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\begin{document}

\title{PhenomeCentral: 7 years of rare disease matchmaking}



\author[1]{Matthew Osmond}%
\author[1]{Taila Hartley}%
\author[2]{Brittney Johnstone}%
\author[3]{Sasha Andijc}%
\author[3]{Marta Girdea}%
\author[1]{Meredith Gillespie}%
\author[4]{Orion Buske}%
\author[3]{Sergiu Dumitriu}%
\author[3]{Veronika Koltunova}%
\author[5]{Arun Ramani}%
\author[6]{Kym Boycott}%
\author[7]{Michael Brudno}%
\affil[1]{Children's Hospital of Eastern Ontario Research Institute}%
\affil[2]{Sunnybrook Health Sciences Centre}%
\affil[3]{University Health Network}%
\affil[4]{Phenotips}%
\affil[5]{University of Toronto Department of Computer Science}%
\affil[6]{Children’s Hospital of Eastern Ontario Research Institute}%
\affil[7]{University of Toronto}%


\vspace{-1em}



  
  \date{May 22, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
A major challenge in validating genetic causes for patients with rare
diseases (RDs) is the difficulty in identifying other RD patients with
overlapping phenotypes and variants in the same candidate gene. This
process, known as matchmaking, requires robust data sharing solutions in
order to be effective. In 2014 we launched PhenomeCentral, a RD data
repository capable of collecting computer-readable genotypic and
phenotypic data for the purposes of RD matchmaking. Over the past 7
years PhenomeCentral's features have been expanded and its dataset has
consistently grown. There are currently 1,615 users registered on
PhenomeCentral, which have contributed over 12,000 patient cases. Most
of these cases contain detailed phenotypic terms, with a significant
portion also providing genomic sequence data or other forms of clinical
information. Matchmaking within PhenomeCentral, and with connections to
other data repositories in the Matchmaker Exchange, have collectively
resulted in over 60,000 matches, which have facilitated multiple gene
discoveries. The collection of deep phenotypic and genotypic data has
also positioned PhenomeCentral well to support next generation of
matchmaking initiatives that utilize genome sequencing data, ensuring
that PhenomeCentral will remain a useful tool in solving undiagnosed RD
cases in the years to come.%
\end{abstract}%



\sloppy


\section*{PhenomeCentral: 7 years of rare disease
matchmaking}

{\label{phenomecentral-7-years-of-rare-disease-matchmaking}}

\section*{}

{\label{section}}

Matthew Osmond\textsuperscript{1}, Taila Hartley\textsuperscript{1},
Brittney Johnstone\textsuperscript{2}, Sasha Andjic\textsuperscript{3},
Marta Girdea\textsuperscript{3}, Meredith Gillespie\textsuperscript{1},
Orion Buske\textsuperscript{5}, Sergiu Dumitriu\textsuperscript{3},
Veronika Koltunova\textsuperscript{3}, Arun Ramani\textsuperscript{6},
Kym M. Boycott\textsuperscript{1, 7}, Michael Brudno\textsuperscript{3,
4, 6*}

\textsuperscript{1}Children's Hospital of Eastern Ontario Research
Institute, University of Ottawa, ON, Canada

\textsuperscript{2}Cancer Genetics and High Risk Program, Sunnybrook
Health Sciences Centre and Department of Molecular Genetics, University
of Toronto, Toronto, ON, Canada

\textsuperscript{3}DATA Team and Techna Institute, University Health
Network, Toronto, ON, Canada

\textsuperscript{4}Department of Computer Science, University of
Toronto, ON, Canada

\textsuperscript{5}PhenoTips, Toronto, ON, Canada

\textsuperscript{6}Hospital for Sick Children, Toronto, ON, Canada

\textsuperscript{7}Department of Genetics, Children's Hospital of
Eastern Ontario, ON, Canada

* To whom communication should be addressed: brudno@cs.toronto.edu

\textbf{Grant Numbers:} Ontario Genomics Institute (OGI-147), CIHR
Foundation Grant (FDN-154279)

\section*{Abstract}

{\label{abstract}}

A major challenge in validating genetic causes for patients with rare
diseases (RDs) is the difficulty in identifying other RD patients with
overlapping phenotypes and variants in the same candidate gene. This
process, known as matchmaking, requires robust data sharing solutions in
order to be effective. In 2014 we launched PhenomeCentral, a RD data
repository capable of collecting computer-readable genotypic and
phenotypic data for the purposes of RD matchmaking. Over the past 7
years PhenomeCentral's features have been expanded and its dataset has
consistently grown. There are currently 1,615 users registered on
PhenomeCentral, which have contributed over 12,000 patient cases. Most
of these cases contain detailed phenotypic terms, with a significant
portion also providing genomic sequence data or other forms of clinical
information. Matchmaking within PhenomeCentral, and with connections to
other data repositories in the Matchmaker Exchange, have collectively
resulted in over 60,000 matches, which have facilitated multiple gene
discoveries. The collection of deep phenotypic and genotypic data has
also positioned PhenomeCentral well to support next generation of
matchmaking initiatives that utilize genome sequencing data, ensuring
that PhenomeCentral will remain a useful tool in solving undiagnosed RD
cases in the years to come.

