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\begin{document}

\title{863 different causes of Rett syndrome and lessons learned from data
integration.}



\author[1]{Friederike Ehrhart}%
\author[2]{Annika Jacobsen}%
\author[3]{Maria Rigau}%
\author[4]{Mattia Bosio}%
\author[5]{Rajaram Kaliyaperumal}%
\author[5]{Jeroen Laros}%
\author[1]{Egon Willighagen}%
\author[6]{Alfonso Valencia}%
\author[2]{Marco Roos}%
\author[3]{Salvador Capella-Gutierrez}%
\author[1]{Curfs Leopold}%
\author[1,1]{Chris Evelo}%
\affil[1]{Maastricht University}%
\affil[2]{Leiden University Medical Centre}%
\affil[3]{Barcelona Supercomputing Center}%
\affil[4]{Centre de Regulacio Genomica}%
\affil[5]{Leiden University Medical Center}%
\affil[6]{Barcelona Supercomputing Centre (BSC-CNS)}%


\vspace{-1em}



  
  \date{May 04, 2024}


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\selectlanguage{english}
\begin{abstract}
Rett syndrome (RTT) is a rare neurological disorder mostly caused by a
genetic variation in MECP2. Various RTT causing and benign variants in
MECP2 have been identified and due to the advent of sequencing in
clinical diagnosis new variants are identified daily. Making new MECP2
variants and the related phenotypes available provides data for better
understanding of disease mechanisms and faster identification of
variants for diagnosis. This is, however, currently hampered by the lack
of interoperability between genotype-phenotype databases. Here, we
demonstrate on the example of MECP2 in RTT that by making the
genotype-phenotype data more Findable, Accessible, Interoperable, and
Reusable (FAIR), we can facilitate prioritization and analysis of
variants. In total, 10,968 MECP2 variants were successfully integrated.
Among these variants 863 unique confirmed RTT causing and 209 unique
confirmed benign variants were found. This dataset was used for
comparison of pathogenicity predicting tools, protein consequences, and
identification of ambiguous variants. Prediction tools generally
recognised the RTT causing and benign variants, however, there was a
broad range of overlap. 19 variants were identified, which were
annotated as both, disease-causing and benign suggesting that there are
more disease-causing factors than a mutation contributing to the disease
development.%
\end{abstract}%



\sloppy


\section*{Keywords}

{\label{keywords}}

genotype-phenotype databases, FAIR data, genetic variants, rare
diseases, data integration, Rett syndrome

\section*{Introduction}

{\label{introduction}}

Rett syndrome (RTT) is a rare neurological disorder first described in
1956 by Andreas Rett occurring predominantly in females (Rett, 1966). In
most cases, the disorder is caused by a loss-of-function variation on
the X-bound gene for MECP2 (methyl-CpG-binding protein 2) (Amir et al.,
1999; Percy et al., 2007). Function affecting variations in several
other genes can cause a RTT phenotype whereas several of these are
involved directly in the up or downstream pathway of MECP2 or in the
same biological processes (Ehrhart, Sangani, \& Curfs, 2018; Lopes et
al., 2016; Lucariello et al., 2016; Sajan et al., 2017; Vidal et al.,
2017).

The disorder usually undergoes a development in four stages. In the
first stage, it is typical that pre- and postnatal development are
almost normal. However, in stage two, at the age of about 6 - 18 months,
deceleration and stop of motoric and communication learning becomes
apparent. In the third stage, patients are usually stable and typical
phenotypes include moderate to severe intellectual disability, lack of
motoric and (oral) communication skills, abnormal breathing patterns,
sleep problems, stereotypic movements (hand wringing). Further, due to
dystonia they often develop scoliosis (Neul et al., 2010). During
development, the patients often appear to have autistic features due to
the lack of communication skills. This is the reason why the disorder is
often misclassified within the autism spectrum. In stage four, the
motoric abilities continue on slowly decreasing while social and
communication skills improve. The spectrum and development of RTT
patients' phenotype was investigated in several large natural history
studies (Percy et al., 2010; Weaving et al., 2003). The phenotype
severity is thought to vary generally due to X-inactivation, mosaicism,
severity of the variation (loss of function vs. impaired function),
genetic background ((Pizzo et al., 2018) and literature cited therein)
and environmental factors.

On the molecular level, the MECP2 protein recognizes and binds to
specific methylated and hydroxymethylated DNA regions, and attracts
several other proteins to form a transcription repression block. This
block makes the DNA sequence accessible for histone deacetylases, which
increases the packing density of these regions, reducing their
transcriptional activity (Nan et al., 1998). Thus, MECP2 represses
transcription on the level of chromatin organization. MECP2 has several
phosphorylation sites that when phosphorylated, e.g., after an incoming
electric signal in a neuron, releases the DNA and allows gene
transcription (Ebert et al., 2013; Tao et al., 2009). As MECP2 regulates
the expression of many genes, the molecular downstream effects are very
broad (Ehrhart et al., 2016; Liyanage \& Rastegar, 2014). Several meta
studies on omics data revealed that the influence of MECP2 affects
dominantly dendritic connectivity, synapse function, glial cell
differentiation, mitochondrial function, mRNA processing and
translation, inflammation, and cytoskeleton (Bedogni et al., 2014; F
Ehrhart et al., 2018; Shovlin \& Tropea, 2018).

The MECP2 protein has five different domains: N-terminal domain (NTD),
methyl-DNA binding domain (MDB), transcription repressor binding domain
(TRD), intermediate domain between methyl-DNA binding and transcription
repressor binding domain also called interdomain (ID), C-terminal domain
(CTD) (Adams, McBryant, Wade, Woodcock, \& Hansen, 2007). Ballestar and
coworkers found that \emph{MECP2} variations that slightly decrease the
specific recognition of the binding site on DNA are able to cause RTT
(Ballestar et al., 2005). The majority of RTT causing missense
variations are found in the methyl-DNA binding domain, but RTT causing
variations have been found in all parts of the protein (Christodoulou,
Grimm, Maher, \& Bennetts, 2003). Some studies have found a distinctive
correlation of phenotype severity and variation type (Neul et al.,
2008), while others found a rather small or insignificant correlation
(Amir et al., 2000; Auranen et al., 2001; Huppke, Laccone, Kramer,
Engel, \& Hanefeld, 2000; Nielsen et al., 2001).

