Return of Results and Clinical Follow-up
Participants are given the option to receive their report by standard
mail or by secure file transfer over email. The report is accompanied by
the consultation letter, which summarizes the primary research findings
and, if applicable, plans for clinical follow-up. The study GC and
clinical Geneticist meet to review pertinent cases and develop a
structure for referrals and follow-up before meeting with the
participant over Zoom or telephone conference. Genetic counseling for
clinically significant findings, including risks for medically
actionable and rare Mendelian conditions, is provided. During the
results counseling session, the study GC collects a detailed medical and
family history, delivers results, and discusses the plan for follow-up.
The family physician is copied on the final report and any referrals
made by the study team with the participant’s consent. Counseling and
referrals for results related to carrier status, pharmacogenomics
findings, PRS, and/or additional research findings (e.g. ancestry, HLA,
blood group, etc.) are not initiated by the study team, however
participants may request an appointment with the study GC to discuss
their results if they wish.
DISCUSSION
We developed a genetic counseling and reporting framework for the return
of comprehensive GS results to a large cohort of ostensibly healthy
individuals. Although aspects of our report have been returned to
participants before, never have they been presented collectively within
a single, all-inclusive document and accompanied by a counseling and
referral structure. Particularly novel is the return of a genetic
ancestry estimate and HLA genotype within the research context.
Individuals may seek ancestry testing to discover information about
their ethnic background or genealogy. Determining genetic ancestry is
important because there are often discrepancies between a patient’s
self-reported ethnicity and genetic ancestry, which can impact risk
calculations in the context of reproductive counseling (Shraga et al.,
2017). Therefore, clinical applications of reporting genetic ancestry
may include identifying founder populations with high carrier
frequencies for autosomal recessive conditions to ensure accurate risk
assessments (Kirkpatrick & Rashkin, 2017). HLA type has been reported
specifically in the context of pharmacogenomic interactions to avoid
adverse reactions to medications (e.g. HIV-positive patients with
HLA*B57:01 are hypersensitive to Abacavir), but also has clinical use in
the context of organ transplantation, blood product donations and
transfusions (Fung & Benson, 2015). For instance, HLA-matching improves
graft and host survival, as well as reduces alloimmunization rates in
solid organ and hematopoietic stem cell transplantation (Fung & Benson,
2015). Universal reporting and registration of HLA and blood group
genotype, alongside genetic ancestry, may increase the identification of
unrelated organ and stem cell donors, especially for non-White/European
individuals. HLA type has also been associated with increased risk for
autoimmune conditions such as celiac disease (Schweiger et al., 2016).
Reporting of HLA type may provide an at least partial underlying
explanation for a patient’s chronic condition.
Counseling and educational materials were important given the novelty
and complexity of the information being returned to participants. Due to
limited time and resources it was not feasible to provide results
counseling to all study participants. Patient-facing materials are
helpful when it comes to increasing understanding of genomic information
(Dwyer et al., 2021). To ensure appropriate understanding and
interpretation of results, we developed appendices with additional
information and resources on each section (i.e. ancestry, HLA,
pharmacogenomics, blood type, etc.). For example, the PRS appendix
contains sections titled “What is a polygenic risk score?” and “How
can I reduce my risk?” as well as a list of external resources and
websites with information on common health conditions like breast and
prostate cancer. Likewise, we describe what HLA and genetic ancestry are
in lay language, along with important limitations of the testing and
results. We anticipate this will reduce the amount of time the study GC
spends counseling participants who otherwise do not require clinical
follow-up. In addition to detailed, patient-friendly appendices, the
consultation letter is provided to all participants, regardless if they
meet with the GC or not, which summarizes the primary SF (i.e.
pharmacogenomics results, carrier status, monogenic disease risks). The
consultation letter also suggests that participants contact their family
physician should they have questions about their current medications in
light of pharmacogenomics findings, or if they are actively family
planning and wish to seek a referral to a local Genetics clinic for
reproductive counseling. In the future, we aim to assess the
understandability and overall acceptance of our reports and
patient-facing materials through participant surveys and interviews. It
will also be crucial to assess the uptake of clinical referrals and
screening recommendations made through the GENCOV study. This type of
longitudinal follow-up is essential in order to understand the impact of
GS results on health outcomes and behaviors (e.g. disease diagnoses and
smoking cessation). We may also adapt our reporting template,
educational materials, and overall counseling framework to improve
participants’ understanding and overall experiences post-return of GS
results.