\textbf{Keywords:} PhenomeCentral, Matchmaker Exchange, rare disease,
patient matchmaking, HPO, data-sharing



\section*{Introduction}

{\label{introduction}}

After genome-wide sequencing, the majority of patients with rare disease
(RD) remain without an identified genetic cause for their condition (Lee
et al., 2014; Wright et al., 2018; Yang et al., 2013). Oftentimes,
finding even one other unrelated patient with a similar phenotype and
available genomic data can lead to the identification of a shared
genetic cause (Bamshad et al., 2011). Given that RDs are just that,
rare, for many disorders no one clinician will see two patients with the
same unsolved RD. Instead, the ``matching'' patients (having similar
manifestations with the same genetic cause) may be located across the
globe.

A significant barrier to identifying matching patients has been the
storage of clinical and genomic data in isolated silos. Furthermore,
much of the clinical data is collected in unstructured, non-standardized
formats, which impedes computation and sharing across groups. Data is
shared by clinicians in numerous ways including case discussion at
conferences and publication of case reports. However, given the rarity
of the conditions being discussed, these means of sharing are
ineffective and inefficient, which translates to delays in gene
discovery and answers for patients.

In the field of genetics, data generation is occurring at unprecedented
rates in both the clinical and research settings. The subsequent
abundance of data in different siloes has led to an interpretation
bottleneck and data sharing plays a crucial role in addressing this
issue. Broad data sharing will lead to better data quality and
interpretation, faster answers for patients, and rapid advancements in
the field of genetics. There is consensus among professional societies,
patients, and experts that responsible data sharing is imperative (ACMG
Board of Directors, 2017; Bush et al., 2018; Darquy et al., 2016; Rehm,
2017). Consideration must be given to the social, societal, privacy, and
policy challenges to implementing international data sharing
responsibly, and these are being tackled by groups such as the Global
Alliance for Genomics and Health (Rahimzadeh, Dyke, \& Knoppers, 2016).
There is no question that data sharing will be essential to solving the
currently unsolved patients with rare diseases. Furthermore, it will
also support better understanding of complex, non-Mendelian conditions
and facilitate the shift toward personalized medicine.

To being to address the problem of responsible data sharing, in 2014 we
launched PhenomeCentral, a web portal designed for the matchmaking of
cases entered by clinicians and researchers working with rare diseases
(Buske et al., 2015a). Since its entrance into the rare disease space,
PhenomeCentral has been facilitating matches and the identification of
second cases or case series leading to gene discovery and answers for
families often experiencing a diagnostic odyssey. PhenomeCentral is a
founding member of the MME (Philippakis et al., 2015), a collaborative
effort to solve genetic disorders by building an international network
of rare disease databases connected by a common application interface
(API; Buske et al., 2015b). In this manuscript we describe the growth of
PhenomeCentral over the past 7 years, improvements we have made for the
portal, and goals of our future work.

\section*{Materials and Methods}

{\label{materials-and-methods}}

\textbf{Original functionality}

The PhenomeCentral web portal is based on the PhenoTips software (Girdea
et al., 2013), which is used in both clinical and genomics research
applications across the world. Clinicians or researchers (users) can
create detailed patient records (cases) which enables the sharing of
both genotypic and phenotypic information with other PhenomeCentral
cases and across the MME. When a case is first created, users are
prompted to select what level of data sharing a patient has consented
for, which can include medical and family history, genome-wide
sequencing data, or photographs. Genotypic information can be entered as
manually curated gene entries and/or uploaded variant call format (VCF)
files. Any VCF data is processed by the Exomiser software to produce a
computationally prioritized list of candidate genes (Smedley et al.,
2015). Case phenotypes are entered as lists of phenotypic terms, which
are then mapped to a standardized phenotype vocabulary known as the
Human Phenotype Ontology (HPO; K\selectlanguage{ngerman}öhler et al., 2021). These HPO terms can
be used to generate a list of ``Suggested Genes'', which lists all known
gene associations for each of a case's terms. Finally, users have the
ability to provide additional clinical information including a pedigree,
growth charts and measurements, and other medical history details using
free text boxes.

Once a case has been added to PhenomeCentral, internal matching is
automatically performed and a list of similar PhenomeCentral cases is
displayed. Users also have the option to indicate that their case has
consented to share across the MME, which adds similar matches from other
rare disease repositories in the MME (called nodes) to the results.