Due to the rareness of RTT (prevalence about 1:10.000 (Laurvick et al.,
2006)), it is important to share and communicate information about
disease causing variations to increase the success of identifying
genetic causes. In a previous study, we investigated the status of RTT
genotype-phenotype databases and the methods that different resources
use to share newly identified genetic variants on the example of RTT
(Townend et al., 2018). Thirteen different genotype-phenotype databases
were identified that are used to collect and share genetic variants
annotated with observed or predicted effects. Our main conclusion was
that databases store and provide information in very different ways,
such that now it is technically infeasible to query multiple databases
and combine the results in an efficient and automated way. In line with
the IRDiRC aims for rare diseases
(\emph{http://www.irdirc.org/about-us/vision-goals/}), the
bioinformatics infrastructure should contribute to store, curate and
make data about known disease causing and benign variations available.
Therefore, the interoperability of these databases needs to improve to
be able to efficiently use their contents in combination.

In this study, we show how to integrate the available RTT genetic and
phenotypic data across multiple databases and use the integrated data
for further analysis about RTT, in order to investigate variant
abundance and distribution and to test variant effect prediction
algorithms. We followed the FAIRification workflow (Jacobsen et al.,
2020) to make the data more findable, accessible, interoperable, and
reusable for computer processing. In line with the FAIR data point
specification, a combination of DCAT and Re3Data vocabularies were used
to describe the data set
{[}\emph{https://github.com/FAIRDataTeam/FAIRDataPoint-Spec/blob/v0.1.0/spec.md}{]}.
The resulting `FAIR data point' refers to two distribution formats: one
in RDF and one in CSV. RDF was used to create a self-describing, machine
interpretable version of the data using existing global ontologies. The
CSV distribution is also shared on Figshare (see DOI in results). To our
knowledge, the combined data created and used in this study is the
largest collection on disease causing and benign \emph{MECP2} variations
available at this moment.

\section*{Materials and Methods}

{\label{materials-and-methods}}

\subsection*{Workflow of genetic variant data
integration}

{\label{workflow-of-genetic-variant-data-integration}}

\subsubsection*{Data selection and
retrieval}

{\label{data-selection-and-retrieval}}

In a recent study (Townend et al., 2018), we identified 13
genotype-phenotype databases containing RTT-specific
\emph{MECP2}variation data. We evaluated each of these for specific
requirements for data integration. Data should be 1) available and
permitted to be re-used and redistributed, 2) the given description of
genetic variants should be for an unambiguous variation. The latter
means that the exact position (chromosome build and location) as well as
the variation of the genetic variants are available or retrievable by
conversion, thus, they can be described using the HGVS nomenclature. For
this study, we selected eight databases and downloaded all \emph{MECP2}
genetic variants with available linked phenotype information from each
of these databases: ClinVar (Landrum et al.,
2016),\emph{https://www.ncbi.nlm.nih.gov/clinvar/}, DECIPHER (Firth et
al., 2009),\emph{https://decipher.sanger.ac.uk}/, EVA
(\emph{http://www.ebi.ac.uk}), EVS
(\emph{http://evs.gs.washington.edu}), ExAC (Lek et al.,
2016),\emph{http://exac.broadinstitute.org/}, KMD
(\emph{https://kmd.nih.go.kr}), LOVD (Fokkema et al., 2011), MECP2
collection:\emph{https://databases.lovd.nl/shared/genes/MECP2}), and
RettBASE (Krishnaraj, Ho, \& Christodoulou,
2017),\emph{http://mecp2.chw.edu.au/}. Additionally, an anonymized
dataset from local RTT patients were included (Maastricht Rett dataset,
permission granted by Niet-WMO verklaring 2018-0597, Maastricht
University METC approval). Either the integrated download function was
used to get the data or data was extracted from HTML (see the
availability of download functions in (Townend et al., 2018). Figure 1
shows the data processing (step 1-3) and analysis (step 4) workflow of
this study.

Best place for figure 1

\subsubsection*{Liftover to enable compatible genetic variant description
formats}

{\label{liftover-to-enable-compatible-genetic-variant-description-formats}}

The MECP2 genetic variant descriptions from the different sources were
made compatible and therefore comparable by application of the HGVS
nomenclature and the same reference sequence. This is the first step to
make the data interoperable. For this, we used the reference sequence
for chromosome 23 (X) NC\_000023.11, which is part of the current human
genome reference assembly (GRCh38). Genomic descriptions were used to
ensure that variations in and outside the gene region (exonic, intronic,
up- and downstream) were included. The process of re-describing all
variants with the HGVS nomenclature using the same reference build,
liftover, was done by using the Mutalyzer position converter webtool
{[}\emph{https://mutalyzer.nl/}{]} (Wildeman, van Ophuizen, den Dunnen,
\& Taschner, 2008). Mutalyzer can perform a conversion between different
reference sequences and categories (e.g. complete genomic regions NC and
mRNA NM) but requires nomenclature compliant input. Manual correction
was performed on genetic variant descriptions that did not have the
complete and correct format for conversion but provided enough
information to correct the format.

\subsubsection*{Creation of phenotype annotated
collections}

{\label{creation-of-phenotype-annotated-collections}}

Genetic variants were assigned by their linked phenotype information to
three different categories: 1. RTT causing (verified by identification
as disease causing variant according to the requirements of the
databases), 2. benign (verified by finding them in a healthy control
subject), and 3. unknown evidence (only pathogenicity prediction scores
provided by database). These lists are collected and used for further
analysis.

