontology for conditionspecific phenotypes
The Resource Description Framework (RDF) is a W3Cstandard for representing data for interchange on the web
and SPARQL is a query language for RDF datasets that
requires indicating relationships between entities of interest.
For example, the structure of the query: Find clinical history, family history, signs and symptoms associated with the
disease Long QT Syndrome (DOID:2843) or any of its descendants is represented in Figure 2.
Figure 2. RDF graph corresponding to the above query
3.5
A framework for population health management with genomic sequencing data
We propose a framework for using a population genomics
dataset, existing knowledge resources (i.e., ClinVar and the
ACMG recommendations for reporting incidental findings),
This query involves finding a path in the RDF graph using a
predetermined set of semantic relationships. The is_a relationship is a transitive relation that allows us to express the
condition on the Disease Ontology annotation “Long QT
syndrome (DOID:2843) or any of its descendants” by ‘is_a
DOID:2843.’ The is_a relationship is also mapped on different properties e.g., rdfs:subClassOf or skos:narrower
(see Box 1 for a query using the rdfs:subClassOf relationship). The link between Disease Ontology terms and symptoms can be expressed by ‘?d has_symptom ?s’. Similarly
the link between Disease Ontology terms and clinical and
3
C. Overby et al.
family history measures can be expressed by ‘?d occurs_with ?c’ and with disease name by ‘?d
has_exact_synonym ?n’.
tures that need to be collected for individuals with reported
diseases (Figure 1, step 4).
4.4
Box1. Example of SPARQL query: What are descendants of Long QT
syndrome (DOID:2843)?
PREFIX obo-term: <http://purl.obolibrary.org/obo/> SELECT DISTINCT ?label from <http://purl.obolibrary.org/obo/merged/DOID> WHERE { ?x rdfs:subClassOf obo-term:DOID_2843. ?x rdfs:label ?label. } Results: Jervell-Lange Neilsen syndrome Andersen-Tawil syndrome We can make explicit use of the semantics of some Disease
Ontology predicates in the notification rule described in the
following section. For example, relationships from Table 2
used in the DO are relevant properties for determining condition-specific phenotypes. (Figure 1, step 3).
Table 2. DO properties for determining condition-specific phenotypes
Category of condition-specific
phenotypes
Disease Ontology Object Property
Clinical and family history
Signs and symptoms
occurs_with
has_symptom
4.3
Screening population clinical data for clinical
features
Clinical data was extracted from the PGP Participant Surveys, PGP10 Trait Surveys and health record data of 13
PGP participants and stored in the population clinical data
collection. The PGP Participant Surveys included questions
spanning a variety of topics, race, health record data, and
whether they have a known genetic disease. Participants
completed PGP10 Trait Surveys after personal results reporting the genetic trait and putative associated phenotypes
were returned to them. Therefore, these surveys are available only for as subset of participants. Health record data was
also provided by a subset of participants. We explored the
dataset published with ref[20] (includes for each condition,
a description, ICD9 code if available, Google codes, and
participant ids). Among our sample of participants, we identified for those with reportable ACMG genes, which also
had condition-specific phenotypes needed for disclosing an
incidental finding (disease associated clinical history, family
history, signs and symptoms). Further, we created a PGP10trait mapping table that includes condition-specific phenotypes collected in the PGP10 survey and a mapping to
ACMG reportable conditions. The DO and PGP10-traitdisease mapping table were used to determine clinical fea-
4
Defining rules for population health management
Hypothetically, patients with reportable variants in ACMG
genes may appear on a dashboard that is viewable by clinic
staff with a recommendation to notify the study team that a
trained individual should contact that participant. Depending
on which condition-specific phenotypes are available for
those patients, the notification action will classify clinical
features that need to be assessed for the disease as (a) collected or (b) need to be collected. A rule can be implemented with results from screening population datasets (See Box
2 and Figure 1, step 5). We illustrate our approach to address this scenario with a sample of 13 PGP participants.
Box 2. Pseudocode for population health management notification rule
IF clinical and family history data associated
with the disease Long QT Syndrome (DOID:2843)
exist in the clinical record of hu034DB1
THEN create a list of signs and symptoms
associated with the disease Long QT Syndrome
(DOID:2843)with “Not collected” as the
default classification.