The matchmaking philosophy of PhenomeCentral is that phenotypic data is
a valuable, but often overlooked resource (Baynam et al., 2015), and can
lead to higher quality matches and decrease time spent exploring false
positives (potential matches that are disproven with further
exploration). Without phenotypic data, gene level matching may yield
many false positive matches (Osmond et al., in press). Consequently,
matches in PhenomeCentral are evaluated based on both phenotypic
similarity and genotypic similarity. A score for phenotypic similarity
is generated using the simGIC algorithm, a semantic similarity measure
capable of comparing multiple lists of HPO terms (Pesquita et al.,
2008). Genotypic similarity between cases is denoted either as having
the same manually curated candidate gene, or in situations where a
PhenomeCentral case had VCF data, a match between a manually entered
gene and VCF file data with a variant identified in the matching gene
(Buske et al., 2015a). After reviewing the matches for a PhenomeCentral
case, users are given the option to send an introductory email through
PhenomeCentral to other users who have matches of interest.

Any case deposited in PhenomeCentral remains available for future cases
to match against. New matches with other PhenomeCentral cases are
updated automatically on the web portal, and users can manually refresh
their case to identify new matches across the MME.

\textbf{New and Noteworthy}

Since PhenomeCentral's initial release, additional features have been
added to facilitate a more efficient matchmaking workflow. Matches for a
specific case are now displayed under a new ``Matching Patients'' tab,
which initially displays all matches for every candidate gene listed for
that case. These matches can then be prioritized using a number of
filters including the MME node, genotypic/phenotypic scores, case
identifiers, whether a matching case is listed as diagnosed, and whether
a match has already been contacted. The communication history is also
listed for each match, allowing users to view when each match was
contacted by email or manually flag that communication for a match
occurred outside of PhenomeCentral. Each match can be flagged with its
status (i.e. ``Saved'' for matches of interest and ``Rejected'' for
matches that were ruled out), and a notes section allows users to record
a summary of any communication that has occurred regarding the match.
Finally, the features added to filter and track matches for an
individual case have also been applied to track larger cohorts within
PhenomeCentral. Each user has access to the ``My Matches'' table, which
displays matches for all cases where a user has edit privileges
(\textbf{Figure 1} ).

To make it easier for users to use the portal there is a video series
detailing PhenomeCentral's functions for effective genotype and
phenotype-driven matchmaking (). The videos provide instructions on how
to register for an account on PhenomeCentral, how to add genotypic and
phenotypic data to a case, how to modify case access permissions for
collaborators, how to review matches for an individual case, and how to
utilize the My Matches table for larger cohorts.

Additional support has also been provided for facilitating matchmaking
connections. While users can still refresh their cases manually to
identify new matches across the MME, cases consented for MME matching
are now re-queried across the full MME network on a weekly basis. A
genetic counselor manually reviews new matches produced by this refresh
each week. Case owners are then sent email notifications for any matches
with their cases that have a high degree of phenotypic and genotypic
similarity.

Finally, as the MME network has expanded to include more rare disease
data repositories, PhenomeCentral has added support to match with these
newer databases. In addition to matching with similar cases in
GeneMatcher and DECIPHER, PhenomeCentral now allows for matching to
occur with cases from \emph{seqr} (Arachchi et al., 2018), MyGene2
(Chong et al., 2016), and RD-Connect (Thompson et al., 2014). With these
new connections, PhenomeCentral users are now capable of querying over
103,600 cases when making matching requests through the MME
(``Matchmaker Exchange Statistic and Publications,'' 2021).

\section*{Results}

{\label{results}}

PhenomeCentral presently has 1,615 users that have entered 12,292 cases
(\textbf{Figure 2} ) from all over the world. PhenomeCentral has become
a major data repository for several large scale rare disease research
programs, which have collectively contributed approximately 69\% of the
total cases within PhenomeCentral. PhenomeCentral is endorsed by
multiple rare disease consortia and initiatives and is a recommended
resource by the International Rare Diseases Research Consortium (IRDiRC;
). The Care for Rare Canada Consortium (C4R; ) has a user base of 159
accounts which have deposited over 2,900 of their cases into
PhenomeCentral. Other groups contributing a significant number of cases
and using PhenomeCentral regularly include the Genetics of Development
Disorders Team (GAD; ), the NIH Undiagnosed Diseases Program and Network
(UDN; ), and the Telethon Institute of Genetics and Medicine (TIGEM; ).
The last two are members of the Undiagnosed Diseases Network
International (UDNI; ), which has endorsed PhenomeCentral as a data
sharing solution. Collectively, the RD research programs mentioned above
bring together 389 users that have contributed over 8,400 cases to date.

