Qingqing Wang1, Xia Tang1, Ke Yang,
Xiaodong Huo, Hui Zhang, Keyue Ding*, and Shixiu
Liao*
Medical Genetic Institute of Henan Province, Henan Provincial People’s
Hospital, Henan Key Laboratory of Genetic Diseases and Functional
Genomics, National Health Commission Key Laboratory of Birth Defect
Prevention, Henan Provincial People’s Hospital of Henan University,
People’s Hospital of Zhengzhou University, Zhengzhou, Henan Province
450003, P.R. China
1 Contributed equally
* Corresponding authors
Keyue Ding, PhD, E-mail: ding.keyue@igenetics.org.cn
Shixiu Liao, MD, E-mail:
ychslshx@henu.edu.cn
Abstract
Neurodevelopmental disorders, a group of early-onset neurological
disorders with significantly clinical and genetic heterogeneity, remain
a diagnostic odyssey for clinical genetic evaluation. In a total of 45
parent-child trios/quads with these disorders that was ‘not yet
diagnosed’ by the traditional testing methods, we assessed the
diagnostic yield by the combined use of standardized phenotypes and
whole-exome sequencing data. Using a standardized vocabulary of
phenotypic abnormalities from Human Phenotype Ontology (HPO), we
performed deep phenotyping for these pedigrees to characterize multiple
clinical features that was extracted from Chinese electronic medical
records (EMRs). By matching HPO terms with known human diseases or
cross-species comparison, together with whole-exome sequencing data, we
prioritized candidate mutations/genes that underlies these pedigrees. We
obtained a diagnostic yield of 49% (22 out of 45) with probably or
possibly genetic diagnosis, of which the compound heterozygosity andde novo mutations accounted for the half of the diagnosis. Of
note, the pedigrees with probable or possible diagnosis accompanied with
a greater number of phenotypes implicated in non-nervous systems. The
combined use of deep phenotyping and whole-exome sequencing provide
implications for etiological evaluation for neurodevelopmental disorders
in the clinical setting.
Keywords: Neurodevelopmental disorders, Human Phenotype Ontology,
Whole-exome sequencing, Diagnosis
Introduction
Neurodevelopmental disorders - a group of early onset neurological
disorder - affect more than 3% of children [1]. According to the
DSM-5 [2], it can be classified into disorders of intellectual
disability, communication, autism spectrum, attention-deficit
hyperactivity, specific learning, motor, and others. Neurodevelopmental
disorders are common reasons of referrals to genetic counselor [3]
and there remains great challenges in genetic evaluation due to the
heterogeneous clinical presentation [4].
Clinical laboratory investigations for neurodevelopmental disorders
include neuroimaging, metabolic screening, traditional genetic testing
(e.g., karyotype, chromosomal microarray analysis, or gene panel
sequencing), and invasive tests [5]. However, >50% of
patients of neurodevelopmental disorders did not receive an etiologic
diagnosis [6,7]. Recently, the application of whole-exome or -genome
sequencing for its diagnosis has been assessed. A large family-based
study (n = 4,293) showed that approximately 42% of patients with
development disorders harbored de novo pathogenic mutations
[8]. Specifically, a diagnosis rate of 36%-48% was obtained for
patients with neurodevelopmental disorders [9–13]. Furthermore, the
diagnostic yield can be increased using an improved analysis pipeline,
e.g., it was increased from 27% to 40% by re-analyzing 1,133 families
with developmental disorders [14], and a 15.4% of additional
diagnosis for 416 children with congenital anomalies or mental
retardations was achieved [15]. However, in a clinical setting,
there remains great interest in implementing novel approaches for
increasing the diagnostic yield for ‘not yet diagnosed’ patients.
