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\begin{document}

\title{AutoCaSc: Prioritizing candidate genes for neurodevelopmental disorders}



\author[1]{Johann Lieberwirth}%
\author[1]{Benjamin Büttner}%
\author[1]{Chiara Klöckner}%
\author[1]{Konrad Platzer}%
\author[1]{Bernt Popp}%
\author[1]{Rami Abou Jamra}%
\affil[1]{Institute of Human Genetics, University of Leipzig Medical Center, Leipzig 04103, Germany}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Routine exome sequencing (ES) in individuals with neurodevelopmental
disorders (NDD) remains inconclusive in \textgreater{}50\%. Research
analysis of unsolved cases can identify novel candidate genes but is
time consuming, subjective, and hard to compare between labs. The field
therefore needs automated and standardized assessment methods to
prioritize candidates for matchmaking. We developed AutoCaSc
(https://autocasc.uni-leipzig.de) based on our candidate scoring scheme
(CaSc). We validated our approach using synthetic trios and real
in-house trio ES data. AutoCaSc consistently (94.5\%) scored variants in
valid novel NDD genes in the top three ranks. In 93 real trio exomes,
AutoCaSc identified most (97.5\%) previously manually scored variants
while evaluating additional highly scoring variants missed in manual
evaluation. It identified candidate variants in previously undescribed
NDD candidate genes ( \emph{CNTN2}, \emph{DLGAP1}, \emph{SMURF1},
\emph{NRXN3}, \emph{PRICKLE1}). AutoCaSc enables anybody to quickly
screen a variant for its plausibility in NDD. After contributing
\textgreater{}40 descriptions of NDD associated genes, we provide usage
recommendations based on our extensive experience. Our implementation is
capable of pipeline integration and therefore allows screening of large
cohorts for candidate genes. AutoCaSc empowers even small labs to a
standardized matchmaking collaboration and to contribute to the ongoing
identification of novel NDD entities.%
\end{abstract}%



\sloppy


\section*{INTRODUCTION}

{\label{introduction}}

Routine genetic analyses and exome sequencing (ES) are able to clarify
the cause of neurodevelopmental disorders (NDD) in about 30-50\% of
affected individuals(Trujillano et al., 2017; Deciphering Developmental
Disorders Study et al., 2020; Shashi et al., 2014; Yavarna et al., 2015;
Chong et al., 2015). Due to the extreme heterogeneity of NDD (1,534
primary genes in the SysID(Kochinke et al., 2016) database as of
2021-12-06), this diagnostic yield will increase with identification of
novel genetic associations. (Chong et al., 2015; Eilbeck et al., 2017;
Kaplanis et al., 2019)

Large screening studies have focused on the identification of \emph{de
novo} variants (DNV) in NDD (Vissers et al., 2010). The DDD (Deciphering
Developmental Disorders) (Deciphering Developmental Disorders Study et
al., 2020) study evaluated 31,058 trio-exomes of individuals with NDD
with the aim to identify causal DNVs. By mere statistical enrichment, 28
novel genes (10.0\% of 281 total enriched genes) could be associated
with NDD. Cases from the DDD cohort were subsequently contributed to
several descriptions of novel NDD associations based on clinically and
functionally guided matchmaking. Thus it is clear that a more
distributed approach is needed that goes beyond analyzing ever large
cohorts of only a few centers, one that assesses gene and variant
characteristics in, at best, all unsolved trio exomes to identify
further NDD entities.

In principle, any rare variant segregating with a disorder can cause a
disease. (Najmabadi et al., 2011; Tarpey et al., 2009; Vissers et al.,
2010) Wet lab or model organism analyses of all candidate variants would
be slow and expensive. Therefore, a systematic procedure for
pre-selection of the most promising candidates is needed to close the
gap between the large number of candidates and the experimental proof of
their causality. Scientific evaluators usually consider aspects of
predicted effect on the protein, segregation in the family, minor allele
frequency in population databases like gnomAD (Genome Aggregation
Database Consortium et al., 2020), functional aspects of the protein and
other information from literature and from public sources. (MacArthur et
al., 2014) This approach is highly evaluator dependent, making it both
subjective and not scalable. Furthermore, it is time-consuming and needs
expert knowledge, thus deterring many labs from scientific evaluation of
their ES data. A standardized and quick evaluation in a measurable
framework is therefore needed.