AND FOR EACH sign and symptom
IF the sign or symptom exists in the
clinical record of hu034DB1
THEN re-classify that sign or symptom as
“Collected”
AND notify the study team to contact hu034DB1 to
disclose findings and discuss the list of
classified signs and symptoms needed to be
assessed for the disease Long QT Syndrome
(DOID:2843)
(DOID:2843)
5
5.1
RESULTS
Data repository for PGP participants
The subset of 13 PGP participants as data sources comprised 13 records in the population genomic data collection,
1 record in the population ACMG reportable data collection, and 13 records in the population clinical data collection. Within our population clinical data collection, PGP
Participant Survey data was available for all 13 participants,
a PGP10 Trait Survey was available for 1 participant, and 8
of our PGP participants reported health record data.
5.2
Screening and notification result
Among 13 PGP participants, our approach to screen population genomic data files for reportable ACMG genes identified one participant (hu034DB1) with a reportable ACMG
gene (SCN5A) and created one reportable ACMG genedisease record (Figure 1, step 1). The variant found in this
participant was classified by ClinVar as “likely pathogenic”
(rs12720452). Our mapping table links SCN5A with Long
QT syndrome 3 (Figure 1, step 2).
Use of the Disease Ontology to Screen Genomic Sequencing Study Participants for Condition-specific Phenotypes: Application to
Population Health Management
We found that for Long QT Syndrome (DOID:2843), DO
has not defined data associated with the occurs_with and
has_symptoms properties (Figure 1, step 3). We therefore
propose areas to build upon the DO in order to better represent Long QT Syndrome for population health management
scenarios. Table 3 describes information that could potentially be added to DO for Long QT Syndrome including
clinical and family history, signs and symptoms, typical age
of onset, inheritance pattern and implicated genes.
Table 3. Disease Ontology properties for applying rules for population
health management
Category of conditionspecific phenotypes
Proposed information for Ramano-Ward
Syndrome[30, 31]
Clinical history and family
history
clinical history of syncope; clinical history
of congenital deafness; clinical history of
tornadoes de pointes; family history with
clinical diagnosis of LQTS; family history
with sudden death at age < 30
Signs and symptoms
long QT interval on EKG; syncope; aborted
cardiac arrest
Typical age of onset
infancy through middle-age
Inheritance pattern and im- autosomal dominant inheritance of a mutaplicated genes
tion at least one of the KCNQ1, KCNH2,
SCN5A, KCNE1, KCNE2 genes
We also found that Romano-Ward Syndrome is currently
missing from DO as a subclass of Long QT Syndrome. Given Romano-Ward Syndrome is most relevant to the pathogenic variation in SCN5A identified for this patient, we propose adding this subclass. In further exploration, we determined whether DO terms were mentioned within the descriptions of the Human Phenotype Ontology (HP) classes.
We specified a query to: Find HP classes that mention the
regular expression “DOID:”(i.e., any DO term) in the description of that class (See Box 3). We found that currently
DOIDs are not mentioned in HP class descriptions, however, this may be an approach to build upon HP in order to
represent relationships between DO diseases and HP phenotypes. Toward this goal, DO terms are already used in the
HP browser (http://www.human-phenotype-ontology.org).
Box 3. Example of SPARQL query: How many HP phenotypes mention
“DOID” (long QT syndrome) in the definition.
#The OBO HP ontology annotation term IAO_0000115
("definition") is used.
PREFIX obo-term: <http://purl.obolibrary.org/obo/>
SELECT DISTINCT count(?s)
from <http://purl.obolibrary.org/obo/merged/HP>
WHERE
{
?s a owl:Class .
?s rdfs:label ?label .
?s obo-term:IAO_0000115 ?annotation .
FILTER regex(?annotation, "DOID:”) .
}
Given the current absence of condition-specific phenotypes
from DO for Long QT Syndrome, we used the PGP10-traitdisease mapping table to identify clinical features that need
to be measured for individuals with Long QT Syndrome
(Figure 1, step 4). Questions assessing a clinical and/or
family history of deafness, long QT syndrome, sudden
death, and congenital heart defect were mapped to Long QT
Syndrome 3. Upon evaluating the population clinical data
collection, our notification rule indicated for one PGP participant (hu034DB1) that the study team should be notified
with the list of clinical features that need to be assessed for
the disease and recommended that a qualified individual
contact that participant. For this study participant, all relevant clinical features were classified as “collected.”
This action is consistent with the approach taken when this
participant was identified as having a reportable incidental
finding in the PGP.[20, 22] Specifically, PGP researchers
called and confirmed with the participant that she reported
no family history consistent with Long QT Syndrome and
that she pursued clinical evaluation after learning of the
variant.