\subsubsection*{Data FAIRification}

{\label{data-fairification}}

We made the prepared genetic variant and phenotype data more Findable,
Accessible, Interoperable, and Reusable for humans and computers
following the FAIR guiding principles (Wilkinson et al., 2016). The data
was made machine-readable (in RDF format) using a semantic data model
(see below) and a general-purpose FAIRifier tool (Thompson, Burger,
Kaliyaperumal, Roos, \& Bonino da Silva Santos, 2020) based on the
OpenRefine data cleaning and wrangling tool
(\emph{http://openrefine.org/}) and an RDF plugin
(\emph{https://github.com/stkenny/grefine-rdf-extension}). Similarly,
machine-readable metadata (information about the data) was generated
using the Metadata Editor (Thompson et al., 2020). The machine-readable
metadata was made available on a FAIR Data Point ((Bonino da Silva
Santos et al., 2016) https://github.com/FAIRDataTeam/FAIRDataPoint-Spec)
available via:\emph{http://purl.org/biosemantics-lumc/rettbase/fdp}. The
FAIR Data Point metadata provides URIs that resolve to the RDF and CSV
files for each of the nine sources on Figshare
(\emph{https://doi.org/10.6084/m9.figshare.c.4769153.v1}).

We applied and extended the semantic data model of a genetic variant
described in (Horst; et al., 2015) to convert the prepared data to RDF.
The model is available on GitHub
(\emph{https://github.com/LUMC-BioSemantics/rett-variant}) and describes
the important data elements of the datasets: 1) the genetic variant:
HGVS nomenclature, start/end position of the variation, and genome
build, and 2) the phenotype information that describes whether a variant
is thought to be RTT causing, benign or unknown.

\subsection*{Downstream analysis}

{\label{downstream-analysis}}

\subsubsection*{Network analysis of data distribution in RTT
databases}

{\label{network-analysis-of-data-distribution-in-rtt-databases}}

To analyse the distribution of \emph{MECP2} variations in the RTT
databases a network was created where the nodes represent databases and
the node size the number of available MECP2 variations. The thickness of
the lines connecting the databases indicate how many MECP2 variations
they share. Network visualization and analysis software Cytoscape
(Shannon et al., 2003) was used for this purpose.

\subsubsection*{Variant annotation and characterization by genomic
features}

{\label{variant-annotation-and-characterization-by-genomic-features}}

To characterize all the collected \emph{MECP2} variants, we developed an
automatic analysis pipeline for variant annotation. We used the HGVS
corrected variants to integrate custom scripts with HGVS conversion tool
from\emph{https://github.com/counsyl/hgvs}and generated VCF files for
annotation within an automated pipeline available
at\emph{https://github.com/mbosio85/HGVSparse}. Afterwards, we proceeded
to annotate variants with Ensembl Variant Effect Predictor, VEP,
(McLaren et al., 2016) v94 using the GRCh38 assembly, selecting all
available features, plus optional plugins to estimate variant
pathogenicity (i.e., PolyPhen (Adzhubei et al., 2010), SIFT (Sim et al.,
2012), MetaLR (Dong et al., 2015), CADD (Kircher et al., 2014),
FATHMM-MKL (Shihab et al., 2015) from dbNSFP and dbscSNV scores (Liu,
Wu, Li, \& Boerwinkle, 2016)) both in coding and splicing regions.

The resulting VEP annotated data was processed with R scripts, available
at\emph{https://gitlab.bsc.es/mbosio85/rtt\_summary\_plot}, to compare
RTT causing and benign variants as subsets, and to generate summary
statistics for these. The scripts allow to compare and visualize the two
classes in terms of any of the available VEP annotation features, (e.g.
variant frequency in the population, estimated variant consequence, and
conservation score of the genomic location). Using this we compared the
two datasets of RTT causing and benign variants by pathogenicity scores,
impact (i.e. estimation of the consequence of each variant on the
protein sequence), variant frequency, and genomic location. Because a
few variations appear both as RTT causing and benign, we represented
this subset of variants as a third class (``both'') in all
visualizations.

Finally, we focused on exonic missense variants and used VEP information
about the amino acid change and position within the MECP2-e2 transcript
to visualize the variation distribution across protein domains and
conserved regions (as described in (Lombardi, Baker, \& Zoghbi, 2015)).
This allowed us to make a finer characterization of differential
distribution of RTT causing and benign variants across MECP2 domains.

\section*{Results}

{\label{results}}

\subsection*{Data integration challenges
identified}

{\label{data-integration-challenges-identified}}

We encountered several challenges while integrating data from the
different RTT databases: 1) different descriptions of genetic variants
were used, 2) liftover process and limitations in automated liftover,
and 3) findability of terms of use/re-use, detailed below.

1. For the descriptions of genetic variants, the most commonly used
nomenclature was HGVS. HGVS still comes in different, correct, flavours,
e.g. using genomic or cDNA positions or different (versions of)
reference sequences, which still need conversions from one to the
other\textsubscript{,} using for instance Mutalyzer. The other most
common standard was the RS number (reference SNP identifier, from
dbSNP). These are usually linked to loci and can therefore not be used
as unambiguous identifiers for a variant. Databases that give only RS
identifiers were therefore not included in further analysis. The same
problem occurred with the annotation of diagnosis and/or phenotypes. As
described before (Townend et al., 2018) only a few databases link
original diagnostic information to the genetic information. If this
information was given different formats or definitions were used.

2. For the liftover to one common, comparable variant description
(GRCh38 (hg19)), genomic position)\emph{Mutalyzer} was used. It can be
used programmatically via API (Application programming interface) or via
Graphical User Interface (GUI). After liftover to HGVS nomenclature it
was possible for the majority of variants (90.7\% - 100\% per dataset)
to use Mutalyzer without further curation (Table 1). Nevertheless, for
up to 9.3\% of the variations in a dataset (Maastricht Rett dataset, the
average was 4.3\%, Table 1) the data needed curation due to typos,
incorrect nomenclature (e.g., symbols which are not in the official
nomenclature), or outdated/historic position description (e.g., Genbank
variation description nomenclature). Mutalyzer itself cannot deal with
insertions of a number on unknown base pairs (e.g., ins3 instead of
insATT), round brackets ( ) to indicate uncertainty (they are gone after
translation while square brackets {[} {]} to indicate different alleles
or group alleles are fine), asterisk * to indicate stop (protein)
according to the official HGVS nomenclature. These variations required
manual curation, e.g. changing round brackets to square brackets, use
Mutalyzer to do the liftover, changing square brackets back to round
brackets. Furthermore, it is currently not possible to do a direct
liftover from one genomic reference sequence to another (e.g.,
NC\_000023.10:g.153282026G\textgreater{}A to
NC\_000023.11:g.154016575G\textgreater{}A) due to the size of the
reference sequence. At the moment, this must be done in two steps via
transcript (NC -\textgreater{} NM -\textgreater{} NC).