Deep phenotyping - the precise and comprehensive analysis of phenotypic
abnormalities - aims to provide the best clinical care for each patient
according to disease stratification [16]. The human phenotype
ontology (HPO), a standard vocabulary for describing the phenotypic
abnormalities in human disease, provides the most comprehensive
resources for deep phenotyping [16,17]. Using the standardized human
phenotype ontology, several tools (e.g., Phenolyzer ,Phenomiser , and Exomiser ) have been developed for clinical
and genetic diagnosis. Phenolyzer (i.e., phenotype-based gene
analyzer) discovers genes implicated in diseases based on prior
phenotype or disease information [18]; and Exomiserprioritizes disease-associated genes/mutations by analyzing whole-exome
sequencing data with its matched phenotypes [19,20]. To the best of
our knowledge, incorporating deep phenotyping with whole-exome
sequencing for the assessment of the diagnostic yield for
neurodevelopment disorders in family-based study remains limited.
Here, we performed the phenotype-driven diagnosis for ‘not yet
diagnosed’ pedigrees with neurodevelopment disorders. Deep phenotyping
for heterogenous clinical features, together with whole-exome
sequencing, prioritized potentially pathogenic genes/mutations
underlying these families using phenotype matching algorithms.
Materials and Methods
The conceptual framework for the phenotype-driven diagnosis for nuclear
pedigrees with neurodevelopment disorders was composed of deep
phenotyping, whole-exome sequencing, variant filtering, and
phenotype-matching based prioritization (Fig 1 ).
The recruitment of pedigree with neurodevelopmental
disorders
We recruited nuclear pedigrees (i.e., parent-offspring) with chief
complaints of ‘developmental delay’, ‘intellectual disability or
’seizure’ (Table S1 ), who had genetic counseling visit our
medical genetic institute. We excluded patients with known etiologies
based on traditional genetic testing or metabolic screening (Table
S2 ).
Deep phenotyping and phenotype
standardization
Various clinical notes including medical history, laboratory tests
and/or radiologic reports, were collected from the Chinese electronic
medical records at the Henan Provincial People’s Hospital. Clinical
features of symptoms, signs, laboratory and radiologic tests were
extracted manually for each proband (i.e., deep phenotyping), as present
in our previous studies [21,22]. The extracted phenotypes in Chinese
were then standardized by searching for HPO terms in Chinese Human
Phenotypic Ontology browser (http://www.chinahpo.org), and one of
the most matched HPO term was selected if multiple terms were noted
(Table S3 ). The packages of ‘ontologyIndex’ and ‘hpoPlot’ inR were used to perform HPO-based analyses [23].
Whole-exome sequencing
Genomic DNA was extracted from the peripheral blood lymphocytes using
the QIAamp DNA Blood Mini kit (Qiagen, Hilden Germany), and was then
fragmented into 250-300bp by sonicator (Covaris, Woburn MA). The
sequencing library was constructed using SureSelect Human All Exon V6
kit (Agilent, Santa Clara CA) and whole-exome sequencing was performed
on HiSeq Xten sequencing platform (Illumina, San Diego CA) at the
Beijing Genomics Center (Shenzhen, China). Sequencing data with
paired-end length of 150 bp were obtained from 45 nuclear pedigrees
(including 55 affected cases and 97 individuals without phenotypes),
with an approximately mean sequencing depth of 30\(\times\).
Data analysis
Whole-exome sequencing reads were aligned to the hg19 reference genome
with BWA [24], applied GATK (v.4.1) for indel realignment, duplicate
removal and base score quality recalibration, and single nucleotide
variant (SNV) and small insertion and deletion (indel) across all
individuals in a family was identified using standard hard filtering
according to GATK Best Practice Guide [25].
Following variant calling, we annotated and classified mutations into
pathogenic, likely pathogenic, variants with uncertain significance
(VUS), likely benign and benign according to the ACMG/AMP guideline
[26], as implemented in InterVar [27]. The minor allele
frequency (MAF) was ascertained from the 1000 Genome Project (1000G)
[28] or the Genome Aggregation Database (gnomAD, v.2.0.2) [29].
