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\begin{document}

\title{DECIPHER: Supporting the interpretation and sharing of rare disease
phenotype-linked variant data to advance diagnosis and research}



\author[1]{Julia Foreman}%
\author[1]{Simon Brent}%
\author[1]{Daniel Perrett}%
\author[1]{Andrew Bevan}%
\author[2]{Sarah Hunt}%
\author[3]{Fiona Cunningham}%
\author[1]{Matthew Hurles}%
\author[4]{Helen Firth}%
\affil[1]{Wellcome Sanger Institute}%
\affil[2]{EMBL-EBI}%
\affil[3]{European Bioinformatics Institute}%
\affil[4]{Cambridge University Hospitals NHS Foundation Trust}%


\vspace{-1em}



  
  \date{May 23, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
DECIPHER (https://www.deciphergenomics.org) is a free web platform for
sharing anonymised phenotype-linked variant data from rare disease
patients. Its dynamic interpretation interfaces contextualise genomic
and phenotypic data to enable more informed variant interpretation,
incorporating international standards for variant classification.
DECIPHER supports almost all types of germline and mosaic variation in
the nuclear and mitochondrial genome: sequence variants, short tandem
repeats, copy-number variants and large structural variants. Patient
phenotypes are deposited using Human Phenotype Ontology (HPO) terms,
supplemented by quantitative data, which is aggregated to derive
gene-specific phenotypic summaries. It hosts data from \textgreater{}250
projects from \textasciitilde{}40 countries, openly sharing
\textasciitilde{}40,000 patient records containing \textgreater{}51,000
variants and \textgreater{}172,000 phenotype terms. The rich
phenotype-linked variant data in DECIPHER drives rare disease research
and diagnosis by enabling patient matching within DECIPHER and with
other resources, and has been cited in \textgreater{}2,600 publications.
In this paper, we describe the types of data deposited to DECIPHER, the
variant interpretation tools, and patient matching interfaces which make
DECIPHER an invaluable rare disease resource.%
\end{abstract}%



\sloppy


\textbf{Title}

DECIPHER: Supporting the interpretation and sharing of rare disease
phenotype-linked variant data to advance diagnosis and research

\textbf{Authors}

Julia Foreman, Simon Brent, Daniel Perrett, Andrew P. Bevan, Sarah E.
Hunt, Fiona Cunningham, Matthew E. Hurles and Helen V. Firth

\textbf{Author's institutional affiliations}

Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge,
CB10 1SA, United Kingdom.

European Molecular Biology Laboratory, European Bioinformatics

Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United
Kingdom

East Anglian Medical Genetics Service, Cambridge University Hospitals
NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, CB2 0QQ
United Kingdom.

\textbf{Grant Numbers}

This research was funded in whole, or in part, by the Wellcome Trust
{[}Grant numbers WT206194, WT200990/Z/16/Z, WT108749/Z/15/Z{]}. For the
purpose of open access, the author has applied a CC BY public copyright
licence to any Author Accepted Manuscript version arising from this
submission.

\textbf{Abstract and Keywords}

DECIPHER (\emph{https://www.deciphergenomics.org}) is a free web
platform for sharing anonymised phenotype-linked variant data from rare
disease patients. Its dynamic interpretation interfaces contextualise
genomic and phenotypic data to enable more informed variant
interpretation, incorporating international standards for variant
classification.

DECIPHER supports almost all types of germline and mosaic variation in
the nuclear and mitochondrial genome: sequence variants, short tandem
repeats, copy-number variants and large structural variants.

Patient phenotypes are deposited using Human Phenotype Ontology (HPO)
terms, supplemented by quantitative data, which is aggregated to derive
gene-specific phenotypic summaries. It hosts data from \textgreater{}250
projects from \textasciitilde{}40 countries, openly sharing
\textasciitilde{}40,000 patient records containing \textgreater{}51,000
variants and \textgreater{}172,000 phenotype terms.

The rich phenotype-linked variant data in DECIPHER drives rare disease
research and diagnosis by enabling patient matching within DECIPHER and
with other resources, and has been cited in \textgreater{}2,600
publications. In this paper, we describe the types of data deposited to
DECIPHER, the variant interpretation tools, and patient matching
interfaces which make DECIPHER an invaluable rare disease resource.

Rare Diseases; Genetic Disorders; Genotype Phenotype Correlation;
Variant Interpretation; MatchMaker Exchange

\textbf{Main text}

\textbf{Introduction}

The population prevalence of rare disease has recently been estimated to
be 3.5--5.9\%, which equates to 263--446 million people affected
globally. A large proportion of these rare diseases, approximately 72\%,
are known to have a genetic basis (Wakap \emph{et al} ., 2020 PMID:
31527858). Advances in genomic technologies to determine causal
variants, such as whole-exome sequencing, are identifying the genetic
basis of disease for only 25--40\% of patients (Stranneheim \emph{et
al.} , 2021, Quaio \emph{et al.} , 2020, Sawyer \emph{et al.} , 2016).
As a result, many patients undergoing diagnostic genetic testing do not
receive a genetic diagnosis, and often experience long delays which have
a substantial emotional impact on the family (Miller, 2021) and
significant healthcare costs (Monroe \emph{et al} ., 2016). A genetic
diagnosis has multiple benefits for the patient and their family,
including better understanding of the prognosis, personalised treatment,
tailored management and surveillance, improved access to health and
social care, and increased reproductive choice (Wright \emph{et al} .,
2018).

The number of rare Mendelian diseases with known molecular aetiology is
estimated to be 5,000--6,000 (Hartley \emph{et al} ., 2018), however,
for the majority of disease-associated genes it is not known which
variants are disease-causing, and which are benign. Different pathogenic
variants in the same gene can cause different diseases, for example
variants in\emph{FGFR3} can cause multiple diseases including Muenke
Syndrome, Hypochondroplasia, and Achondroplasia. Different diseases
caused by variants in the same gene must be considered distinct due to
their disparate clinical presentation and differing treatment options.
The sharing of patient level variants and phenotypes is therefore
essential to accelerate our understanding of the molecular basis of
genetic disease.