Computational approaches have been developed to predict novel disease
genes based on confirmed gene associations (e.g. ToppGene Suite (Chen et
al., 2009), Endeavour (Tranchevent et al., 2016)). Other approaches use
evolutionary constraints to predict the importance of a gene for the
organism in general (e.g. pLI (Exome Aggregation Consortium et al.,
2016)) or the deleteriousness of single genetic variants (e.g. CADD
(Kircher et al., 2014)). Few projects (eXtasy (Sifrim et al., 2013),
PHIVE (Robinson et al., 2014), Phenolyzer (Yang et al., 2015), OMIM
explorer (James et al., 2016), DeepPVP (Boudellioua et al., 2019)) use
both variant and gene-specific information for candidate prioritization;
a quick review and comparison of the tools combining gene and variant
data for candidate gene prediction, showed that they are often
unmaintained, unusable or do not offer a visual user interface in the
first place (compare Table S1).

Here we describe the development of a candidate scoring scheme (CaSc)
specifically intended for the NDD research field and based on commonly
used complementary data sources for genes and variants. The webAutoCaSc
web tool enables broad applicability while the command line tool
vcfAutoCaSc is designed for screening cohorts and pipeline integration.
We validated our approach using synthetic trios and real in-house trio
ES data. The successful application of these principles and tools at our
Institute resulted in \textgreater{}40 descriptions of rare NDD
entities. Based on this experience, we recommend a workflow for
re-evaluation of sequencing data in unsolved NDD trios.



\section*{MATERIALS AND METHODS}

{\label{materials-and-methods}}

\subsection*{The Leipzig NDD research
cohort}

{\label{the-leipzig-ndd-research-cohort}}

An ongoing study at the Institute of Human Genetics at Leipzig
University Medical Center investigates the genetics of rare diseases
using high throughput sequencing (also commonly referred to as Next
Generation Sequencing or NGS). This study was approved by the ethics
committee of the University of Leipzig, Germany (224/16-ek and
402/16-ek) and all individuals or their parents or legal guardians
consented to genetic testing.

All individuals received high throughput sequencing. Genomic DNA was
enriched using either a clinical exome target design (CES; TruSight One
v1 panel, 4811 genes, Illumina, Inc., San Diego, USA) or different exome
target designs (TWIST Human Core Exome Kit, TWIST Bioscience, San
Francisco, CA, USA; BGI Exome capture 59M kit, BGI, Shenzhen, China;
SureSelect Human All Exon V6, Agilent Technologies, Santa Clara,
California; TruSeq DNA Exome and Nextera Rapid Capture Exomes, Illumina,
Inc., San Diego, CA, USA). Resulting libraries were sequenced with
paired end reads (100bp or 150bp) on either Illumina (NextSeq 550 or
NovaSeq 6000) or BGI sequencers (BGISEQ-500). Most samples were
initially sequenced using an affected only (``single'') approach. The
data was first analyzed in a diagnostic setting. In unsolved cases with
research consent and parental DNA samples, we subsequently complemented
the analysis by trio ES and re-evaluated the case.

Research re-evaluation in unsolved cases included specially trained
staff at our institute manually re-checking the data to identify
candidate genes and variants based on gene annotations and literature/
database review. Training and supervision were performed by principal
investigators with long lasting experience in molecular genetics
diagnostics and NDD research.

\subsection*{Establishing a comparable candidate score
(CaSc)}

{\label{establishing-a-comparable-candidate-score-casc}}

In order to standardize this approach and make results comparable
throughout genes and by different analysts, we established a candidate
score (CaSc) as a set of criteria to evaluate variants in novel
candidate genes for NDD. Roughly, the set of criteria included variant
attributes (missense or loss-of-function (LoF) variants, \emph{in
silico} (computational) prediction tools, conservation, minor allele
frequency), inheritance aspects (segregation and zygosity, and the
relation to higher pLI-scores(Exome Aggregation Consortium et al., 2016)
for LoF variants and missense Z-scores (Genome Aggregation Database
Consortium et al., 2020)), and the gene's plausibility for causing NDD.
This was calculated based on reviewing OMIM and PubMed entries to check
if the gene is involved in neurological processes, using MGI (Bult et
al., 2019) and PubMed for animal models, STRING (Szklarczyk et al.,
2019) for protein interactions, GTEx (Consortium, 2020) for gene
expression in central nervous system, as well as a list of resources to
find out if variants in the gene have been described in association with
autism spectrum disorder or NDD (the resources included the DDD study,
the Human Gene Mutation Database (HGMD), ClinVar, cooperation partners,
and entries in GeneMatcher). An overview of the manual version of CaSc
is provided in Figure S1 and a detailed description in the corresponding
preprint (B\selectlanguage{ngerman}üttner et al., 2019). Evaluators manually applied these rules
to all candidate variants and focused on following up the highest
scoring and thus most promising genes through matchmaking. Evaluating a
variant took between 5 and 15 minutes. As we moved on to the automatic
version of CaSc, there have been several changes to the scoring logic
(see below and detailed in the Supplementary notes).

