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\begin{document}

\title{Identifying novel putative genes linked to stuttering in highly
multiplex Indian families using exome sequencing}



\author[1]{SRIKUMARI CR}%
\author[1]{NANDHINI G}%
\author[1]{Chandru  J}%
\affil[1]{University of Madras}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Abstract Stuttering is a complex speech disorder and heritability of
this trait is persuasive, with multiple afflicted families showing
phenotypic segregation across generations, yet no conclusive genetic
etiology could be identified. Analyzing multiplex families using exome
sequencing(ES) may help in identification of putative genes and scope
for understanding the mutational burden for speech implicated pathways.
In this study ES was performed in six individuals from two clinically
well characterized, multiple affected, south Indian families (STU-65 and
STU-66) showing stuttering across five generations. From ES to variant
prioritization, a sequential bioinformatics approach was implemented to
search for putative gene targets. In the two multiplex families studied,
ES data analysis resulted in an enriched list of 14 genes (with
variants) (COL4A2,COL6A3,COL6A6,ITGAX,LAMA5,ADAMTS9,CSGALNACT1,
TMOD2,HTR2B,RSC1A1,TRPV2,WNK1,ARSD and SPTBN5) involved in neural
functions. Additionally, a homozygous variant in NLRP11 gene and a
heterozygous variant in NAGPA gene were identified in STU-65 family that
needs further confirmation. Our results support the fact that stuttering
is a polygenic disorder. The putative gene targets identified in our
study can drive the research prospects to understand the underlying
mechanisms. We hypothesize multiple and combined mechanisms to be
involved in the genesis of stuttering. Keywords: Stuttering, exome
sequencing, neural pathways%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Stuttering is a disorder of fluency that disrupts the normal flow of
speech wherein people who stutter (PWS) know what they want to say but
have trouble saying it. The condition is generally outgrown by children
but it persists in some as an enduring problem. Emergence of stuttering
is multifactorial that includes genetic, environmental and neurological
factors. Environmental factors though not a cause, can interact with
genes (cause epigenetic modifications) and may aggravate the dysfluency
under conditions of stress, anxiety and also depends on family dynamics,
child's temperament, etc\textsuperscript{1}.

The evidence for genetic factors were indicated in multiple studies
worldwide\textsuperscript{2}. Worldwide search for causative genes began
with linkage studies, as applicable to any inherited disorder, to
elucidate the genetic and non-genetic etiology relevant to stuttering.
Nevertheless since stuttering is a complex trait, linkage analysis
resulted in mapping multiple loci across studies. Identification of
genes was not possible until studies focused on consanguineous
families\textsuperscript{3}. The causative genes linked to stuttering
were identified to be \emph{GNPTAB} , \emph{GNPTG} , \emph{NAGPA}
and\emph{AP4E1} that explains 20\% of stuttering
cases\textsuperscript{4}. All these genes point to intracellular
trafficking deficits, an emerging concept in neurological disorders,
implicating lysosomal dysfunction but the mechanism linking these genes
to the phenomena of stuttering is unknown. However these deficits are
said to have unique impact on specific neurons responsible for speech. A
recent mouse model study tried to illuminate the neurons that are
central to the mystery of stuttering and indicated that abnormality lies
in astrocytes (essential for nerve activity) of corpus callosum (largest
white matter structure). This limited astrocytes population slows down
inter-hemispheric communication by a tiny bit, noticeable only in
speech\textsuperscript{5}.

Speech production requires efficient communication between central
nervous system (cortical and sub-cortical regions) and peripheral
nervous system (cranial nerves that innervate respiratory, phonatory,
articulatory and resonance systems). Age of onset of stuttering
parallels the developments in anatomy of speech system, language and
articulatory skills. Interferences in these maturational processes may
contribute to stuttering, hence brain imaging studies in children could
enhance our understanding of pathophysiology\textsuperscript{6}.