DECIPHER (Firth \emph{et al.} , 2009; Swaminathan \emph{et al} ., 2012;
Bragin \emph{et al} ., 2014; Chatzimichali \emph{et al} . 2015) is a
global web-based platform which shares phenotype-linked variant data
from rare disease patients (Fig. 1A). It is freely available via a web
interface at\emph{https://www.deciphergenomics.org}. Approximately
40,000 of the patient records held by DECIPHER have explicit patient
consent for open sharing on the website (Fig. 1B). These openly shared
records contain more than 51,000 variants and more than 172,000
phenotype terms. The integration of this phenotype and variant data
enables the discovery of new gene-disease and variant-disease
relationships, driving diagnosis and our understanding of human biology.
Since DECIPHER was established in 2004, the platform has been used and
cited in more than 2,600 published manuscripts.

Patient records in DECIPHER are deposited by academic clinical centres,
which are affiliated both to a hospital which oversees the treatment of
patients with genetic disorders, and to a local university department of
human/clinical genetics. Eligible centers
(\emph{https://www.deciphergenomics.org/join/overview}) can apply to
join DECIPHER using an online application form. Data from a centre is
stored within a DECIPHER project, and a senior clinician at that centre
(clinical coordinator), sometimes in conjunction with a senior clinical
scientist (lab coordinator), has the responsibility for
approving/rejecting applications from individuals working at that centre
who wish to access the data in the project.

The platform supports the deposition of almost all types of genetic
variation, including sequence variants, short tandem repeats,
copy-number variants (CNVs) and large structural variants. Variant
interpretation interfaces are provided, including genome and protein
browsers, which contextualise genetic and phenotype information to
enable accurate interpretation. These interfaces integrate external
datasets such as the Genome Aggregation Database (gnomAD,
Karczewski\emph{et al} ., 2020), which can be used to exclude variants
seen at appreciable frequency in the general population, in addition to
disease relevant datasets such as ClinVar (Landrum \emph{et al} ., 2018
PMID: 29165669) and DECIPHER records themselves. DECIPHER also
encourages the use of global standards to promote good practice,
including the American College of Medical Genetics (ACMG) guidelines for
sequence variant interpretation (Richards \emph{et al.,} 2015) and
ACMG/ClinGen technical standards for interpreting CNVs (Riggs \emph{et
al} ., 2020).

In the following sections we present examples of the genotype/phenotype
data deposited and shared with the rare disease community. In addition
we present the tools provided by DECIPHER to assess the pathogenicity of
variants according to international standards, and the utility of
DECIPHER to map the clinically relevant parts of the genome.

\textbf{DECIPHER patient records}

DECIPHER associates variants and phenotypes through individual patient
records, each of which are connected to a particular depositing centre.
DECIPHER itself cannot re-identify individuals, and technical and
organisational measures are in place to safeguard data. These measures
are reviewed and updated in line with evolving best practice.

On deposition, each patient record is given a DECIPHER Patient ID as a
reference, which is shown on the website and forms part of the URL for
the patient record (e.g. https://www.deciphergenomics.org/patient/283351
- note that URLs of the form
https://decipher.sanger.ac.uk/patient/283351 continue to be supported).
Each patient record also has an internal ID (e.g. a lab number), which
is only displayed to users of the depositing centre. The internal ID
allows the depositing centre (only) to link the record to an individual
patient.

Through the DECIPHER platform it is possible to send a patient's
clinician a message to request further information about the patient,
for example in the case where there is a potential patient match, or if
a researcher is carrying out a functional study on the gene in which
that patient's variant is situated. Below we will describe in more
detail the clinical and research utility of this notification system.

\textbf{Deposition and breadth of sharing}

DECIPHER has been carefully designed to ensure that the depth and
breadth of sharing is proportionate to the scientific/clinical needs and
level of consent. For example, a user who does not belong to a DECIPHER
project can only access the openly shared patient data, while data which
is visible to registered users who are logged in reflects their project
and consortium memberships.

Patient genotype and phenotype data can be deposited to DECIPHER in
three ways:

\begin{enumerate}
\tightlist
\item
  Via the web interface for an individual patient's data.
\item
  By uploading Excel or csv files via the web interface (bulk upload)
  for data from multiple patients.
\item
  Using the deposition API to allow programmatic uploading of data and
  synchronisation of data across systems (e.g. synchronisation between a
  centre's electronic health records and the patient records in that
  centre's DECIPHER project).
\end{enumerate}

DECIPHER users at the depositing centre determine the sharing level of
each patient record and variant. Patient records, and individual
variants within these records, can be kept private to the depositing
centre. This allows DECIPHER's tools to be used for assessing variant
pathogenicity to inform the conversation with the patient before seeking
consent for wider sharing. With explicit patient consent, patient
records are shared openly, with the data available to anyone who visits
the website. DECIPHER also supports consortium sharing. This allows
sharing of patient records between a defined group of centres, where
there is an expectation of collaboration for patient care, again before
explicit patient consent for open sharing has been obtained. DECIPHER
currently hosts six consortia, which share more than 63,000 patient
records. Consortia include the United Kingdom National Health Service
consortium, the Deciphering Developmental Disorders (DDD) consortium
which shares research data from the DDD study (Wright \emph{et al} .,
2014), and a data-sharing consortium covering New South Wales and
Western Australia.

DECIPHER is a live interface and data deposited is available to view,
interpret, and share in real time. Patient records can be added and
edited iteratively as more information becomes available, e.g.
additional phenotype terms, the inheritance status of a variant, or new
functional data. Information can be added to a record by a clinician and
clinical scientist working asynchronously and in different locations.

