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\begin{document}

\title{Next-generation phenotyping contributing to the identification of a
non-coding deletion in KANSL1 causing Koolen-de Vries syndrome}



\author[1]{Peter Krawitz}%
\author[1]{Fabian Brand}%
\author[1]{Claudia Perne}%
\affil[1]{University of Bonn}%


\vspace{-1em}



  
  \date{May 20, 2024}


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\selectlanguage{english}
\begin{abstract}
Next-generation phenotyping (NGP) is the application of advanced methods
of computer vision on medical imaging data such as portrait photos of
individuals with rare disorders. NGP on portraits results in gestalt
scores that can be used for the selection of appropriate genetic tests,
and for the interpretation of the molecular data. Here, we report on an
exceptional case of a young girl that was presented at the age of eight
and fifteen and enrolled in NGP diagnostics at the latter occasion. The
girl had clinical features associated with Koolen-de Vries syndrome and
a suggestive facial gestalt. However, chromosomal micro array (CMA),
Sanger sequencing, multiplex ligation-dependent probe analysis (MLPA),
and trio exome sequencing remained inconclusive. Based on the highly
indicative gestalt score for Koolen-de Vries, the decision was made to
perform genome sequencing to also evaluate non-coding variants. This
analysis revealed a 4.7 kb deletion at the end of intron 6 of the KANSL1
gene, which is the smallest reported structural variant to date for this
phenotype. The case illustrates how NGP can be integrated into the
iterative diagnostic process of test selection and interpretation of
sequencing results.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Many genetic syndromes are associated with a distinctive facial gestalt
which can be used to expedite the diagnostic process. Although
high-throughput sequencing has helped to address the considerable
heterogeneity of many syndromes in a single test, the rare expertise of
dysmorphologists, which is still required for the data interpretation,
is often the bottleneck. In recent years, advances in machine learning
have enabled the development of NGP tools, that can be used to analyze
facial dysmorphology in patient portrait photos (Ferry et al., 2014;
Kuru et al., 2014; Gripp et al., 2016; Wang and Luo, 2016; Dudding-Byth
et al., 2017; Hadj-Rabia et al., 2017; Valentine et al., 2017; Liehr et
al., 2018; Gurovich et al., 2019; van der Donk et al., 2019; Hsieh et
al., 2022). Amongst them is GestaltMatcher, which is a deep
convolutional neural network that was trained on thousands of
molecularly confirmed cases and achieves high accuracies in the
identification of hundreds of syndromes (Hsieh et al., 2022). In this
paper, we describe how the results of this artificial intelligence
helped to solve a case with a typical phenotype of Koolen-de Vries
syndrome but an unusual disease-causing mutation.

\textbf{Results}

We report a female patient who first presented to a syndromic
consultation at the age of eight because auf multiple phenotypic
abnormalities. The girl had muscular hypotonia since early childhood.
During infancy a developmental delay became noticeable and later she
scored in the moderate range of intellectual deficiency. Brain MRI
showed two heterotopic foci as well as symmetrically clumped hippocampi.
Facial dysmorphism, which became more prominent as a teenager, includes
a long face, slightly up slanting palpebral fissures, ptosis of the left
eye, a prominent, bulbous nasal tip and low-hanging columella (Figure
1). Furthermore, she had pale skin with many moles, thick curly hair,
and a missing left upper canine tooth. Her family described her as
extremely friendly, but anxious in contact with other children. A
chromosome analysis, a chromosomal microarray (CMA) and diagnostics for
Fragile X Syndrome, which have been performed after the first
consultation at the age of eight years, were unremarkable.

At re-consultation seven years later, the fifteen-year-old female was
enrolled into a study protocol that also involved NGP. The
computer-assisted assessment of portrait images yielded high gestalt
scores for Koolen-de Vries Syndrome (Figure 1). The feature score, which
is based on the clinical abnormalities annotated in HPO terminology, was
in the lower range reflecting the rather unspecific phenotypic
manifestations in the young female (Robinson et al., 2008; Peng et al.,
2021). Although some characteristic aspects of the facial gestalt, such
as the elongation of the face and the pear-shaped nose, become more
prominent with age, the gestalt score for the portrait at the age of
eight years was already comparably high (Figure 1).