Currently, 5,554 cases in PhenomeCentral have a manually curated
candidate gene listed, spanning a total of 3,234 unique genes. Most
cases have a single candidate gene, with only 12\% of cases listing two
or more genes as candidates. Additional genotypic data in the form of
VCF files are available in 1901 cases, which can be used to generate
matches between candidate genes and variants in genomic sequencing data.

The majority of cases in PhenomeCentral also have some degree of
computer-readable phenotyping (10250/12292, 83\%). Of these cases, 94\%
have two or more phenotypes documented, with a median of 8 phenotypic
terms per case. In total, 118,059 HPO annotations are documented across
this dataset, spanning across 7,938 unique HPO terms. When grouped into
general phenotypic categories, abnormalities of the nervous system are
the most commonly reported type of HPO term, followed by abnormalities
of the head or neck and abnormalities of the skeletal system
(\textbf{Figure 3a)} . More specifically, the most prevalent HPO terms
within this dataset (\textbf{Figure 3b} ) include global developmental
delay (2867/12292, 23\%), seizures (1572/12292, 13\%), and short stature
(1531/12292, 12\%). In addition to HPO terms, many cases have additional
information entered, including growth charts (3005/12292, 24\%), a
pedigree (2555/12292, 21\%), and additional medical history information
(1972/12292, 16\%).

A total of 4,665 matches have been made internally between
PhenomeCentral cases. In addition to internal matchmaking with other
PhenomeCentral cases, currently 70\% of PhenomeCentral cases
(8479/12292) have been consented for matching with the other MME nodes.
Such cases submitted queries for over 2,900 unique candidate genes, and
these 8,479 cases collectively contained over 98,500 HPO annotations.
Queries involving PhenomeCentral as a node in the MME have resulted in
61,362 matches with other nodes, with the majority of these originating
from cases in GeneMatcher (40701/61362, 66\%) and DECIPHER (17097/61362,
28\%).

In collaboration with the MME, PhenomeCentral has enjoyed success in
facilitating matches leading to gene discovery (e.g., Chelban et al.,
2019; den Hoed et al., 2021; Faden et al., 2015; Ito et al., 2018;
Johnstone et al., 2017; Kernohan et al., 2017; Y.-R. Lee et al., 2020;
Lessel et al., 2020; Martinelli et al., 2018; Oud et al., 2017;
Salpietro et al., 2019; Simons et al., 2017; Skraban et al., 2017;
Stray-Pedersen et al., 2016; Vavassori et al., 2021), and, ultimately,
answers for families.

\section*{Discussion}

{\label{discussion}}

Assessing the current landscape of the PhenomeCentral dataset shows a
steady growth in the deposition of cases for the purposes of
matchmaking. Most of these cases are deeply phenotyped, with an average
of 11 HPO terms annotated per case, and many cases containing additional
medical and family history information. A high amount of genotypic
diversity was also observed within the PhenomeCentral dataset, with over
3200 unique candidate genes flagged in total. Finally, all
PhenomeCentral cases were subjected to internal matching, and about 70\%
of cases were also consented to matching with other MME data
repositories. Both internal and external matchmaking queries resulted in
over 62,000 matches being returned across the entire dataset, ultimately
leading to the identification of multiple novel disease-gene
associations.

PhenomeCentral is based on the PhenoTips software, which has a number of
advantages. Over the past five years, PhenoTips has been actively
implemented into hospital systems around the world to enable improved
care for RD patients, resulting in a lower barrier to entry as more
physicians become familiar with the similar interfaces. Having PhenoTips
software at the core of clinical and research databases also expedites
the migration of clinical data into research, as all PhenoTips instances
support the export and import of the same standardized data files, as
well as automated deposition of de-identified cases into PhenomeCentral.

Based on user feedback, we devoted considerable resources to developing
the revised matchmaking filters and the My Matches table. As the MME
network continues to grow and more data is deposited for matchmaking, we
have begun to approach a point where nearly every candidate gene returns
matches with other cases (Osmond et al., in press). Combined with the
reality that older matchmaking submissions continue to receive new
matches years after their initial submission, it is critical that
matchmaking nodes provide users with the tools to filter and track up to
thousands of matches simultaneously. The new filters and My Matches
table represent initial steps towards providing users with such tools,
however additional changes will be required so that matchmaking remains
efficient for users.

