Detection of Clinically Relevant Copy Number Variations and Genes in a Bangladeshi Cohort of Neurodevelopmental Disorders

doi:10.21203/rs.3.rs-948388/v1

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Detection of Clinically Relevant Copy Number Variations and Genes in a Bangladeshi Cohort of Neurodevelopmental Disorders

https://doi.org/10.21203/rs.3.rs-948388/v1

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Background: Copy number variations (CNVs) play a critical role into the pathogenesis of neurodevelopmental disorders (NDD) among children. In this study, we aim to identify clinically relevant CNVs, genes and their phenotypic characteristics in an ethnically underrepresented homogenous population of Bangladesh.

Methods: We have conducted genome-wide chromosomal microarray analysis (CMA) for 212 NDD patients with male to female ratio of 2.2:1.0 to identify rare chromosomal abnormalities (deletion /duplication/ rearrangements). To identify candidate genes within the rare CNVs, multiple gene constraint metrics (i.e. “Critical-Exon Genes (CEGs)”) were applied to the population data. Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) was followed in a subset of 95 NDD patients to assess the severity of autism and all statistical tests were performed using R package.

Results: In our cohort, the head circumference of males are significantly greater than females (p=0.0002). Of all samples assayed, 12.26% (26/212) and 47.17% (100/212) patients carried pathogenic and variant of uncertain significance (VOUS) CNVs, respectively. 2.83% (6/212) pathogenic CNVs are located at the subtelomeric regions. Further burden test identified females are significant carriers of pathogenic CNVs in comparison to males (OR=4.2; p=0.0007). ADOS-2 subset show severe social communication deficit (p=0.014) and overall ASD symptoms severity (p=0.026) among the patients carrying duplication CNV compared to the CNV negative group. Candidate gene analysis identified 153 unique CEGs in pathogenic CNVs and 31 in VOUS. Of the unique genes, 18 genes were found to be in smaller (<1 MB) focal CNVs and identified PSMC3 gene as a potential candidate gene for Autism Spectrum Disorder (ASD). Moreover, we hypothesized that KMT2B gene duplication might be associated with intellectual disability.

Conclusion: Our results show the utility of CMA for precise genetic diagnosis and its integration into the diagnosis therapeutics and management of NDD patients.

Cellular & Molecular Neuroscience

Neurodevelopmental Disorders (NDD)

Chromosomal Microarray Analysis (CMA)

Copy number variation (CNV)

Autism Diagnostic Observation Schedule-Second Edition (ADOS-2)

Variant of Uncertain Significance (VOUS)

Critical Exon Gene (CEG)

Autism Spectrum Disorder (ASD)

Neurodevelopmental disorders (NDDs) are a group of developmental deficits that disrupt normal physiology and function of the brain. These disorders are referred as a collection of early onset conditions that include autism spectrum disorders (ASDs), intellectual disability (ID), epilepsy encephalopathy, attention deficit hyperactive disorders (ADHD), obsessive compulsive disorder (OCD), and cognitive skills disorders [1–5]. Such disorders, when isolated, are termed non-syndromic; when associated with the presence of dysmorphisms or apparent congenital anomalies (CA), are termed syndromic [6]. The incidence of DD/ID is 3% in the general population [7] while the statistics from USA shows that ASD affects 1 in 54 live births [8]. Individuals affected with NDDs usually present reduced adaptive skills, limited intellectual ability, motor difficulties, CA and problems with social interaction. Phenotypically, there are major overlaps among ASDs with epilepsy encephalopathy, ADHD, Fragile X syndrome (FXS), motor abnormalities and intellectual disability [9, 10].