There are several limitations to our reporting. Repeat expansion
disorders were not included in the report. We plan to use tools like
ExpansionHunter (https://github.com/Illumina/ExpansionHunter/) for
detecting repeat in order to report these types of results in the
future. Currently, we do not have health outcome data on our cohort and
are therefore unable to calculate specific risk percentiles or ORs for
PRS for common health conditions. Therefore, it did not seem appropriate
to model PRS using visuals reflective of continuous risk estimates (i.e.
a bell curve with a threshold value), despite reports suggesting that
these, along with percentiles and verbal explanations, may be better
understood by research participants (Brockman et al., 2021). We may
consider amending PRS results in the future after assessing health
outcome data and calculating ORs for common conditions in our cohort. We
chose to use HapMap3 over other commonly used datasets like 1000Genomes
(https://www.internationalgenome.org) to generate an ancestry
estimate due to reduced sample processing time. We recognize that
1000Genomes contains a larger number of reference populations and may
increase the specificity of ancestry estimations. We plan to validate
our genetic ancestry results against the 1000Genomes dataset in the
future. Ancestry estimates and PRS are overall less accurate for
individuals of non-White/European ethnicity given the lack of
representation within publicly available datasets and genomic research
studies. The GENCOV study population is recruited from within the
Greater Toronto Area of Ontario, Canada, which represents a
substantially diverse population. Correlations between self-reported and
estimated genetic ancestry as well as contribution of GS data from
non-White/European individuals to genomic datasets may help to identify
discrepancies and potentially improve the accuracy of ancestry
estimations and PRS calculations in the future. The traditional standard
for HLA genotyping is PCR-based sequencing methodologies (Mimori et al.,
2019). Compared to PCR-based methods, HLA-VBSeq is 66% and 52%
accurate at determining approximate and exact HLA class I/II genotype to
4-digit resolution from GS data, respectively (Bauer et al., 2018).
Therefore, clinical testing is recommended to confirm HLA genotype and
phase at this time. Although, for applications like solid organ
transplantation, complete matching of HLA loci is not necessarily
required and lower resolution HLA genotyping may be sufficient alongside
confirmation of ABO compatibility (Fung & Benson, 2015).
In summary, we developed an analysis and reporting structure for GS
performed in healthy individuals. Our framework is in alignment with
ideas about the future of genomic medicine, which involves the
availability of information beyond what is currently considered
clinically actionable to patients. Although more research is required to
evaluate both the personal and medical implications of returning
comprehensive GS data to healthy individuals, the integration of genomic
data across multiple aspects of health, including knowledge of risks for
hereditary diseases, pharmacogenomics interactions, PRS, as well as
genetic ancestry and HLA/blood group genotyping is a step towards more
personalized healthcare.
DATA AVAILABILITY
Raw, de-identified GS, viral, and clinical data will be submitted to and
available for access through the databases listed in Table 3.
ACKNOWLEDGEMENTS
We thank Jo-Anne Hebrick and Miranda Lorenti of The Centre for Applied
Genomics, The Hospital for Sick Children, Toronto, Canada for assistance
with DNA extraction. We thank Karan Singh at William Osler Health System
for recruitment support. We thank Andrew Wong at Sinai Health for IT
support. We thank Paul Krzyzanowski at the Ontario Institute for Cancer
Research (OICR), and Andrew McArthur at McMaster University for support
generating viral sequence data. We thank the Canadian Institutes of
Health Research (CIHR) for research funding.
