\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Diagnostic testing laboratories are valuable partners for disease gene
discovery: 5-year experience with GeneMatcher}



\author[1]{Meghan Towne}%
\author[1]{Mari Rossi}%
\author[1]{Bess Wayburn}%
\author[1]{Jennifer Huang}%
\author[1]{Kelly Radtke}%
\author[1]{Wendy Alcaraz}%
\author[1]{Kelly Farwell}%
\author[1]{Deepali  Shinde }%
\affil[1]{Ambry Genetics Corp}%


\vspace{-1em}



  
  \date{October 31, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Clinical and research laboratories extensively use exome sequencing due
to its high diagnostic rates, cost savings, impact on clinical
management, and efficacy for disease gene discovery. While the rates of
disease gene discovery have steadily increased, only
\textasciitilde{}16\% of genes in the genome have confirmed disease
associations. Here we describe our diagnostic laboratory's disease gene
discovery and ongoing data-sharing efforts with GeneMatcher. In total,
we submitted 246 candidates from 243 unique genes to GeneMatcher, of
which 45.93\% are now clinically characterized. Submissions with at
least one case meeting our candidate genes reporting criteria were
significantly more likely to be characterized as of October 2021
compared to genes with no candidates meeting our reporting criteria
(p=0.025). We reported relevant findings related to these gene-disease
associations for 480 probands. In 219 (45.63\%) instances, these results
were reclassifications after an initial candidate gene (uncertain) or
negative report. Since 2013, we have co-authored 105 publications
focused on delineating gene-disease associations. Diagnostic
laboratories are pivotal for disease gene discovery efforts and can
screen phenotypes based on genotype matches, contact clinicians of
relevant cases, and issue proactive reclassification reports.
GeneMatcher is a critical resource in these efforts.%
\end{abstract}%



\sloppy


\textbf{Diagnostic testing laboratories are valuable partners for
disease gene discovery: 5-year experience with GeneMatcher}

Meghan C. Towne, Mari Rossi, Bess Wayburn, Jennifer Huang, Kelly Radtke,
Wendy Alcaraz, Kelly D. Farwell Hagman, Deepali N. Shinde Ambry Genetics
1 Enterprise Aliso Viejo CA, USA Corresponding authors: Meghan C. Towne
- mtowne@ambrygen.com Deepali N. Shinde - dshinde@ambrygen.com No grant
funding was used for this work

\textbf{Conflict of interest}All authors are salaried employees of Ambry
Genetics.

Abstract

Clinical and research laboratories extensively use exome sequencing due
to its high diagnostic rates, cost savings, impact on clinical
management, and efficacy for disease gene discovery. While the rates of
disease gene discovery have steadily increased, only
\textasciitilde{}16\% of genes in the genome have confirmed disease
associations. Here we describe our diagnostic laboratory's disease gene
discovery and ongoing data-sharing efforts with GeneMatcher. In total,
we submitted 246 candidates from 243 unique genes to GeneMatcher, of
which 45.93\% are now clinically characterized. Submissions with at
least one case meeting our candidate genes reporting criteria were
significantly more likely to be characterized as of October 2021
compared to genes with no candidates meeting our reporting criteria
(p=0.025). We reported relevant findings related to these gene-disease
associations for 480 probands. In 219 (45.63\%) instances, these results
were reclassifications after an initial candidate gene (uncertain) or
negative report. Since 2013, we have co-authored 105 publications
focused on delineating gene-disease associations. Diagnostic
laboratories are pivotal for disease gene discovery efforts and can
screen phenotypes based on genotype matches, contact clinicians of
relevant cases, and issue proactive reclassification reports.
GeneMatcher is a critical resource in these efforts.

Keywords (5-6): Disease gene discovery; gene-disease validity; clinical
validity; data-sharing; GeneMatcher; Matchmaker Exchange; exome
sequencing

\textbf{1 \textbar{} INTRODUCTION}

The past decade has been a time of rapid disease gene discovery, driven
by the rise in popularity of next-generation sequencing (NGS)
technologies and the increasing use of web-based collaborative
data-sharing initiatives, such as the Matchmaker Exchange (Chong, 2015;
Sobreira, 2015; Sobreira, 2017; Boycott, 2019; Bamshad, 2019).
Matchmaker Exchange enhances data-sharing and characterization of novel
gene-disease associations by connecting multiple genomic and phenotypic
databases through a common application programming interface (API)
(Sobreira, 2017). One of the components of Matchmaker Exchange is
GeneMatcher (http://www.genematcher.org) which launched in 2013 to
connect scientists and clinicians to share standardized data on
candidate genes of interest and the associated phenotypes of individuals
with presumed but unidentified Mendelian disorders. By sharing candidate
gene information through GeneMatcher, researchers can assemble a
critical mass of probands to support the characterization of new
gene-disease associations (Sobreira, 2015). As more genes are associated
with Mendelian disorders, the overall diagnostic rate of genomic
technologies and the potential to identify new therapeutic targets
inherently increase (Myers, 2018; Bamshad, 2019). More directly, disease
gene discovery impacts patients by ending the notorious `diagnostic
odyssey,' providing more tailored clinical care, and informing
reproductive risks.