Brain imaging studies revealed structural and functional connectivity
differences in Children Who Stutter (CWS) involving neural networks that
support self-initiated timing of speech movements (BGTC - Basal Ganglia
Thalamo-cortical loop, SMA and Putamen) and in integration of auditory
feedback to speech motor control process. Structural (white matter)
connectivity involving corpus callosum and those among the putamen and
cortical motor and auditory regions is attenuated in the left hemisphere
in children who stutter\textsuperscript{7,8}. Over activity of the
dopamine neurotransmitter has been implicated as a potential factor in
developmental stuttering\textsuperscript{9}. Thus it is evident that the
problem is not confined to a single part of brain rather connecting
different parts of the brain.

The findings in stuttering research - overactive dopamine, deficits in
neural circuits and genes involved in cellular waste disposal - all
point to communication deficits. All these findings hint that stuttering
may result from slight delay in communication with different parts of
the brain.

Research studies on the genetics of stuttering in India are limited and
nearly unexplored. Our preliminary genetic epidemiology study in a large
cohort of 74,544 school children in the state of Tamil Nadu, reported
the prevalence of stuttering to be\textbf{0.46\%}
\textsuperscript{10}\textbf{.} Investigational mutation screening for
the recurrence of previously reported stuttering genes showed a minimal
resolution of 3.1\% (2/64) that could be ascribed to these genes but
remains elusive, which is reported as the first paper in this series.

Since stuttering is a complex trait that segregates in families,
analyzing multiplex families would help in either identification of
highly penetrant rare variants or variants that are common in affected
individuals. Subsequently this forms the concept of this paper as the
second in this series. ES was employed in two multiple affected families
from existing database\textsuperscript{10}. The aim of the study is to
identify new candidate genes for stuttering, by analyzing the pathogenic
variants inherited in the multiplex families but not present in public
databases.

\textbf{RESULTS}

ES is a powerful technology to reveal the complex genetic architecture
and unravel the missing heritability. ES was performed in six
individuals from two highly multiplex families with severe stuttering:
STU-66 nuclear family (figure 1) comprising of affected father (IV-2),
unaffected mother (IV-3), unaffected brother (V-4) and the proband
(V-5); STU-65 family (figure 2) includes two affected siblings
(V-33;V-35). Further details of the families are mentioned in the
methodology section.

\textbf{Variant filtering}

\textbf{STU-66 family:} ES data of the four members, resulted in 73,426
variants in affected father, 73,689 in unaffected mother, 73,141 in
unaffected sibling and 73,198 variants in proband.

Applying variant filter criteria (detailed in methodology), resulted in
a mean of 2245 variants (ranging from 2188-2296). The variant data of
the four individuals were analyzed using Venn diagram that produced 15
possible combinations of the data (figure 3). One such combination
revealed 276 genes that were common to affected father and the proband
(figure A1). On filtering further for the common variants in them ensued
260 variants harbored in 248 genes. Of these 260 common variants, 218
were previously known (dbSNP database) and 42 were novel variants
(hitherto unreported). Only 85 of the 218 known variants and 12 of the
42 novel variants fall in the coding region. Applying a four step
exclusion criteria (detailed in methodology) to known variants,
shortened the list to 18 genes (all heterozygous variants). The genes of
interest thus sums up to 19 known variants and 12 novel variants, spread
across 30 genes (table A1).

\textbf{STU-65 family:} ES data of the two affected sibs (V-33, proband
and V-35, affected brother) resulted in 58,346 and 51,062 variants
respectively. Applying similar criteria as described above using Venn
diagram approach, 294 genes were found common to the sib-pair (figure
A2). The downstream analysis of the variants observed in the 294 common
genes is depicted in the flow chart (figure A3). Those variants common
to both sibs were sorted out resulting in 161 common variants harbored
in 153 genes. Of these, 158 were known variants and 3 were novel
variants that were all found in the coding regions. Applying the same
four step exclusion criteria (as in STU-66) to 158 known variants,
narrowed down the gene list to 34 variants (4 homozygous and 30
heterozygous). Thus the genes of interest sum up to 31 known and 3 novel
variants, in 34 genes. (table A2).

No significant intronic/UTR variation was detected, other than the
indels and missense variations, in the exome data of both families.
Considering stuttering to be a less common trait, an allele count of
more than 20 in gnomAD database were excluded, narrowing the list to 35
genes.