With facial dysmorphism typical for Koolen-de Vries Syndrome and some
matching phenotypic features such as the friendly personality, this
diagnosis was suspected despite the inconclusive CMA results. While
\textasciitilde{}95\% of the cases with Koolen-de Vries syndrome are due
to 500 to 650 kb deletion in 17q21.31, only \textasciitilde{}5\% are due
to sequence variants in \emph{KANSL1} (Koolen et al., 2006, 2012, 2016;
Sharp et al., 2006; Shaw-Smith et al., 2006; Zollino et al., 2012,
2015). Around the microdeletion in 17q21.31 large clusters of low
complexity repeats at the breakpoints were described, suggesting an
underlying mechanism of non-allelic homologous recombination (NAHR)
(Stankiewicz and Lupski, 2002; Dubourg et al., 2011). Up to now, these
deletions were found by CMA. So far, only few atypical deletions had
been reported for individuals affected by Koolen-de Vries Syndrome, the
smallest of these still 68 kb in size (Cooper et al., 2011; Dubourg et
al., 2011; Koolen et al., 2012; Zollino et al., 2015). All of these
deletions were also detected by CMA.

As the recurrent microdeletion in 17q21.31 was not supported by CMA we
initiated Sanger sequencing and multiplex ligation-dependent probe
amplification (MLPA) of \emph{KANSL1} . Both analyses did not show any
abnormal findings. Next, a trio exome analysis in the patient and her
parents was performed. Data for the patient and her parents was
generated using the NovaSeq platform (Illumina) and the SureSelect v6
exome capture kit (Agilent). Initial bioinformatics analysis was focused
on relevant single nucleotide variants (SNVs) and indels using a local
implementation of GATK best practices pipelines optimized for data from
the NovaSeq sequencer. Copy number variants (CNVs) were initially
generated using cn.MOPS (Klambauer et al., 2012). No variants
in\emph{KANSL1} nor any other gene were detected that could explain the
phenotype. Following the inconclusive results of the trio exome
analysis, a genome sequencing was conducted. The bioinformatics analysis
was performed using the NVIDIA Parabricks toolkit. This toolkit enables
accelerated genome analysis by utilizing NVIDIA GPU resources. Several
algorithms from this toolkit have been used to call SNVs and indels on
the genomic data of the patient. In particular, accelerated versions of
BWA-mem and the HaplotypeCaller were crucial for fast processing and
yielded variant calls of high quality. To determine candidates for
structural variants (SVs) and CNVs, we used manta (Chen et al., 2016),
delly (Rausch et al., 2012) and lumpy (Layer et al., 2014). Variant
calls of all three tools were merged using a vote-based scheme to find
candidates supported by all callers. A 4,708 bp deletion affecting the
end of intron 6 and only the first 46 bp of exon 7
(NM\_015443.4:c.1849-4611\_1895del) was detected by all three tools.
Furthermore, the deletion was also clearly visible by a drop of coverage
and by split reads in the sequence alignment (Figure 2). In a careful
re-analysis of the exome data, that was guided by the results from
genome sequencing data, the deletion could also be detected using Pindel
(Ye et al., 2009) (Figure 2). Changing some alignments preferences in
Integrative Genome Viewer enabled the visualization of the deletion
in\emph{KANSL1} exome sequencing data (Figure 2). The deletion was also
subsequently verified by qPCR.

\textbf{Discussion}

Many SV and CNV tools for exome data rely on depth of coverage signals
to identify likely candidates for structural changes in the genome in
short read Illumina data. For both, exome and genome data, the
effectiveness of this approach is limited by the availability of good
normalized control data from other genomic regions in the same
individual or other individuals of the same sequencing run. In case of
the trio-exome sequencing experiment from our patient, this baseline was
formed by other unrelated samples sequenced in parallel. Depth and
variability of the coverage in certain genomic regions also has an
influence on the ability of those callers to detect structural change to
the genome. Other CNV detection methods rely on a mix of other factors
to find likely candidates for variation. Pindel incorporates signals
from split reads. These are read pairs in which one of the two reads
cannot be aligned to the reference genome and is assumed to carry the
precise breakpoint information of insertion or deletion events. Similar
metrics are used also by other callers that were used for subsequent
genome sequencing data analysis (e.g. manta, delly, lumpy).

The initial negative result using other CNV calling methods is due to
the suboptimal coverage distribution at some of the \emph{KANSL1} exons
and intronic regions and the fact that the deletion reaches only 46 bp
into the exon. The variant in question is mainly in the end of intron 7
making coverage-based detection of structural changes based on exome
data substantially more difficult than in genome sequencing data. As a
result, from sequence analysis, 130 pathogenic or likely pathogenic
variants have been reported for \emph{KANSL1} in the database ClinVar
(Landrum et al., 2020). In contrast, the 4.7 kb deletion that we
identified, is the first entry in ClinVar for a variant length in
between 51 bp and 50 kb.