AUTHOR CONTRIBUTIONS
Conceptualization: S.Ca., E.F., M.C., C.M., H.F., Y.B., J.T., J.L-E.;
Data curation: E.F., M.C.; Methodology: S.Ca., E.F., M.C., L.S., T.J.P,
J.S., E.G., M.L., L.H., W.L., A.N., J.T., J.L.E.; Project
administration: G.M., S.Ch., J.T. J.L-E.; Software: E.F., M.C., T.J.P,
J.S., M.L., L.H., W.L., J.L-E.; Visualization: S.Ca., E.F., M.C., G.M.,
H.F., M.L., L.H., W.L., A.N., J.L.E.; Writing-original draft: S.Ca.;
Writing-review & editing: S.Ca., E.F., M.C., G.M., S.Ch., C.M., H.F.,
Y.B., L.S., T.J.P, J.S., E.G., M.L., L.H., W.L., A.N., J.T., J.L.E.;
Supervision: J.T., J.L-E.; Funding acquisition: H.F., T.J.P., J.S.,
L.S., J.T., J.L-E.
ETHICS DECLARATION
This study is approved by the Mount Sinai Hospital research ethics board
(Study ID: 424901). All institutions involved in recruitment of study
participants received local ethics board approval. Informed consent was
obtained from all participants in the study. Participant data was
de-identified.
CONFLICTS OF INTEREST
The authors have no conflicts of interest to disclose.
FUNDING
This work was supported by the Canadian Institutes of Health Research
(Funding Reference Number VR4-172753).
GENCOV STUDY WORKGROUP
Saranya Arnoldo1,2, Navneet Aujla1,
Erin Bearss3, Alexandra Binnie2,
Bjug Borgundvaag1,3, Howard
Chertkow4, Marc Clausen5, Marc
Dagher1,6, Luke Devine1,3, David Di
Iorio1, Steven Marc Friedman3,7,
Chun Yiu Jordan Fung3,8, Anne-Claude
Gingras1,3,8, Lee W. Goneau9,
Deepanjali Kaushik2, Zeeshan Khan10,
Elisa Lapadula3,8, Tiffany Lu3, Tony
Mazzulli1,3, Allison McGeer1,3,8,
Shelley L McLeod1,3, Gregory
Morgan1,3,8, David Richardson2,
Harpreet Singh1, Seth Stern10, Ahmed
Taher1,7,10, Iris Wong10, Natasha
Zarei10
- University of Toronto
- William Osler Health System
- Mount Sinai Hospital, Sinai Health
- Baycrest Health Sciences
- Unity Health Toronto
- Women’s College Hospital
- University Health Network
- Lunenfeld-Tanenbaum Research Institute
- Dynacare Medical Laboratories
- Mackenzie Health
REFERENCES
Adam, M. P., Ardinger, H. H., Pagon, R. A., Wallace, S. E., Bean, L. J.
H., Gripp, K. W., & Mirzaa, G. M. (1993-2022). GeneReviews .
University of Washington, Seattle.
Alexander, D. H., Novembre, J., & Lange, K. (2009). Fast model-based
estimation of ancestry in unrelated individuals. Genome Res ,19 (9), 1655-1664. https://doi.org/10.1101/gr.094052.109
Altshuler, D. M., Gibbs, R. A., Peltonen, L., Dermitzakis, E.,
Schaffner, S. F., Yu, F., . . . Consortium, I. H. (2010). Integrating
common and rare genetic variation in diverse human populations.Nature , 467 (7311), 52-58.
https://doi.org/10.1038/nature09298
Bauer, D. C., Zadoorian, A., Wilson, L. O. W., Thorne, N. P., &
Alliance, M. G. H. (2018). Evaluation of computational programs to
predict HLA genotypes from genomic sequencing data. Brief
Bioinform , 19 (2), 179-187. https://doi.org/10.1093/bib/bbw097
Bellcross, C. A., Page, P. Z., & Meaney-Delman, D. (2012).
Direct-to-consumer personal genome testing and cancer risk prediction.Cancer J , 18 (4), 293-302.
https://doi.org/10.1097/PPO.0b013e3182610e38
Brockman, D. G., Petronio, L., Dron, J. S., Kwon, B. C., Vosburg, T.,
Nip, L., . . . Khera, A. V. (2021). Design and user experience testing
of a polygenic score report: a qualitative study of prospective users.BMC Med Genomics , 14 (1), 238.
https://doi.org/10.1186/s12920-021-01056-0
Ceyhan-Birsoy, O., Machini, K., Lebo, M. S., Yu, T. W., Agrawal, P. B.,
Parad, R. B., . . . Rehm, H. L. (2017). A curated gene list for
reporting results of newborn genomic sequencing. Genet Med ,19 (7), 809-818. https://doi.org/10.1038/gim.2016.193
Cochran, M., East, K., Greve, V., Kelly, M., Kelley, W., Moore, T., . .