Databases of ClinVar [30], OMIM (omim.org) and HGMD [31] were
used to identify the known pathogenic variants implicated in the
neurodevelopmental disorders.
To identify potentially causal genes/mutations underlying the affected
pedigree and thus make a genetic diagnosis, we used Exomiser[32] to prioritize mutations by integrating the calculation of
phenotype similarity between the known human diseases and mouse models
(i.e., cross-species comparisons) with evaluation of mutations according
to pathogenicity, MAF (< 0.1%), and mode of inheritance. A
detailed protocol for conducting such analyses was provided recently
[33]. We also applied Phenolyzer [18] to discover genes
implicated in neurodevelopmental disorders using HPO terms alone,
leveraging prior biological knowledge and phenotype information.
Sanger sequencing
The identified potentially causal mutations were validated using Sanger
sequencing. PCR products were sequenced in bidirection using ABI 3700
sequencer, and were analyzed using SnapGene Viewer
(https://www.snapgene.com/snapgene-viewer/).
Results
The recruited pedigrees
During 2019-01 to 2019-06, we recruited a total of 45 ‘not yet
diagnosed’ pedigrees with neurodevelopmental disorders, who were
referred to the outpatient clinic at our institute (Table 1 ). The
ratio of male: female of the proband was approximately 2:1, and all
probands had age at symptom onset < 8 years. The majority of
the pedigrees were referred for prenatal genetic counseling but five
affected adults came for clinical genetic evaluation. These pedigrees
included 31 parent-child trios, 12 parent-child quads, one parent-child
quin, and one family with a second-degree relative. Consanguinity was
not documented for the parents of the proband.
HPO encoded phenotypes recapitulating significant clinical
heterogeneity for neurodevelopmental
disorders
Deep phenotyping compiled the clinical features extracted from the
Chinese electronic medical records and ascertained a total of 121 HPO
terms via standardization (Table S3 and Fig S1 ). An
ontology plot showed the annotated HPO terms (i.e., as nodes indicated)
as subgraphs of the full ontology, where a lineage represented a system
hierarchy (Fig 2A ), for example, two branches of ‘nervous system
physiology’ and ‘nervous system morphology’ were under the lineage of
‘phenotypic abnormality of nervous system’. The plot also showed that
the population frequency of HPO terms differed significantly among
branches. Of note, phenotypes ‘is-a ’ relation with ‘HP:0001249
(intellectual disability)’ or ‘HP:00012758 (neurodevelopmental delay)’
showed relatively higher frequencies.
Overall, more than half of HPO terms were associated with nervous system
(n = 66, 55%), whereas the remaining terms were noted to be
implicated in multiple non-nervous systems (Fig 2B ). Per
pedigree, a median number of eight HPO terms were annotated; only one
pedigree present with ‘seizure’ and one had at most 16 phenotypes
(Fig 2C ). Cumulatively, nearly all families (n = 44)
present with more than two phenotypes that such a multi-morbidity has
important clinical implications [34]. We also noted that the
phenotypes presenting in the affected individuals from the sam pedigree
varied, in part due to incomplete penetrance or at a later symptom onset
(Table S4 ). One example in undiagnosed pedigree (UDP) #7 showed
that the proband (p701) exhibited ‘HP:0001250 (seizures)’, ‘HP:0001270
(motor development delay)’ and ‘HP:0000750 (language development delay)’
at six months of age, whereas his sibling (p702) developed only
‘seizures’ at four years of age.
The most frequent phenotypes associated with nervous system were
‘HP:0000750 (delayed speech and language development)’, ‘HP:0001270
(motor delay)’, ‘HP:0001249 (intellectual disability)’, ‘HP:0012434
(delayed social development)’, and ‘HP:0001263 (global developmental
delay)’ (Fig 2D ). However, approximately half of these phenotypes
were singleton (n = 35, 53%) or doubleton (n = 6, 9%).