\subsection*{Automatic Candidate score
(AutoCaSc)}

{\label{automatic-candidate-score-autocasc}}

The manual scoring process showed to be suboptimal regarding time needed
and inter-evaluator variability when applied without extensive prior
training and constant supervision, which is hard to maintain in a
university setting with continuous workforce change. We thus decided to
develop a software tool to remove subjectivity and enable lasting
comparability and continuous quick re-evaluation of older candidate
genes. AutoCaSc is written using the Python programming language and all
code is publicly available through a GitHub repository
(https://github.com/JohannKaspar/AutoCaSc) under a creative commons
license (CC BY-NC-SA 4.0).

The three categories ``Inheritance'', ``Gene constraint'' and ``Variant
attributes'' were implemented as simple decision trees based on an
updated CaSc logic (detailed in Table S2). For the ``Gene plausibility''
category, we reviewed public databases providing information on gene
expression, annotated literature, animal models and reported variants.
Criteria for using a resource were the uniqueness of the used data
sources, its completeness and regular updates or sustained maintenance.
We selected the Genotype-Tissue Expression (GTEx (Consortium, 2020))
project for expression data, Mouse Genome Informatics (MGI (Bult et al.,
2019)) database for model organism data, STRING (Szklarczyk et al.,
2019) database for protein-protein interactions, PubTator Central (PTC)
(Wei et al., 2019) for annotations of PubMed abstracts, DisGeNET (Piñero
et al., 2017) for gene disease association (GDA) and PsyMuKB (Lin et
al., 2019) for reported DNVs. DisGeNET is a partially redundant source
because it calculates GDA based on published literature and from various
repositories and databases. However, we found that the integration of
DisGeNET overall improves the gene plausibility score as it includes
data beyond literature.

When submitting genomic or transcript coding variants to AutoCaSc, they
are annotated using the Ensembl Variant Effect Predictor (VEP) (McLaren
et al., 2016) via the Ensembl REST (representational state transfer) API
(application programming interface). Latest allele counts are queried
through the GraphQL API of gnomAD (Genome Aggregation Database
Consortium et al., 2020). Gene constraint metrics such as pLI and
Z-score(Exome Aggregation Consortium et al., 2016), predicted impact of
the variant on protein function, mode of inheritance, GERP++ (Davydov et
al., 2010) rankscore (RS), frequency in gnomAD (Genome Aggregation
Database Consortium et al., 2020), SIFT (Kumar et al., 2009) converted
RS, MutationTaster (Schwarz et al., 2014) converted RS and
MutationAssessor (Reva et al., 2011) RS are then used to calculate the
variant score. This is then summed with the precalculated gene
plausibility score. Please refer to Figure 1b, Table S2 and Table S3 for
an overview of all criteria and data sources used.

We provide webAutoCaSc (https://autocasc.uni-leipzig.de/) as an online
implementation. The webtool (user interface mock in Figure 1d) is
intended for quick manual scoring of a few variants. It does not store
any query information on the server side. All server access logs are
deleted on a daily basis using a cron command.

The command line implementation of our algorithm (vcfAutoCaSc) uses
slivar (Pedersen et al., 2021) to pre-filter variants in multi-sample
VCF (Danecek et al., 2011) files and automatically scores all passing
variants. Family information in the standard PED pedigree format can be
provided as input. This allows AutoCaSc to be implemented in existing
pipelines for rare variant analysis where it can be used to
automatically prioritize candidates in large datasets.

\subsection*{Synthetic trio benchmark}

{\label{synthetic-trio-benchmark}}

To evaluate AutoCaSc, we searched for recent publications in high
quality genetic journals describing novel NDD associated genes. We
curated a list of 79 variants in 11 novel NDD genes described in 9
publications from a total of 21 reviewed publications (File S1). We
reasoned that due to their novelty at the time of review (2021-01-18),
these would be comparable to candidate variants that a research lab
would evaluate. Next, we used two publicly available trios of healthy
individuals (CEU and ASH trio from Genome In A Bottle (GIAB) (Zook et
al., 2016)) and incorporated the reviewed variants into these VCFs files
according to their segregation. We then applied vcfAutoCaSc to these
synthetic benchmark trios and evaluated the performance by ranking the
passing variants by their CaSc. For the scope of our evaluation, we
defined two variants in compound heterozygous state as one candidate
finding.