\textbf{Genetic Data}

As our knowledge of rare disease genetics develops and the interaction
between genetic loci are more fully understood, there is a pressing need
for the visualization of all types of genetic variation within a single
interface. DECIPHER fulfills this need, supporting many types of genetic
variation including sequence variants, CNVs, aneuploidy, uniparental
disomy (UPD), inversions, insertions and short tandem repeats (STRs)
(Fig. 2).

\emph{Variant deposition:} Variants are deposited using genomic
coordinates. Sequence variants can also be deposited using a relevant
subset of HGVS nomenclature (den Dunnen \emph{et al} ., 2006), and will
be normalised (left aligned, parsimonious) during the deposition process
(Tan \emph{et al} ., 2015). For known STRs, the disease-relevant STR can
be selected from a dropdown in the web interface. Additional information
about the variant such as inheritance, genotype, pathogenicity, and
contribution to phenotype can also be recorded.

\emph{Mosaicism:} For \emph{de novo} mosaic variants, it is possible to
record the mosaicism observed in each tissue, as a percentage. This
information is clinically important as it can help explain the
variability of clinical symptoms, for example the difference between
nevus sebaceous or Schimmelpenning syndrome (where extracutaneous
abnormalities are present), caused by \emph{HRAS} and
\emph{KRAS}variants (Groesser \emph{et al} ., 2012).

\emph{Mitochondrial variants:} DECIPHER supports the deposition and
interpretation of variants in the nuclear and mitochondrial genomes.
Mitochondrial diseases are the most common form of inherited
neuro-metabolic disorders, and are caused by mutations in the nuclear or
mitochondrial genomes. In addition, nuclear genetic factors have been
shown to influence clinical outcomes for mitochondrial DNA mutations
(Boggan \emph{et al} ., 2019). Thus the display of both genomes in a
single interface is clinically important. In DECIPHER it is possible to
record homoplasmy or the percentage of heteroplasmy per tissue, which is
clinically essential as it has been shown to contribute to disease
progression (Grady \emph{et al} ., 2018).

\emph{Variant haplotypes:} Variants may work \emph{in cis} to create or
modify a disease allele or \emph{in trans} to cause a biallelic
disorder. For this reason DECIPHER users can assign variants to a
haplotype, e.g. for compound heterozygous variants, the variants will be
shown as \emph{in trans} . As our understanding of rare disease genetics
improves, the representation of its complexity is becoming even more
essential. It is known that genetic modifiers alleviate or exacerbate
the severity of the disease (Rahit and Tarailo-Graovac 2020) and there
are recent examples where rare pathogenic haplotypes have been shown to
cause disease, such as an albinism-causing TYR haplotype
(Campbell\emph{et al} , 2019).

\emph{Pathogenicity predictors:} For all sequence variants deposited to
DECIPHER, predictions from the Ensembl Variant Effect Predictor (VEP;
McLaren \emph{et al} ., 2016) are displayed across all Ensembl/GENCODE
transcripts. Predictions include the consequence (e.g. missense,
frameshift), the protein change, and several pathogenicity scores: SIFT
(Sim \emph{et al} ., 2012), PolyPhen-2 (Adzhubei \emph{et al} ., 2013),
CADD (Kircher \emph{et al} ., 2014), REVEL (Ioannidis \emph{et al} .,
2016), and SpliceAI (Jaganathan \emph{et al.} , 2019). DECIPHER seeks
advice from experts in the field and refers to benchmarking studies for
pathogenicity predictors (e.g. Gunning \emph{et al} ., 2021) prior to
the inclusion of additional scores, assisting in the application of good
practice.

\emph{Reference genome:} All genomic information is displayed in the
GRCh38 assembly version of the human genome, allowing the most
up-to-date genome and transcript information to be used to enable
accurate variant interpretation. The display of genomic data in GRCh38
permits DECIPHER to promote the use of Matched Annotation from NCBI and
EMBL-EBI (MANE) transcripts, where the RefSeq and Ensembl/GENCODE
transcripts from a protein-coding gene pair are identical (5' UTR,
coding region, and 3' UTR). DECIPHER currently promotes and highlights
MANE Select transcripts, one high-quality representative transcript per
protein-coding gene that is well-supported by experimental data and
represents the biology of the gene
(https://tark.ensembl.org/web/mane\_project). Describing variants
relative to a single, recommended transcript, along with sequence
variant normalisation, assists in the standardisation of variant
reporting.

\emph{Reference conversion tools:} Deposition with GRCh37/hg19
coordinates is still supported: prior to normalisation, DECIPHER remaps
GRCh37 coordinates onto the GRCh38 assembly, using an algorithm based on
the UCSC LiftOver tool
(\emph{https://genome.ucsc.edu/cgi-bin/hgLiftOver}, Kuhn \emph{et al} .,
2013). A range of tools are also provided to allow users to visualise
the differences between assemblies. These include GRCh37 and GRCh38
comparative genome browsers, gene lists for variants lifted over by
DECIPHER which display genes that no longer overlap the variant, and a
liftover mapping genome browser track (Fig. 3).

\textbf{Phenotypic Data}

DECIPHER supports detailed phenotype data capture (Fig. 4A) which
enables the in-depth comparison of patient phenotypes, as well as the
delineation of new syndromes. Much of the phenotype is represented using
Human Phenotype Ontology (HPO) terms - a standardised, controlled
vocabulary which supports deep phenotyping (K\selectlanguage{ngerman}öhler \emph{et al} .,
2019). This allows phenotypic information to be described unambiguously,
and for phenotypic similarity between patients to be established
computationally by comparing related terms in the ontology. This is
essential for finding potential patient matches. The DECIPHER phenotype
deposition interface provides a search tool, allowing HPO terms to be
added to a patient record quickly and easily. DECIPHER also supports the
recording of the absence of clinically relevant phenotypes, and of
manifestations of HPO terms (clinical modifiers), such as severity, age
of onset, and pace of progression. This information can be helpful to
users trying to determine the accuracy of a patient match, especially
when the number of patient phenotypes is small.