Due to the high volume of testing, diagnostic laboratories that offer
diagnostic exome sequencing (DES) are valuable partners for disease gene
discovery (Bamshad, 2019). However, most of the data generated by DES
are not adequately available for data-sharing and matchmaking (Boycott,
2019). Some diagnostic laboratories evaluate and report rare variants in
uncharacterized genes as part of their DES protocol (Retterer, 2016;
Farwell Hagman, 2017). At our laboratory, we have a standardized and
validated scoring metric for evaluating gene-disease validity (GDV)
(Smith, 2017). Genes with no clinical evidence or limited evidence are
considered uncharacterized and those with a GDV score of moderate or
higher are considered characterized. Both characterized and
uncharacterized genes may be reported if meeting our DES reporting
criteria and have strong evidence for their association with a proband's
phenotype (Farwell Hagman, 2017). Reporting criteria for uncharacterized
candidate genes can vary widely between diagnostic laboratories with
published reports of 5.8-24.2\% of DES cases having a reported candidate
gene (Farwell Hagman, 2017; Retterer, 2016).

Because GDV scores are based on a gene-disease relationship, having
access to comprehensive phenotypic data ideally in the form of clinical
notes that summarize the salient points of the medical history are
crucial for accurately assessing what genes may be relevant for a
proband (Seaby, 2020). Genes that meet reporting criteria for our
uncharacterized genes are entered into GeneMatcher on a rolling basis.
This process allows us to enter high-confidence, potentially
disease-causing variants representing the strongest candidates and is
consistent with the ``gene-to-patient'' model proposed by Seaby et al.
(2021) to reduce the burden of sifting through large volumes of unvetted
variants (``analytical noise''). A thoughtful approach to identifying
what variants are the most likely to be disease-causing in a proband is
needed before submitting to GeneMatcher to ensure the highest positive
outcomes to matches. This in turn leads to newly published data which
ultimately can lead to gene characterization (Figure1).

Rates of disease gene discovery have steadily increased over time with a
spike occurring as the adoption of NGS technologies became more
prominent. However, the rates of publications reporting these
discoveries are not keeping up (Bamshad, 2019). The elusive gene-disease
relationships that remain to be described may be due to several factors,
including complex inheritance or the difficulty in ascertaining probands
with extremely rare disorders. Publications may be delayed until the
collection of a large enough cohort with robust clinical data curation
and paired functional studies. This may be hindering the
characterization of gene-disease associations in extremely rare cases
with highly specific clinical findings that are less conducive to cohort
studies. Moving forward, participation by commercial laboratories in
these data-sharing initiatives is even more imperative to help identify
the elusive, ultrarare diagnoses.

Here, we report our laboratory's experience with GeneMatcher, how it has
impacted characterization of gene-disease associations, and
collaborations for additional research investigations into the clinical
validity of the reported gene-disease associations.

\textbf{2 \textbar{} MATERIALS AND METHODS}

\textbf{2.1\textbar{} Gene disease validity assessment and gene
characterization}

Our diagnostic laboratory has established standardized guidelines for
curating and scoring GDV which have been independently validated (Smith,
2017). Briefly, the tiered system is similar to the ClinGen model and
scores evidence of gene-disease relationships as definitive, strong,
moderate, limited, no reported evidence, and conflicting evidence
reported (Strande, 2017). A team of scientists reviews published
peer-reviewed literature on an ongoing basis and determines the GDV
score for each gene-disease relationship. Scores of ``moderate'' or
higher are considered characterized, and those with a score of
``limited'' or lower are uncharacterized. Some genes have evidence to
support associations with more than one phenotype, inheritance pattern
or disease mechanism. This could include genes with multiple
characterized associations or more a combination of characterized and
uncharacterized associations. Gene-disease relationships classified as
``limited'' by GDV assessment may be included as an uncharacterized
candidate for clinical reporting purposes. Genes with supporting
evidence, but that do not meet reporting criteria are not included on
reports but may be reported as `notable' findings and are available to
clinicians by way of filtered variant lists (Farwell Hagman, 2017).

\textbf{2.2 \textbar{} Exome Sequencing}

DES was performed as previously described (Farwell, 2015; Farwell
Hagman, 2017). In brief, samples were prepared using either the
SureSelect Target Enrichment System (Agilent Technologies, Santa Clara,
CA), SeqCap EZ VCRome 2.0 (Roche NimbleGen, Madison, WI), or the IDT
xGen Exome Research Panel V1.0 (Integrated DNA Technologies, Coralville,
IA). Sequencing was performed using paired-end, 100 or 150-cycle
chemistry on the Illumina HiSeq, NovaSeq, or NextSeq (Illumina, San
Diego, CA). Bioinformatics filtering removed common benign variants,
intergenic and 3'/5' UTR variants, intronic variants outside \selectlanguage{ngerman}±6, and
non-splice-related synonymous variants. Family history-based filtering
and inheritance models were applied to the data.