The etiology of NDDs is principally genetic. Advancement of genomic techniques such as high-resolution microarrays and next-generation sequencing have yielded significant insights into the genetic etiology of NDDs [11]. For decades, structural genomic variation known to be a major contributor into the etiology of a proportion of children diagnosed with NDDs [12–14]. In the last decade, many large international genomic consortiums have profiled NDD cases mostly from European ancestry to identify genomic alterations and NDD associated genes. More than 100 genes and genomic loci [15] have been consistently found to be involved in the etiology of NDDs. Studies based on ASD cohorts have identified an increased burden of rare genic copy-number variations (CNVs) and have characterized rare, usually de novo, recurrent CNV loci that are thought to contribute to the genetic risk [16]. Specific genes within these CNV regions that are implicated in the etiology of ASD and other NDDs include SHANK3, SYNGAP1, NRXN1, GRM7 and DLGAP2, etc. [17–21]. As the number of candidate genes and loci have increased, a striking recurrence of candidates identified in multiple disorders has been uncovered, which may account for a proportion of the significant comorbidity that has been noted among neurodevelopmental disorders [22, 23]. The availability of microarray related technologies and the contribution of structural variations to NDD enabled whole genome chromosomal microarray (CMA) as one of the first tier diagnostic tests for NDD cases in developing countries. In 2010, Miller et al. demonstrated the utility of CMA as a first-tier clinical diagnostic test to enable early diagnosis of individuals with NDDs [24].

In Bangladesh, autism is referred as a great economic burden indicating a significant health problem. A community based study reported the increase in incidence of ASD from 0.2 (2005) to 0.84 (2009) per 1000 children in Bangladesh [25, 26]. In a well characterized NDD suspected cohort, a gold standard observational assessment tool ADOS-2 [27] confirmed 73.85% (209/283) ASD cases rest 26.15% (74/283) are broader NDD cases [28]. The prevalence of NDDs including cerebral palsy, developmental delay and ASD among rural community is 5.6/1000, 2.6/1000 and 0.75/1000etc [29]. Here, the diagnosis of NDDs is mostly done by clinical conditions of the patients and psychological assessment tools like (DSM-IV & ADOS/ADOS2) [28, 30]. Due to overlapping and complex clinical presentation of NDDs, it is difficult to confirm diagnosis by psychological assessment. Therefore, early diagnosis of NDD cases in children may lead to better outcomes through expeditious educational planning and therapeutics [31]. In Bangladesh, the genetic cause of breast cancer, intellectual disability and rare diseases were uncovered by whole genome sequencing [32], whole exome sequencing [33], targeted sequencing [34, 35], whole genome microarray [36] and quantitative PCR [37]. But there is no comprehensive genetic study with large NDD cohort. Our study analyzed a cohort of 212 NDD patients of Bangladesh that underwent microarray testing to identify copy number variations (CNVs) from 2017 to 2020 for diagnostic purpose. To our knowledge, this is a first cohort of NDD patients reporting a significant number of clinically relevant variants and genes from Bangladeshi population.

Cohort description

The cohort comprised of 212 neurodevelopmental disorder patients with autism spectrum disorders (ASDs), developmental delay (DD), seizures/epilepsy, intellectual disability (ID), syndromic features, psychiatric or behavioral issues, hypotonia, speech and language disability, attention deficit hyperactivity disorder (ADHD). Almost all the patients have more than one phenotype. The age of the patients ranged from 9 days to 31 years with 54.25% (115/212) categorized in the range of 1-5 years while 3.8% (8/212) patients were of less than 1 year of age (Table 1). Around 68.40% (145/212) of the cohort were males. Of the 212 patients, ten (7 male and 3 female) were not included in the statistical analysis of age, weight, OFC and BMI as it was not possible to obtain corresponding data (Table 1 and Table 2). Moreover, a subset of patients (n=95) were evaluated for autism spectrum disorders by ADOS-2 method. Among these 95 participants (70 male, 25 female), 71meet the criteria of autism positive (51 male, 20 female) and the remaining 24 were classified as autism negative. Besides the 71, there were more 24 autism patients who were diagnosed by different assessment tool DSM-V (n=14) and ADOS (n=10). Therefore, in total there are 95 ASD patients (44.81%=95/212) diagnosed by different types of psychological assessment tools, DSM-V (n=14), ADOS (n=10) and ADOS-2 (n=71), with the male to female ratio of 2.65:1 (Table 2) and age ranging from 1.5 years to 19 years. We have also collected occipito frontal head circumference, measured in centimeters using a non-stretchable plastic tape and body weight measured in kilograms using a calibrated weight machine.

Ethics statement

The study protocol was approved by Institutional Review Board of Holy Family Red Crescent Medical College and Hospital. Before participant enrolment, written informed consent including the use of peripheral blood and clinical data for research use and publication was obtained from the parents.