According to DSM-5, phenotypes under the lineage of ‘nervous system
physiology’ were grouped each of which showing dominant phenotypes
(e.g., ‘HP:0000750’ in communication disorder, and ‘HP:0001270’ in motor
disorder), whereas phenotypes in attention deficit/hyperactivity
disorder, autism spectrum disorder and specific learning disorder were
less present. Phenotypes associated with non-nervous system were likely
to be present (Fig 2E ), indicative of syndromic features in a
proportion of pedigrees (n = 29, 64%). The vast majority of
these phenotypes were singleton or doubleton but ‘HP:0001252 (muscular
hypotonia)’, ‘HP:0012389 (appendicular hypotonia)’ and ‘HP:0003808
(abnormal muscle tone)’ had a frequency of 12.5%, 10%, and 7%,
respectively.
An increased diagnostic yield by incorporating HPO encoded
phenotypes and whole-exome
sequencing
We filtered the SNVs and indels by removing common variants (MAF
> 0.01 in gnomAD database), and then evaluating the
remaining variants based on the predicted pathogenicity. Given the mode
of inheritance, variants co-segregated with the pedigree were selected.
For example, homozygous or compound heterozygous mutations were required
under a mode of autosomal recessive inheritance. We assigned phenotypic
score for genes based on the comparison with known human diseases or
animal models with mutations in orthologues, and obtained the final
ranking as the sum of the individual scores [32]. We then assigned
the prioritized variants according to the following criteria: 1)
pathogenic variant (PV): a variant presented in HGMD [31], Clinvar
[30] or classified to be ‘pathogenic’ based on ACMG guidelines with
matched phenotypes to neurodevelopmental disorders; 2) likely pathogenic
variant (LPV): a non-HGMD or non-Clinvar variant, but was classified to
be ‘pathogenic’ based on ACMG guidelines in the gene for which
previously reported patients have matched phenotypes to
neurodevelopmental disorders; and 3) variant of unknown significance
(VUS): the variant that do not fulfill the above criteria but the
corresponding gene has matched phenotypes to neurodevelopmental
disorders. Thus, we classified genetic diagnosis into three groups as:
1) probable diagnosis: a PV or LPV identified in a gene relevant to
phenotypes in the patient; (2) possible diagnosis: a VUS variant(s)
identified in a gene relevant to phenotypes in the patient or the gene
was prioritized by walking the interactome [32]; and (3)
undiagnosed: no disease-associated variant(s) detected.
According to the ontology plot (Fig 2A ), we hypothesized that the
replacement of a given phenotype term (e.g., a term located at the
low-level of the ontology) with its ancestral term may affect
phenotype-matching. One striking example of UDP #9 highlighted such
effects on prioritization. Deep phenotyping originally characterized
‘HP:0000253 (progressive microcephaly)’ in the proband (p901), which was
descended from its ancestral term of ‘HP:0000252 (microcephaly)’. When
‘HP:0000252’ was used, the compound heterozygous mutations inTSNE2 were prioritized with a Phenotype score of 0.878 and
Exomiser score of 0.993, significantly greater than ‘HP:0000253’ was
used (0.000 and 0.015, respectively). Thus, we replaced 35 terms with
their corresponding ancestral terms in 27 pedigrees (i.e., one for 19
pedigrees, and two for eight pedigrees) (Table S5 ).
Overall, we achieved 13 probable and nine possible diagnosis from a
total of 45 pedigrees (Fig 3A ), leading to a diagnostic yield of
49%. Of the diagnosed pedigrees, nine were inherited in an autosomal
dominant (AD) manner, four in an autosomal recessive (AR) with compound
heterozygous mutations, eight in X-linked recessive (XR), and one in
X-linked dominant (XD). In addition to the compound heterozygous
mutations, the de novo mutation accounted for seven pedigrees in
an AD manner (Fig 3B ). A detailed annotation for these mutations,
including population frequency, ACMG-guided clinical classification, and
associated clinical syndromes were provided in Table 2 . As
expected, the Phenotype (Fig 3C ) and Exomiser (Fig 3D )
score were slight increased when the original phenotype term was
replaced with its corresponding ancestral term, as noted obviously in
UDP #9.