\textbf{2.3 \textbar{} Candidate gene analysis for diagnostic exome
cases}

DES cases submitted as informative trios, defined as having samples from
family members representative of both the maternal and paternal
lineages, and that were negative after review of characterized
gene-disease relationships are eligible for uncharacterized candidate
gene analysis as previously described (Farwell-Hagman, 2017). If an
identified characterized gene finding only accounts for part of a
patient's phenotype, uncharacterized genes can be analyzed for causes of
the unaccounted-for clinical features. Uncharacterized genes that meet
reporting criteria are considered candidate genes with uncertain
clinical significance (Richards, 2015). Alterations in uncharacterized
genes identified during analysis as having potential relevance undergo
dual scientist and director review to determine if there is adequate
evidence to report as a candidate finding (Smith, 2017).

\textbf{2.4 \textbar{} Data-sharing to GeneMatcher}

Our laboratory began submitting to GeneMatcher in March 2016.
Alterations reported as a candidate gene finding or classified as
``insufficient evidence'' but with substantiation for disease relevance
are entered in GeneMatcher on a rolling basis. A new entry is submitted
for each case with a novel mechanism or inheritance model associated
with a gene.

\textbf{2.5 \textbar{} Follow-up to Matches}

Matches are sent to a designated inbox (genematcher@ambrygen.com) which
is monitored by research staff who oversee both our rare disease
research efforts and recruitment for external research studies. Upon
receiving a match notification, all cases with rare variants and
consistent genotype in a matched gene of interest are reviewed for
clinical overlap. High-level phenotypic data and variant information are
shared in a HIPAA-compliant manner with GeneMatcher collaborators to
assess the integrity of the match. Ordering providers for cases with
consistent matches are contacted by email with a general description of
the collaboration, investigator's contact information, and gene of
interest. A second follow-up message to the ordering provider is sent by
encrypted email and contains the proband's identifiable information,
variant of interest, and other relevant information, for example, why
the alteration did not meet our reporting criteria at the time of the
initial report, and therefore there is no documentation of the
alteration or if it was not Sanger confirmed. We do not share the
clinician or patient's contact information with the collaborating
investigator without consent. Once the connection is established, the
clinician works with the family to obtain appropriate consent for
inclusion in any resulting work. We continue as a partner to share
additional data as requested (Sanger or IGV imaging, testing methodology
details), DNA (if available), and additional family testing for variant
co-segregation. In certain cases, our laboratory issues research-grade
single site analysis reports to document alterations that were not
previously reported to aid in the clinician's discussion with the
family.

\textbf{2.6 \textbar{} Data Analysis}

To assess the overall impact of our laboratory's involvement with
GeneMatcher, we analyzed the outcomes of our submissions to date. For
purposes of this analysis, duplicate entries for a candidate
gene-disease relationship with the same inheritance, zygosity, and
alteration type were consolidated and counted as a single entry. We
retrospectively reviewed all our DES cases from 2011 through January
2021 and tabulated the number of probands with reported alterations in
genes with a GeneMatcher entry. For genes with more than one disease
association, we only counted cases associated with the phenotype related
to the GeneMatcher entry. We assessed the classification of the gene at
the time of the initial report (uncharacterized, characterized, or
notable), the current GDV score (uncharacterized, moderate, strong, or
definitive) and the number of probands who received a reclassification
report due to gene characterization. Fisher exact test was used for
statistical analysis.

To quantify our laboratory's contribution to disease gene discovery
efforts, we queried PubMed (https://pubmed.ncbi.nlm.nih.gov/) for
peer-reviewed publications focused on gene characterization efforts
which listed authors affiliated with Ambry Genetics. These articles were
reviewed for content and categorized as (1) description of new
gene-disease association, (2) follow-up case report or supportive
evidence (i.e., functional or animal model evidence), (3) follow-up
descriptive cohort, and (4) follow-up expansion of phenotype.
Publications which included a broadening and further defining of the
phenotypic spectrum but not a major expansion in the phenotype were
counted as a follow-up descriptive cohort. We cross referenced this
published gene list with our GeneMatcher submissions.

\textbf{3 \textbar{} RESULTS}

In total, 246 candidate entries representing 243 unique genes were
submitted to GeneMatcher from our laboratory between March 2016 and
September 2021 {[}Supplement Table 1{]}. Four gene entries (\emph{EIF2C,
ITGA11, LRFN2,} and \emph{UNC45B} ) were subsequently marked as
`suspended' because they no longer met candidate gene criteria per our
laboratory's GDV scoring metric. Of note, one of these
genes,\emph{UNC45B} was subsequently characterized for an autosomal
dominant congenital myopathy in December 2020; however, we had
originally submitted as an autosomal recessive cause of Joubert syndrome
with skeletal and ocular anomalies. There were three genes \emph{(ANK3,
RYR3,} and \emph{SPTBN4} ) that each had two entries representing
distinct phenotypes of interest including differences in inheritance
models or suspected mechanism of disease.

As of October 2021, 45.93\% of entries have been characterized {[}Table
1{]}. Most candidate gene entries were for autosomal dominant \emph{de
novo} occurrences; however, there are no significant differences for
characterization based on zygosity, inheritance pattern or alterations
type. Of the 246 entries, 153 (62.20\%) had at least one case with a
reported uncharacterized gene finding, meaning the gene, variant,
proband combination met candidate gene reporting criteria described in
Farwell Hagman, et al. (2017). Submissions that had at least one case
meeting our candidate criteria were significantly more likely to be
characterized (79/153; 51.63\%) as of October 2021 compared to genes
with no candidates meeting reporting criteria (34/93; 36.56\%; p=0.025).