ADOS‑2

Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) [27] is a semi-structured, standardized assessment of communication, social interaction, imaginative use of materials, and restricted and repetitive behaviors. ADOS-2 is a gold standard observational assessment for diagnosing ASD. ADOS-2 has five modules and each module offers standard activities designed to elicit behaviors that are relevant to the diagnosis of ASD at different chronological ages and language abilities. For the present study, autism characteristics were measured using Toddler module, Module 1, and Module 2. Each module uses communication, reciprocal social interaction, and restricted and repetitive behaviors to generate a total score. Elevated scores classify an individual in the autism spectrum or autism diagnostic range based on the severity or the frequency displayed.

DNA extraction and quantitation

DNA was extracted from peripheral blood sample using ReliaPrep™ Blood gDNA isolation kit (Promega, USA) followed by the protocols provided by the manufacturer. Extracted DNA samples were checked for the quality using Nanodrop Spectrophotometer. Then samples were electrophoresed on agarose gels and samples with intact genomic DNA showing no smearing on agarose gel were selected for experiment. Intact genomic DNA was diluted to 50 ng/µL concentration based on Quant-iTPicogreen (Invitrogen) quantitation. The whole-genome amplification process requires 200ng of input gDNA.

Whole-genome microarray

We conducted genome-wide microarray to identify chromosomal abnormalities (deletion/duplication/translocation and rearrangements) and investigated the changes in fluorescence intensities between the test specimen and the controls. Illumina Global Screening Array-24+ v1.0 was used applying illumine SNP genotyping technology and Illumina cnv Partition 3.2.1 plug-in of GenomeStudio was used to detect chromosomal abnormalities. This microarray uses 642,824 probes spread across the genome to detect genetic abnormalities (includes >60 loci in DECIPHER database reported for neurodevelopmental disorders) greater than 30kb and also targets sub-telomeric regions that are vulnerable to chromosomal abnormalities. We have used rigorous multiple algorithmic techniques (MathLab and Java) and manual curation of the data to pinpoint genomic variation based on the normalized log2 intensities of the probes. Our algorithm excluded all common CNVs found in the in house (NeuroGen) control population samples (9689 samples) from analysis and only analyzed rare CNVs to infer their contribution to human diseases. Digestion, ligation, PCR, labeling, hybridization, and scanning were performed following the standard protocols.

Frequency determination

We have used commercially available tool GenomeArc (GenomeArc Inc.) annotation software that integrates clinical, genomics and OMICs data. To determine the frequency >50% reciprocal overlap within the same chromosome was considered and we have used 9689 unrelated population control samples from European ancestry [38]. These samples were collected from multiple major population-scale studies that used high-resolution microarray platforms. These included 4,347 control samples assayed by Illumina 1 M from the Study of Addiction Genetics and Environment (SAGE) [39]and the Health, Aging, and Body Composition (HABC) [40]; 2,988 control samples assayed by Illumina Omni 2.5 Mfrom the Collaborative Genetic Study of Nicotine Dependence (COGEND)] [41] and Cooperative Health Research in the Region of Augsburg KORA projects [42]; 2,357 control samples assayed by Affymetrix 6.0 from the OttawaHeart Institute [43] and the PopGen project [44].

CNV classification criteria

To classify and assess the clinical relevance for a particular CNV, at first, all common CNVs (frequency >1%) found in the control population samples (9689 samples) and Database of Genomic Variants (DGV) (http://dgv.tcag.ca/dgv/app/ home) were excluded from further analysis. Then, rare CNVs (frequency<1%) were classified based on their type (gain or loss), size, location, gene content as well as the patients’ clinical data. By searching genomic databases, including Online Mendelian Inheritance in Man (OMIM) (https:// omim.org/), ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), ClinGen Dosage Sensitivity Map (https://dosage.clinicalgenome.org), ISCA database (http://dbsearch.clinicalgenome.org/search/), DECIPHER (https://decipher.sanger.ac.uk/) and published literature (http://www.ncbi.nlm.nih.gov/pubmed/), CNVs were finally classified as pathogenic, variants of uncertain significance (VOUS) and benign following the ACMG guidelines [45].