\textbf{Comparison of variants with available databases}

In the STU-66 family, only one missense variant in \emph{COL4A2} gene
(c.461G\textgreater{}A; p.Gly154Glu) previously reported in a disorder
related to fluency of language\textsuperscript{11} was observed in our
study. \emph{COL4A2} encodes type IV collagen that forms a major
structural component of basement membrane and are involved in
cerebro-vascular disease that include porencephaly and white matter
lesions\textsuperscript{12,13}.

Since the STU-65 family had consanguinity for three generations, we
scrutinized homozygous variants first. Only one \emph{NLRP11} variant
(c.1052G\textgreater{}A; p.Arg351Gln) was present in homozygous
condition in both the sibs. \emph{NLRP11} is a member of NOD like
receptor protein with pyrin domain responsible for formation of
inflammasome but it is said to represses NF-\selectlanguage{greek}κ\selectlanguage{english}B and type I interferon
responses, involved in inflammation
pathways\textsuperscript{14}\textbackslash{}sout. Neuro-immune and
neuro-inflammation has been established as key factors in the
development of Autism, where speech is also
affected\textsuperscript{15}. \emph{NLRP11} could be a possible
candidate gene in this family. In addition a recently reported
heterozygous variant in NAGPA gene (c.322G\textgreater{}A; p.Glu108Lys;
gnomAD database) implicated in stuttering was also observed in sibs.

\textbf{Identification of genes in common pathways using systems biology
approach}

Checking manually for the phenotypic relevance of the 64 genes yielded
nine putative pathways encompassing 41 genes\textbf{.} This can be
directly or indirectly linked to stuttering (table 1). Intriguingly, the
possible candidate genes are different for the two families, but they
fit into nine common pathways. Similarly, pathway browser tool in
Reactome database (https://reactome.org) determine pathways that are
over-represented. In this manner 25 most relevant pathways encompassing
14 genes (table 2), were sorted based on their p-value and the remaining
genes were indicated as interactors (table A3). Thus the enriched gene
list comprised of 14 genes that recurred in the two approaches (tables 1
and 2).

\textbf{Genes commonly observed in the two multiplex stuttering
families}

To determine the genes causing stuttering, the expansive list of genes
with variations, that was exclusive to the affected individuals in each
family was compared (276 genes in STU-66 family and 294 in STU-65).
Seven genes (\emph{SLC36A1, C1orf167, SYNC, TM4SF1, MUC6, SPTBN5}
and\emph{MSRB1)} were found to be common to both families, irrespective
of the variants identified (figure A4). All the affected members
harbored multiple variants in each of these seven genes.

\textbf{DISCUSSION}

The findings in exome sequencing of two multiplex families with severe
stuttering are discussed. Considering the previously implicated genes
(\emph{GNPTAB} , \emph{GNPTG} , \emph{NAGPA} and \emph{AP4E1} ) only one
pathogenic heterozygous missense variant (c.322G\textgreater{}A;
p.Glu108Lys) in \emph{NAGPA} was detected in our study. Since the so far
implicated genes in stuttering are limited to a single lysosomal
pathway, we extended the analyses to the 123 genes also, known in
lysosomal function, but did not identify any significant variation in
them in our two families.

\textbf{Search for other putative pathways}

A segregation based strategy was used to account for those gene variants
overlapping with the affected members (276 genes in STU-66 family and
294 in STU-65) but absent in the unaffected members. Seven
genes\emph{viz., SLC36A1, C1orf167, SYNC, TM4SF1, MUC6, SPTBN5}
and\emph{MSRB1} were common to both the families with stuttering though
they had different sets of multiple variants in each gene.\emph{SLC36A1}
encodes a proton dependent amino acid transporter, highly expressed in
brain. Mutations in this gene involves poor coordination of speech
associated with iminoglycinuria and spinocerebellar ataxia 45. Mutations
in \emph{C1orf167} is associated with neural tube defects.\emph{SYNC}
gene is involved in muscle contraction and is regarded as a marker of
neuromuscular disease. \emph{TM4SF1} and \emph{MUC6} genes form the
extracellular components. \emph{SPTBN5} gene encodes protein that links
membrane lipids, proteins and cytosolic factors to cytoskeletal
filaments and also binds to kinesin and actin proteins.\emph{MSRB1} gene
acts in response to stress and is also involved in actin
repolymerization (https://www.genecards.org). Analysis of
protein-protein interactions (https://string-db.org/) of the protein
products of these seven genes using \emph{in silico tool} (figure A5),
the gene products did not show any interaction with each other but they
seem to be involved in a common biological mechanism: within cell or
cell-cell communication. These deficits at neuromuscular junctions might
contribute to stuttering as speech involves 40,000 neromuscular events
per second that act in coordination\textsuperscript{16}.