In total, 196 probands received a candidate gene report involving one of
the genes entered in GeneMatcher. Of these, 32 genes had more than one
uncharacterized report {[}range 2-4{]}. Currently, 480 probands received
a reportable finding after the gene was characterized, which accounts
for 11.96\% (480/4012) of all characterized gene reports from our
laboratory (data not shown). Of these 480 cases, 219 (45.63\%) were
reclassifications of an original result {[}Figure 2{]}.
Reclassifications include upgrades from candidate gene results
(uncertain) or the addition of the finding to a previous report. 75
genes had more than one reported characterized case {[}range of 2-31{]}.

Our laboratory has co-authored 105 peer-reviewed papers describing
gene-disease associations for 104 unique genes between 2013 and 2021
{[}Supplement Table 2{]}. 39.42\% (41/104) of these genes were
candidates we had submitted to GeneMatcher. Majority of publications
were the initial description of a gene-disease association, but also
follow-up publications that further defined rare Mendelian disorders
{[}Figure 3{]}.

\textbf{4 \textbar{} DISCUSSION}

Identifying patients is a critical but limiting step in the disease gene
discovery process for rare disorders. Diagnostic laboratories provide
meaningful contributions to these efforts by screening large testing
cohorts for relevant phenotypes based on genotype matches, contacting
clinicians of appropriate cases, and issuing proactive reclassification
reports. Over the last 5 years, our laboratory has entered 246 candidate
entries into GeneMatcher of which 45.93\% are now characterized. There
was no significant difference for the likelihood to be characterized
based on inheritance type, zygosity, or alteration type. However,
entries which met our candidate gene reporting criteria were
significantly more likely to be characterized, suggesting that
while\emph{de novo} and biallelic alterations are easier to identify and
represent a large percentage of our GeneMatcher entries (56.91\% and
33.74\%, respectively), our process of evaluating existing evidence for
a gene-disease association is a strong predictor of disease association
regardless of mechanism.

By integrating standardized methods for evaluating the evidence
available for gene-disease associations into the clinical DES analysis
framework, diagnostic laboratories can be valuable partners in screening
genes for potential disease associations. All probands with informative
trios and negative results after characterized gene review undergo
additional candidate gene analysis at our laboratory. Analysts consider
the published literature and in-house DES data from previous cases. In
some instances, we amass sufficient evidence to characterize a
gene-disease association based on internal data. For example, we
identified six internal probands with \emph{de novo} rare
\emph{ZNF292}alterations and similar neurodevelopmental phenotypes more
than one year before the manuscript describing the association was
published. Our laboratory submits data on genes with substantial
evidence for a disease association into GeneMatcher. This creates a more
meaningful contribution to data-sharing compared to automated `data
dumping' practices where every rare variant in an uncharacterized gene
is entered. Broad data deposits without evaluating the potential
clinical relevance can create analytical noise and superfluous
communications (Seaby, 2021). Follow-up communications to screen matches
are laborious, so contributors to GeneMatcher should have standardized
methods to appropriately vet candidate genes before submitting.

One advantage of DES is the ability to analyze the expanding catalog of
characterized genes over time. Gene characterization has a cascading
effect with the potential for multiple patients to receive a
reclassified diagnostic finding once a gene-disease relationship is
established. Here, we found GeneMatcher entries ultimately resulted in
480 probands receiving a reportable finding. This accounts for a
substantial portion (11.96\%) of our total DES cohort with reported
characterized findings. Importantly, nearly half of the probands with
reported variants in these genes received a reclassification report.
Laboratories should have processes in place to curate and score new
evidence GDV in real-time, and retrospectively evaluate cases for
proactive reclassification. This is an ongoing effort and several cases
reported here are under review for clinical overlap and reporting
{[}Figure 2{]}. Without a process to re-evaluate previous cases, nearly
half of this cohort would not have received an updated report.
Establishing an accurate molecular diagnosis has profound impacts on
patients, especially for individuals with rare disorders. A diagnosis
can precipitate tailored clinical management, clearer recurrence risk
counseling, and in some cases, eligibility for clinical trials.

Further, because of the ability to reclassify previously reported DES
cases, diagnostic laboratories can quickly identify additional probands
and further power studies to clarify genotype-phenotype correlations and
phenotypic spectrum expansions. Often after an initial cohort describes
a new gene-disease relationship there is a follow-up publication of a
larger cohort to further delineate the newly characterized disorder. For
example, we co-authored Peng et al. (2017) which described the
association between biallelic \emph{FDXR} alterations and auditory
neuropathy and optic atrophy. The following year, we collaborated with
Slone et al. (2018) on a follow-up study to include additional patient
reports and functional evidence. We participated in 24 follow-up cohort
studies as well as 14 publications describing a single case report or
other data in support of the association. Lastly, there were 5
publications we participated in that described a phenotypic expansion
for a gene to include major features not previously reported
{[}citations listed in supplement Table 2{]}. Diagnostic laboratories
can serve as a recruitment hub for researchers where more than one
proband of interest can be identified through a single source. We can
screen the phenotype of individuals with rare variants in a candidate
gene of interest, identify the probands with phenotypic overlap, and
reach out to multiple clinicians. In some cases, a candidate gene may
not have met reporting criteria. In these instances, we can provide
reports documenting the presence of an alteration, but not commenting on
the clinical relevance until sufficient information is amassed and
reported that meets our characterization status. Clinicians find these
reports useful to facilitate discussion with families about an
alteration which was not originally included on a DES report and the
option to enroll in a study further investigating a disease association.
Laboratories can further assist the research process by sharing
remaining banked DNA (if available and with the appropriate permissions)
with researchers and providing cascade testing to other family members
for purposes of risk prediction, recurrence risk estimates, and variant
segregation within a family.