3. The permission to reuse and redistribute was difficult to find for
some databases (RettBase, KMD).

Best place for Table 1

\subsection*{Size and content of the FAIR
dataset}

{\label{size-and-content-of-the-fair-dataset}}

\textbf{Number of disease causing and benign \emph{MECP2} genetic
variants available}

Based on the 13 genotype-phenotype databases identified in (Townend et
al., 2018), the inclusion criteria for this study were not met by
DisGeNET, dbSNP, dbVAR, Caf\selectlanguage{ngerman}é Variome, and HGMD. DisGeNET, dbSNP and
dbVAR did not provide unambiguous descriptions of variations as the RS
identifier only indicates a location of polymorphism and needs
evaluation of the, sometimes ambiguous, additional information about the
nucleotide change. Café Variome provided only protein change which,
although very relevant itself, cannot be translated back to an
unambiguous genetic change. HGMD, the only commercial database, did not
allow re-use and re-distribution of the content. The eight databases
that did fulfil our inclusion criteria and data previously anonymized
from local RTT patients were used in this study (see Table 2). At the
time of research, in total 12,158 \emph{MECP2} variation entries were
found in these databases. The databases contained between 34 (DECIPHER)
and 4,706 (RettBASE) MECP2 variations (Table 2). Between 15\% and 100\%
of these variations were unique database entries (occur only once in one
single database). Multiple entries of one variation were found
frequently in disease specific databases, giving an indication of the
abundance of this variant and also confirming its pathogenicity. In
total we identified 4,573 RTT causing \emph{MECP2} variants (of which
863 were unique) that annotate genetic information with diagnosis
(RettBase, ClinVar, Maastricht Rett dataset, KMD) and/or clear phenotype
descriptions (DECIPHER) clearly stating that they cause RTT (or similar
e.g., X-linked mental retardation) (intake criteria Sup. Table 1). We
identified 617 benign \emph{MECP2} variants, of which 209 were unique,
from two of the databases that annotate with diagnosis information
(RettBase and ClinVar). These were clearly stated to be benign. 19
variants were found annotated both as RTT causing and benign (Sup. Table
2).

In total, we collected 12,158 \emph{MECP2} variants, which resulted in a
collection of 10,968 (5,038 unique) curated and integrated variants.
These processed datasets are available as csv on gdrive (\emph{link}).
Out of the 10,968 curated \emph{MECP2} variations only 11 occur in more
than 1\% of all database entries, and these account for 53.7\% of all
database entries (data not shown).

Best place for Table 2

The 863 unique RTT causing variations are distributed over 4,573
database entries. Also here, only 12 variations are found in more than
1\% of all database entries (Table 3) and these 12 make in total 60\% of
the database entries. The most abundantly found \emph{MECP2} variations
were found in seven of nine databases (Table 3). The majority (eight) of
these are C\textgreater{}T transitions at CpG hotspots (Wan et al.,
1999). These eight \emph{MECP2} hotspot variations contribute to 49.7\%
of all \emph{MECP2} variation entries. The most abundant
\emph{MECP2}variation in this dataset is
NC\_000023.11:g.g.154031355G\textgreater{}A
(NM\_004992.3:c.473C\textgreater{}T, NP\_004983.1:p.(Thr158Met)) with
463 counts (Table 3). In total 54\% of RTT causing variations are a
deletion, 9\% insertion, 37\% substitution, and 9\% duplication. Many of
the database entries contain multiple variations (e.g., a deletion and
insertion) on the same or different chromosomes. 452 RTT causing
variations have only one single database entry and of these 269 are a
deletion, 43 insertion, and/or 153 substitution.

Best place for Table 3

\textbf{Distribution of the variants across databases}

Table 2 shows the number of unique MECP2 variations for each
investigated database. The relative number of unique MECP2 variations in
each of the databases differ. The number of how many variations in one
database are unique can also give an indication whether it is a database
focusing on collecting pathogenic variations (RettBase, ClinVar,
Maastricht Rett dataset, DECIPHER) (exception KMD) or general population
sequencing results (no disease annotation) (EVA, LOVD, ExAC) (exception
EVS). LOVD for example lists all different variations and provides
background information about the abundance of one variation in the
variations' information sheet. RettBase also gives the reference from
where this specific entry is from. From Table 2 it also becomes clear
that every database has unique MECP2 variations, which are found in no
other database. The number of such unique variants differ between 3,329
(EVA) and 1 (EVS).

Figure 2 shows the size of \emph{MECP2} variation collections in the
different databases, their shared and their unique variations. There are
databases that focus on collections of genome and/or exome sequencing
data of mostly healthy individuals (EVA, EVS, ExAC), curated collections
of disease causing variants (LOVD, RettBase, ClinVar, Decipher), and
hospital derived collections (KMD, Maastricht Rett dataset). The overlap
or shared MECP2 variations between databases can be explained by the
occurrence of this variation in multiple patients, data exchange between
databases, or by recruitment from the same resources. For instance, ExAC
and LOVD share 559 unique variants, LOVD and ClinVar 546, LOVD and
RettBase 512, RettBase and ClinVar 504.