\subsection*{Reevaluation of trio ES}

{\label{reevaluation-of-trio-es}}

We used AutoCaSc to re-evaluate 93 recent ES trios (TWIST target design)
from our in-house NDD cohort. Evaluation in a diagnostic setting (Zacher
et al., 2021) using the browser-based ``Varvis'' genomics software
(Limbus Medical Technologies GmbH, Rostock, Germany) revealed a
pathogenic SNV in 16 trios (17.2\%) and a pathogenic CNV in three trios
(3.2\%). In the remaining cases, no diagnostic variants were reported
and manual analysis in a research setting including the application of
the manual version of CaSc was performed. To evaluate the performance of
vcfAutoCaSc, we produced a cohort multi-sample VCF file (see
Supplementary notes) and annotated it using the above described pipeline
of slivar and vcfAutoCaSc.

\section*{}

{\label{results}}

\section*{RESULTS}

{\label{results}}

\subsection*{Candidate yield using
CaSc}

{\label{candidate-yield-using-casc}}

Between January 2016 and December 2020, we evaluated 2,977 cases of
individuals with NDD at the Leipzig Center for Rare Diseases and
clarified 1,055 of these (35.4\%). In 1,922 (65.6\%) individuals no
clinically relevant variant was identified. Of these, 1,192 cases
(62.0\%) were reevaluated in a research setting, mostly as trios (1,038;
87.1\%) and using CaSc for variant prioritization.

Overall, in 932 families we identified 1,561 candidate variants in 1,309
genes. From these we contributed to 43 publications describing novel NDD
entities and are currently working on the clinical and molecular
description of 91 candidate genes. A complete and regularly updated list
of our in-house candidates(Abou Jamra, Rami \& Platzer, Konrad, 2022)
can be found online. In 569 families (61.1\%), we identified one
candidate gene, in 218 (23.4\%) we identified two candidate genes, in
137 (14.7\%) we identified three to five candidate genes, and in 9
families we identified 6 or more candidate genes (most of these were
homozygous in consanguineous families).

\subsection*{Novel NDD genes are highly ranked in synthetic
trios}

{\label{novel-ndd-genes-are-highly-ranked-in-synthetic-trios}}

We applied vcfAutoCaSc to 158 (79 CEU and 79 ASH based) synthetic trios,
containing novel NDD-causing variants from recent publications (Figure
2a). The number of variants remaining after prefiltering varied between
5 and 26, depending on the base trio (CEU or ASH), the sex of the index
individual and the parents' affected status.

CaSc of the inserted variants varied between 4.8 and 10.8 with a median
of 8.0, whereas the median of all other rare variants, which passed the
prefiltering in the trios was 4.3. Of all inserted variants, 75\% had a
CaSc equal to 6.7 or higher. Comparing true positive and false positive
rates, a CaSc cutoff of 6.0 seems optimal (Figure 2b). In the CEU trio,
inserted variants were ranked as the top variant in median (mean rank
1.5; top rank in 47/79 (59.5\%); second rank in 26/79 (32.9\%); third
rank in 4/79 (5.0\%)). In the ASH trio, inserted variants were ranked as
the second highest variant in median (mean rank 2.3; top rank in 0/79
(0\%); second rank in 63/79 (79.7\%); third rank in 7/79 (8.9\%)). The
variant that consistently ranked first in the ASH trio was a \emph{de
novo} variant in \emph{DNMT3A} present in the HG002 sample.\emph{DNMT3A}
is a known NDD gene and the variant type would be typical for the
associated disorders (MIM \#618724 or MIM \#615879, compare Figure S2
for detailed discussion). In 147/158 (93.0\%) of simulations, the
inserted variant (or the compound-heterozygous candidate finding) was in
the three highest scoring candidates. Comparing the filtered ranks of
the inserted vs. all other trio specific variants passing the
prefiltering showed that the inserted variants were scored significantly
higher (ASH Trio: p = 7.5e-17; CEU Trio: p = 1.4e-05; Wilcoxon rank-sum
test). Compare File S1 for complete results of the synthetic trio
scoring.

\subsection*{Identification of novel candidate variants beyond confined
manual
evaluation}

{\label{identification-of-novel-candidate-variants-beyond-confined-manual-evaluation}}

We applied vcfAutoCaSc to 93 trio exomes from our in-house cohort. The
male to female ratio in this sub-cohort was 57:36 (1.58). Eight (8.6\%)
families self-reported consanguinity.

Automated filtering and scoring identified 309 unique candidate variants
(median 2.0, average 3.3 per case) in 289 genes. The maximum number of
candidates per case was 22 variants (1/93 cases; 1.1\%) and the minimum
was zero variants (15/93 cases; 16.1\%). Most (79/81 variants, 97.5\%)
manually scored candidates were also scored by vcfAutoCaSc, including
all 16 SNVs and indels that had been reported to treating physicians
Figure 2c). In addition, 230 further candidates, which had not been
manually scored before, passed all pre-filtering steps and were
automatically scored (Figure 2c). In 15/93 cases (16.1\%), no variant
passed the filters. In 42/93 (45.2\%) cases, the variant evaluated
highest by AutoCaSc was also considered in the manual evaluation. In
35/93 cases (37.6\%), it was not evaluated manually. In nearly half
(24/35; 68.6\%) of these cases, there was no single manually evaluated
variant reported, but at least one scored by AutoCaSc. The overall
highest candidate score in these 24 cases was 10.0, while the median of
the highest score was 5.5 in these cases. The number of candidates in
these cases varied between one and 10 (median 2). A table with all
evaluated variants can be found in File S2.