While diagnostic laboratories can be an efficient tool for finding
matches given there is a warehouse of clinical data to screen, the level
of clinical data available is dependent on the information submitted at
the time of testing. Comprehensive clinic notes that summarize the
salient points of a proband's phenotype should be submitted with all DES
testing (Seaby, 2020). A complete clinical description of a proband's
presentation is not only important for variant classification, but also
for characterizing new disease genes. In reviewing previously reported
cases for collaborators, those with more specific and complete clinical
information are more evident as positive matches compared to vague
descriptions. Further, robust clinical data can help identify links
between a proband's features and a gene with limited evidence-based on
animal models and other functional data.

There is still much work to be done, and many suspected gene-disease
associations with insufficient evidence for characterization exist. In
total, we have 70 genes that have been reported as a candidate, but
remained uncharacterized for more than 2 years, indicating that there
was sufficient experimental evidence at the time to associate the gene
with disease; however, robust patient data has not yet become available
despite the use of GeneMatcher. More than one novel candidate report was
issued in only nine of these genes, making these true ``n-of-one'' cases
in need of additional patient reports to corroborate the finding. For
example, in 2015, compound heterozygous predicted loss-of-function
alterations in the \emph{NMNAT2} gene were identified in a fetus with
multiple congenital brain anomalies, contractures, and absence of
muscle, which matched perfectly with a mouse model published years
earlier (Hicks, 2012). Follow-up in-house structural studies and
functional studies through a collaborator support the pathogenicity of
these alterations (Lukacs, 2019); however, this gene remains
uncharacterized as no additional patient reports have been published.
Another example of the importance of robust phenotypic data is
the\emph{CHD3} gene, which was reported as a gene of interest in
patients with neurodevelopmental disorders in several large cohort
studies (O'Roark, 2012; Iossifov, 2014; DDD, 2016), but did not meet our
criteria for characterization until a case series with detailed
phenotypic data five years after the first published report (Snijders
Blok, 2017). Robust case series like this are needed to garnish enough
evidence to meet the strict criteria for gene-disease characterization.
However, these manuscripts are more likely to take longer to publish,
delaying the necessary evidence for characterization to be available.
This delay is most profound for gene-disease associations in extremely
rare cases that are less conducive to cohort studies and disorders with
clinical heterogeneity which require large cohorts to identify the
phenotypic commonalities. Collaborative efforts such as GeneMatcher are
needed to power these studies.

By involving diagnostic laboratories in research collaborations there is
also a direct clinical impact that results in diagnostic findings going
to patients faster. We previously found that clinically relevant
reclassification results were issued faster for publications on which we
were collaborators compared to genes for which we were not included on
the publication (internal data). Involvement of diagnostic laboratories
in data-sharing platforms like the Matchmaker Exchange may shorten the
time from publication to reclassification report issuance.

\textbf{Next steps}

Trio testing is powerful for the identification of \emph{de novo} and
biallelic alterations confirmed \emph{in trans} . In fact, 90.53\% of
our GeneMatcher entries were for autosomal dominant \emph{de
novo}occurrences or autosomal recessive reports. Future identification
of elusive disease genes, such as those with lower penetrance or complex
inheritance patterns, will require extensive involvement in data-sharing
initiatives to further power disease gene discovery efforts. This will
be even more important as clinical DES use extends into varied clinical
settings. For example, expanding the clinical uses into the prenatal
clinic may ascertain more severe phenotypes not compatible with life and
therefore yet to be identified. As more commercially available panels
are run on an exome backbone, the ability to tailor gene content based
on the latest literature and proactively provide reclassification
results can extend to other testing platforms. Ongoing participation in
GeneMatcher and other Matchmaker Exchange platforms will heighten the
diagnostic and disease gene discovery power of DES.

\textbf{Conclusion}

Due to the high volumes of DES data generated and the clinical data
curation efforts needed for accurate variant classifications, diagnostic
laboratories are a powerful partner for clinical research initiatives,
especially those involving disease gene discovery efforts. GeneMatcher
is a data-sharing initiative for identifying and collecting
genotype-phenotype paired data for gene-disease characterization and has
been crucial in our laboratory's gene characterization efforts. In turn,
this has a direct and meaningful impact on the diagnostic rates for
clinical genetic testing both for prospective tests and retrospectively
providing reclassification of variants to previously tested individuals.

\textbf{Acknowledgments}

We are thankful for all the families for their participation, and for
the clinicians for sharing the clinical histories and playing a critical
role in recruitment, and researchers for their ongoing collaboration.
Thank you to Sha Tang, Zoe Powis, and Taylor Cain for their work with
GeneMatcher entry evaluations and submissions.