To assess the use of HPO-encoded phenotype alone in clinical diagnosis,
we used Phenolyzer [18] for identifying the associated
clinical syndromes underlying the pedigrees and its corresponding causal
genes. The rank of the prioritized genes identified in Exomiserwas compared with the rank of the genes seeded from Phenolyzer .
Our findings indicated that, incorporating pathogenic mutations
significantly increased the ranking for the prioritized genes, whereas a
large set of seed genes generated by Phenolyzer created more
difficulty in the prioritization process (Wilcoxon test,\(p=6\times 10^{-5}\)) (Fig 3E ).
We finally investigated whether the phenotypic structure differed
between the ‘diagnosed’ and ‘undiagnosed’ pedigrees, as shown in the
landscape of HPO-encoded phenotypes for all pedigrees (Fig S1 ).
Of note, the number of phenotypes associated with non-nervous system in
the diagnosed pedigrees (n = 22) was significantly greater than
that in the undiagnosed pedigrees (n = 23) (Wilcoxon test,\(p=9\times 10^{-4}\)), suggesting an increased power for the
diagnosis of pedigrees with syndromic features (Fig 3F ).
Case examples
For the purpose of illustration, we summarized the analyses for five
pedigrees accompanied with various phenotypes (Fig 4A ). The
confirmation of Sanger sequencing for mutations prioritized for the
remaining 17 pedigrees was shown in Fig S2 .
UDP #1. The pedigree illustrated the differential diagnosis for
clinical genetic evaluation according to the prioritized mutations
(Fig 4B ). The proband (p102, a 14-year-old boy) was delivered by
Cesarean section at full term accompanied with hypoxia (Apgar score
unknown) in 2005. At 18 months of age, he was able to walk with support;
and he could not speak and has a lower self-reported intelligence
quotient when compared with children at the same age. He was diagnosed
with ‘ischemic hypoxic encephalopathy’ at a local hospital and received
rehabilitation but the response to therapy was limited. His sibling
(p101, a 9-year-old boy) had similar clinical features. Both siblings
present with short-stature (127 cm and 117 cm, respectively), multiple
facial scars, wind ears, strabismus, and multiple alopecia aerate
scattered on head. Multiple coffee skin spots on back, right
cryptorchid, horseshoe valgus on both feet, and knee-jerk hyperactivity,
were recorded in medical history. A novel pathogenic splicing mutation
(NM_004187.5:c.2517_2622del) in KDM5C and a hemizygous VUS
mutation (NM_021120.4:c.1334G>A, p.Arg445His) inDLG3 were initially prioritized. DLG3 is associated with
mental retardation, X-linked 90 (OMIM:300850), but their healthy uncle
harbored the same mutation. KDM5C is associated with X-linked
intellectual disability (XLID) (OMIM:300534), and the splicing-site
mutation was co-segregated with the phenotypes. Both siblings present
with typical features of XLID, and we therefore inferred that the
splicing-site mutation in KDM5C was causal.
UDP #21. The pedigree demonstrated that whole-exome sequencing
in parent-child quad may increase the power to identify the causal
mutation (Fig 4C ). Both affected siblings were referred to a
genetic counselor due to intellectual disability, accompanied with
macrocephaly, postnatal overgrowth, delayed speech and language
development, motor deterioration, and Chiari malformation. The proband
(p2101) was delivered by Cesarean section at full term without hypoxia.
At three years of age, his head circumference was 54 cm (>3
s.d.), and received a decompression surgery for subamygdala hernia in
cerebellum six months later. Her sibling (p2102) had a similar medical
history and present with delays in language development and motor
skills, poor coordination and social adaptability at 19 months of age.