Droplet Digital PCR

Copy Number Assays were performed using Droplet Digital PCR (ddPCR) System (Bio-Rad Laboratories, Inc.). GeneAssist™ Copy Number Assay Workflow Builder (ThermoFischer, USA) was used to design TaqMan assays on Chr11, PKNOX2 (Hs03289418_cn), Chr15, SNHG14 (Hs05375107_cn) and Chr21, TSPEAR (Hs02835328_cn) with FAM dye. TaqMan™ Copy Number Reference Assay, human, RNase P with VIC dye was used as a reference assay. A total of 22µl reaction mix was prepared, containing 3.5µl of template DNA (20ng/µl) without restriction digestion, 10 µl of 2X ddPCRsupermix for probes (No UDP) (Bio-RAD Laboratories Inc.), 1µl each 20X TaqMan Target Probe (FAM) and 20X TaqMan Reference probe (VIC) (Applied Biosystems, USA) and 6.5µl of RNase-/Dnase Free water. All reactions were prepared in triplicates with one negative control. The reaction mixtures were partitioned using QX200 Droplet Generator™ and then transferred to a 96 well plate and amplified using C1000 Touch thermal cycler, as per the manufacturer’s protocol, then the samples were read by QX200 Droplet Reader™. Data acquisition and analysis were performed using QuantaSoft Version 1.7.4.0917. Poisson algorithm was used to determine the concentrations of the targets as copies/µl.

Candidate gene and pathway analysis

Gene set enrichment analysis (GSEA) [46] was performed using the gene overlap package of R and cytoscape [47] was used to analyze the pathways interrupted by pathogenic CNVs. The enriched set of genes along with their GMT file (consisting of KEGG and GO[48] pathways) were uploaded in the Cytoscape. A pathway was considered to be significant if the false discovery rate (FDR) and p-value cut off was <0.01 and 0.001, respectively. Then, the network was built using the enrichment map and the auto-annotate cytoscape application. P-values are denoted using color gradient (low p values with darker colors). Further, “critical-exon” method [38, 49] was applied to identify candidate genes within the pathogenic and VOUS breakpoints.

Statistical analysis

Welch t-test and one way ANOVA were used to test the physical and phenotypic characteristics difference based on sex, autism diagnosis, and mutation. Significance level of 0.05 was determined to test differences. The analysis was done using statistical analysis in social science (SPSS) version 23. For qualitative data we applied Fisher's exact test.

The cohort comprised of 212 neurodevelopmental disorder patients with male to female ratio of 2.2:1.0 (Table 1). Among 212, 202 were included for descriptive statistical analysis and showed significant differences between the male and female groups with respect to weight (p=0.001), head circumference (p=0.0002) and BMI (p=0.001) (Table 1 and Figure 1). Patients carrying rare CNVs (pathogenic/VOUS) and patients carrying no rare CNVs are defined as CNV positive and CNV negative group respectively. Significant differences with respect to head circumference (p=0.0001), weight (p=0.002) and BMI (p=0.007) were also found in the male CNV positive group compared to female CNV positive group (Table 1, Figure S1) while no significant differences were observed in terms of head circumference and weight in the CNV negative groups (Table S2). It was observed that females are the substantial carriers of pathogenic CNV than males (OR=4.2; p=0.00077) (Figure 3d).

Table 1: Physical and phenotypic characteristics difference based on sex and CNV types