In collaboration with ophthalmologists, DECIPHER has developed forms for
groups of HPO phenotypes for the eye community, to assist phenotyping in
the clinic. These forms contain a predetermined list of HPO terms which
can be marked absent or present, and include common retinal and
non-retinal disease, and symptoms and signs (extraocular features,
ocular features, and electrodiagnostic testing and imaging).

\emph{Family history:} In the case of inherited disorders, it is
important to capture family phenotype history. In DECIPHER, users can
record whether or not relevant family members are affected with similar
or related phenotypes. Presence of absence of HPO terms can also be
indicated for each family member if known.

\emph{Quantitative data:} In addition to HPO terms, DECIPHER supports
quantitative phenotype data capture (Fig. 4B). Developmental milestones
(age of social smile, sat independently, walked independently, first
words) and anthropometric measurements (growth, visual function, fundus
imaging) can be deposited. Aggregated observations from open-access
patient records are shared openly (See Quantitative phenotypic data to
confirm fit with diagnosis section and Fig. 6A). DECIPHER also provides
an interface to record birth and pregnancy information, such as age of
the mother/father at birth of the patient, consanguinity, maternal
illness, and gestation (which is also used to adjust growth charts);
this information is not currently shared openly, but is shared within a
consortium.

\textbf{Genotypic summaries to assist variant interpretation}

DECIPHER provides a suite of tools to assist in assessing the
pathogenicity of variants, including genome and protein browsers.

\emph{Protein browser:} A protein browser is available for
protein-coding genes, showing a genotypic summary which helps users to
determine if a variant is located in a mutational hot spot or
established functional domain (Fig. 5A). The protein browser is fully
interactive and is customisable via a settings menu. In the centre of
the protein browser, Pfam domains (Mistry \emph{et al} ., 2021) are
displayed allowing users to identify distinct functional/structural
elements of the protein. Clinically relevant variants from DECIPHER and
ClinVar are plotted above and below the Pfam domains, with annotated
pathogenicity and predicted molecular consequence (e.g. missense, likely
loss-of-function (LOF)) indicated through colouring. In addition to the
location of the variants being shown, for likely LOF variants, the
location of the protein truncating codon is indicated, since this
information is essential in determining if a transcript will escape
nonsense mediated decay (NMD). A predicted (NMD) track is also
displayed. The location of variation in the general population is shown
through display of gnomAD missense and LOF tracks. Regional missense
constraint data is also available (regional missense constraint improves
variant deleteriousness prediction, Samocha \emph{et al} .,
https://www.biorxiv.org/content/10.1101/148353v1), in addition to exon
structure. Protein secondary structures (e.g. locations of helices and
turns) and the locations of 3D structures (where available from the
Protein Data Bank in Europe (PDBe)) are displayed at the bottom of the
protein browser. Clicking on these 3D structures will display an
interactive 3D protein viewer (Marco Biasini, 2015, pv v1.8.1. Zenodo.
https://doi.org/10.5281/zenodo.20980) which provides zooming, panning
and rotation, and hovering over an amino acid with a pointing device
identifies the visualised amino acid and position (similar behaviour
exists for ligands). DECIPHER variants are shown in this 3D view,
allowing users to determine, for example if the variants are all within
a DNA binding pocket or enzyme active site.

When looking at the protein browser from a patient record with a
sequence variant, the location of the patient's variant is displayed by
a vertical line, allowing easy orientation. In the case of a patient
with a CNV, the protein browser is accessible from the CNV's genes tab,
which displays a table of genes that overlap the CNV, along with other
relevant information such as gene/disease association information and
predictive scores. Clicking on a row displays further information about
that gene, including the protein browser. An additional track is shown
on the protein browser, indicating which part of the protein overlaps
the CNV.

\emph{Genome browser:} The Genoverse genome browser
(\emph{http://genoverse.org}), developed by the DECIPHER team, is a
portable, interactive, customizable genome browser which allows the user
to explore data. It displays a number of tracks containing information
relevant to variant pathogenicity assessment such as genes associated
with disease phenotypes (as curated and maintained by Online Mendelian
Inheritance in Man (OMIM,\emph{https://omim.org}, Amberger et\emph{al.,}
2019), protein ortholog sequences from Ensembl indicating conversation,
transcripts (as maintained by Ensembl), and regional missense
constraint. Information from population resources such as gnomAD and
Database of Genomic Variants (DGV) Gold (Church \emph{et al} ., 2010)
are displayed to enable users to determine if their patient's variant
has been observed in healthy individuals. Disease relevant variant
tracks are also available, which include DECIPHER sequence variants and
CNVs, ClinVar sequence and structural variants, and variants from Human
Gene Mutation Database (HGMD) public (Stenson\emph{et al} ., 2020). The
tracks which are displayed by default are tailored according to the type
of variant being assessed.

\textbf{Tools supporting diagnostic assessment}

\textbf{Assessing pathogenicity according to international standards}

DECIPHER supports the annotation and sharing of variant pathogenicity
using ACMG guidelines for sequence variants and ACMG/ClinGen technical
standards for CNVs, which helps to standardise the classification of
variants across centres. When interpreting a CNV it is possible for
users to choose to assess the variant using sequence variant guidelines,
which may be more applicable for small CNVs since the distinction
between a sequence variant and a CNV is blurred (Brandt \emph{et al} .,
2019).

\emph{Criteria selection:} In both pathogenicity interfaces (Fig. 5A,
5B), types of evidence (such as population data and functional data) are
displayed, along with the relevant evidence criteria used to determine
if data supports the variant being pathogenic or benign. Relevant
criteria can be selected with a single click. Some of the criteria have
additional information links. These either provide information about how
the criteria can be used according to the original paper (e.g. \emph{de
novo} CNV evidence), or in the case of sequence variants they provide
information about ClinGen Sequence Variant Interpretation (SVI) Working
Group guidelines (e.g. recommendation for functional assays (PS3/BS3)
Brnich \emph{et al} ., 2019). As new guidelines become available these
pathogenicity interfaces are updated to provide the latest relevant
recommendations. Criteria strengths can be modified as required in the
interface.