In conclusion, we reported a 4.7 kb deletion in \emph{KANSL1} that is
mainly non-coding and was therefore first detected by genome sequencing.
However, retrospectively it could also be confirmed in exome sequencing
data with fine-tuning of the filter settings. Since high accuracy in CMA
analysis is limited to a resolution of 50 kb or higher, and in exome
analysis to a resolution of 50 bp or lower, deletions in the order of
few kilobases are not detected in the diagnostic tests most often used
today. In genome sequencing data, on the other hand SV and CNVs in this
size range can be identified more easily, but are usually more difficult
to interpret, if they are non-coding.

Therefore, our case exemplifies, how computer-assisted analysis of the
portrait can make a significant contribution to the diagnostic process.
First, NGP has the potential to speed up data analysis. If our Koolen-de
Vries patient would have carried the recurrent microdeletion, a SNV or
indel, the high gestalt score would have made the molecular confirmation
of the suspected clinical diagnosis straightforward using protocols such
as the PEDIA workflow (Hsieh et al., 2019). Second, highly suggestive
results of NGP can be used to request genome sequencing if exome or CMA
analysis were inconclusive. Third, NGP can help with the classification
of the pathogenicity of novel variants found in the genome.

According to the guidelines from 2015, a matching phenotype is only
considered as supporting evidence for pathogenicity of a sequence
variant (PP4) (Richards et al., 2015). However, experienced
dysmorphologists may attribute a higher level of evidence to the
pathogenicity of a variant in a gene if the associated phenotype is
highly specific (Zhang et al., 2020). Most clinicians that are
confronted for the first time with such a specific diagnosis will be
hesitant to apply these higher weights. Here, computer-assisted analysis
could help, since syndromic distinctiveness can be measured and the
similarity of a portrait to other molecularly confirmed cases can be
quantified (Hsieh et al., 2022). By this means, NGP makes the visual
inspection of a patient applicable to a Bayesian classification
framework (Tavtigian et al., 2018). Interestingly, the specificity of
the facial gestalt of Koolen-de Vries Syndrome ranges only in the upper
half of dysmorphic phenotypes and is exceeded for example by the
distinctiveness of Baraitser-Winter syndrome or Seckel syndrome. For
disorders in this category high gestalt scores should therefore be
handled with even greater attention and could justify more comprehensive
tests such as genome sequencing if molecular confirmation is still
pending.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\selectlanguage{ngerman}\textbf{Figure 1. Graphical heatmaps of the patient´s photos.} Photos
were taken at A) the age of fifteen and B) at the age of eight. Image
comparison shows a high similarity between the descriptor of the
patient´s photo and the composite image of Koolen-de Vries syndrome. The
heatmaps illustrate the facial elements that informed the DeepGestalt
algorithm in this patient. For calculation of the Feature score the
following HPO-terms were used: generalized muscle weakness; nevus;
intellectual disability, moderate; poor fine motor coordination;
tonsillitis; agenesis of canine; abnormal morphology of the hippocampus;
decreased serum iron.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2. \emph{KANSL1} whole exome and whole genome sequencing
data.} \emph{KANSL1} sequencing data visualized in IGV. A) A screenshot
of the deletion in whole exome sequencing data. Reads are sorted by
start location and grouped by read pairs. Soft clipped bases are
included, making the exact breakpoint in DNA visible, even against the
complete lack of other reads in the region. B) A Screenshot of whole
genome sequencing data is shown. The deletion causes a noticeable drop
of coverage. Additional support for the detected deletion is provided by
split reads, marked in red.

Black arrow = 3`- end of deletion (Exon 7)

Blue arrow = 5`-end of deletion (intron 6)

References

Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, K\selectlanguage{ngerman}ällberg M,
Cox AJ, Kruglyak S, Saunders CT. 2016. Manta: rapid detection of
structural variants and indels for germline and cancer sequencing
applications. Bioinformatics 32:1220--1222.

Cooper GM, Coe BP, Girirajan S, Rosenfeld JA, Vu TH, Baker C, Williams
C, Stalker H, Hamid R, Hannig V, Abdel-Hamid H, Bader P, et al. 2011. A
copy number variation morbidity map of developmental delay. Nat Genet
43:838--846.