General description of full cohort					Male			Female			Total		Significance of group difference (p)
Number (Frequency in %)					145 (68.40%)			67 (31.60%)			212		0.0001
Mean Age in years (age range)					5.828 (6M to 30.8Y)			4.794 (8M to18.9Y)			212		0.077
Male to female ratio					2.16:1
1 to 5 years old patients					75 (65.22%)				40 (34.78%)		115		0.0019
< 1 year old patients					6 (75.00%)				2 (25.00%)		8		0.2367
Physical characteristics vs gender									Physical characteristics vs male/female CNV status
Physical characteristics		Male (138)		Female (64)			Significance of difference		Male CNV positive (78)		Male CNV negative (60)		Female CNV positive (41)		Female CNV negative (23)		Significance of difference
		Mean		Mean			p		Mean		Mean		Mean		Mean		p
OFC (cm)		50.098		47.891			0.0002^*		50.558		49.500		47.732		48.174		0.0004^*
Weight (kg)		22.088		16.547			0.001^*		23.133		20.730		16.354		16.891		0.021
Height (m)		1.066		1.022			0.214		1.066		1.065		0.994		1.073		0.375
BMI (kg/m²)		18.809		15.304			0.001^*		20.308		16.860		15.601		14.775		0.014
Physical characteristics vs male/female CNV status
Phenotype vs M/F CNV status	Male CNV pos (78)		Female CNV pos (41)			Significance of difference		Male CNV neg (60)		Female CNV negative (23)		Significance of difference		Male CNV positive (78)		Male CNV negative (60)		Significance of difference
	Mean		Mean			t (p)		Mean		Mean		t (p)		Mean		Mean		t (p)
OFC (cm)	50.558		47.732			0.0001^*		49.500		48.174		1.276 (0.211)		50.558		49.500		0.078
Weight(kg)	23.133		16.354			0.002^*		20.730		16.891		1.713 (0.09)		23.133		20.730		0.308
Height (m)	1.066		0.994			0.087		1.065		1.073		0.134 (0.894)		1.066		1.065		0.984
BMI (kg/m²)	20.308		15.601			0.007^*		16.860		14.775		2.162 (0.038)		20.308		16.860		0.042

^* p values are Bonferroni corrected

In this cohort, 95 patients were evaluated for autism spectrum disorders by ADOS-2 method and 71 met the ADOS-2 cut-off criteria of autism positive (51 male, 20 female, male/female ratio 2.55:1) while the remaining 24 were classified as autism negative (non-spectrum). The entire cohort (p=0.0096) and subset of ASD positive cases (p=0.019) show head circumference of males was significantly greater than females (Table 2, Figure 2a and 2b) while no significant differences were observed among male and female in terms of age, weight, height, BMI, social affect score, and ADOS-2 total score. We also observed that patients carrying duplication CNV showed severe social communication deficit (p=0.014) and overall ASD symptoms severity (p=0.026) compared to the CNV negative group (Table 2, Figure 2d and 2e). Moreover, a trend of increased number of CNVs in autism patients was observed in comparison to non spectrum individuals (OR=2.29; p=0.06) (Figure 2c). The detailed statistical analysis of the subset was embedded in Table S3.

Table 2: Physical and phenotypic characteristics difference based on sex, autism and CNV types

General description of autism cohort				Male	Female		Significance of group difference (p)
Patient's number and frequency (N=95)				69 (72.63%)	26 (27.37%)		0.0001
Mean Age in months				65.343	58.92		0.42
Autism male to female ratio				2.55:1
Physical and phenotypic characteristics vs gender				Physical and phenotypic characteristics vs ASD male-female
	Male (70)	Female (25)	Significance of difference	Male ASD positive (51)		Female ASD positive (20)	Significance of difference
	Mean	Mean	(p)	Mean		Mean	(p)
OFC (cm)	50.957	49.600	0.0096	51.216		49.800	0.019
Weight (kg)	21.964	18.600	0.106	23.118		18.850	0.100
Height (m)	1.094	1.105	0.842	1.109		1.116	0.914
BMI (kg/m²)	17.122	15.640	0.094	17.247		15.734	0.168
Social affect score	11.229	11.920	0.462	13.392		13.100	0.728
ADOS 2 total score	13.000	13.720	0.539	15.745		15.300	0.662
Phenotype and physical characteristics vs CNV types
	Del CNV (27)	Dup CNV (24)	Significance of difference	Dup CNV (24)		CNV negative (37)	Significance of difference
	Mean	Mean	t (p)	Mean		Mean	t (p)
OFC (cm)	50.333	51.250	0.264	51.250		50.486	0.310
Weight (kg)	22.241	24.667	0.530	24.667		19.108	0.046
Height (m)	1.082	1.154	0.204	1.154		1.095	0.258
BMI (kg/m²)	17.216	17.671	0.682	17.671		15.927	0.071
Social affect score	10.926	13.208	0.041	13.208		10.541	0.014
ADOS 2 total score	12.667	15.333	0.051	15.333		12.216	0.026