Best place for figure 2

\subsection*{Biological questions answered using this
data}

{\label{biological-questions-answered-using-this-data}}

\subsubsection*{Variant pathogenicity prediction vs. curated
datasets}

{\label{variant-pathogenicity-prediction-vs.-curated-datasets}}

To explore differences between RTT causing and benign
\emph{MECP2}genetic variants we analyzed the annotated results from VEP
(see Methods) from six descriptive features (Figure 3). We chose to
visualize the obtained scores about conservation (i.e., PolyPhen),
pathogenicity estimation scores (i.e., SIFT, CADD, MetaLR, FATHMM-MKL),
and the variant frequency in normal population from GnomAD (Lek et al.,
2016) (i.e., GnomAD\_AF).

We split variants by benign, both and RTT causing, as we identified a
subset of 19 variants appearing in both datasets. Overall, we see
expected results: the RTT causing variants were found to be in positions
significantly more conserved than the benign or both variants (Figure 3,
PolyPhen (Wilcoxon test)), as well as less frequent than benign
variations even though, all variants presented here are not abundant in
the normal population (Figure 3, GnomAD\_AF). Analysis of the obtained
estimation of pathogenicity from multiple scores (Figure 3 panels SIFT,
CADD, MetaLR and FATHMM-MKL), shows that RTT causing variants are on
average predicted as more damaging than the benign and both variants (p
\textless{} 0.0001 in all cases after applying Wilcoxon test). Note that
SIFT associates more pathogenic variants to lower scores, whereas CADD,
MetaLR and FATHMM-MKL associates more pathogenic variants to higher
scores. MetaLR is better than the other three pathogenicity scores in
distinguishing benign and RTT causing variant types. This may be because
this novel meta-score integrates more features than the other three
prediction tools, amongst other pathogenicity scores and frequency
information.

The characterization of the both group is located in three of five
predictions between the benign and RTT causing, and in two of five
closer to the benign group.

Best place for figure 3

\subsubsection*{Distribution of pathogenic and benign missense variations
to protein
domains}

{\label{distribution-of-pathogenic-and-benign-missense-variations-to-protein-domains}}

In this experiment the position of RTT causing and benign missense
variants in different domains and conserved regions of MECP2 are
compared (Table 4 and Figure 4). Most RTT causing missense variations
are found in the methyl-DNA binding domain (MDB) (68.3\%) and in the
transcription repressor binding domain (TRD). However, at lower
frequencies, RTT causing missense variations can also be found in the
other domains. The benign variants are most frequent in the C-terminal
domain (55.1\%) and the interdomain (28.1\%), but can likewise also be
found in the other domains at lower frequencies. The distribution across
the conserved regions of \emph{MECP2} shows that 93.6\% of the missense
RTT causing variants are found in conserved regions while only 16.3\% of
the benign variants are found in conserved regions.

Best place for figure 4

Considering protein consequences, in 1350 cases, which is about half of
the RTT causing single nucleotide mutations, are protein truncating
variants, changing an Arginine into a stop codon. Also frequently,
Arginine is changed into a Cysteine (533) or Tryptophan (179) which are
major changes considering protein 3D structure. The average BLOSUM62
value of all amino acid changes for the RTT causing dataset is -1.8. For
the benign \emph{MECP2} variations, the most abundant variations are
silent (= not amino acid changing), coding for Serine (65), Threonine
(44) and Proline (40). The most abundant amino acid change is Glutamic
acid to Lysine (33) and the average BLOSUM62 value of all amino acid
changes indicates with -0.3 less severe consequences for the protein
structure than the RTT causing group.

\section*{Discussion}

{\label{discussion}}

\subsubsection*{Added-value of integration of data across different
sources}

{\label{added-value-of-integration-of-data-across-different-sources}}

This is to our knowledge the first study that integrates genetic
variation data from multiple databases on \emph{MECP2} . Despite best
efforts of individual sources to reach the largest possible coverage,
our results demonstrate that the number of usefully annotated variants
increases when databases are combined. The greatest advantage of the
integrated approach is therefore that more variants become available for
further research and diagnosis. This is especially interesting for rare
diseases which have relatively small study populations. By mapping to a
common reference sequence, the information of different sources becomes
comparable and we are getting closer to the ``true'' number of variants
known. In this study, we were able to increase the previously estimated
numbers of a few hundred RTT causing unique sequence variations to 863.
However, databases, at least the active ones, get regular updates and
input of data. In the time from the beginning of this study the number
of variants in e.g. RettBase increased within six months from 4738
(March 2018, (Townend et al., 2018)) to 4757 (November 2018) to 4806
(NM\_004992.3, April 2020). Consequently, the number of 863 known RTT
causing variants is likely outdated when this study is published. We
argue that it is unrealistic to assume that any single database will
ever be completely comprehensive, unless it automatically pulls in
updates from other databases. A possible contribution to the solution of
this problem would be to create the combined list of pathogenic variants
by automated workflows that find and summarize data from across
databases on demand or continuously. To make that possible we need to
standardize how databases provide data for machine processing. The role
of FAIR data principles to achieve this is discussed later in more
detail.

This integrated dataset gives the possibility to study abundance and
prevalence of certain variations in a larger population than any of the
study populations published before. There are several studies on
relatively small (Das, Raha, Sanghavi, Maitra, \& Udani, 2013; Inui et
al., 2001) or large populations (e.g. (Bienvenu et al., 2002; Percy et
al., 2010)) that have published their data in the previous years.
(Bienvenu et al., 2002) analysed 301 different MECP2 alleles in a French
population and found 69 different variations, which cause 64\% of RTT.
They identified NP\_004983.1:p.R168*, R255*, R270*, T158M, and R306C
(Table 5) as the most abundant variations and 59 variations were found
in only one or two patients. In the list from the US national history
study (819 participants (Percy et al., 2010)) the variations R106W,
R133C, T158M, R168*, R255*, R270*, R294*, and R306C were responsible for
more than 60\% of RTT. The MECP2 variation content of RettBase was
analyzed recently by (Krishnaraj et al., 2017) and the following eight
hotspot variations are responsible for a total of 47\% of RTT cases (of
total number of \emph{MECP2} entries was at that time 4668, disease
causing and benign): R106W, R133C, T158M, R168\emph{*} , R255\emph{*} ,
R270*, R294*, and R306C. (Percy et al., 2007) provides information about
eleven more datasets from different countries.