In each family the downstream analysis of genes that overlap only among
affected members resulted in a set of 30/276 genes in STU-66 and 34/294
genes in STU-65. These 64 genes when subjected to pathway browser tool
(that interact with Reactome biological pathways), to look for common
biological mechanisms, resulted in an enriched gene list of 14 target
genes (\emph{COL4A2, COL6A3, COL6A6, ITGAX, LAMA5, ADAMTS9, CSGALNACT1,
TMOD2} , \emph{HTR2B} , \emph{RSC1A1} , \emph{TRPV2, WNK1} , \emph{ARSD}
and\emph{SPTBN5)} with P-values ranging from 0.125-0.0001 (figure 4).
Since the genes that interact significantly tend to have a common
function and may be involved in the same pathway, functional dissection
of 14 possible candidate genes was attempted to identify the pathways
related to stuttering.

Genes like \emph{COL4A2, COL6A3, COL6A6} are involved in formation and
degradation of collagens\textsuperscript{17}, \emph{ITGAX} for
integrin\textsuperscript{18} and \emph{LAMA5} for
laminin\textsuperscript{19} that together form the extra cellular matrix
(ECM) components. \emph{ADAMTS9} gene codes for metalloproteinase
involved in ECM degradation\textsuperscript{20}. \emph{CSGALNACT1} is
involved in chondroitin sulfate biosynthesis, one of the GAGs associated
to core protein as proteoglycans that also form the component of
ECM\textsuperscript{21}.

ECM has three basic parts that involves basal lamina, interstitial
matrix and perineuronal networks. ECM is dynamic, provides structural
stability in the form of basement membrane, plays role in signaling and
are critical for neuroplasticity\textsuperscript{22,23}. ECM provides an
appropriate environment for the development and functioning of cells,
like muscle and nerve cells\textsuperscript{24,25}. In the present
study, variations in genes encoding collagen and laminins may disrupt
basement membrane which has important role in neuroplasticity and
maintaining synapses.

Other gene variants observed in the present study are in
\emph{TMOD2}gene involved in muscle contraction, \emph{HTR2B} in
signaling serotonin receptors, \emph{RSC1A1} , \emph{TRPV2} and
\emph{WNK1} genes in transport of small molecules in stimuli-sensing
channels. All of them are involved in communication between neuron and
muscle cells and may coordinate production of speech. At neuro muscular
junctions (NMJ), motor neurons release neurotranmitters that bind to
post-synaptic receptors, leading to contraction of
muscles\textsuperscript{26}. Hence disturbances in signaling system
might occur at NMJ that may disrupt speech.

\emph{ARSD} belongs to cluster of sulfatase genes that hydrolyse GAGs
that are found to represent truncated
pseudogenes\textsuperscript{27}.\emph{SPTBN5} is involved in vesicle
transport and NCAM signaling (neural cell adhesion molecule) that plays
an important role in nervous system development and synaptic
plasticity\textsuperscript{28}.

Thus most of the genes identified form extra cellular matrix molecules,
involved in signaling functions indicating overall cell communication
deficits. These pathways very well coincide with findings of
neuroimaging (deficits in white matter tracts)\textsuperscript{8,29} and
genetic animal studies (deficits in corpus
callosum)\textsuperscript{5}which conclude cell communication deficits
as the major cause for stuttering. Shugart \emph{et al.,}
\textsuperscript{30} implicated, interesting candidate genes (on
chromosome 18) like desmoglein/desmocolin family and neuronal cadherin 2
gene that helps in cell-cell communication and cell adhesion. Such
communications are important in neurons involved in the production of
speech. As opposed to traditional thinking that origin of speech and
language could be localized to one part of our brain, today neural bases
are in fact found in the circuits that connect different regions of
brain\textsuperscript{31}. In a more elaborate GWAS study, 10 candidate
genes were identified to be strongly associated with
stuttering\textsuperscript{32}. Globally the genes implicated in
stuttering till date, in all affected individuals were reported to have
only one mutant copy with reduced level of functional protein
(haploinsufficiency) rather than total absence\textsuperscript{33}.