The presence of high quality phenotypic data in cases submitted to
matchmaking represents another solution to reducing the time required to
resolve an increasing number of matches. The matchmaking experiences of
the Care4Rare Canada research team suggest that while most MME nodes
support the storage of standardized phenotypic data, more than half of
cases in the MME are submitted with little to no information on clinical
features (Osmond et al., in press). As a result, most matches are
difficult to resolve on an initial review, and require lengthy email
exchanges with the matching user to determine whether a given match is
of interest. Conversely, matches with cases from nodes where phenotypic
data is frequently provided, such as PhenomeCentral, could be ruled out
on initial review over 50\% of the time, drastically reducing the number
of follow-up emails required. As the number of cases and candidate genes
submitted to matching continue to grow, it will be critical for nodes to
emphasize the importance of submitting phenotypic data to ensure current
matchmaking solutions remain practical. The philosophy of PhenomeCentral
is that the upfront effort of contributing phenotypic data to cases
ultimately saves time in the matchmaking process

We believe that the current design of PhenomeCentral is well positioned
for novel approaches to matchmaking, which will utilize genomic sequence
data to increase the number of matches made for a given gene. The MME
framework is currently based on two-sided matchmaking, an approach where
both cases in a match have the same identified candidate gene. An
iteration on this approach, called one-sided matchmaking, would instead
allow users to directly query the genomic sequence data of patient
records in a database for variants in a candidate gene. In the future,
zero-sided matchmaking, a process in which algorithms use genomic
sequence data and phenotypic information to highlight matches of
interest, may also become a reality. One-sided and zero-sided
matchmaking, while requiring patient consent to a greater degree of data
sharing, both have the potential to increase the number of matches made
for a candidate gene. They will also have a greater need for detailed
phenotypic data associated with cases to ensure that the larger numbers
of matches can be reviewed without resorting to lengthy email exchanges.
PhenomeCentral is perfectly positioned for these matchmaking approaches,
as it already allows users to upload sequencing data for cases, provides
consent checkboxes to indicate which cases may use this data for
matchmaking, and can display matches between candidate genes and
variants identified in sequencing data.

In summary, PhenomeCentral has continued to grow as a data repository
dedicated to gene discovery and finding diagnoses for unsolved rare
disease patients through matchmaking. The current dataset consists of
cases from both large research groups and individual researchers, and
contains a wide variety of candidate genes and computer-readable
phenotypes. The development of new features such as robust matching
filters and cohort-wide matching tables have helped PhenomeCentral users
more efficiently manage an ever-growing number of matches. Finally, an
emphasis on contributing high quality genotypic and phenotypic data to
matchmaking has both aided MME users in the quick resolution of many
matches (Osmond et al., in press), and has positioned PhenomeCentral to
contribute to more sophisticated forms of matchmaking in the future.

\section*{Acknowledgements}

{\label{acknowledgements}}

This work was performed under the Care4Rare Canada Consortium funded by
Genome Canada and the Ontario Genomics Institute (OGI-147), the Canadian
Institutes of Health Research, Ontario Research Fund, Genome Alberta,
Genome British Columbia, Genome Quebec, and Children's Hospital of
Eastern Ontario Foundation. T.H. was supported by a Frederick Banting
and Charles Best Canada Graduate Scholarship Doctoral Award from CIHR.
K.M.B. was supported by a CIHR Foundation Grant (FDN-154279) and a Tier
1 Canada Research Chair in Rare Disease Precision Health.

\section*{Conflicts of Interest}

{\label{conflicts-of-interest}}

OJB and MB have an equity interest in, and OJB is an employee of
PhenoTips, which provides support for software that underlies the
PhenomeCentral database.

\section*{Data Availability Statement}

{\label{data-availability-statement}}

The data that support the findings of this study are available on
request from the corresponding author. The data are not publicly
available due to privacy or ethical restrictions.



\section*{References}

{\label{references}}

ACMG Board of Directors. (2017). Laboratory and clinical genomic data
sharing is crucial to improving genetic health care: a position
statement of the American College of Medical Genetics and
Genomics.\emph{Genetics in Medicine} , \emph{19} (7), 721--722.
https://doi.org/10.1038/gim.2016.196

Arachchi, H., Wojcik, M. H., Weisburd, B., Jacobsen, J. O. B., Valkanas,
E., Baxter, S., \ldots{} Rehm, H. L. (2018). matchbox: An open-source
tool for patient matching via the Matchmaker Exchange. \emph{Human
Mutation} , \emph{39} (12), 1827--1834.
https://doi.org/10.1002/humu.23655

Bamshad, M. J., Ng, S. B., Bigham, A. W., Tabor, H. K., Emond, M. J.,
Nickerson, D. A., \& Shendure, J. (2011). Exome sequencing as a tool for
Mendelian disease gene discovery. \emph{Nature Reviews Genetics}
,\emph{12} (11), 745--755. https://doi.org/10.1038/nrg3031

Baynam, G., Walters, M., Claes, P., Kung, S., Lesouef, P., Dawkins, H.,
\ldots{} Goldblatt, J. (2015, April 1). Phenotyping: Targeting
genotype's rich cousin for diagnosis. \emph{Journal of Paediatrics and
Child Health} , Vol. 51, pp. 381--386. Blackwell Publishing.
https://doi.org/10.1111/jpc.12705