She received the decompression surgery at two years of age, and
developed intermittent seizures thereafter. She had a head circumference
of 54 cm (>3 s.d.) at 28 months of age. Currently, she is
able to speak and walk with support. Both siblings have facial
abnormalities including prominent forehead, long face, short philtrum,
dental malocclusion, everted lower lip vermilion, narrow mouth with
open-mouth appearance, and down slanted palpebral fissures. A
heterozygous nonsense mutation (NM 002501.4:c.935G>A,
p.Trp312*) in NFIX was prioritized, which was associated with
Sotos syndrome 2 (OMIM:614753) or Marshall-Smith syndrome (OMIM:602535).
Sanger sequencing confirmed the same mutation in the mother but with a
low mutation fraction, and the mechanism for the potentially gonadal
chimeras needs to be further investigated.
UDP #10. The family identified a de novo mutation in the
well-known gene associated with Rett syndrome (Fig 4D ). The
naturally delivered proband (p1001) developed torticollis at five months
of age and got worse seven months later. She was able to sit but fail to
crawl at eight months of age, and was able to stand at 14 months of age.
At 18 months of age, she gradually showed hand involuntary movements,
short attention span, and limited language ability. Neuropsychological
examination showed that her intellectual development, social
adaptability, and motor development was equivalent to eight months, nine
months, and 11 months, respectively. At 33 months of age, she presented
a global developmental delay with a head circumference of 45 cm, a
height of 90 cm, and a weight of 11 kg. She showed tongue extension and
astigmatism in the left eye. Our analysis identified a de novoknown nonsense mutation (rs61749721, NM_004992.3:c.763C>T,
p.Arg255*) in MECP2 - a well-known gene causing X-linked dominant
Rett syndrome (OMIM:312750).
UDP #9. The pedigree showed that the identification of the
pathogenic mutation may benefit for prenatal genetic diagnosis and thus
reduce risks of birth defects (Fig 4E ). The proband (p901) was
delivered at full term but showed microcephaly at birth (< 3.2
s.d., compared to age matched and normal standards). He got feeding
difficulty and intermittent opisthotonus progressively. At 12 months of
age, he developed an encephalopathy with refractory tonic myoclonic
epilepsy, failed to fix or follow vision, and present with limbic
hypertonia. At eight years of age, his microcephaly had deteriorated
considerably (<5.45 s.d.), and was not able to speak, and he
could not stand alone and present muscle atrophy in lower limbs. MRI
scan could not be performed due to un-cooperation. A compound
heterozygosity for two pathogenic mutations in TSEN2(NM025265.4:c.904G>A, p.Glu302Lys and
NM025265.4:c.1354C>A, p.Arg452*;) was prioritized,
associated with pontocerebellar hypoplasia type 2B (OMIM: 612389), which
is characterized by abnormalities in the cerebellum and brainstem with a
progressive microcephaly combined with extrapyramidal dyskinesia and
epilepsy. The mother came for prenatal diagnosis in her 20th week of
pregnancy. An ultrasound imaging showed that the head circumference of
the fetus (p902) was less than the normal gestational age, and further
genetic testing identified the same compound heterozygous mutations inTSEN2 .
UDP #30. This pedigree showed that the identified pathogenic
mutation may accelerate our understanding of the pleiotropic effect of
the causal gene (Fig 4F ). The proband (a 2-year-old girl) was
born naturally but developed epileptic seizures 10 days later with a
frequency of 2 to 30 times per day, lasting for 5 seconds to 5 minutes
for each occurrence, occasionally with continuous attacking and
shortness of breath. She showed developmental delays at nine months of
age, e.g., unable to raise his head, with a low-pitched crying,
unwilling to laugh, abnormal gaze, without ocular pursuit, and pupillary
light reflex disappeared. MRI scan showed a C-shaped spine, thinning of
the corpus callosum on back, and dysplastic white matter. An
electroencephalogram showed a periodic eruption-suppression wave, and
visual evoked potentials indicated severe abnormalities in bilateral
visual pathways. An echocardiography reported an atrial septal defect
(type II). Family history revealed that one of her siblings had similar
symptoms and died three months after giving birth. A compound
heterozygosity of a frameshift mutation (NM_016373.3:c.854dupA,
p.N285fs*10; pathogenic) and a missense mutation
(NM_016373.3:c.1063G>C, p.G355R; VUS) in WWOX was
identified, derived from maternally and paternally, respectively.