\emph{Relevant evidence:} Within the interfaces there is a customised
section displaying `evidence to consider' which provides information
relating to the specific evidence type being assessed. For example, for
computational and predictive data evidence, predictive pathogenicity
scores (SIFT, PolyPhen-2, CADD, REVEL, SpliceAI) are displayed. Links
are also provided to relevant DECIPHER interpretation interfaces, for
example to the in-built tolerated population variation calculator, which
can be used to determine if a variant observed in the reference sample
is too common to cause a given Mendelian disorder of interest
(Whiffin\emph{et al} ., 2017). External links (e.g. PubMed literature
search) are also provided.

\emph{Calculation of variant pathogenicity:} As criteria are added,
DECIPHER uses these to calculate the variant pathogenicity. For sequence
variants, this is calculated according to the combining rules detailed
in the original 2015 ACMG guidelines. In addition, DECIPHER calculates
the posterior probability of pathogenicity and classification according
to the ClinGen SVI Working Group's Bayesian classification framework,
which provides a mathematical foundation for the combining rules
(Tavtigian \emph{et al} ., 2018). DECIPHER highlights cases where these
classifications disagree, and ultimately all pathogenicity assessments
are made by depositors using their professional discretion. For CNVs,
the evidence can be scored according to ACMG/ClinGen technical standards
instead.

\emph{ClinGen Expert Panel specifications:} For some genes there are
ClinGen Variant Curation Expert Panel Specifications, which recommend
adaptations of the sequence variant ACMG guidelines (e.g. Rett and
Angelman-like Disorders Variant Curation Expert Panel for \emph{MECP2}
,\emph{CDKL5} , \emph{FOXG1} , \emph{UBE3A} , \emph{SLC9A6,}
and\emph{TCF4} ). When interpreting variants in genes for which these
recommendations exist, detailed information about how to apply the
criteria is provided along with a link to the relevant Clinical Domain
Working Group, so that patients with variants in these genes benefit
from interpretation in accordance with these recommended standards.

\textbf{Confirming variant-phenotype association and diagnosis}

DECIPHER provides an assessment interface (Fig. 5D) which is designed to
be used in a multidisciplinary team meeting to evaluate whether one or
more variants explain the clinical features seen in a patient, and
record if a diagnosis has been made (or excluded). Depositors can report
evidence from several evidence lines, such as the age at presentation or
additional clinical investigation, to weigh evidence for or against a
genotype-phenotype relationship. An OMIM gene-disease pair and assertion
is recorded, for example `genetic diagnosis confirmed', `uncertain
genetic diagnosis', or `non-penetrant (or pre-symptomatic) for a
dominant genetic disorder'. The output of the assessment is a
date-stamped report providing the patient's variants and phenotypes, in
addition to the diagnosis and evidence on which that diagnosis was made.

There are many published examples of patients having compound phenotypes
due to pathogenic variants in more than one gene, for example, in
Ferrer\emph{et al} ., 2019, the patient had three independent rare
disease diagnoses due to pathogenic variants in \emph{SIN3A}
(Witteveen--Kolk syndrome), \emph{FLG} (dermatitis), and \emph{EDAR}
(ectodermal dysplasia). A recent study has suggested that multiple
molecular diagnoses occur in approximately 5\% of cases (Posey \emph{et
al} ., 2017). The assessment interface allows multiple assessments to be
created for a patient, allowing the genetic basis of compound phenotypes
to be recorded and shared.

\textbf{Quantitative phenotypic data to confirm fit with diagnosis}

\emph{Quantitative phenotype data and gene-specific centile
charts:}Quantitative phenotype data (developmental milestones or
anthropometric measurements) can be recorded in DECIPHER, and are
aggregated on a gene-by-gene basis and shared openly (Fig. 6A). In order
for this information to be shown for a given gene there must be at least
five patients with both quantitative phenotype data and openly shared
sequence variants annotated as pathogenic/likely pathogenic. Once this
threshold is met, DECIPHER automatically aggregates and shares the
information as a series of graphs on which expectations for the
predominantly healthy population (`Normal'), the DECIPHER population as
a whole, and the gene-specific data is plotted. Anthropometric
measurements are plotted around the standard deviation (adjusted for sex
and gestation, where possible), while developmental milestones are
plotted against time. The standard deviation for each population is
displayed at the bottom of the graph as a boxplot. For users logged into
DECIPHER and looking at a patient record from their centre, a vertical
line indicates their patient's measurement or age at attainment of the
milestone, allowing them to easily judge whether it is consistent with a
pathogenic/likely pathogenic variant in the gene. The display of the
DECIPHER population allows users to determine if a particular
measurement is particularly discriminative for a given disorder. These
gene-specific centile charts can also be used in the clinic to determine
how a child is developing relative to other children with the same
disorder.

\emph{Composite facial images:} For certain genes there also are
composite faces, which highlight facial dysmorphologies specific to a
gene. These anonymised composite face images have been created from
individuals with \emph{de novo} mutations in the affected genes that
were collected through the DDD study (Deciphering Developmental
Disorders, Study, 2017).