Although our study resulted in a different ranking of the eight hotspots
we could confirm these as the most abundant ones which occur in our
dataset in 54.6\% of all RTT causing database entries. All eight hotspot
mutations are C\textgreater{}T transitions leading in seven of eight
cases to a change from Arginine to a stop codon, Cysteine or Tryptophan
which are changes with a high probability to change the 3D structure of
the protein. The special vulnerability of certain Cytosine positions to
errors in base excision repair was described before (Wang, Tang, Lai, \&
Zhang, 2014).

Best place for Table 5

In our integrated dataset most pathogenic mutations in \emph{MECP2}occur
in the methyl-DNA or transcription repressor binding domain. This has
been found and confirmed before (Ballestar, Yusufzai, \& Wolffe, 2000;
Ghosh, Horowitz-Scherer, Nikitina, Gierasch, \& Woodcock, 2008; Heckman,
Chahrour, \& Zoghbi, 2014; Krishnaraj et al., 2017). The functionality
of the methyl-DNA binding domain is reported to be extremely sensitive
for changes (Ballestar et al., 2000). The importance of the domain also
shows from the observation that a construct consisting only of
methyl-DNA binding and transcription repressor domain could preserve
some basic functions of MECP2 (Tillotson et al., 2017). There is also a
clear distinction between conserved and non-conserved regions. As
expected, disease-causing mutations occur much more often in the
conserved regions. However, the data shows clearly that mutations in all
domains, both conserved and non-conserved regions, can cause RTT. The
open question here remains how much influence does a particular mutation
has and how much is contributed by other genetic aspects or
environmental influences. This question becomes more important
considering the discovery of variants that in one individual can be
benign and RTT causing in another.

\subsubsection*{How can the same variation be benign AND cause RTT in
different
individuals?}

{\label{how-can-the-same-variation-be-benign-and-cause-rtt-in-different-individuals}}

The majority of the \emph{MECP2} genetic variations, which are described
as RTT causing in one, and benign in another database entry, are
predicted to be benign (Figure 3). Possible explanations why a variant
can be disease causing in one individual and benign in another could be
due to the location of the gene on the X chromosome which may result in
a subclinical phenotype in females but a fully-fledged RTT in male
patients. The sex of patients is usually not given in these
genotype-phenotype database. Also, X inactivation patterns (Weaving et
al., 2003) and genetic background related to other participating genes
in \emph{MECP2} related pathways (Pizzo et al., 2018) influence the
severity of a rare monogenic (X-linked) disease and can possibly even
save individuals with a documented pathogenic variation from disease
development (Chen et al., 2016). In principle, patients could also have
an unreported second mutation that could cause the effect either alone
or through epistatic interaction.

For several variations, a high pathogenicity score was predicted but
they were still documented in healthy individuals. This has been
observed before in a girl with RTT who inherited a germline disease
causing \emph{MECP2} c.1160C\textgreater{}T (P387L,
NC\_000023.11:g.154030668G\textgreater{}A) variation from a healthy (!)
father (Bhanushali, Mandsaurwala, \& Das, 2016). Exactly this variant we
found only in our RTT causing dataset (documented in ClinVar and
RettBase), the annotation with the benign outcome was not added to one
of these databases yet. To unravel the different influences
of\emph{MECP2} variations in the context of an individual patient, we
need to evaluate how genetic background can affect other process related
genes. For this, genotype-phenotype databases with detailed phenotype
capture will be highly important and data integration tools and methods
must be developed to investigate this further.

There is also a significant number of patients (54 in our integrated
dataset) whose \emph{MECP2} gene carries more than one variation. In
these cases, we presume that the disease is caused by one
(pathogenic)\emph{MECP2} variation while the other variation \emph{can}
be benign if it occurs alone. Other possibilities are positive or
negative epistatic effects if these variants occur on the same allele.
All of these possibilities may lead to wrong classification of variants.

\subsubsection*{Making the MECP2 genetic variant data
FAIR}

{\label{making-the-mecp2-genetic-variant-data-fair}}

The FAIR guiding principles have emerged from analysing the general, and
often repeated, process that data scientists go through when preparing
data from multiple sources for data integration and analysis
The\emph{MECP2} genotype-phenotype data from this study were retrieved
from nine heterogeneous resources, which we prepared for analysis by
making them more FAIR. This was first and foremost done to enable
integration of the data for analysis as correctly as possible, which
also facilitates integration with other interoperable data such as
protein functionality data from for instance UniProt, NextProt or Phyre
databases. Another reason was to ensure reusability of the integrated
data for other research studies. Note, all the FAIRified resources allow
redistribution.

The FAIRified data was described with machine-readable metadata and
distributed at a new location, which prospectively allows other
researchers to reuse this data. Thus, as data users, we made the data
FAIR after retrieving them from their respective distributions. This was
necessary, because the way that the data were provided by the different
sources was not sufficiently uniform for machines to integrate multiple
sources. The disadvantage of leaving the implementation of FAIR
principles to data consumers is that they are more likely to make
mistakes in the interpretation of the meaning of the data, which may not
be the same as the sources. Ideally, data are made FAIR at the source to
minimize that risk and optimize transparency. This would have allowed us
to directly use the data in automated workflows that can be run
regularly to update our findings.