Our ES approach in two multiplex stuttering families point to
interesting putative genes involved in signaling and transport. However
in STU-65 family, a homozygous variant in \emph{NLRP11} gene and a
heterozygous variant in \emph{NAGPA} gene were also identified. The role
of these variants must be explored by confirming its segregation in the
other affected members of the family and also through functional
validation.

Our investigation is of a descriptive type and the potential candidate
genes identified should be viewed with caution because some of the gene
variants may not have direct role in the development of the disorder. ES
in additional members would further narrow down the variants. Functional
studies of the identified variants would help in understanding the
biological mechanism. Yet, our results support the fact that stuttering
is a polygenic disorder with a complex genetic background and we
hypothesize multiple and combined mechanisms to be involved in the
genesis of stuttering. The variable expressivity of the disorder in our
families could be explained by this genetic heterogeneity.

ES addresses only protein coding regions of genes within genome (1.5\%);
80\% of DNA that was believed to be junk is now biologically active that
makes RNA, of which 20\% make functional RNAs involved in regulation
(http://jonlieffmd.com/blog/mind-and-molecular-genetics-in-the-neuron-3-evolution).
The regulatory regions are 20 times bigger than protein coding regions.
Thus the complexity lies not only in the number of genes involved but
also may be due to the huge regulatory dynamics.

\textbf{CONCLUSION}

Dissecting the genetic architecture will not only contribute to a better
understanding of the etiology of stuttering but also facilitate the
identification of new targets with implications to prevention, diagnosis
and treatment. Using ES, we generated an enriched list of 14 target
genes (\emph{COL4A2, COL6A3, COL6A6, ITGAX, LAMA5, ADAMTS9, CSGALNACT1,
TMOD2} , \emph{HTR2B} , \emph{RSC1A1} , \emph{TRPV2, WNK1} , \emph{ARSD}
and\emph{SPTBN5)} involved in neural related pathways, in the two
multiplex families studied. We propose that these 14 genes having role
in signaling and transport may be putatively linked to stuttering.
However in STU 65 family, a homozygous variant in \emph{NLRP11} gene and
a heterozygous variant in \emph{NAGPA} gene were also identified. ES in
additional members and further functional validation would ensure the
role of these genes in stuttering.

\textbf{METHODOLOGY}

Two multiplex families (STU-65 and STU-66) were recruited from database
and clinically characterized for stuttering by speech pathologist using
Stuttering Severity Instrument-3\textsuperscript{34}. Using a structured
questionnaire family history and other details were noted. Eight
milliliters of blood was collected from probands and their family
members. Genomic DNA was isolated using PCI extraction
method\textsuperscript{35} and quantified by Nano drop (Thermo- Fisher
Scientific, Wilmington, USA). About 500ng of DNA per sample was
quantified and 20 uL volume of each sample was sent to Medgenome Labs
Ltd., Bengaluru for comprehensive exome sequencing. ES was performed in
six individuals; four individuals from STU-66 family and two affected
siblings from STU-65 family.

\textbf{STU-66 family:} A Telugu speaking family showed seven affected
across three generations with three inbreeding loops in the pedigree
(figure 1). The proband (V-5) was born to non-consanguineous parents,
where the father (IV-2) had mild stuttering but mother (IV-3) was
unaffected. Both the proband (V-5) and his younger brother (V-6) had
moderate but progressive stuttering, with age at onset of 2.5 years; his
elder brother (V-4) was unaffected. His grandmother (recovered; III-1),
uncle (mild stuttering; IV-7) and two first cousins (severe; V-2,V-3)
were also affected with stuttering. The nuclear family comprising of
affected father (IV-2), unaffected mother (IV-3), unaffected brother
(V-4) and the proband (V-5) were selected for ES.

\textbf{STU-65 family:} A Tamil speaking family from a major endogamous
Mudaliar caste had more than 20 PWS across six generations. Availability
of senior informants starting from third generation helped to trace
intense multigenerational inbreeding, tracing the phenotype to a common
female founder I-2 (figure 2).