\textbf{Identifying patient matches to supporting diagnosis}

\textbf{Matchmaking within DECIPHER}

The phenotype-linked variant data in DECIPHER allows for effective
patient matching. DECIPHER presents a powerful, flexible matching
patient interface (Fig. 6B), which allows users to view DECIPHER records
which overlap a deposited copy-number, sequence, or insertion variant,
or a gene. The matching patient interface displays useful summary
information about the potential matches, for example, for sequence
variants, this is consequence, inheritance, and pathogenicity. To allow
users to quickly identify the most prominent clinical features in
overlapping patients, a list of phenotypes present in multiple matching
patients is displayed. When viewing this interface from a patient
record, additional lists showing which of the patient's phenotypes are
present or not recorded in matching patients are also displayed. This
allows users to easily determine if there is a good phenotypic match
between their patient and other matching patients. Beneath this is a
table containing information about the individual matching patient
records. The table columns can be sorted and all matching phenotypes are
shown in bold.

\emph{Customisable data display:}A series of filters are provided in the
matching patient interface so that users can drill into the most
relevant patient data. This allows users to filter on, for example,
functional similarity, consequence, inheritance, and/or pathogenicity.
This can be particularly useful when different variant consequences are
associated with different syndromes (e.g. \emph{SCN2A} , where loss of
function variants are associated with nonspecific severe intellectual
disability, and missense variants with infantile epileptic
encephalopathy).

\emph{Functionally identical variants:} If the same variant has
previously been deposited to DECIPHER, a `Functionally Identical
Variant' interface is present, displaying variant pathogenicity and
evidence, in addition to phenotype information from these patient
records. This ensures that users are alerted to other patients carrying
the same variant, and assists in the standardisation of variant
classification across centres.

\emph{Discriminative phenotypes:} The wealth of the phenotype-genotype
linked data in DECIPHER also allows the aggregation of data associated
with pathogenic variants in disease genes. Within DECIPHER, aggregated
phenotype data is used to identify the most discriminating phenotypes
associated with disease genes (Fig. 6C). Recognising distinctive
clinical characteristics associated with a disorder can be key to a
diagnosis. The interface presents a table displaying the percentage of
phenotyped patients with sequence variants in a gene of interest with a
particular phenotype, compared with the percentage of phenotyped
patients in DECIPHER with the same phenotype, and the odds ratio
and\emph{p} -value from a Fisher's exact test, which indicate the most
discriminative phenotypes associated with a gene.

\emph{Clinician contact:} If a matching patient is discovered, it is
possible to contact the clinician responsible for the patient's care
through DECIPHER. DECIPHER depositors are able to send messages
directly, and since October 2014, over 4,500 collaboration requests have
been sent amongst these registered DECIPHER users. In the case where a
user is not registered with DECIPHER, the DECIPHER team first moderates
such contact requests, and if the request appears to be legitimate and
appropriate, forwards the message to the clinician responsible for the
patient, asking them to contact the requestor directly to discuss
collaboration. Over 2,900 such contact requests have been sent since
January 2018.

\textbf{Matchmaking through the Matchmaker Exchange}

DECIPHER is a founding member of the Matchmaker Exchange (MME,
https://matchmakerexchange.org), a Global Alliance for Genomics and
Health (GA4GH) driver project which enables the federated discovery of
similar rare disease patient data in connected databases. This worldwide
collaboration allows automated matchmaking of genetic and/or phenotypic
data between databases, via an application programming interface (API).
Through MME, DECIPHER is currently connected to Broad-seqr
(https://seqr.broadinstitute.org/matchmaker/matchbox, Arachchi et al.,
2018), GeneMatcher (https://genematcher.org, Sobreira\emph{et al} .,
2015), MyGene2 (https://www.mygene2.org/, MyGene2, 2016), PhenomeCentral
(https://phenomecentral.org, Buske\emph{et al} ., 2015) and RD-Connect
(https://platform.rd-connect.eu, Lochmüller \emph{et al} ., 2018). Since
2020, DECIPHER depositors have made approximately 1,500 requests for
matches from connected databases and received details of more than 4,100
potential patient matches. In the same time period, DECIPHER has
received more than 55,000 requests for matches from connected databases,
and has returned details of more than 255,000 potential patient matches.

Within DECIPHER, users with write access to a patient are able to query
the MME. It is essential that the patient record in DECIPHER has
explicit consent for open sharing, as some connected databases have dual
notification i.e. they provide their user with details of any potential
patient match, and unshared patient records will not be available to
users of the other databases. Once MME is queried and the connected
databases have responded, details of potential patient matches are
displayed within the DECIPHER interface. Potential matches from each
database are displayed in a tabular format with matching phenotypes in
bold, assisting users in determining the level of phenotype similarity
(Fig. 6D). DECIPHER supports the querying of MME for patients with at
least one open-access sequence or a copy-number variant which overlaps
one gene. Other types of variants present in the patient record will not
be included in the MME request.

When a MME request is sent to DECIPHER which contains genomic
information, all open-access patient sequence or copy-number variants
which overlap a single gene, and all DDD consortium research variants
(see Driving rare disease research section) are evaluated for similarity
based on functional overlap. Many of the variant requests received from
connected databases provide genomic coordinates in GRCh37, and in these
cases DECIPHER performs liftover to convert the coordinates to GRCh38
prior to identifying matches. A score for each potential patient match
is provided, ranging from 0 to 1, with 1 indicating a better match.
DECIPHER's scoring algorithm for genomic matches takes into account the
Ensembl VEP predicted consequence, assessing the severity and similarity
of the consequence to those provided in the request.

If only phenotypic data is provided, all open-access patients with
phenotypes are evaluated for a match. This takes into account all HPO
ancestor terms for both the patient in the request, and patients within
DECIPHER. These matches are scored by generating an Intersection over
Union score comparing the HPO ancestor terms of the request patient and
the patient in DECIPHER.

DECIPHER returns the 20 highest scoring matches per MME request. In the
case where there are many matches, the patients' chromosomal sex is
taken into account in addition to the score, in order to prioritise the
best possible matches. The returned matches include variant, phenotype
(including absent phenotypes) and diagnosis information.

