Hello my name is Jules Savatt. I am a genetic counselor at Geisinger, a member
of the ClinGen education workgroup, and coordinator for the ClinGen patient
registry -- GenomeConnect. This presentation will provide a brief
introduction to patient data sharing and will provide information on how patient-
shared genotype and phenotype data can inform variant classification. Data
sharing is vital to the process of understanding the relationship between
genes, genetic variants, health, and disease. ClinGen encourages data sharing
from a number of stakeholders but patients can directly share their
information by participating in a patient registry that shares data, such
as GenomeConnect. Individuals that have had genetic testing enroll online and
provide their health history using a health survey. After completing this
health survey, participants upload a copy of their genetic testing report so that
our genetic counseling team can collect information about any genetic variants
that were found. A participant's genetic and health information is then
de-identified and shared with NCBI's ClinVar. Phenotype information from
participant health surveys is mapped to human phenotype ontology terms, allowing
for structured data sharing. In terms of genomic data, the variant, clinical
significance reported by the laboratory, report date, reporting institution, and
associated conditions are collected directly from the participants report,
and these data are submitted as a "Phenotyping Only" submission. This
submission type highlights that GenomeConnect does not make an attempt to
interpret the variant independently, and makes it clear that the laboratory that
completed the participant's testing may also submit to ClinVar. Once this
information is available, how can it be used to inform variant classification?
From our experience with GenomeConnect, we recognize that patients opting to
share their information are an important source of genomic data that otherwise
may not be publicly available. The more we aggregate genomic data, the better
we'll be able to classify variants. It is clear that the more a variant has been
identified, the more we are able to understand if that variant causes
disease. More than that, if you examine the ACMG sequence variant interpretation
guidelines shown here, you can see that even if a variant at the same amino acid
residue has been identified previously, variant classification can be impacted.
In this way, patient-contributed variants can also contribute to the understanding
of other variants. Second, patient- provided data affords additional case-
level information that can be used to inform variant classification. Laboratory
submissions are often in aggregate and may include a few phenotypic details.
Patient-provided data is case-level, rather than aggregate, and often includes
additional phenotypic information and provides the ability to recontact over time for
additional data, or as health changes. Patient data includes segregation
information if available, and can inform if any other variants were detected in
that individual that could explain their phenotype. This case-level information
can be used to inform classification in a number of ways highlighted here. For
example, GenomeConnect submitted this SCN8A variant. The variant was
interpreted as likely pathogenic by Lab 2 in 2015 on the participant report. The
variant was also found to be de novo through that testing. Ultimately, Lab 1,
another laboratory that had identified this variant previously, reached out to
the GenomeConnect team to inquire whether maternity and paternity had been
confirmed during testing, and whether the variant was in fact de novo in our
participant. This segregation data allowed Lab 1 to update their
classification to likely pathogenic as well. Finally, GenomeConnect facilitates
connections to inform gene-disease relationships. GenomeConnect submits
variants in genes of uncertain significance to Matchmaker Exchange, a
federated network of rare disease datasets established to facilitate
candidate gene matching. It is necessary to understand whether a gene is
associated with a specific phenotype before a variant can be classified, and
GenomeConnect contributes data to these efforts through Matchmaker Exchange
submissions. So how can you leverage patient-shared data to inform your
classification efforts? First, review ClinVar. Variant information from Genome-
Connect participants or other registries engaging in data
sharing is submitted to ClinVar periodically. We are happy to answer
questions regarding our submissions, including whether other variants may
have been detected in that individual, whether maternity and paternity were
confirmed when the variant was reported as de novo, or to invite participants to
participate in functional studies or additional research. In addition to
providing information that can inform variant classification, patient data
sharing allows patients to remain up-to-date regarding their genetic test
results. If the GenomeConnect team learns that a participant's report is out
of date and the reporting laboratory has updated their classification, this
information is provided back to participants that elect to receive it. To
summarize, genome Connect offers a structured way for patients to provide
additional phenotypic information, case- level data, and novel genomic information
to inform variant interpretation and genomic medicine. GenomeConnect is
currently submitting patient-provided data to ClinVar, and is working to
enable other registries to do the same. You can find a list of registries
recognized by ClinGen for their data sharing efforts here. I hope this has
been a useful introduction to patient data sharing and its potential impact on
variant classification. Please visit www.clinicalgenome.org for additional educational
resources and to learn more about ClinGen initiatives. Our funding sources are
listed at the bottom of this slide. Please feel free to contact ClinGen by
emailing clingen@clinicalgenome.org, or contact the GenomeConnect team
directly at info@genomeconnect.org with questions or feedback. Thank you.