Our microarray analysis from the cohort identified 1053 CNVs (excluding <30kb deletion and <50kb duplication) that includes 395 duplications and 658 deletions that were interpreted and classified according to the ACMG guideline [45] into benign, pathogenic and variants of uncertain clinical significance (VOUS) (Figure 3b). We have also determined the frequency of all 1053 CNVs within 212 Bangladeshi NDD patients and 9689 control CNVs from Ontario population with European ancestry. Using the frequency distribution of both rare and common CNVs of NDD patients of Bangladesh, circos map was constructed[50] (Figure 4). Of all samples assayed, 12.26% (26/212) (Table 3, Figure 3a) and 47.17% patients carried pathogenic and variant of uncertain significance (VOUS) CNVs (Figure 3a). Recurrent deletions and duplications were identified in the 15q11.2q13.1 and 21q11.1q22.3 loci (Table 3). All the CNVs in these regions were further confirmed by ddPCR (Table S4 and Figure S2). In this cohort, 2.83% (6/212) and 20.28% (43/212) patients were carrying pathogenic and VOUS subtelomeric CNVs respectively (Table 3 and Table S1). Of the pathogenic 26 samples, one patient was carrying double terminal deletion impacting chromosome 18 (Table 3) and 11 along with a pathogenic CNV also presented VOUS (Table S5). The 27 pathogenic CNVs comprised of 16 deletions, and 11 duplications (Table 3 and Figure 3C). The average length of deletion and duplication are 5365.26kb and 17451.72kb respectively and the highest frequency group for pathogenic CNV deletion and duplication are 30-2000kb and >20000kb respectively (Figure 3e). To exclude false CNV calls, we have randomly chosen 9 pathogenic CNV for ddPCR validation and yielded (8/9) 88.89% validation rate (Table S4 and Figure S2).

Larger numbers of genes were found to be disrupted in pathogenic deletion (n=1846) and duplication CNVs (n=2925) compared to VOUS deletions (n=490) and duplications (n=991) (Figure 3C). The average length of VOUS deletion and duplication is 129.17kb and 476.76kb respectively and the highest frequency group for VOUS CNV deletion and duplication are 30-100kb and >500kb respectively (Figure 3f). In this cohort, we have also found 1591 loss of heterozygosity (LOH) variants in almost all samples of the cohort (210/212). Of the 1591, 62.10% (988/1591) and 37.90% (603/1591) LOH CNVs are rare and common, respectively. The distribution of frequency has been shown in Figure 4a and 4b.

Our custom pathway enrichment (comprised of Gene Ontology and KEGG databases) analysis of the impacted genes within the CNV breakpoints of all pathogenic deletions identified “ubiquitin-like protein transferase activity (GO:0019787)”, “negative regulation of cell death (GO:0060548)” and “vesicle-mediated transport in synapse (GO:0099003)”pathways to be highly significant (FDR P <6.53 × 10-7), (FDR P <7.2 × 10-6) and (FDR P <7.1 × 10-5) after correction for multiple tests (Figure 5a). Pathway enrichment on the genes impacted by pathogenic duplications identified “interspecies interaction between organisms (GO:0044419), “dependent protein catabolic process (GO:0030163)”, “regulation of neuron death (GO:1901214)” and synaptic signaling (GO:0099536)” pathways to be highly significant (FDR P <1.51 × 10-5), (FDR P <1.85 × 10-5), (FDR P < 5.34 × 10-7) and (FDR P <1.72× 10-4) after correction for multiple tests (Figure 5b).

Analysis of both pathogenic and VOUS CNVs for critical exon genes (CEGs) yielded 153 unique CEGs in pathogenic CNVs and 31 in VOUS, including 3 genes found in both pathogenic CNVs and VOUS alike. On average, there were significantly more CEGs per pathogenic variant (7.5) compared with VOUS (0.19) (p=0.0002) (Table 4). In “critical-exon” analysis, we have found total 24 focal CNVs of <1Mb that disrupt 22 unique CEGs in 22 NDD patients. We have excluded 4 CEG genes; PTPRD, RBFOX1, PCDH9 and LRRC4C from further analysis as only intronic region of the genes were disrupted by corresponding CNVs. After excluding these genes, rest 18 genes; RAB11FIP3, ASTN2, NRXN1, PRPF8, PITPNA, FGFR2, TSC2, PKD1, PSMC3, STK11, APC, ADAR, MAP4, ABCA2, PRKDC, PRKCE, ARID4B, PLCB1, were taken for further analysis (Table S6 and Figure 6). Of the 18, we have found PSMC3 gene as a possible candidate gene for ASD. We have also found a patient (#106), carrying 2.01Mb and 891Kb duplication CNVs, had most similar phenotypes to the KMT2B related disorder patients previously described [51–56] (Figure 7 and Table S8).