Homozygous or compound heterozygous mutations in WWOX - a tumor
suppressor gene - cause spino-cerebellar ataxia autosomal recessive,
type 12 (OMIM:614322) or epileptic encephalopathy, early infantile, 28
(EIEE28, OMIM:616211). A pedigree with EIEE28 reported to have atrial
septal defect may be explained by the WWOX homozygous deletion or
the compound heterozygous mutations in HSPG2 [35].
Discussion
In the present study, we assessed the diagnostic yield for ‘not yet
diagnosed’ pedigrees with neurodevelopment disorders in a clinical
setting through the combined use of HPO-based deep phenotyping,
whole-exome sequencing, and phenotype-matching algorithms (Fig
1 ). Clinical whole-exome sequencing has improved our ability to
discover genetic causes for various rare undiagnosed disorders although
the currently diagnostic rate is 25%~40% [11,36].
Hurdles remain for the establishment of genotype-phenotype correlation
in the context of genetic evaluation [36,37]. In the clinical
practice, the use of HPO in annotating phenotypic information remains
unexplored [38]. Here, we compiled multiple clinic features from
Chinese electronic medical records using HPO-based deep phenotyping
(Fig 2 ). Together with the filtered pathogenic mutations, we
obtained an improved diagnostic yield of 49%, and identified a large
proportion of de novo and compound heterozygosity mutations
underlying these pedigrees (Fig 3 and Table 2 ).
The current study refined and extended the mutational and phenotypic
spectrum of the known neurodevelopmental disorders. We reported, for the
first time, at least 13 novel pathogenic mutations in the known genes
associated with neurodevelopmental disorders, providing a basis for
further functional validation and/or the elucidation of molecular
mechanism. In addition, we characterized a cardiac phenotype (i.e.,
atrial septal defect) in both affected siblings in UDP #30 diagnosed
with EIEE28 with the compound heterozygous mutations in WWOX .
Expanding the mutational spectrum in the causal genes and phenotypic
spectrum of neurodevelopmental disorders could offer accurate and
reliable genetic counseling and prenatal diagnosis to the patients, and
thereby minimize the birth of affected individuals [39].
Our results provide take-home messages for health professionals in the
clinical management of patients with neurodevelopment disorders. The
annotated phenotypes recapitulate syndromic features of
neurodevelopmental disorders, which cannot be neglected in clinical
diagnosis and genetics evaluation. Phenotypes associated with
non-nervous system may have an increased power to make a genetic
diagnosis, and precision phenotyping enables revealing causal genes with
pleiotropic effects [40]. Furthermore, return of actionable genetic
findings for genetic counselor will accelerate prenatal genetic
diagnosis, and control the risk/burden for birth defects [41,42].
There are several explanations for the remaining 23 undiagnosed
pedigrees, including five child-parent quads. First, deep phenotyping
based on electronic medical records cannot extract all phenotypes
related to patients, such as phenotypes associated with non-nervous
system. Second, the selection of phenotype according to the hierarchical
ontology has an obvious effect on phenotype-matching (e.g., UDP #9). An
algorithm by iterating all corresponding ancestral terms may enable
identifying the most matched disorder. Finally, whole-exome sequencing
cannot capture genetic aberrations outside the exon regions, and a
sequencing depth of approximately 30\(\times\) has limited power to
accurately identify copy number variants [43,44]. Therefore, the use
of whole-genome sequencing for clinical diagnosis for rare undiagnosed
pedigree is urgently needed.