\subsection*{Exemplary cavities in automatic
filtering}

{\label{exemplary-cavities-in-automatic-filtering}}

One of the 79 variants (ENST00000159111:c.288C\textgreater{}T
p.(Gly96=), \emph{KDM4B} , CaSc 10.0) in the simulation experiment was
filtered out by slivar during prefiltering because it was not predicted
to lead to a change in the amino acid sequence of the protein (silent)
and the impact on protein function was predicted to be low. This variant
has been implied as pathogenic by Duncan and colleagues(Duncan et al.,
2020) because RNA analyses showed a splice donor loss combined with the
gain of a new donor site (r.287\_317del) which was predicted to cause a
frameshift at protein level (p.Glu97Thrfs*66).

One of the 81 previously manually scored variants (ENST00000513312:
c.487C\textgreater{}T, p.(Arg163Trp), \emph{MCIDAS} , CaSc 6.2) from the
in-house cohort was filtered out, because the corresponding
gene\emph{MCIDAS} already has an associated non-NDD phenotype (Ciliary
dyskinesia, \#618695). Genes for which an associated phenotype is noted
in OMIM but which are not listed as NDD (candidate) genes in SysID are
scored by vcfAutoCaSc but not included in the ranking (compare filtering
steps in the Supplementary Methods). Secondly, a pair of variants in
compound heterozygous state ((1) ENST00000250937: c.446C\textgreater{}G,
p.(Pro149Arg) and (2) ENST00000250937: c.224T\textgreater{}G,
p.(Val75Gly), \emph{DOHH} , CaSc 4.4) did not pass slivar VCF quality
filters as the read depth (DP = 17) of variant (1) was below our defined
cutoff of 20.

\subsection*{Example of candidate variants identified through automated
scoring}

{\label{example-of-candidate-variants-identified-through-automated-scoring}}

Several known NDD genes, such as \emph{SCN1A} , \emph{PIK3CA}
,\emph{CLTC} , \emph{FOXP1} or \emph{SOX5} , were among the strongest
candidate variants (Table 1).

The highest scoring variant in a gene currently with unclear association
to NDD (according to SysID and PanelApp as of 2021-11-27) was a
homozygous LoF variant in \emph{CNTN2} (ENST00000331830:
c.940C\textgreater{}T, p.(Arg314*), CaSc 11.4). The predicted impact of
the variant was very high (CADD 34.0) and it did not occur in
gnomAD.\emph{CNTN2} is highly expressed in the brain (GTEx), and
directly interacts (STRING) with known NDD associated genes like
\emph{CNTNAP2}(Strauss et al., 2006) and \emph{L1CAM} (Rosenthal et al.,
1992).\emph{CNTN2} encodes contactin-2 which, together with the
\emph{CNTNAP2}gene product, is responsible for organizing voltage-gated
potassium channels at juxtaparanodal regions. (Stogmann et al., 2013)
Seizures are described in knockout mice (MGI). Stogmann and colleagues
(Stogmann et al., 2013) have linked a homozygous frameshifting variant
in a consanguineous family to familial adult myoclonic epilepsy; of the
five affected siblings in this published family, at least two had
borderline intelligence, and two individuals had average
neuropsychological test scores. In our case, the male individual had
epileptic encephalopathy and developmental delay and his brother,
segregating the LoF variant, was similarly affected while a healthy
sister did not carry the variant in homozygous state. Taken together, an
impairment of neuronal functions through biallelic loss of \emph{CNTN2}
seems plausible justifying the high score and this gene as a good NDD
candidate. Interestingly however there is one one homozygous
\emph{CNTN2} LoF variant (c.1169C\textgreater{}A, p.(Ser390*)) listed in
gnomAD. Together with the high consanguinity in the case described here
this may point to additional aggravating variants (``dual diagnosis'')
in this family (e.g. homozygous variants in \emph{SELL} with CaSc of
8.09, \emph{FRAS1}a SysID primary gene and \emph{ERMARD} a SysID
candidate gene).