\textbf{Data Availability}

Ambry Genetic supports open data sharing. All data supporting the work
presented here can be found at www.genematcher.org or in the
supplemental files attached. Further, classified alterations identified
during diagnostic testing are deposited into ClinVar.

\textbf{Ethics approval}

All HIPAA-covered patient identifiers were removed. Western
Institutional Review Board determined the study to be exempt from the
Office for Human Research Protections Regulations for the Protection of
Human Subjects (45 CFR 46) under category 4. Retrospective data analysis
of anonymized data exempted the study from the requirement to receive
consent from patients.



\textbf{References}

Bamshad, M. J., Nickerson, D. A., \& Chong, J. X. (2019). Mendelian Gene
Discovery: Fast and Furious with No End in Sight.~\emph{American journal
of human genetics} ,~\emph{105} (3), 448--455.
https://doi.org/10.1016/j.ajhg.2019.07.011

Boycott, K. M., Hartley, T., Biesecker, L. G., Gibbs, R. A., Innes, A.
M., Riess, O., Belmont, J., Dunwoodie, S. L., Jojic, N., Lassmann, T.,
Mackay, D., Temple, I. K., Visel, A., \& Baynam, G. (2019). A Diagnosis
for All Rare Genetic Diseases: The Horizon and the Next
Frontiers.~\emph{Cell} ,~\emph{177} (1), 32--37.
https://doi-org./10.1016/j.cell.2019.02.040

Chong, J. X., Buckingham, K. J., Jhangiani, S. N., Boehm, C., Sobreira,
N., Smith, J. D., Harrell, T. M., McMillin, M. J., Wiszniewski, W.,
Gambin, T., Coban Akdemir, Z. H., Doheny, K., Scott, A. F.,
Avramopoulos, D., Chakravarti, A., Hoover-Fong, J., Mathews, D., Witmer,
P. D., Ling, H., Hetrick, K., \ldots{} Bamshad, M. J. (2015). The
Genetic Basis of Mendelian Phenotypes: Discoveries, Challenges, and
Opportunities.~\emph{American journal of human genetics} ,~\emph{97}
(2), 199--215. https://doi-org./10.1016/j.ajhg.2015.06.009

Deciphering Developmental Disorders Study (2017). Prevalence and
architecture of de novo mutations in developmental
disorders.~\emph{Nature} ,~\emph{542} (7642), 433--438.
https://doi.org/10.1038/nature21062

Farwell, K. D., Shahmirzadi, L., El-Khechen, D., Powis, Z., Chao, E. C.,
Tippin Davis, B., Baxter, R. M., Zeng, W., Mroske, C., Parra, M. C.,
Gandomi, S. K., Lu, I., Li, X., Lu, H., Lu, H. M., Salvador, D., Ruble,
D., Lao, M., Fischbach, S., Wen, J., \ldots{} Tang, S. (2015). Enhanced
utility of family-centered diagnostic exome sequencing with inheritance
model-based analysis: results from 500 unselected families with
undiagnosed genetic conditions.~\emph{Genetics in medicine : official
journal of the American College of Medical Genetics} ,~\emph{17} (7),
578--586. https://doi.org/10.1038/gim.2014.154

Farwell Hagman, K. D., Shinde, D. N., Mroske, C., Smith, E., Radtke, K.,
Shahmirzadi, L., El-Khechen, D., Powis, Z., Chao, E. C., Alcaraz, W. A.,
Helbig, K. L., Sajan, S. A., Rossi, M., Lu, H. M., Huether, R., Li, S.,
Wu, S., Nuñes, M. E., \& Tang, S. (2017). Candidate-gene criteria for
clinical reporting: diagnostic exome sequencing identifies altered
candidate genes among 8\% of patients with undiagnosed
diseases.~\emph{Genetics in medicine : official journal of the American
College of Medical Genetics} ,~\emph{19} (2), 224--235.
https://doi-org/10.1038/gim.2016.95

Hicks, A. N., Lorenzetti, D., Gilley, J., Lu, B., Andersson, K. E.,
Miligan, C., Overbeek, P. A., Oppenheim, R., \& Bishop, C. E. (2012).
Nicotinamide mononucleotide adenylyltransferase 2 (Nmnat2) regulates
axon integrity in the mouse embryo.~\emph{PloS one} ,~\emph{7} (10),
e47869. https://doi-.org/10.1371/journal.pone.0047869

Iossifov, I., O'Roak, B. J., Sanders, S. J., Ronemus, M., Krumm, N.,
Levy, D., Stessman, H. A., Witherspoon, K. T., Vives, L., Patterson, K.
E., Smith, J. D., Paeper, B., Nickerson, D. A., Dea, J., Dong, S.,
Gonzalez, L. E., Mandell, J. D., Mane, S. M., Murtha, M. T., Sullivan,
C. A., \ldots{} Wigler, M. (2014). The contribution of de novo coding
mutations to autism spectrum disorder.~\emph{Nature} ,~\emph{515}
(7526), 216--221. https://doi.org/10.1038/nature13908

Myers, K. A., Johnstone, D. L., \& Dyment, D. A. (2019). Epilepsy
genetics: Current knowledge, applications, and future
directions.~\emph{Clinical genetics} ,~\emph{95} (1), 95--111.
https://doi-org/10.1111/cge.13414