Conclusions
The combined use of deep phenotyping and whole-exome sequencing
increased the diagnostic yield for nuclear pedigrees with
neurodevelopmental disorders. Our analysis may provide an avenue for
shortening the diagnostic odyssey for such rare undiagnosed disease in
the clinical setting, and an economical and practical approach will be
widely applied for their evaluation of genetic etiology.
Ethics, consent and
permissions
The study was approved by the Institutional Research Board (IRB) at the
Henan Provincial People’s Hospital, and all participants or their
guardians signed the informed consent.
Consent to publish
We have confirmed that we have obtained consent to publish from the
participant (or their guardians).
Availability of data and
materials
The datasets supporting the conclusions of this article are included
within the article and its additional files.
Acknowledgements
This work was supported by National Natural Science Foundation of China
(No. 81650010, S.L.), the Science and Technology Cooperation Project of
Henan Province (No.182106000058, S.L.), and a grant from National Health
Commission Key Laboratory of Birth Defects Prevention (ZD201907, K.D.).
Competing interests
The authors disclose no conflicts of interest.
Authors contributions
Q.W., K.Y., X.H., and S.L. recruited pedigrees, Q.W. conducted deep
phenotyping with guidance from K.D., X.T. and K.D. performed computation
and data analysis, Q.W. established genotype-phenotype correlation with
input from H.Z., K.D., and S.L., X.T. prepared the graphics and tables
with input from Q.W., K.D. and Q.W. wrote the manuscript with input from
X.T. and S.L., and K.D. and S.L. conceived and designed the project.
Web resources
CHPO: http://www.chinahpo.org/
HPO: https://hpo.jax.org/app/
GATK: https://gatk.broadinstitute.org/hc/en-us
gnomAD: https://gnomad.broadinstitute.org/
Exomiser: https://www.sanger.ac.uk/tool/exomiser/
Phenolyzer: http://phenolyzer.wglab.org/
Intervar: https://github.com/WGLab/InterVar
Clinvar: https://www.ncbi.nlm.nih.gov/clinvar/
OMIM: https://omim.org/
HGMD: http://www.hgmd.cf.ac.uk/ac/index.php
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Tables
Table 1. Clinical characteristics of the recruited pedigrees.
Table 2. Probably and possibly genetic diagnosis for nuclear pedigrees
with neurodevelopmental disorders.
Figure legends
Fig 1. - A framework of clinical genetic evaluation for ‘not yet
diagnosed’ nuclear pedigrees with neurodevelopmental disorders.
Fig 2. - Deep phenotyping characterized phenotypes for pedigrees with
neurodevelopmental disorders, which were standardized using HPO terms.
(A). An ontology plot indicated the relationship of phenotypes. The
color represents the frequency of a term in the HPO database (dark
green: high; and yellow: low). The solid circle represents the abnormal
phenotype present in the recruited pedigrees; (B). The distribution of
HPO terms grouped by nervous and non-nervous systems; (C). The
distribution of HPO terms per pedigree; (D). The distribution of HPO
terms associated with nervous system. The terms under the lineage of
nervous system physiology were grouped using DSM-5; and (E). The
distribution of HPO terms associated with non-nervous system.
Fig 3. - Genetic diagnosis for nuclear pedigrees with neurodevelopmental
disorders. (A). The diagnosis of 22 pedigrees and their inheritance
patterns; (B). The distribution of compound heterozygosity and de
novo mutations; (C-D). An increased Phenotype andExomiser scores when a given term was replaced with its
corresponding ancestral term according to the ontology; (E). The
comparison of the rank of the prioritized genes identified inExomiser and phenolyzer ; and (F). The distribution of HPO
terms in diagnosed and undiagnosed pedigrees. ***, p <
0.001; NS, not significant.
Fig 4. - Case examples. (A). A landscape of HPO terms; and (B-F). Sanger
sequencing confirmed the genetic diagnoses for five pedigrees.