Bush, L. W., Beck, A. E., Biesecker, L. G., Evans, J. P., Hamosh, A.,
Holm, I. A., \ldots{} Rehm, H. L. (2018). Professional responsibilities
regarding the provision, publication and dissemination of patient
phenotypes in the context of clinical genetic and genomic testing:
points to consider. A statement of the American College of Medical
Genetics and Genomics (ACMG). \emph{Genetics in Medicine} , \emph{20}
(2), 169-171. https://doi.org/10.1038/gim.2017.242

Buske, O. J., Girdea, M., Dumitriu, S., Gallinger, B., Hartley, T.,
Trang, H., \ldots{} Brudno, M. (2015a). PhenomeCentral: A portal for
phenotypic and genotypic matchmaking of patients with rare genetic
diseases. \emph{Human Mutation} , \emph{36} (10), 931--940.
https://doi.org/10.1002/humu.22851

Buske, O. J., Schiettecatte, F., Hutton, B., Dumitriu, S., Misyura, A.,
Huang, L., \ldots{} Brudno, M. (2015b). The Matchmaker Exchange API:
Automating patient matching Through the exchange of structured
phenotypic and genotypic profiles. \emph{Human Mutation} , \emph{36}
(10), 922--927. https://doi.org/10.1002/humu.22850

Chelban, V., Wilson, M. P., Warman Chardon, J., Vandrovcova, J.,
Zanetti, M. N., Zamba\selectlanguage{english}-Papanicolaou, E., \ldots{} Houlden, H. (2019).
PDXK mutations cause polyneuropathy responsive to pyridoxal 5\selectlanguage{english}'\selectlanguage{english}-phosphate
supplementation. \emph{Annals of Neurology} , \emph{86} (2), 225--240.
https://doi.org/10.1002/ana.25524

Chong, J. X., Yu, J.-H., Lorentzen, P., Park, K. M., Jamal, S. M.,
Tabor, H. K., \ldots{} Bamshad, M. J. (2016). Gene discovery for
Mendelian conditions via social networking: de novo variants in KDM1A
cause developmental delay and distinctive facial features.\emph{Genetics
in Medicine} , \emph{18} (8), 788--795.
https://doi.org/10.1038/gim.2015.161

Darquy, S., Moutel, G., Lapointe, A.-S., D'Audiffret, D., Champagnat,
J., Guerroui, S., \ldots{} Duchange, N. (2016). Patient/family views on
data sharing in rare diseases: study in the European LeukoTreat
project.\emph{European Journal of Human Genetics} , \emph{24} (3),
338-343. https://doi.org/10.1038/ejhg.2015.115

den Hoed, J., de Boer, E., Voisin, N., Dingemans, A. J. M., Guex, N.,
Wiel, L., \ldots{} Vissers, L. E. L. M. (2021). Mutation-specific
pathophysiological mechanisms define different neurodevelopmental
disorders associated with SATB1 dysfunction. \emph{American Journal of
Human Genetics} , \emph{108} (2), 346--356.
https://doi.org/10.1016/j.ajhg.2021.01.007

Faden, M., AlZahrani, F., Mendoza-Londono, R., Dupuis, L., Hartley, T.,
Kannu, P., \ldots{} Alkuraya, F. S. (2015). Identification of a
recognizable progressive skeletal dysplasia caused by RSPRY1
mutations.\emph{American Journal of Human Genetics} , \emph{97} (4),
608--615. https://doi.org/10.1016/j.ajhg.2015.08.007

Girdea, M., Dumitriu, S., Fiume, M., Bowdin, S., Boycott, K. M.,
Chenier, S., \ldots{} Brudno, M. (2013). PhenoTips: Patient phenotyping
software for clinical and research use. \emph{Human Mutation} ,\emph{34}
(8), 1057--1065. https://doi.org/10.1002/humu.22347

Ito, Y., Carss, K. J., Duarte, S. T., Hartley, T., Keren, B., Kurian, M.
A., \ldots{} Raymond, F. L. (2018). De novo truncating mutations in
WASF1 cause intellectual disability with seizures. \emph{American
Journal of Human Genetics} , \emph{103} (1), 144--153.
https://doi.org/10.1016/j.ajhg.2018.06.001

Johnstone, D. L., Nguyen, T.-T.-M., Murakami, Y., Kernohan, K. D.,
Tetreault, M., Goldsmith, C., \ldots{} Campeau, P. M. (2017). Compound
heterozygous mutations in the gene PIGP are associated with early
infantile epileptic encephalopathy. \emph{Human Molecular Genetics}
,\emph{26} (9), 1706--1715. https://doi.org/10.1093/hmg/ddx077