\subsubsection*{Next step:
automatization}

{\label{next-step-automatization}}

The first step in the integration of genetic variation data across
multiple resources was a time consuming study, which included a lot of
manual data acquisition and curation. Additionally,
analysing\emph{MECP2} variations as the causative entities in RTT was
the leading example in this study but this method should be available
and applicable for any other gene, too. The next step therefore would be
to automate this process. This can only be efficient and robust when the
data resources provide an interface by which machines can predict how to
find, access, and use their data. This interface is complementary to the
specific features that each source provides for its users. FAIR
principles provide useful guidance here: they do not prescribe any
specific implementation, but do enforce a higher level of transparency
for machines. In other words, the feasibility and quality of automation
depends on the resources being FAIR. Consequently, a workflow can be
developed and used as a tool to retrieve the known disease causing,
benign, or other variants of yet unknown significance for any gene. The
FAIRification of the databases is a process, which has already started
and will hopefully continue to support efficient data science.
Interesting for the implementation of FAIR principles are activities
towards new standards for processing variant data, such as by the
genetic variant workstream of the Global Alliance for Genomics and
Health as well as its GA4GH Beacon project, which allows cross database
search for variants. Generic FAIR services and service specifications,
such as produced in FAIRtrain, FAIRsFAIR, and EOSC-Life (e.g.
FAIRsharing.org and the FAIR Data Point specification (Bonino da Silva
Santos et al., 2016) https://github.com/FAIRDataTeam/FAIRDataPoint-Spec,
enable the general task of identifying and visiting interoperable RDF)
from restricted access databases.

\section*{Acknowledgements}

{\label{acknowledgements}}

The authors would like to thank the Mutalyzer team for support and
feedback, Henk van Kranen for support in liftover of ancient genetic
variant descriptions, Eric Smeets for collection of the Maastricht Rett
dataset, and Mark Wilkinson for information about automatization.

\textbf{Conflict of Interest Statement}

The authors report no conflict of interest.

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Shovlin, S., \& Tropea, D. (2018). Transcriptome level analysis in Rett
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Sim, N. L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., \& Ng, P. C.
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Tao, J., Hu, K., Chang, Q., Wu, H., Sherman, N. E., Martinowich, K., . .
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Thompson, M., Burger, J., Kaliyaperumal, R., Roos, M., \& Bonino da
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Tillotson, R., Selfridge, J., Koerner, M. V., Gadalla, K. K. E., Guy,
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\par\null

\section*{Tables}

{\label{tables}}

Table 1: Overview for the different databases, their phenotype
annotation format, number of available \emph{MECP2} variants, and data
liftover success rates using automated (Mutalyzer) and manual curation.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Phenotype annotation format & Database & Number of \emph{MECP2} or RTT
variations & Number of variations meeting annotation criteria & Number
of variations with sufficient genetic annotation & Number of variations
with sufficient genetic annotation & Number of variations with
sufficient genetic annotation & Number of variations with sufficient
genetic annotation\tabularnewline
& & & & Using Mutalyzer & Using Mutalyzer & After manual curation &
After manual curation\tabularnewline
& & & & \# & \% & \# & \%\tabularnewline
Phenotype & DECIPHER & 34 & 25 & 25 & 100 & 25 & 100\tabularnewline
Diagnosis & Maastricht Rett dataset & 429 & 428 & 388 & 90.7 & 393 &
91.8\tabularnewline
& ClinVar & 1,134 & 743 & 681 & 91.7 & 726 & 97.7\tabularnewline
& RettBase & 4,705 & 3,986 & 3,798 & 95.3 & 3,980 & 99.8\tabularnewline
& KMD & 35 & 35 & 35 & 100 & 35 & 100\tabularnewline
Pathogencity scores & EVS & 190 & 190 & 190 & 100 & 190 &
100\tabularnewline
& LOVD & 808 & 808 & 738 & 91.3 & 804 & 99.5\tabularnewline
& EVA & 4,226 & 4,226 & 4,193 & 99.2 & 4216 & 99.8\tabularnewline
& ExAC & 599 & 599 & 559 & 93.3 & 599 & 100\tabularnewline
TOTAL & TOTAL & 12,158 & 11,040 & 10,607 & average 95.7\% & 10,968 &
average 98.7\%\tabularnewline
\bottomrule
\end{longtable}

Table 2: Numbers of total and unique \emph{MECP2} variations in each
database.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Database & Number of total \emph{MECP2} variation entries & Number of
unique \emph{MECP2} variations & Number of unique \emph{MECP2}
variations & Number of unique variations which occur only in this
database\tabularnewline
& & \# & \% of total \emph{MECP2} variation entries &\tabularnewline
EVA & 4,226 & 4,192 & 99.2 & 3,329\tabularnewline
LOVD & 808 & 802 & 99.3 & 144\tabularnewline
RettBase & 4,705 & 740 & 15.7 & 209\tabularnewline
ExAC & 599 & 599 & 100.0 & 40\tabularnewline
ClinVar & 1,134 & 716 & 63.1 & 126\tabularnewline
EVS & 190 & 95 & 50.0 & 1\tabularnewline
Maastricht Rett dataset & 429 & 68 & 15.9 & 34\tabularnewline
KMD & 35 & 35 & 100.0 & 9\tabularnewline
DECIPHER & 34 & 23 & 67.6 & 2\tabularnewline
\bottomrule
\end{longtable}