Lukacs, M., Gilley, J., Zhu, Y., Orsomando, G., Angeletti, C., Liu, J.,
Yang, X., Park, J., Hopkin, R. J., Coleman, M. P., Zhai, R. G., \&
Stottmann, R. W. (2019). Severe biallelic loss-of-function mutations in
nicotinamide mononucleotide adenylyltransferase 2 (NMNAT2) in two
fetuses with fetal akinesia deformation sequence.~\emph{Experimental
neurology} ,~\emph{320} , 112961.
https://doi.org/10.1016/j.expneurol.2019.112961

O'Roak, B. J., Stessman, H. A., Boyle, E. A., Witherspoon, K. T.,
Martin, B., Lee, C., Vives, L., Baker, C., Hiatt, J. B., Nickerson, D.
A., Bernier, R., Shendure, J., \& Eichler, E. E. (2014). Recurrent de
novo mutations implicate novel genes underlying simplex autism
risk.~\emph{Nature communications} ,~\emph{5} , 5595.
https://doi.org/10.1038/ncomms6595

Peng, Y., Shinde, D. N., Valencia, C. A., Mo, J. S., Rosenfeld, J.,
Truitt Cho, M., Chamberlin, A., Li, Z., Liu, J., Gui, B., Brockhage, R.,
Basinger, A., Alvarez-Leon, B., Heydemann, P., Magoulas, P. L., Lewis,
A. M., Scaglia, F., Gril, S., Chong, S. C., Bower, M., \ldots{} Huang,
T. (2017). Biallelic mutations in the ferredoxin reductase gene cause
novel mitochondriopathy with optic atrophy.~\emph{Human molecular
genetics} ,~\emph{26} (24), 4937--4950.
https://doi.org/10.1093/hmg/ddx377

Retterer, K., Juusola, J., Cho, M. T., Vitazka, P., Millan, F.,
Gibellini, F., Vertino-Bell, A., Smaoui, N., Neidich, J., Monaghan, K.
G., McKnight, D., Bai, R., Suchy, S., Friedman, B., Tahiliani, J.,
Pineda-Alvarez, D., Richard, G., Brandt, T., Haverfield, E., Chung, W.
K., \ldots{} Bale, S. (2016). Clinical application of whole-exome
sequencing across clinical indications.~\emph{Genetics in medicine :
official journal of the American College of Medical Genetics}
,~\emph{18} (7), 696--704. https://doi-org./10.1038/gim.2015.148

Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J.,
Grody, W. W., Hegde, M., Lyon, E., Spector, E., Voelkerding, K., Rehm,
H. L., \& ACMG Laboratory Quality Assurance Committee (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.~\emph{Genetics in
medicine : official journal of the American College of Medical Genetics}
,~\emph{17} (5), 405--424. https://doi-org/10.1038/gim.2015.30

Seaby, E. G., \& Ennis, S. (2020). Challenges in the diagnosis and
discovery of rare genetic disorders using contemporary sequencing
technologies.~\emph{Briefings in functional genomics} ,~\emph{19} (4),
243--258. https://doi-org/10.1093/bfgp/elaa009

Seaby, E. G., Rehm, H. L., \& O'Donnell-Luria, A. (2021). Strategies to
Uplift Novel Mendelian Gene Discovery for Improved Clinical
Outcomes.~\emph{Frontiers in genetics} ,~\emph{12} , 674295.
https://doi-.org/10.3389/fgene.2021.674295

Slone, J., Peng, Y., Chamberlin, A., Harris, B., Kaylor, J., McDonald,
M. T., Lemmon, M., El-Dairi, M. A., Tchapyjnikov, D.,
Gonzalez-Krellwitz, L. A., Sellars, E. A., McConkie-Rosell, A.,
Reinholdt, L. G., \& Huang, T. (2018). Biallelic mutations in FDXR cause
neurodegeneration associated with inflammation.~\emph{Journal of human
genetics} ,~\emph{63} (12), 1211--1222.
https://doi.org/10.1038/s10038-018-0515-y

Smith, E. D., Radtke, K., Rossi, M., Shinde, D. N., Darabi, S.,
El-Khechen, D., Powis, Z., Helbig, K., Waller, K., Grange, D. K., Tang,
S., \& Farwell Hagman, K. D. (2017). Classification of Genes:
Standardized Clinical Validity Assessment of Gene-Disease Associations
Aids Diagnostic Exome Analysis and Reclassifications.~\emph{Human
mutation} ,~\emph{38} (5), 600--608. https://doi-org/10.1002/humu.23183

Snijders Blok, L., Rousseau, J., Twist, J., Ehresmann, S., Takaku, M.,
Venselaar, H., Rodan, L. H., Nowak, C. B., Douglas, J., Swoboda, K. J.,
Steeves, M. A., Sahai, I., Stumpel, C., Stegmann, A., Wheeler, P.,
Willing, M., Fiala, E., Kochhar, A., Gibson, W. T., Cohen, A., \ldots{}
Campeau, P. M. (2018). CHD3 helicase domain mutations cause a
neurodevelopmental syndrome with macrocephaly and impaired speech and
language.~\emph{Nature communications} ,~\emph{9} (1), 4619.
https://doi.org/10.1038/s41467-018-06014-6