The proband (V-33) and his affected brother (V-35) were born to
consanguineous parents (III-21,IV-8) who were also affected with
stuttering. Though his mother complained of severe stuttering in the
early days, her dysfluencies have reduced now but with a reminiscence of
fast speech rate and jaw clenching. Father also had repetitions and jaw
clenching.

Both the proband (V-33) and his younger brother (V-35) developed
stuttering gradually at 2.5 years and were moderate and severe
respectively with no birth complications. The proband was a dropout from
school and had situational increase, with strangers. Dysfluencies
observed include hard contacts in initial syllable, prolongation, silent
pauses, syllable and part-word repetitions with iterations of 2-3.
Secondary behaviours included eye blinks, clicking sounds, fixed
articulatory posture, nose flaring, tension in the neck, jaw jerking and
frequent left side head nod. His rate of speech was slow and
intelligibility in speech was fair. His brother had repetitions and
prolongations along with eye blinking, facial grimace, hand fidgeting,
etc. He also had situational increase but continued beyond school
education. Their grandmother (mild), maternal aunts and cousins were
also affected with severe stuttering. The extended family members have
been extensively phenotyped (table A4).

ES was commercially carried out at MedGenome Labs Ltd., Bangalore
facility. ES library was prepared using Agilent-Sure Select XT Reagent
Kit (Illumina). Biotinylated oligonucleotide capture probes (V5+UTR)
designed for all the coding exons were used to enrich the region of
interest (exome) by hybridization. The library obtained was diluted to
final concentration of 2nm in 10ul and subjected to Cluster
amplification. The flow cell was loaded on to the sequencer (Hi Seq X10)
to generate 2X150 bp sequence reads at 100x sequencing depth. Sequenced
data with Q30 values was considered as qualified and processed to
generate FASTQ files for further downstream variant analysis.

The raw data was quality trimmed and reads (using fastq-mcf command line
tool) were aligned to Human Reference genome (alignment to hg19) using
BWA-MEM tool. The output in SAM format is converted to BAM file (using
Samtools) and processed to obtain SNVs (Single Nucleotide Variation) and
INDELs (small insertions and deletions) in a standard VCF (Variant call
format) file. Coverage of the genes were analyzed using Bedtools.

The variants were called using GATK software and annotated using
MedGenome in-house variant annotation pipeline (VariMAT - Variation and
Mutation Annotation Toolkit). It integrates multiple clinical grade
databases {[}GWAS
(https://www.genome.gov/genetics-glossary/Genome-Wide-Association-Studies),
ClinVar (https://www.ncbi.nlm.nih.gov/clinvar), OMIM
(https://www.omim.org), UniProt (https://www.uniprot.org/), ExAC
(http://exac. broadinstitute.org; https://gnomad.broadinstitute.org),
dbSNP (https://www.ncbi.nlm. nih.gov/snp/), 1000 genomes
(https://www.coriell.org/1/NHGRI/
Collections/1000-Genomes-Collections/1000-Genomes-Project){]}, variant
class prediction and variants pathogenicity prediction tools for
annotating the variants. VariMAT annotated variants contain information
on the population frequency, computational pathogenicity prediction,
variant type and predicted impact of the variant on the protein
(missense, loss of function, etc.).

\textbf{Sequencing data of STU-66 family:} The paired-end ES, generated
a data of 10-14 Gb for each of the four individuals sequenced. More than
93\% of the data showed variant quality scores above Q30 (Q score
measures the base calling accuracy by estimating base calling error
probabilities and Q30 indicates the probability of incorrect base call
of 1 in 1000 which means the base call accuracy is 99.9\%). The overall
alignment and the passed alignment percentage in all the samples was
around 99.99 and 97.28 \% respectively. The analysis was performed after
alignment using SS-V5-UTR panel (74,557,381bp) which covers 23690 genes.
The average panel depth for each of the sample ranged from 80.42 to
99.27X.