Other high scoring variants with previously undescribed or unclear
associations to NDD at the time of data retrieval were \emph{DLGAP1}
,\emph{HDAC4} , \emph{H3F3A} , \emph{ANKRD17} , \emph{SMURF1} ,
\emph{NRXN3} ,\emph{PRICKLE1} and \emph{CASC5} (compare Table 2 for
detailed sub-scores). Members of our institute have contributed to a
recent publication associating heterozygous LoF variants in
\emph{ANKRD17} with a NDD entity based on a cohort of 34 individuals
from 32 families (Chopra et al., 2021, p. 17) and pathogenic variants in
the histone 3 family (\emph{H3F3A} and \emph{H3F3B} ) have been in the
meantime published to cause a NDD entity with neurodegeneration (Bryant
et al., 2020). For a complete list of all candidates found in the real
trios, see File S2.



\section*{DISCUSSION}

{\label{discussion}}

Matchmaking platforms such as GeneMatcher (Sobreira et al., 2015) have
transformed international collaborations for identifying novel
gene-disease associations (GDA) by pooling the results of many genetics
laboratories. Thus, very rare GDA can be confirmed, which would be
unlikely in the cohort of a single laboratory.

The question, which candidates should be uploaded to matchmaking
platforms remains a challenge, since as the number of uploaded
candidates increases, so does the effort required to track and follow up
all matches. In terms of specificity, it also makes sense to pre-select
the candidates present per individual analysis. AutoCaSc offers an
approach to accelerate and systematize this pre-selection in cases of
NDD.

In recent years, consistent application of CaSc for candidate gene
prioritization at the Center for Rare Diseases in Leipzig has
contributed to the identification of 43 new NDD genes, with an
additional 91 candidates in ongoing projects and submissions.
Considering the relatively small cohort size of just under 3,000 cases
of NDD, this is a high yield of new GDA, exemplifying that focusing
resources on the most promising candidates is worthwhile.

To demonstrate utility beyond this anecdotal single center experience,
we used synthetic trios showing that the programmatic implementation of
AutoCaSc prioritizes pathogenic variants in very novel NDD associations
with high confidence. In the vast majority (147/158, 93.0\%) of
simulations, the inserted pathogenic variant was among the three highest
scoring variants. As a prospective, real-world benchmark we compared the
results of previous manual expert application of the CaSc criteria with
the automatic results from AutoCaSc using in-house trio ES. Again, the
AutoCaSc filtering and scoring pipeline performed on par with expert
curation and identified nearly all (79/81, 97.5\%) manually evaluated
variants. Based on our validation experiments and long term user
experience, we demonstrated that AutoCaSc has a high sensitivity to
identify potentially causative candidate variants in genes not yet
associated with NDD and that it is able to score a high number of
candidates in a short time. It is unbiased and systematically runs the
same procedure for all variants that meet certain quality standards and
are eligible by inheritance. When applied with similar pre-filtering
criteria or on single pre-selected variants, it largely eliminates
subjectivity and enables cross-laboratory comparability.

The CaSc score is designed to rank candidate variants in a single
analysis or in cohorts of individuals with NDD and thus does not have a
universal cutoff. However, candidate variants with a CaSc of
\textgreater{}6 were most promising in our synthetic trio experiments
and also in our practical use this cutoff seemed reasonable. In the trio
simulation, 85.9\% of the inserted variants and 16.3\% of the trio
specific variants were above the CaSc \textgreater{}6 threshold, which
is also supported by the receiver operating characteristic curve (ROC)
for this experiment (Figure 2b). A cutoff of 5 would result in higher
sensitivity and basically identify all true inserted variants, albeit
with a higher false positive rate. A high CaSc \textgreater{}9 typically
indicates a very good candidate that likely already has an active
GeneMatcher collaboration.