Donk R van der, Jansen S, Schuurs-Hoeijmakers JHM, Koolen DA, Goltstein
LCMJ, Hoischen A, Brunner HG, Kemmeren P, Nellåker C, Vissers LELM,
Vries BBA de, Hehir-Kwa JY. 2019. Next-generation phenotyping using
computer vision algorithms in rare genomic neurodevelopmental disorders.
Genet Med 21:1719--1725.

Dubourg C, Sanlaville D, Doco-Fenzy M, Le Caignec C, Missirian C,
Jaillard S, Schluth-Bolard C, Landais E, Boute O, Philip N, Toutain A,
David A, et al. 2011. Clinical and molecular characterization of
17q21.31 microdeletion syndrome in 14 French patients with mental
retardation. Eur J Med Genet 54:144--151.

Dudding-Byth T, Baxter A, Holliday EG, Hackett A, O'Donnell S, White SM,
Attia J, Brunner H, Vries B de, Koolen D, Kleefstra T, Ratwatte S, et
al. 2017. Computer face-matching technology using two-dimensional
photographs accurately matches the facial gestalt of unrelated
individuals with the same syndromic form of intellectual disability. BMC
Biotechnol 17:90.

Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP, Zisserman A,
Nellåker C. 2014. Diagnostically relevant facial gestalt information
from ordinary photos. Elife 3:e02020.

Gripp KW, Baker L, Telegrafi A, Monaghan KG. 2016. The role of objective
facial analysis using FDNA in making diagnoses following whole exome
analysis. Report of two patients with mutations in the BAF complex
genes. Am J Med Genet A 170:1754--1762.

Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N, Gelbman D,
Basel-Salmon L, Krawitz PM, Kamphausen SB, Zenker M, Bird LM, Gripp KW.
2019. Identifying facial phenotypes of genetic disorders using deep
learning. Nat Med 25:60--64.

Hadj-Rabia S, Schneider H, Navarro E, Klein O, Kirby N, Huttner K, Wolf
L, Orin M, Wohlfart S, Bodemer C, Grange DK. 2017. Automatic recognition
of the XLHED phenotype from facial images. Am J Med Genet A
173:2408--2414.

Hsieh T-C, Bar-Haim A, Moosa S, Ehmke N, Gripp KW, Pantel JT, Danyel M,
Mensah MA, Horn D, Rosnev S, Fleischer N, Bonini G, et al. 2022.
GestaltMatcher facilitates rare disease matching using facial phenotype
descriptors. Nat Genet.

Hsieh T-C, Mensah MA, Pantel JT, Aguilar D, Bar O, Bayat A,
Becerra-Solano L, Bentzen HB, Biskup S, Borisov O, Braaten O, Ciaccio C,
et al. 2019. PEDIA: prioritization of exome data by image analysis.
Genet Med 21:2807--2814.

Klambauer G, Schwarzbauer K, Mayr A, Clevert D-A, Mitterecker A,
Bodenhofer U, Hochreiter S. 2012. cn.MOPS: mixture of Poissons for
discovering copy number variations in next-generation sequencing data
with a low false discovery rate. Nucleic Acids Res 40:e69.

Koolen DA, Kramer JM, Neveling K, Nillesen WM, Moore-Barton HL, Elmslie
FV, Toutain A, Amiel J, Malan V, Tsai AC-H, Cheung SW, Gilissen C, et
al. 2012. Mutations in the chromatin modifier gene KANSL1 cause the
17q21.31 microdeletion syndrome. Nat Genet 44:639--641.

Koolen DA, Pfundt R, Linda K, Beunders G, Veenstra-Knol HE, Conta JH,
Fortuna AM, Gillessen-Kaesbach G, Dugan S, Halbach S, Abdul-Rahman OA,
Winesett HM, et al. 2016. The Koolen-de Vries syndrome: a phenotypic
comparison of patients with a 17q21.31 microdeletion versus a KANSL1
sequence variant. Eur J Hum Genet 24:652--659.

Koolen DA, Vissers LELM, Pfundt R, Leeuw N de, Knight SJL, Regan R, Kooy
RF, Reyniers E, Romano C, Fichera M, Schinzel A, Baumer A, et al. 2006.
A new chromosome 17q21.31 microdeletion syndrome associated with a
common inversion polymorphism. Nat Genet 38:999--1001.

Kuru K, Niranjan M, Tunca Y, Osvank E, Azim T. 2014. Biomedical visual
data analysis to build an intelligent diagnostic decision support system
in medical genetics. Artif Intell Med 62:105--118.