\textbf{Driving rare disease research}

The \textasciitilde{}40,000 openly consented patient records in DECIPHER
contain more than 51,000 variants and \textasciitilde{}172,000
phenotypes, and represents a rich dataset to drive rare disease
research. Since its inception in 2004, DECIPHER has been cited more than
2,600 times in peer reviewed publications (Fig. 7A); a testimony to its
impact on rare disease research. In some cases there is a large
genotypic patient series, which allows, for example, the full spectrum
of phenotypes associated with a gene to be recognised. At the time of
writing, the genes with the most open access sequence variants
were\emph{NF1} (162), \emph{ANKRD11} (123), \emph{ARID1B}
(107),\emph{KMT2A} (107), and \emph{DDX3X} (78) (Fig. 7B).

\emph{Search:} To identify the most relevant patient records and gene
information DECIPHER offers a powerful search function allowing users to
search using many different categories including gene, phenotype, HPO
identifier, genomic position (in GRCh37 or GRCh38), chromosome band,
pathogenicity, and inheritance. Advanced searches are supported, such as
searching for multiple terms either from the same category (e.g.
multiple phenotypes) or different categories (e.g. gene plus phenotype).
Results are displayed in a tabular format, in addition to genome
browser-based representations.

\emph{Driving discovery:} The genotype-linked phenotypic data allows,
for example, new variant-disease associations to be discovered, such as
loss-of-function variants in \emph{ARFGEF1} causing developmental delay
and epilepsy (Thomas \emph{et al} ., 2021). The dataset also enables the
extension of phenotypes for new syndromes to be uncovered (e.g.
Witteveen-Kolk syndrome a \emph{SIN3A} -related disorder
Balasubramanian\emph{et al} ., 2021), in addition to well established
syndromes (e.g.\emph{ALG13} congenital disorder of glycosylation
Alsharhan \emph{et al} ., 2021). It also permits the understanding of
contiguous gene effects, such as that around \emph{ERF} which causes a
novel craniosynostosis syndrome with varying degrees of intellectual
disability (Calpena \emph{et al} ., 2021).

\emph{DDD Research variants:} In addition to the openly consented
patient data, DECIPHER openly shares the DDD research variants, which
are variants of unknown significance identified in undiagnosed probands
with developmental disorders in the DDD study. These include
functional\emph{de novo} variants and rare loss-of-function homozygous,
compound heterozygous, and hemizygous variants in genes that are neither
developmental disorder genes, nor OMIM-morbid genes. At present this
dataset comprises nearly 5,000 variants. High-level phenotype terms are
provided for each variant (Fig. 7C). The number of patients with each
variant in the DDD dataset is displayed, in addition to the number of
patients identified in the GeneDx and Radboud University Medical
Center\emph{de novo} variant dataset as described by Kaplanis \emph{et
al.} , 2020. This dataset enables the discovery of new gene-disease
associations.

\emph{Bulk data for research:} The openly consented patient data is
available for bulk download for research purposes, subject to a data
access agreement. In bulk the data can be used, for example, for
developing new analytical methods, in understanding patterns of
polymorphism, and in refining critical intervals to map genes involved
in specific phenotypes and diseases. The dataset has recently been used
to associate phenotypes with functional systems (Jabato \emph{et al} .,
2021), and to develop a new tool to assist clinical interpretation of
CNVs (Requena \emph{et al} ., 2021). DECIPHER also shares the data in
bulk for display, subject to a Data Display Agreement. This allows
third-party variant analysis companies and the academic genome browser
providers such as Ensembl and UCSC to display the data, maximising the
possibility of finding patient matches.

\textbf{Summary}

DECIPHER is a free web-based platform which enables the visualisation of
genomic and phenotypic relationships to aid variant interpretation,
diagnosis, and discovery. The platform supports the interpretation and
sharing of almost all types of genetic variation, providing variant
interpretation interfaces which contextualise the genotypic and
phenotypic data. These interfaces include a genome browser, protein
browser, matching patient variant displays, and tools to assess the
variant according to internationally-accepted standards. Potential
matching patients in other connected databases can also be identified
through the MME. The platform enables the flexible and proportionate
sharing of patient-level data, so that the depth and breadth of sharing
is tailored to the scientific/clinical needs and the level of patient
consent attained. DECIPHER currently openly shares
\textasciitilde{}40,000 rare disease patient records, and supports the
more limited sharing of \textgreater{}63,000. DECIPHER is under
continuous development, ensuring that it keeps up to date with the fast
moving field of rare genetic disease. New user-facing features are
released approximately every six weeks, along with updates to reference
data sources (such as the Ensembl/GENCODE gene set, HPO, ClinVar).
DECIPHER enables clinical use of selected new datasets and tools
developed by the research community. This makes them directly available
to clinicians and clinical scientists, thereby assisting in the rapid
translation of research into the diagnostic arena. Since its inception
in 2004, the platform has made a huge impact on rare genetic disease
research, and is cited in more than 2,600 publications. The rich
phenotype-linked variant data hosted by DECIPHER, and the tools it
provides, enable DECIPHER to advance its mission of mapping the
clinically relevant parts of the genome.

\textbf{Acknowledgements}

The authors thank the patients and their families for their permission
to include their information in DECIPHER. The authors would also like to
thank all registered DECIPHER users for depositing and seeking consent
to share patient data. The DECIPHER project was given a favourable NRES
REC opinion by Cambridge South (previously Cambridgeshire 4 REC), REC
reference 04/MRE05/50, in November 2004. DECIPHER submits annual
progress reports to ensure this favourable opinion applies for the
duration of the research. DECIPHER is supported by Wellcome funding,
grant WT206194. Helen Firth is supported by The Wellcome award
WT200990/Z/16/Z Designing, developing and delivering integrated
foundations for genomic medicine. Fiona Cunnigham and Sarah E. Hunt
receive funding from the Wellcome Trust (grant number WT108749/Z/15/Z)
and the European Molecular Biology Laboratory. This research was funded
in whole, or in part, by the Wellcome Trust {[}Grant numbers WT206194,
WT200990/Z/16/Z, WT108749/Z/15/Z{]}. For the purpose of open access, the
author has applied a CC BY public copyright licence to any Author
Accepted Manuscript version arising from this submission. Matthew Hurles
is a co-founder, shareholder and non-executive director of Congenica
Ltd., a diagnostic software company. Data sharing not applicable to this
article as no datasets were generated or analysed during the current
study.