Table 3: Most abundant RTT causing variants in this study.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Genomic position +\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
count\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\%\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
cDNA ++ and protein change \selectlanguage{ngerman}§\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Effect and previous reports\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
g.154031355G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
463\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
10.1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
c.473C\textgreater{}T, p.(Thr158Met) \selectlanguage{ngerman}¶\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Missense variation (Bienvenu et al., 2002; Krishnaraj et al., 2017;
Percy et al., 2010)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
g.154031326G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
409\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
8.9\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
c.502C\textgreater{}T, p.(Arg168*) \selectlanguage{ngerman}¶\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Nonsense variation, leading to truncation (Bienvenu et al., 2002;
Krishnaraj et al., 2017; Percy et al., 2010)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
g.154031065G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
345\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
7.5\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
c.763C\textgreater{}T, p.(Arg255*) \selectlanguage{ngerman}¶\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Nonsense variation, leading to truncation (Bienvenu et al., 2002;
Krishnaraj et al., 2017; Percy et al., 2010)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
g.154031020G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
309\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
6.8\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
c.808C\textgreater{}T, p.(Arg270*) \selectlanguage{ngerman}¶\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Nonsense variation, leading to truncation (Bienvenu et al., 2002;
Krishnaraj et al., 2017; Percy et al., 2010)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
g.154030948G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
281\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
6.1\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
c.880C\textgreater{}T, p.(Arg294*) \selectlanguage{ngerman}¶\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Nonsense variation, leading to truncation (Krishnaraj et al., 2017;
Percy et al., 2010)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
g.154030912G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
279\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6.1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
c.916C\textgreater{}T, p.(Arg306Cys) \selectlanguage{ngerman}¶\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Missense variation (Bienvenu et al., 2002; Krishnaraj et al., 2017;
Percy et al., 2010)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
g.154031431G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
249\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
5.4\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
c.397C\textgreater{}T, p.(Arg133Cys) \selectlanguage{ngerman}¶\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Missense variation (Krishnaraj et al., 2017; Percy et al., 2010;
Zappella, Meloni, Longo, Hayek, \& Renieri, 2001)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
g.154032268G\textgreater{}A\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
161\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
3.5\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
c.316C\textgreater{}T, p.(Arg106Trp) \selectlanguage{ngerman}¶\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Missense variation (Krishnaraj et al., 2017; Percy et al., 2010)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
g.154031373G\textgreater{}C\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
80\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1.7\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
c.455C\textgreater{}G, p.(Pro152Arg)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Missense variation (Cheadle et al., 2000)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
g.154031022delC\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
67\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1.5\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
c.806delG, p.(Gly269fs)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Frameshift deletion leading to missense (Das et al., 2013)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
g.154030621\_154030664del44\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
50\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
c.1164\_1207del44, p.(Pro389*)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Frameshift deletion leading to truncation\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
g.154030631\_154030671del41\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
49\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
c.1157\_1197del41, p.(Leu386fs)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Deletion leading to frameshift\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

RefSeq + NC\_000023.11, ++ NM\_004992.3, \selectlanguage{ngerman}§ NP\_004983.1

¶ one of the eight hotspot variations (Wan et al., 1999)

\textbf{Table 4:} Location of RTT causing and benign missense variants
in different domains and conserved regions of MECP2.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{RTT causing}\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Benign}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Domain length (\% of total)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\% of missense variations per region\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\% of missense variations per region\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Domains\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
N-terminal domain\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
78 (16.0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Methyl-DNA binding domain\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
84 (17.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
68.3\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Interdomain\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
45 (9.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.9\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
28.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Transcription repressor domain\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
103 (21.2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
24.1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
C-terminal domain\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
176 (36.2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5.5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
55.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Conserved regions\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Conserved regions\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
93.6\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
16.3\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 5: Comparison of most abundant \emph{MECP2} variations in
different studies.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Sample size and citation & Variations mentioned in studies (abundance in
\% if known) & Variations mentioned in studies (abundance in \% if
known) & Variations mentioned in studies (abundance in \% if known) &
Variations mentioned in studies (abundance in \% if known) & Variations
mentioned in studies (abundance in \% if known) & Variations mentioned
in studies (abundance in \% if known) & Variations mentioned in studies
(abundance in \% if known) & Variations mentioned in studies (abundance
in \% if known)\tabularnewline
4573 variations annotated with RTT causing (this study) & Thr158Met
(10.1) & Arg168* (8.9) & Arg255* (7.5) & Arg270* (6.8) & Arg294* (6.1) &
Arg306Cys (6.1) & Arg133Cys (5.4) & Arg106Trp (3.5)\tabularnewline
301 RTT patients (Bienvenu et al., 2002) & Thr158Met (7.8) & Arg168*
(11.5) & Arg255* (10.9) & Arg270* (10.5) & & Arg306Cys (6.8) &
&\tabularnewline
819 RTT patients (Percy et al., 2010) & Thr158Met (11.0) & Arg168*
(10.4) & Arg255* (11.0) & Arg270* (5.5) & Arg294* (6.3) & Arg306Cys
(6.9) & Arg133Cys (3.8) & Arg106Trp (3.1)\tabularnewline
RettBase 4668 total entries (Krishnaraj et al., 2017) & Thr158Met &
Arg168* & Arg255* & Arg270* & Arg294* & Arg306Cys & Arg133Cys &
Arg106Trp\tabularnewline
\bottomrule
\end{longtable}

RefSeq: NP\_004983.1:p.

\section*{Figure legends}

{\label{figure-legends}}

Figure 1: Schematic drawing of the workflow of this study: data
collection, preparation, FAIRification and downstream analysis.

Figure 2: Network illustrating the number of unique and
overlapping\emph{MECP2} variations within and between nine Rett syndrome
databases: DECIPHER, Maastricht Rett dataset (MRD), ClinVar, Rettbase,
KMD, EVS, LOVD, EVA, and ExAC. Each node (circle) represents a database.
The node size correlates with the number of variants (between 30 and
4775), the edge thickness correlates with the number of
overlapping/shared variants between the two databases (between 0 and
500). The colour of the charts in the nodes represent the proportion of
unique variants (blue) versus variants shared with other databases
(yellow).

Figure 3: Boxplots comparing prediction score value distribution
calculated by different tools from the benign, both and RTT
causing\emph{MECP2} genetic variants. The effect prediction was done
based on conservation score (PolyPhen), four pathogenicity scores (SIFT,
CADD, MetaLR, and FATHMM.MKL), and the variant allele frequency in the
GnomAD dataset.

Figure 4: Distribution of RTT causing and benign \emph{MECP2} missense
variations. Amino acid positions correspond to isoform MECP2-e2 (the
result of translation initiated at exon 2). Frequency is represented as
the percentage of missense variations falling in each position, from the
total of missense variations in cases or controls. In A) each MECP2
domain is coloured differently, while in B) conserved deletions are
coloured in yellow. Domain abbreviations: N-terminal domain (NTD),
methyl-DNA binding domain (MDB), interdomain (ID), transcription
repressor binding domain (TRD), C-terminal domain (CTD).

\section*{Appendices}

{\label{appendices}}

Supplementary table 1 and 2\selectlanguage{english}
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