While manual curation is time consuming and limited to only a few
variants per case, AutoCaSc automatically scored and ranked a further
230 candidates passing prefiltering in the real trio benchmark. A
possible reason why these variants were not manually considered for
scoring by the human evaluators, is that these did not at first sight
seem promising enough to score to the time-limited evaluators. This
hypothesis is in agreement with the fact that the majority of these
variants received a relatively low score by AutoCaSc. Another
possibility is that the evaluators identified publications on the
candidate gene that seemed to exclude it as a causal factor; this could
be, for example, refuted associations or associations with different
disorders but without a NDD phenotype. For example, in
the\emph{TrioReal\_66} case, a candidate was scored by vcfAutoCaSc that
was not documented manually. This was a \emph{de novo} missense variant
in\emph{HDAC4} (ENST00000345617: c.1792G\textgreater{}C, p.(Glu598Gln),
CaSc 10.0). \emph{HDAC4} is listed in SysID as a known NDD gene. Wheeler
and colleagues(Wheeler et al., 2014) demonstrated that
haploinsufficiency of \emph{HDAC4} does not cause mental retardation.
Based on this, the variant might not have appeared convincing to the
evaluators leading to it not being scored. However, certain missense
variants in \emph{HDAC4} were recently described to cause a syndromic
NDD entity and a gain-of-function effect was discussed based on
nucleocytoplasmic mislocation of the protein (Wakeling et al., 2021, p.
4). The variant in \emph{TrioReal\_66} affects a different protein
region, which is however highly conserved and represents a structured
alpha helix in the AlphaFold protein model of HDAC4. Together with
multiple \emph{in silico} tools predicting a detrimental effect,
its\emph{de novo} occurrence and the high constraint for missense
variation of HDAC4 this variant could now be classified as likely
pathogenic. This example shows that human evaluation can incorporate
more complex concepts like refuted associations, which are currently not
implemented in AutoCaSc. It also shows that manual evaluation introduces
unreproducible bias, which can lose interesting variants for follow-up.
Reproducible automatic scoring of all filtered variants instead enables
research labs to keep an eye on future publications. As it can
repeatedly update candidate gene scores at basically no additional cost,
AutoCaSc can also be used for continuous re-evaluations of cases to
incorporate new knowledge and recent NDD literature which is impossible
to do manually. This will be possible with future regular updates and
versioning to the score.

While we show the superiority of automated candidate scoring through
AutoCaSc, our current implementation has some cavities. AutoCaSc has
been validated for trios and lacks functionality for affected only
sequencing or more complex family structures like duo or quad
approaches. It is possible to score variants with unknown inheritance
and segregation, but these variants artificially score low. Also,
AutoCaSc missed one of the reviewed \emph{KDM4B} variants in the
simulated trios because the pre-filtering removed the variant which was
annotated as silent change. This exemplifies that the scoring,
especially in vcfAutoCaSc, works only as well as the upstream software
and databases. If annotation software incorrectly classifies a variant
as irrelevant, it will not be adequately analyzed. Interestingly, this
same variant also evades scoring by a recently published decision tool
for the PVS1 ACMG criterion (Xiang et al., 2020). Further two variants
previously scored manually in the real trios were missed. One was
filtered out after scoring by vcfAutoCaSc because the corresponding gene
was already associated with a phenotype which was not linked to NDD. We
implemented this known disease blacklist filter to remove the high
scoring impact of well known (e.g. many publications and associations in
the literature) and thus highly investigated genes on filtering results,
as well as to remove known reappearing local artifacts (e.g. mucin
genes). The second variant was removed in pre-filtering by slivar
because its read depth was below our defined cutoff of 20x read
coverage. By relaxing the quality settings for prefiltering, more
variants could be scored by AutoCaSc if a higher expenditure of time for
scoring is accepted. The speed is currently limited by the APIs of VEP
and gnomAD, which AutoCaSc uses to retrieve data. By using these,
AutoCaSc requires very few resources on the server side and is always
up-to-date. If the goal is to apply AutoCaSc to thousands of trios, it
should be considered to install VEP and gnomAD locally to avoid the
bottleneck introduced through rate limiting of these APIs.

Faster scoring would also allow pre-filtering to be less strict, more
variants to be scored, and quality filters to be manually adjusted in
the results table, leaving a reasonably large set of candidates. Future
implementations and updates to our tools could integrate fast annotation
tools like slivar not only for pre-filtering but directly to provide
information needed in the scoring process instead of relying on APIs.
Future versions of the AutoCaSc tools will allow for sequencing designs
beyond trio exomes (single, duo, quad). Furthermore, cosegregation can
currently be entered as a supporting argument in the command line
version only but will be implemented in the webtool with the next
update. Its modularity makes AutoCaSc flexible to easily
integrate\emph{in silico} tools with better performance or other omics
resources in the future. The web interface also offers possibilities for
expansion and automation. For example, a submission to GeneMatcher or
ClinVar and sharing of scoring results from authenticated sources would
be possible if requested and adopted by the user community.

In summary, we suggest that AutoCaSc should be integrated into existing
ES filtering workflows (as depicted in Figure 1a) and the gene scores
should be used to prioritize for follow-up. The various interfaces of
the AutoCaSc tools will facilitate this integration. Assessing the NDD
association of a candidate variant in our framework does not require
in-depth literature and database review nor programming knowledge.
AutoCaSc can be implemented, in principle, in the routine of all genetic
labs doing NDD genetic diagnostics with minimal additional cost. With
widespread continuous usage and subsequent upload of the most promising
candidate genes to matchmaking platforms like GeneMatcher, we strongly
believe it can accelerate the identification of novel monogenic causes
of NDD.