Landrum MJ, Chitipiralla S, Brown GR, Chen C, Gu B, Hart J, Hoffman D,
Jang W, Kaur K, Liu C, Lyoshin V, Maddipatla Z, et al. 2020. ClinVar:
improvements to accessing data. Nucleic Acids Res 48:D835--D844.

Layer RM, Chiang C, Quinlan AR, Hall IM. 2014. LUMPY: a probabilistic
framework for structural variant discovery. Genome Biol 15:R84.

Liehr T, Acquarola N, Pyle K, St-Pierre S, Rinholm M, Bar O, Wilhelm K,
Schreyer I. 2018. Next generation phenotyping in Emanuel and
Pallister-Killian syndrome using computer-aided facial dysmorphology
analysis of 2D photos. Clin Genet 93:378--381.

Peng C, Dieck S, Schmid A, Ahmad A, Knaus A, Wenzel M, Mehnert L, Zirn
B, Haack T, Ossowski S, Wagner M, Brunet T, et al. 2021. CADA:
phenotype-driven gene prioritization based on a case-enriched knowledge
graph. NAR Genom Bioinform 3:.

Rausch T, Zichner T, Schlattl A, Stütz AM, Benes V, Korbel JO. 2012.
DELLY: structural variant discovery by integrated paired-end and
split-read analysis. Bioinformatics 28:i333--i339.

Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW,
Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL, et al. 2015.
Standards and guidelines for the interpretation of sequence variants: a
joint consensus recommendation of the American College of Medical
Genetics and Genomics and the Association for Molecular Pathology. Genet
Med 17:405--424.

Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. 2008. The
Human Phenotype Ontology: a tool for annotating and analyzing human
hereditary disease. Am J Hum Genet 83:610--615.

Sharp AJ, Hansen S, Selzer RR, Cheng Z, Regan R, Hurst JA, Stewart H,
Price SM, Blair E, Hennekam RC, Fitzpatrick CA, Segraves R, et al. 2006.
Discovery of previously unidentified genomic disorders from the
duplication architecture of the human genome. Nat Genet 38:1038--1042.

Shaw-Smith C, Pittman AM, Willatt L, Martin H, Rickman L, Gribble S,
Curley R, Cumming S, Dunn C, Kalaitzopoulos D, Porter K, Prigmore E, et
al. 2006. Microdeletion encompassing MAPT at chromosome 17q21.3 is
associated with developmental delay and learning disability. Nat Genet
38:1032--1037.

Stankiewicz P, Lupski JR. 2002. Genome architecture, rearrangements and
genomic disorders. Trends Genet 18:74--82.

Tavtigian SV, Greenblatt MS, Harrison SM, Nussbaum RL, Prabhu SA,
Boucher KM, Biesecker LG. 2018. Modeling the ACMG/AMP variant
classification guidelines as a Bayesian classification framework. Genet
Med.

Valentine M, Bihm DCJ, Wolf L, Hoyme HE, May PA, Buckley D, Kalberg W,
Abdul-Rahman OA. 2017. Computer-Aided Recognition of Facial Attributes
for Fetal Alcohol Spectrum Disorders. Pediatrics 140:.

Wang K, Luo J. 2016. Detecting Visually Observable Disease Symptoms from
Faces. EURASIP J Bioinform Syst Biol 2016:13.

Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. 2009. Pindel: a pattern
growth approach to detect break points of large deletions and medium
sized insertions from paired-end short reads. Bioinformatics
25:2865--2871.

Zhang J, Yao Y, He H, Shen J. 2020. Clinical Interpretation of Sequence
Variants. Curr Protoc Hum Genet 106:e98.

Zollino M, Marangi G, Ponzi E, Orteschi D, Ricciardi S, Lattante S,
Murdolo M, Battaglia D, Contaldo I, Mercuri E, Stefanini MC, Caumes R,
et al. 2015. Intragenic KANSL1 mutations and chromosome 17q21.31
deletions: broadening the clinical spectrum and genotype-phenotype
correlations in a large cohort of patients. J Med Genet 52:804--814.

Zollino M, Orteschi D, Murdolo M, Lattante S, Battaglia D, Stefanini C,
Mercuri E, Chiurazzi P, Neri G, Marangi G. 2012. Mutations in KANSL1
cause the 17q21.31 microdeletion syndrome phenotype. Nat Genet
44:636--638.

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