Kernohan, K. D., Dyment, D. A., Pupavac, M., Cramer, Z., McBride, A.,
Bernard, G., \ldots{} Thiffault, I. (2017). Matchmaking facilitates the
diagnosis of an autosomal-recessive mitochondrial disease caused by
biallelic mutation of the tRNA isopentenyltransferase (TRIT1)
gene.\emph{Human Mutation} , \emph{38} (5), 511--516.
https://doi.org/10.1002/humu.23196

Kohler, S., Gargano, M., Matentzoglu, N., Carmody, L. C., Lewis-Smith,
D., Vasilevsky, N. A., \ldots{} Robinson, P. N. (2021). The Human
Phenotype Ontology in 2021. \emph{Nucleic Acids Research} ,\emph{49}
(D1), D1207. https://doi.org/10.1093/nar/gkaa1043

Lee, H., Deignan, J. L., Dorrani, N., Strom, S. P., Kantarci, S.,
Quintero-Rivera, F., \ldots{} Nelson, S. F. (2014). Clinical Exome
Sequencing for Genetic Identification of Rare Mendelian
Disorders.\emph{Journal of American Medical Association} , \emph{312}
(18), 1880-1887. https://doi.org/10.1001/jama.2014.14604

Lee, Y.-R., Khan, K., Armfield-Uhas, K., Srikanth, S., Thompson, N. A.,
Pardo, M., \ldots{} Schwartz, C. E. (2020). Mutations in FAM50A suggest
that Armfield XLID syndrome is a spliceosomopathy. \emph{Nature
Communications} , \emph{11} (1), 3698.
https://doi.org/10.1038/s41467-020-17452-6

Lessel, D., Zeitler, D. M., Reijnders, M. R. F., Kazantsev, A., Hassani
Nia, F., Bartholomaus, A., \ldots{} Kreienkamp, H.-J. (2020). Germline
AGO2 mutations impair RNA interference and human neurological
development. \emph{Nature Communications} , \emph{11} (1), 5797.
https://doi.org/10.1038/s41467-020-19572-5

Martinelli, S., Krumbach, O. H. F., Pantaleoni, F., Coppola, S., Amin,
E., Pannone, L., \ldots{} Mirzaa, G. M. (2018). Functional dysregulation
of CDC42 causes diverse developmental phenotypes. \emph{American Journal
of Human Genetics} , \emph{102} (2), 309-320.
https://doi.org/10.1016/j.ajhg.2017.12.015

Matchmaker Exchange Statistic and Publications. (2021). Retrieved
September 30, 2021, from
https://www.matchmakerexchange.org/statistics.html

Osmond, M., Hartley, T., Dyment, D., Kernohan, K. D., Brudno, M., Buske,
O. J., \ldots{} Boycott, K. M. (in press). Outcome of over 1500 matches
through the Matchmaker Exchange for rare disease gene discovery: the two
year experience of Care4Rare Canada. \emph{Genetics in Medicine} .

Oud, M. M., Tuijnenburg, P., Hempel, M., van Vlies, N., Ren, Z.,
Ferdinandusse, S., \ldots{} Kuijpers, T. W. (2017). Mutations in EXTL3
cause neuro-immuno-skeletal dysplasia syndrome. \emph{American Journal
of Human Genetics} , \emph{100} (2), 281-296.
https://doi.org/10.1016/j.ajhg.2017.01.013

Pesquita, C., Faria, D., Bastos, H., Ferreira, A. E., Falcao, A. O., \&
Couto, F. M. (2008). Metrics for GO based protein semantic similarity: a
systematic evaluation. \emph{BMC Bioinformatics} , \emph{9} (S5), S4.
https://doi.org/10.1186/1471-2105-9-S5-S4

Philippakis, A. A., Azzariti, D. R., Beltran, S., Brookes, A. J.,
Brownstein, C. A., Brudno, M., \ldots{} Rehm, H. L. (2015). The
Matchmaker Exchange: A Platform for Rare Disease Gene
Discovery.\emph{Human Mutation} , \emph{36} (10), 915--921.
https://doi.org/10.1002/humu.22858

Rahimzadeh, V., Dyke, S. O. M., \& Knoppers, B. M. (2016). An
international framework for data sharing: Moving forward with the Global
Alliance for Genomics and Health. \emph{Biopreservation and Biobanking}
,\emph{14} (3), 256--259. https://doi.org/10.1089/bio.2016.0005

Rehm, H. L. (2017). A new era in the interpretation of human genomic
variation. \emph{Genetics in Medicine 2017 19:10} , \emph{19} (10),
1092--1095. https://doi.org/10.1038/gim.2017.90