6
DISCUSSION
Overall, we demonstrated that our approach has potential to
support population health management with WGS study
participants. We also validated our screening approach
through identifying a reportable incidental finding according
to ACMG recommendations adapted to this study, and subsequently recommended a notification action that was consistent with the true action taken for one PGP participant.
We recognize that this feasibility study was conducted with
a narrow view of reportable incidental findings.
A recent survey of genetics professional’s found that decisions to disclose incidental findings are impacted the most
by three factors: the condition-specific phenotype (e.g. age
of onset and disease severity), test accuracy, and evidence
indicating pathogenicity.[32] Our planned work will explore
approaches to support these factors.
6.1
DO expansion to include condition-specific
phenotypes for decisions about returning incidental findings from research studies
Here we explored use of ACMG guidelines as potential criteria for disclosing incidental findings from WGS in research studies. We hope to explore use of condition-specific
phenotypes in order to address two complexities we did not
consider in this work: (a) circumstances where the same
criteria may not apply for reporting of adult-onset conditions in adults and children, and (b) and consideration of
patient choice in decisions regarding what types of findings
to report.
Result: 0 5
C. Overby et al.
In the ACMG Workgroup update to their 2013 Guidelines,
the authors report no clear consensus among the ACMG
membership regarding use of the same policy to return
pathogenic variants associated with severe but preventable
diseases in adults and children in a clinical setting.[33] Our
proposal is to include a property for age of onset in the DO
to facilitate more granular rules specifying actions for reporting findings to adults and children.
In this study, we also have not considered all of the complexities that arise from considering patient choice given the
consent model used in the PGP. The debate surrounding
incidental findings now is whether to return all results (both
medically actionable and non-medically actionable) to patients, or allow patients to make decisions about receiving
certain types of results. Effectively incorporating patient
choice and a physician’s fiduciary duty to report medically
relevant data will be the next step in determining what research results to return as incidental findings to patients.
Our current proposal to include properties for disease severity and mode of inheritance of disease will provide opportunities to explore rules for reporting findings given
quality of life values and knowledge about the potential
impact on family members.
6.2
DO support for reasoning with evidence for
variant pathogenicity
Given the complexities of determining when to report incidental findings, rather than specifying a list of reportable
incidental findings as ACMG has, some have specified alternative processes for classifying findings as reportable. In
one study, researchers specify a “binning” approach to categorize WGS and incidental findings data for reporting.[34]
In another study eMERGE Network institutions outlined
five methods for returning genome-wide association study
research incidental findings to study participants. A “return
of results” committee examined four diseases and opinions
surrounding the return of these results. There was agreement
among the committee to report three of the conditions, and
for one condition, agreement could not be reached.[35]
Findings from these studies highlight the need for tools that
can assist with evaluating varying levels of evidence in
complex contexts for reporting findings.
We also note that there are current limitations due to missing information for rare conditions in disease and phenotype
ontologies.[36] As part of future efforts, we propose to expand the DO and improve linkages with phenotype and other disease ontologies. For example, current data integration
and mapping efforts include mapping of DO terms with
Orphanet[37] terms. In addition to proposed areas for expanding the DO, we propose leveraging existing clinical
infrastructure for electronic screening (e-screening) and
6
using dashboards to display e-screening results in a
healthcare setting.
6.3
E-screening and dashboards for population
health management
E-screening and electronic whiteboards (dashboards) have
potential to support a number of applications in population
health management. For example, relevant efforts include escreening for coordinating clinical research visits with patient care visiting[38] and for remote device monitoring and
follow-up.[39] Dashboards have been explored by others as
a technical approach to visualize population data in inpatient
and outpatient settings in order to enhance workflows for
activities such as tracking patients, labs, orders and clinical
flowsheets.[40-42] Here we consider a population health
management scenario for which e-screening can potentially
be used to identify patients with reportable variants in
ACMG genes and a dashboard might assist clinic staff with
notifying the study team to contact these patients/study participants.
7
CONCLUSION
Overall, this study serves to motivate the expansion of our
screening approach to more complex scenarios and broader
applications. We propose improvements to our current infrastructure to improve the scalability of screening for population health management.
ACKNOWLEDGEMENTS
We would like to thank Kristin Maloney for her valuable
input. This work is in part supported by grant
R21HS023390.
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