Sobreira, N., Arachchi, H., Buske, O. J., Chong, J. X., Hutton, B.,
Foreman, J., Schiettecatte, F., Groza, T., Jacobsen, J., Haendel, M. A.,
Boycott, K. M., Hamosh, A., Rehm, H. L., \& Matchmaker Exchange
Consortium (2017). Matchmaker Exchange.~\emph{Current protocols in human
genetics} ,~\emph{95} , 9.31.1--9.31.15. https://doi-org/10.1002/cphg.50

Sobreira, N., Schiettecatte, F., Boehm, C., Valle, D., \& Hamosh, A.
(2015). New tools for Mendelian disease gene identification: PhenoDB
variant analysis module; and GeneMatcher, a web-based tool for linking
investigators with an interest in the same gene.~\emph{Human mutation}
,~\emph{36} (4), 425--431. https://doi-org/10.1002/humu.22769

Strande, N. T., Riggs, E. R., Buchanan, A. H., Ceyhan-Birsoy, O.,
DiStefano, M., Dwight, S. S., Goldstein, J., Ghosh, R., Seifert, B. A.,
Sneddon, T. P., Wright, M. W., Milko, L. V., Cherry, J. M., Giovanni, M.
A., Murray, M. F., O'Daniel, J. M., Ramos, E. M., Santani, A. B., Scott,
A. F., Plon, S. E., \ldots{} Berg, J. S. (2017). Evaluating the Clinical
Validity of Gene-Disease Associations: An Evidence-Based Framework
Developed by the Clinical Genome Resource.~\emph{American journal of
human genetics} ,~\emph{100} (6), 895--906. https://doi-org.
/10.1016/j.ajhg.2017.04.015



\textbf{Figure Titles and Captions}

Figure 1: Uncharacterized gene reporting and reclassification workflow

Figure 1: Up until 2020, uncharacterized gene analysis was performed for
informative trios with an uncertain or negative result following
characterized gene analysis. Currently, uncharacterized genes are
analyzed for negative cases or for probands where the identified
characterized gene finding only accounts for part their phenotype
(`partial overlap'). Genes are continually evaluated based on the latest
literature to determine if the published evidence is sufficient to
characterize the gene-disease association. Once characterized, all
previously reported DES cases with rare variants in a gene are assessed
for clinical overlap and proactive reclassification reports are issued
to appropriate cases. Our laboratory has several workflows are
addressing uncertain results for reclassification. New gene
characterizations result in our highest rate of reclassifications.
Participation in data-sharing initiatives include GeneMatcher, ClinVar,
the Undiagnosed Disease Network, and collaborations established directly
with investigators. Only gene and variant information are shared on
these platforms with follow-up clinical details provided on a
case-by-case basis. Ordering clinicians are contacted for a promising
lead and connected to collaborators for additional data sharing and
patient consent to participate in publications or additional studies.
Other efforts for reclassification include performing reanalysis by
clinician request and implementing bioinformatic pipeline upgrades and
other technology upgrades, both of which are largely time-dependent.
Family studies and additional lab studies (i.e. RNA analysis, structural
modeling) are variant-dependent and to date not widely available for
most alterations.

Figure 2: Overall impact of gene characterization following GeneMatcher
entry

Figure 2: 480 characterized gene reports were issued as of October 2021
for gene-disease associations entered in GeneMatcher. In total, 219
(45.63\%) of these were reclassifications proactively issued by the
laboratory. 109 of these reports were upgraded reclassifications from
candidate gene reports, and 110 were reclassifications issued to
individuals who did not initially meet our candidate gene reporting
criteria. After establishing a gene-disease association, 261 additional
probands received a relevant finding in one of these genes, illustrating
the impact disease gene discoveries have on the overall DES diagnostic
rate. For 79 cases for which we issued a candidate gene report, the
gene-disease association remains uncharacterized. In 4 cases, further
review of a reported candidate gene no longer met our criteria. These
findings were removed from the proband's report, and reclassifications
were issued. Additionally, these matches were marked as `suspended' in
GeneMatcher. Currently, we have 4 cases pending review for
reclassification based on literature published this year.

Figure 3: Co-authored peer-reviewed publications

Figure 3: Since 2013, we have co-authored 105 peer-reviewed papers
describing gene-disease associations. These included descriptions of 104
unique genes. 4 genes (\emph{BPTF, FDXR, IQSEC2} , and \emph{SON} ) were
the topic of more than one paper. A comprehensive list of these
publications can be found in supplement Table 2.

\textbf{Table Title and Captions}

Table 1: Characteristics of GeneMatcher Entries

Table 1: Inheritance, zygosity and alteration types for GeneMatcher
entries are listed. Entries with homozygous or compound heterozygous
alterations reported were counted for each alteration type to accurately
record the percentage of each alteration type. There were no statistical
differences within each category.

\textbf{Supplements}

Table 1: All GeneMatcher entries with current gene-disease validity
scores and associated case count

Table 2: List of co-authored peer-reviewed publications involving
gene-disease descriptions\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`Table 1.docx`  available at \url{https://authorea.com/users/443073/articles/543141-diagnostic-testing-laboratories-are-valuable-partners-for-disease-gene-discovery-5-year-experience-with-genematcher}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