\textbf{Sequencing data of STU-65 family:} In this family the proband
and his affected brother, V-33 and V-35, were sequenced generating total
data of 8-11 Gb. More than 90\% of the data showed quality score
distribution above Q30. The overall alignment and passed alignment
percentage (alignment to hg19) in proband and sib was around 99.99 and
94.89 \% respectively. The average panel depth for each sample ranges
from 80 to 100 X.

\textbf{Variant filtering:} The downstream variant analysis protocol
involves short listing of the variants in the affected individuals,
followed by prioritization of variants based on the data from additional
family members. Each of the individual variant files were subjected to
variant filtering criteria, to profile \emph{rare variants} in all
samples under investigation, comprising of:

\begin{enumerate}
\tightlist
\item
  The high impact variant type that includes frameshift, termination,
  start loss type,
\item
  The moderate impact variants being missense, stop loss and indels, and
\item
  Intronic splice site variants comprise of splice donor/acceptor or
  proximal splicing impact variants. These variants are filtered with a
  cutoff for depth being 3X and MAF \textless{}1\% in ExAC, 1000
  genomes, and proprietary Medvar (MedGenome variation) databases.
\end{enumerate}

\textbf{Variant prioritization:} The variant prioritization and
candidate gene identification in family based cohort often rely on
profiling the common and uncommon gene variants present in the affected
and unaffected members in a family. An open source tool called
InteractiVenn\textsuperscript{35} was deployed for this purpose that can
handle up to six data sets (creates Venn diagram). To this resulting
variant list the following exclusion criteria were employed.

\textbf{Exclusion criteria}

\begin{enumerate}
\tightlist
\item
  those predicted as tolerant using MetaSVM (meta-analytic support
  vector machine (SVM)) and MetaLR which accommodate multiple omics data
  to detect consensus genes associated with disease, were excluded. In
  other words, those variants predicted to be damaging by more than one
  prediction tool was considered.
\item
  In sorting the known phenotype, variants in gene with completely
  unmatched OMIM phenotypes were excluded.
\item
  Proximal splice site variants placed at \textgreater{}8 nucleotides
  away from exon were also excluded.
\item
  Variants in zinc finger encoding genes were excluded as they have very
  omnipresent role.
\end{enumerate}

\textbf{Comparison of variants with available databases}

These shortlisted variants were matched with previously reported genes,
attributed to stuttering or any other speech and language disorders

\begin{enumerate}
\tightlist
\item
  Significant SNPs reported by Kraft\textsuperscript{32} in the
  'Genome-wide association study of persistent developmental stuttering'
  of northern European ancestry.
\item
  The 123 genes listed in the lysosomal pathway
  (https://pathcards.genecards.org/ card/lysosome). Since the three
  genes so far known to be implicated in stuttering were identified in
  the lysosomal pathway, this study carefully observed any potential
  variants in genes related to this pathway.
\item
  The genes so far listed for speech and language disorders
  by\textsuperscript{37}.
\end{enumerate}

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\textbf{Table 1: Manually curated list of candidate* genes identified in
9 putative pathways, that can be directly or indirectly linked to
stuttering, in the two families studied}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
\textbf{S.No}\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
\textbf{Gene list from} \textbf{STU-66 family}\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
\textbf{Pathways}\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
\textbf{Gene list from} \textbf{STU-65 family}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
SPTBN5, \emph{PLXNA1}, \emph{RAPIGAP}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
neural developmental pathways\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{\textbf{COL6A6},} \emph{NELL2, DSCAML1}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
ADAMTS9, \emph{COL6A3,} \emph{COL4A2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
extra cellular matrix organisation/degradation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{\textbf{LAMA5}, COL6A6, \textbf{ITGAX}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{TMOD2}, \emph{SYNC}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
muscle contraction\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{MYHI}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
CD3EAP\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
gene expression\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
SPTBN5, \emph{ZNF763,} \emph{CCNJL, KIF13B, ATXN2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
signal transduction/ vesicle transport\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{ATXN2L, HTR2B, STARD13, MYH1 \textbf{LAMA5}, NEK4, MRIP,
KDM5A}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{RSC1A1}, \emph{NIPA2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
transport of small molecules\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
TRPV2, WNK1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{CSGALNACT1}, ADAMTS9, SPTBN5, \emph{ALAS1}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{PAN3, ARSD, WDR27, HSIT3H2A}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{GPR97}, SPTBN5, \emph{ABCA13}, \emph{UNKL}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
immune system\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{CRTAM, \textbf{ITGAX}, \textbf{NLRP11}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Lysosome enzyme targeting\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{NAGPA}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{Note:} *The candidate genes proposed are exclusive to affected
individuals, segregating in heterozygous status, except for NLRP11
segregating in the homozygous state;\textbf{bold} indicates genes
involved in multiple pathways