. Bick, D. (2021). A study of elective genome sequencing and
pharmacogenetic testing in an unselected population. Mol Genet
Genomic Med , 9 (9), e1766. https://doi.org/10.1002/mgg3.1766
de Wert, G., Dondorp, W., Clarke, A., Dequeker, E. M. C., Cordier, C.,
Deans, Z., . . . Genetics, E. S. o. H. (2021). Opportunistic genomic
screening. Recommendations of the European Society of Human Genetics.Eur J Hum Genet , 29 (3), 365-377.
https://doi.org/10.1038/s41431-020-00758-w
Delanne, J., Nambot, S., Chassagne, A., Putois, O., Pelissier, A.,
Peyron, C., . . . Faivre, L. (2019). Secondary findings from
whole-exome/genome sequencing evaluating stakeholder perspectives. A
review of the literature. Eur J Med Genet , 62 (6), 103529.
https://doi.org/10.1016/j.ejmg.2018.08.010
Dwyer, A. A., Au, M. G., Smith, N., Plummer, L., Lippincott, M. F.,
Balasubramanian, R., & Seminara, S. B. (2021). Evaluating co-created
patient-facing materials to increase understanding of genetic test
results. J Genet Couns , 30 (2), 598-605.
https://doi.org/10.1002/jgc4.1348
East, K. M., Cochran, M., Kelley, W. V., Greve, V., Emmerson, K.,
Raines, G., . . . Bick, D. (2019). Understanding the present and
preparing for the future: Exploring the needs of diagnostic and elective
genomic medicine patients. J Genet Couns , 28 (2), 438-448.
https://doi.org/10.1002/jgc4.1114
Fung, M. K., & Benson, K. (2015). Using HLA typing to support patients
with cancer. Cancer Control , 22 (1), 79-86.
https://doi.org/10.1177/107327481502200110
Green, R. C., Berg, J. S., Grody, W. W., Kalia, S. S., Korf, B. R.,
Martin, C. L., . . . Genomics, A. C. o. M. G. a. (2013). ACMG
recommendations for reporting of incidental findings in clinical exome
and genome sequencing. Genet Med , 15 (7), 565-574.
https://doi.org/10.1038/gim.2013.73
Hewett, M., Oliver, D. E., Rubin, D. L., Easton, K. L., Stuart, J. M.,
Altman, R. B., & Klein, T. E. (2002). PharmGKB: the Pharmacogenetics
Knowledge Base. Nucleic Acids Res , 30 (1), 163-165.
https://doi.org/10.1093/nar/30.1.163
Horton, R., Crawford, G., Freeman, L., Fenwick, A., Wright, C. F., &
Lucassen, A. (2019). Direct-to-consumer genetic testing. BMJ ,367 , l5688. https://doi.org/10.1136/bmj.l5688
Kirkpatrick, B. E., & Rashkin, M. D. (2017). Ancestry Testing and the
Practice of Genetic Counseling. J Genet Couns , 26 (1),
6-20. https://doi.org/10.1007/s10897-016-0014-2
Lane, W. J., Westhoff, C. M., Uy, J. M., Aguad, M., Smeland-Wagman, R.,
Kaufman, R. M., . . . Project, M. (2016). Comprehensive red blood cell
and platelet antigen prediction from whole genome sequencing: proof of
principle. Transfusion , 56 (3), 743-754.
https://doi.org/10.1111/trf.13416
Lee, S. B., Wheeler, M. M., Patterson, K., McGee, S., Dalton, R.,
Woodahl, E. L., . . . Nickerson, D. A. (2019). Stargazer: a software
tool for calling star alleles from next-generation sequencing data using
CYP2D6 as a model. Genet Med , 21 (2), 361-372.