Salpietro, V., Dixon, C. L., Guo, H., Bello, O. D., Vandrovcova, J.,
Efthymiou, S., \ldots{} Houlden, H. (2019). AMPA receptor GluA2 subunit
defects are a cause of neurodevelopmental disorders. \emph{Nature
Communications} , \emph{10} (1), 3094.
https://doi.org/10.1038/s41467-019-10910-w

Simons, C., Dyment, D., Bent, S. J., Crawford, J., D'Hooghe, M.,
Kohlschutter, A., \ldots{} Wolf, N. I. (2017). A recurrent de novo
mutation in TMEM106B causes hypomyelinating leukodystrophy.\emph{Brain}
, \emph{140} (12), 3105--3111. https://doi.org/10.1093/brain/awx314

Skraban, C. M., Wells, C. F., Markose, P., Cho, M. T., Nesbitt, A. I.,
Au, P. Y. B., \ldots{} Deardorff, M. A. (2017). WDR26 haploinsufficiency
causes a recognizable syndrome of intellectual disability, seizures,
abnormal gait, and distinctive facial features. \emph{American Journal
of Human Genetics} , \emph{101} (1), 139-148.
https://doi.org/10.1016/j.ajhg.2017.06.002

Smedley, D., Jacobsen, J. O. B., Jager, M., Kohler, S., Holtgrewe, M.,
Schubach, M., \ldots{} Robinson, P. N. (2015). Next-generation
diagnostics and disease-gene discovery with the Exomiser. \emph{Nature
Protocols} , \emph{10} (12), 2004--2015.
https://doi.org/10.1038/nprot.2015.124

Stray-Pedersen, A., Cobben, J.-M., Prescott, T. E., Lee, S., Cang, C.,
Aranda, K., \ldots{} Yoon, G. (2016). Biallelic mutations in UNC80 cause
persistent hypotonia, encephalopathy, growth retardation, and severe
intellectual disability. \emph{American Journal of Human Genetics}
,\emph{98} (1), 202-209. https://doi.org/10.1016/j.ajhg.2015.11.004

Thompson, R., Johnston, L., Taruscio, D., Monaco, L., Beroud, C., Gut,
I. G., \ldots{} Lochmuller, H. (2014). RD-Connect: An integrated
platform connecting databases, registries, biobanks and clinical
bioinformatics for rare disease research. \emph{Journal of General
Internal Medicine} , \emph{29} (S3), 780--787.
https://doi.org/10.1007/s11606-014-2908-8

Vavassori, S., Chou, J., Faletti, L. E., Haunerdinger, V., Opitz, L.,
Joset, P., \ldots{} Pachlopnik Schmid, J. (2021). Multisystem
inflammation and susceptibility to viral infections in human ZNFX1
deficiency. \emph{Journal of Allergy and Clinical Immunology}
,\emph{148} (2), 381--393. https://doi.org/10.1016/j.jaci.2021.03.045

Wright, C. F., McRae, J. F., Clayton, S., Gallone, G., Aitken, S.,
FitzGerald, T. W., \ldots{} Firth, H. V. (2018). Making new genetic
diagnoses with old data: iterative reanalysis and reporting from
genome-wide data in 1,133 families with developmental
disorders.\emph{Genetics in Medicine 2018 20:10} , \emph{20} (10),
1216--1223. https://doi.org/10.1038/gim.2017.246

Yang, Y., Muzny, D. M., Reid, J. G., Bainbridge, M. N., Willis, A.,
Ward, P. A., \ldots{} Eng, C. M. (2013). Clinical whole-exome sequencing
for the diagnosis of mendelian disorders. \emph{The New England Journal
of Medicine} , \emph{369} (16), 1502.
https://doi.org/10.1056/nejmoa1306555

\section*{Figure Legends}

{\label{figure-legends}}

\textbf{Figure 1: Screenshot of the My Matches table in
PhenomeCentral.}PhenomeCentral users can use this table to keep track of
the matches found for all of their cases in one place. Here, they can
perform advanced filtering and sorting, compare genotypes and phenotypes
between matched cases, write individual notes, initiate contact with
other PhenomeCentral or MME users, and access the communication history
associated with a specific match.

\textbf{Figure 2: Total number of cases deposited in
PhenomeCentral.}Since its launch in February 2014, and beginning with
only a handful of cases contributed by the Canadian Care for Rare
Consortium (http://care4rare.ca), PhenomeCentral has seen steady growth
and has now surpassed 12,000 patient profiles available for matching.

\textbf{Figure 3: Overview of the HPO phenotypes documented in
PhenomeCentral cases (N = 12292 cases). a)} Frequency of general
categories of HPO terms; abnormalities in the nervous system,
abnormalities in the head or neck, and abnormalities in the skeletal
system are most prevalent. \textbf{b)} Frequency of specific HPO terms;
global developmental delay, seizures, and short stature are most
prevalent.\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

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