\textbf{Table 2: Tentative list of candidate* genes identified in
enrichment pathways, that can be directly or indirectly linked to
stuttering, in the two families studied}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
\textbf{S.No}\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
\textbf{Gene list from} \textbf{STU-66 family}\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
\textbf{Pathways}\strut
\end{minipage} & \begin{minipage}[b]{0.24\columnwidth}\raggedright\strut
\textbf{Gene list from} \textbf{STU-65 family}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{1}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
ECM proteoglycans\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6, ITGAX, LAMA5}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3,}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Collagen chain trimerization\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{3}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{ADAMTS9, COL4A2, COL6A3,}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Degradation of the extracellular matrix\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6, LAMA5}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{4}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3,}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The activation of arylsulfatases\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{5}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Collagen degradation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{6}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3,}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Assembly of collagen fibrils and other multimeric structures\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6,}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{7}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Laminin interactions\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{LAMA5}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{8}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3,}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Collagen biosynthesis and modifying enzymes\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{10}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3, SPTBN5}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
NCAM signaling for neurite out-growth\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{11}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{TMOD2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Striated Muscle Contraction\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{12}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3,}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Collagen formation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6,}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{13}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Interleukin-4 and Interleukin-13 signaling\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{ITGAX, LAMA5}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{14}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Gamma carboxylation, hypusine formation and arylsulfatase
activation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{ARSD}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{15}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Serotonin receptors\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{HTR2B}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{16}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Anchoring fibril formation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{17}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Integrin cell surface interactions\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6, ITGAX}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{18}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{RSC1A1}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Intestinal hexose absorption\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{19}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{ARSD}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Glycosphingolipid metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{20}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Crosslinking of collagen fibrils\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{21}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{CSGALNACT1}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Chondroitin sulfate biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{22}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Stimuli-sensing channels\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{TRPV2, WNK1}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{23}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{RSC1A1}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Intestinal absorption\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{24}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL4A2, COL6A3}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Extracellular matrix organization\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{COL6A6, ITGAX, LAMA5}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{25}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
TRP channels\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{TRPV2}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{Note:} *The candidate genes proposed are exclusive to affected
segregating in heterozygous status

\textbf{Supplementary tables:}

Table A1: Compilation of the variants common in parent offspring pair of
STU-66 family correlating to speech language disorders that includes 13
novel variants observed along with the bioinformatic predictions for
pathogenicity

Table A2: Compilation of the variants common in sib pair of STU-65
family correlating to speech language disorders that includes 4 novel
variants observed along with the bioinformatic predictions for
pathogenicity

Table A3: Over-representation analysis results of variants identified in
two ES families showing 25 most significant pathways related to
stuttering using Reactome database

Table A4: Characterization of the stuttering phenotype and other
associated findings among 24 individuals examined in the STU-65 family

\textbf{Figure legends:}

Figure 1: Five generation pedigree of stuttering family - STU-66

Figure 2: Six generational pedigree of a stuttering family - STU-65

Figure 3: Venn diagram showing the variants shared among the four
individuals in the STU-66 family (15 combinations)

Figure 4: Flow chart depicting exome analysis of two multiplex
stuttering families

\textbf{Supplementary figures:}

Figure A1: Bioinformatic-analysis pipeline applied to exome sequence
data of a STU-66 family with stuttering

Figure A2: Venn diagram showing the variants shared between the affected
sib pair in the

STU-65 family

Figure A3: Bioinformatic-analysis pipeline applied to exome sequence
data of a STU-65 family with stuttering

Figure A4: Venn diagram showing the genes shared between two stuttering
multiplex families

Figure A5: Functional protein association network (STRING analysis) did
not show any connection for seven proteins identified in common for the
two multiplex stuttering families\selectlanguage{english}
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