https://doi.org/10.1038/s41436-018-0054-0
Lewis, A. C. F., Knoppers, B. M., & Green, R. C. (2021). An
international policy on returning genomic research results. Genome
Med , 13 (1), 115. https://doi.org/10.1186/s13073-021-00928-5
Linderman, M. D., Nielsen, D. E., & Green, R. C. (2016). Personal
Genome Sequencing in Ostensibly Healthy Individuals and the PeopleSeq
Consortium. J Pers Med , 6 (2).
https://doi.org/10.3390/jpm6020014
Marjonen, H., Marttila, M., Paajanen, T., Vornanen, M., Brunfeldt, M.,
Joensuu, A., . . . Kristiansson, K. (2021). A Web Portal for
Communicating Polygenic Risk Score Results for Health Care Use-The P5
Study. Front Genet , 12 , 763159.
https://doi.org/10.3389/fgene.2021.763159
Miller, D. T., Lee, K., Chung, W. K., Gordon, A. S., Herman, G. E.,
Klein, T. E., . . . Group, A. S. F. W. (2021). ACMG SF v3.0 list for
reporting of secondary findings in clinical exome and genome sequencing:
a policy statement of the American College of Medical Genetics and
Genomics (ACMG). Genet Med , 23 (8), 1381-1390.
https://doi.org/10.1038/s41436-021-01172-3
Mimori, T., Yasuda, J., Kuroki, Y., Shibata, T. F., Katsuoka, F., Saito,
S., . . . Yamamoto, M. (2019). Construction of full-length Japanese
reference panel of class I HLA genes with single-molecule, real-time
sequencing. Pharmacogenomics J , 19 (2), 136-146.
https://doi.org/10.1038/s41397-017-0010-4
Prokop, J. W., May, T., Strong, K., Bilinovich, S. M., Bupp, C.,
Rajasekaran, S., . . . Lazar, J. (2018). Genome sequencing in the
clinic: the past, present, and future of genomic medicine. Physiol
Genomics , 50 (8), 563-579.
https://doi.org/10.1152/physiolgenomics.00046.2018
Reble, E., Gutierrez Salazar, M., Zakoor, K. R., Khalouei, S., Clausen,
M., Kodida, R., . . . Bombard, Y. (2021). Beyond medically actionable
results: an analytical pipeline for decreasing the burden of returning
all clinically significant secondary findings. Hum Genet ,140 (3), 493-504. https://doi.org/10.1007/s00439-020-02220-9
Rehm, H. L., Berg, J. S., Brooks, L. D., Bustamante, C. D., Evans, J.
P., Landrum, M. J., . . . ClinGen. (2015). ClinGen–the Clinical
Genome Resource. N Engl J Med , 372 (23), 2235-2242.
https://doi.org/10.1056/NEJMsr1406261
Relling, M. V., & Klein, T. E. (2011). CPIC: Clinical Pharmacogenetics
Implementation Consortium of the Pharmacogenomics Research Network.Clin Pharmacol Ther , 89 (3), 464-467.
https://doi.org/10.1038/clpt.2010.279
Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J.,
. . . Committee, A. L. Q. A. (2015). Standards and guidelines for the
interpretation of sequence variants: a joint consensus recommendation of
the American College of Medical Genetics and Genomics and the
Association for Molecular Pathology. Genet Med , 17 (5),
405-424. https://doi.org/10.1038/gim.2015.30
Schweiger, S. D., Mendez, A., Kunilo Jamnik, S., Bratanic, N., Bratina,
N., Battelino, T., . . . Vidan-Jeras, B. (2016). High-risk genotypes
HLA-DR3-DQ2/DR3-DQ2 and DR3-DQ2/DR4-DQ8 in co-occurrence of type 1
diabetes and celiac disease. Autoimmunity , 49 (4), 240-247.
https://doi.org/10.3109/08916934.2016.1164144
Shickh, S., Hirjikaka, D., Clausen, M.,… Bombard, Y. (2022). The
Genetics Adviser: a Protocol for a Mixed-Methods Randomized Controlled
Trial Evaluating a Digital Platform for Genetics Service Delivery.BMJ Open . In Press.