\section*{}

{\label{acknowledgements}}

\section*{ACKNOWLEDGEMENTS}

{\label{acknowledgements}}

B.P. is supported by the Deutsche Forschungsgemeinschaft (DFG) through
grant PO2366/2--1.

\section*{AUTHORS' CONTRIBUTIONS}

{\label{authors-contributions}}

B.P., B.B. and R.A.J conceived the initial study concept. B.B. and
R.A.J. developed the manual CaSc concept. J.L. programmed the AutoCaSc
tools and performed the computational analyses under the supervision of
B.P. and R.A.J.. B.B. and R.A.J. created the pre-print draft
(https://doi.org/10.1101/588517), concept and figures. B.P. performed
bioinformatic analyses to align, call and annotate the exome files used
in the validation experiments. J.L. and B.P. created Figures 1, 2 and
Table 1, 2 and the Supplementary materials. C.K. and K.P. performed the
manual CaSc annotations and aided in the development of the score. B.P,
J.L. and R.A.J wrote and edited the manuscript. All authors reviewed and
commented on the final draft manuscript.

\section*{AVAILABILITY OF DATA AND
MATERIALS}

{\label{availability-of-data-and-materials}}

All data relevant to this manuscript can be found in the online version
of this article at the publisher's website or has been uploaded to
Zenodo (File S1, File S1: https://doi.org/10.5281/zenodo.6190031).

\section*{}

{\label{supplementary-files}}

\section*{SUPPLEMENTARY FILES}

{\label{supplementary-files}}

Supplementary notes: supplementary methods, results, figures and tables,
references and file links.

\section*{}

{\label{references}}

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\section*{TABLES}

{\label{tables}}

\subsection*{Table 1 \textbar{} Variants in know NDD causing genes found
in the 93 trio
cohort}

{\label{table-1-variants-in-know-ndd-causing-genes-found-in-the-93-trio-cohort}}

\subsection*{Table 2 \textbar{} Variants in genes not yet associated with
NDD}

{\label{table-2-variants-in-genes-not-yet-associated-with-ndd}}



\section*{FIGURES}

{\label{figures}}

\subsection*{Figure 1 \textbar{} AutoCaSc workflow and
categories}

{\label{figure-1-autocasc-workflow-and-categories}}

\textbf{a)} Schematic of the proposed workflow embedding AutoCaSc into a
genetic pipeline for identification of pathogenic variants in
individuals with NDD. When trio exome sequencing does not lead to a
diagnosis, AutoCaSc is intended to prioritize variants and thus focus on
the most promising candidates. \textbf{b)} Composition of the CaSc with
the four categories, as well as the maximum achievable points: Variant
attributes (max. 6 points), Gene plausibility (max. 6 points),
Inheritance (max. 3 points), Gene constraint (max. 1 point). The maximum
number of points for inheritance is awarded for autosomal recessive
variants where the variant occurs in at least one other affected
sibling. Gene constraint metrics, however, are only awarded for
autosomal dominant inherited variants, which is why both categories
together score a maximum of 3 points. \textbf{c)} Depiction of the
AutoCaSc tools front-ends. Schematic illustration of vcfAutoCaSc, which
is intended for when whole VCFs or cohorts are to be screened (left
side). Screenshot of the results page of webAutoCaSc, which is intended
to evaluate single or small groups of variants quickly and without
installation (right side).

\subsection*{Figure 2 \textbar{} Simulated and real trio
scoring}

{\label{figure-2-simulated-and-real-trio-scoring}}

\textbf{a)} Achieved CaSc of the inserted variants, subdivided according
to the affected novel NDD gene. The color of the circles encodes the
mode of inheritance. The red dashed line represents a possible cutoff of
6. \textbf{b)} ROC curve relating the false positive rate and true
positive rate for the simulation experiment. optimal cutoff at CaSc 6
marked in red. \textbf{c)} Scoring results for the 78/93 (83.9\%)
in-house trio exomes with at \textgreater{}0 candidate variants scored.
In the left part of the figure, the trios are plotted on the x-axis in
descending order of the highest candidate score (CaSc), the y-axis shows
the CaSc. The dots represent candidate variants. The large dots are the
variants with the highest score in a trio. The colors indicate whether a
candidate was scored only manually (n=2), only by AutoCaSc (n=230), or
by both (n=79). The right part shows a histogram of the achieved CaSc.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`Table_1.docx`  available at \url{https://authorea.com/users/479253/articles/567112-autocasc-prioritizing-candidate-genes-for-neurodevelopmental-disorders}

\textbf{Hosted file}

\verb`Table_2.docx`  available at \url{https://authorea.com/users/479253/articles/567112-autocasc-prioritizing-candidate-genes-for-neurodevelopmental-disorders}

\selectlanguage{english}
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