\textbf{Conflict of Interests}

Matthew Hurles is a co-founder, shareholder and non-executive director
of Congenica Ltd., a diagnostic software company.

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\textbf{Figure legends}

\textbf{Figure 1. A:} The DECIPHER community is a global network of
academic clinical centres with expertise in genetics. Depositing centres
are able to send messages directly to other registered users about
patient matches through DECIPHER. Since October 2014 over 4,500 such
messages have been sent. Here, each line represents a collaboration
request sent between depositing centres. Unregistered users' messages,
sent through DECIPHER, are not included in this image. \textbf{B:} The
DECIPHER database currently openly shares approx. 40,000 rare disease
patient records, built up over time.

\textbf{Figure 2.} DECIPHER supports the deposition and sharing of
almost all types of genetic variation.

\textbf{Figure 3.} All genomic data is visualised in GRCh38, but
deposition is still supported in GRCh37/hg19. Tools are provided to
visualise the differences between assemblies. These include comparative
genome browsers and gene lists for variants lifted over by DECIPHER, and
a liftover mapping genome browser track.

\textbf{Figure 4. A:} DECIPHER enables the deposition of phenotypes
using HPO terms. \textbf{B:} DECIPHER supports the deposition of
developmental milestones and anthropometric measurements, e.g.
occipitofrontal (head) circumference.

\textbf{Figure 5. A:} DECIPHER has developed a protein browser which
summarises genotypic data. Tracks include: Pfam domains, DECIPHER and
ClinVar variants, gnomAD variants, and region of predicted nonsense
mediated decay (NMD) escape. \textbf{B:} DECIPHER supports the
annotation and sharing of sequence variant pathogenicity assessments
using ACMG guidelines. A pathogenicity evidence interface is available
for depositors. Relevant criteria are selected by clicking on the
criteria displayed on the left under ``Available evidence types''.
``Selected criteria'' are displayed on the right, along with ``Evidence
to consider''. ``Further information'' links provide recommendations for
the use of criteria. In this example, a variant in \emph{SLC9A6} is
being annotated and ClinGen Variant Curation Expert Panel Specifications
exist for this gene. Detailed information about these recommendations
are displayed by clicking on the ``Gene recommendation'' links - expert
panel recommendations for \emph{de novo} criterion PS2 are displayed. As
criteria are added, DECIPHER calculates the variant pathogenicity
according to criteria-combining rules detailed in the original 2015
guidelines, and according to the ClinGen SVI Working Group's Bayesian
classification framework. \textbf{C:} DECIPHER supports the annotation
of copy-number variants according to ACMG/ClinGen technical standards.
Similar to the sequence variant interface, ``Available evidence types''
are displayed on the left, with ``Selected evidence'' and ``Evidence to
consider'' displayed on the right. As criteria are selected, the
classification score and pathogenicity are calculated and displayed at
the bottom of the interface. \textbf{D:} An assessment interface is
provided which is designed to be used in a multidisciplinary team
meeting to evaluate whether one or more variants explain the clinical
features seen in a patient, and record if a diagnosis has been made (or
excluded). Depositors can report several lines of evidence, to weigh
evidence for or against a genotype-phenotype relationship. An OMIM
gene-disease pair and assertion is recorded.

\textbf{Figure 6. A:} Quantitative phenotype data (such as developmental
milestones or anthropometric measurements) is recorded in DECIPHER, and
aggregated on a gene-by-gene basis\textbf{.} The data is shared openly
in a series of graphs which displays expectations for the healthy
population (`Normal'), the DECIPHER population as a whole, and the
gene-specific data. For certain genes, such as \emph{EP300} (displayed
here), there are composite faces, which highlight facial
dysmorphologies. \textbf{B:} The matching patient interface allows users
to view DECIPHER records which overlap a deposited copy-number,
sequence, or insertion variant, or a gene. In this example, the matching
patients overlap \emph{EP300} . Summary information is shown in a series
of pie charts, along with phenotypes present in multiple matching
patients. The individual patient records are displayed at the bottom of
the interface. Filters are available to assist in finding the most
relevant patient matches. \textbf{C:} Within DECIPHER, aggregated
phenotype data is used to identify the most discriminating phenotypes
associated with disease genes. A table shows the percentage of
phenotyped patients with sequence variants in a gene of interest, with a
particular phenotype, compared with the percentage of phenotyped
patients in DECIPHER with the same phenotype. The odds ratio and\emph{p}
-value from a Fisher's exact test are displayed. In this example, data
for \emph{KMT2A} is displayed and sorted by\emph{p} -value. \textbf{D} :
Users with write access to an open-access patient record are able to
query the MatchMaker Exchange to search for potential patient matches.
DECIPHER is currently connected to Broad-seqr, GeneMatcher, MyGene2,
PhenomeCentral and RD-Connect. Details of potential patient matches are
displayed within DECIPHER (patient IDs have been removed in this
example).

\textbf{Figure 7. A:} Since its inception in 2004, DECIPHER has been
cited in more than 2,600 publications. \textbf{B:} The genes with the
most open access sequence variants in DECIPHER (at the time of
writing).\textbf{C:} DECIPHER openly shares variants of unknown
significance identified in undiagnosed probands in the Deciphering
Developmental Disorders study (research variants). For each variant a
page provides details of the variant and high-level phenotype terms. The
number of patients with each variant in the DDD dataset is displayed, in
addition to the number of patients identified in the GeneDx and Radboud
University Medical Center \emph{de novo} variant dataset as described by
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