Shraga, R., Yarnall, S., Elango, S., Manoharan, A., Rodriguez, S. A.,
Bristow, S. L., . . . Puig, O. (2017). Evaluating genetic ancestry and
self-reported ethnicity in the context of carrier screening. BMC
Genet , 18 (1), 99. https://doi.org/10.1186/s12863-017-0570-y
Solomon, B. D., Nguyen, A. D., Bear, K. A., & Wolfsberg, T. G. (2013).
Clinical genomic database. Proc Natl Acad Sci U S A ,110 (24), 9851-9855. https://doi.org/10.1073/pnas.1302575110
Taher, J., Mighton, C., Chowdhary, S., Casalino, S., Frangione, E.,
Arnoldo, S., . . . Lerner-Ellis, J. (2021). Implementation of
serological and molecular tools to inform COVID-19 patient management:
protocol for the GENCOV prospective cohort study. BMJ Open ,11 (9), e052842. https://doi.org/10.1136/bmjopen-2021-052842
Vassy, J. L., McLaughlin, H. M., McLaughlin, H. L., MacRae, C. A.,
Seidman, C. E., Lautenbach, D., . . . Green, R. C. (2015). A one-page
summary report of genome sequencing for the healthy adult. Public
Health Genomics , 18 (2), 123-129.
https://doi.org/10.1159/000370102
Weymann, D., Laskin, J., Roscoe, R., Schrader, K. A., Chia, S., Yip, S.,
. . . Regier, D. A. (2017). The cost and cost trajectory of whole-genome
analysis guiding treatment of patients with advanced cancers. Mol
Genet Genomic Med , 5 (3), 251-260.
https://doi.org/10.1002/mgg3.281
Wise, A. L., Manolio, T. A., Mensah, G. A., Peterson, J. F., Roden, D.
M., Tamburro, C., . . . Green, E. D. (2019). Genomic medicine for
undiagnosed diseases. Lancet , 394 (10197), 533-540.
https://doi.org/10.1016/S0140-6736(19)31274-7
FIGURE LEGENDS
Figure 1: Workflow for genetic counseling and return of GS
results through the GENCOV study. 1) Study participants complete an
online intake survey to collect information on medical history and
COVID-19 symptoms, as well as an educational digital genomics platform
wherein they are able to indicate initial preferences for return of SF
from GS. The participant completes pre-test counseling with the study
GC, either in group format by teleconference or by personal telephone
and/or video appointment. The study GC confirms and records
participants’ preferences for SF from GS. 2) GS data is filtered and
analyzed based on participant preferences. A final GS report is compiled
and reviewed by the study team. 3) The GC and Geneticist meet to review
reports to determine which participants require results counseling and
clinical follow-up. Individuals with clinically significant findings
meet with the study GC (and Geneticist, where applicable) by
teleconference to discuss their results and the plan for follow-up. The
GC writes the consultation letter and initiates clinical referral(s).
The family physician is forwarded a copy of the report, letter, and
referrals with participants’ consent. Result counseling appointments and
referrals are not initiated for other findings, however participants
will receive a copy of their report along with a letter summarizing
their results with the option to speak with the study GC if they have
questions about their report. †Clinically significant
findings include monogenic disease risks for rare Mendelian conditions
as well as medically actionable conditions. Other findings include
carrier status results, PRS for common conditions, pharmacogenomics,
ancestry, HLA, blood group, and viral lineage; GC: Genetic counselor;
SF: Secondary findings; GS: Genome sequencing.
Figure 2: Summary of the elements of the comprehensive GS
report: 1) Clinically significant findings related to the participant’s
risk for hereditary conditions (monogenic disease risks, including
medically actionable and rare Mendelian conditions); 2) Findings
relevant to reproductive planning (carrier status); 3) Pharmacogenomic
variants; 4) PRS for common conditions; 5) HLA genotype; 6) Genetic
ancestry; 7) Blood group genotype; 8) COVID-19 viral lineage; 9) Testing
methodologies and limitations; and, 10) Informational appendices. PRS:
Polygenic risk score; Rh: Rhesus; GS: Genome sequencing; HLA: Human
leukocyte antigen.
TABLES
Table 1: Summary of publically available data sources used in
the GENCOV study.