Table 4: Critical Exon Genes (CEGs) identified in CNVs from neurodevelopmental disorder

	Pathogenic	VOUS
Total CNVs	26	178
Total number of CEGs identified	195	33
Unique CEGs identified	153	31
Average number CEG/CNV	7.50	0.19
*t (p)	4.33 (0.0002)

* Welch t test

In this study, we have found significantly greater head circumference in the male patients compared to female patients in the overall NDD and autism positive patients. This is a replication of previous studies in other ethnically diverse population that also shown similar association with abnormal acceleration of head growth among children of ASD compared to neurotypical children [57–59]. This observation hints that aberrant brain cell proliferation may be a key neurobiological mechanism in the disorder [60]. Some studies also suggest that certain mutations underlying neurodevelopmental disorders may also lead to changes in brain volume, microcephaly or macrocephaly [61, 62]. Significant difference in head circumference was not observed between CNV positive and negative group as well as CNV deletion and duplication group (Table S2 and S3). But larger head circumference and weight were found in male carrying rare CNV compared to female carrying rare CNV (Table 1, Figure 1).

This is the first Bangladeshi NDD cohort conducted whole genome microarray analysis using GenomeArc annotation tool and identified a diagnostic yield of 12.26% (26/212) that is in strong agreement with other ethnically diverse neurodevelopmental disorder cohorts [24, 38, 63]. Of the 12.26% (26/212), 5 patients were carrying different lengths of deletion and duplication CNVs in the 15q11.2q13.1 region of chromosome 15 (Table 3 and Figure S3). The proximal long arm ‘q’ of human chromosome 15q11-q13 harbors a cluster of imprinted genes, which expressions are controlled by the imprinting centre (IC) [64]. Disruptions of this region due to deletion or duplication CNVs are associated with three distinct neurodevelopmental disorders: Prader-Willi syndrome (PWS, #176270), Angelman syndrome (AS, #105830) and 15q11-q13 duplication syndrome (Dup15q syndrome, #608636) [65, 66]. We have also found 3 patients carrying extra copy of chromosome 21q (dup21q) that is associated with Down Syndrome (DS#190685). Moreover, there were 4 other patients carrying3.5Mb deletion in the 17p11.2 (#40), 1.4Mb deletion in the 7q11.23 (#107), 1.69Mb deletion in the 17q12 (#59) and 1.9Mb duplication in the 22q11.22q11.23 (#6) chromosomal regions (Table 3) that overlapped with the critical regions implicated with some well characterized syndrome [66], Smith Magenis syndrome (SMS# 182290), Williams-Beuren Syndrome (WS# 194050), Renal cysts and diabetes syndrome (RCAD#137920) and 22q11.2 distal deletion syndrome (OMIM#611867) respectively.

Table 3

Details of pathogenic CNVs found in the cohort
ID	Sex	Type	Chromosomal band and position	Size (Mb)	Number of affected genes	Critical exon genes	GenomeArc analysis	Clinical conditions	BD frequency (n=212)	Canada control frequency (n=9692)
1	M	Del ^b	4p16.3p16.2 chr4:35,031-4,878,160	4.843	155	CTBP1, PCGF3, FAM193A, HTT	Wolf Hirschhorn Syndrome; PIGG, UVSSA3, IDUA, FGFR3, LRPAP1, SH3BP2, MSX1	Autism, developmental delay (walk at 5 years), behavioural Problem, intellectual disability, frequent falling, Dysmorphic face	0.00472	0
19	M	Del^a	15q11.2q13.1 chr15:22,838,642-28,314,382	5.476	170	GABRB3, CYFIP1, UBE3A, HERC2, GABRA5	PWS/AS; UBE3A, GABRB3, MAGEL2	Global developmental delay, cannot stand spontaneously, fatty body, Syndromic child	0.01415	0
20	F	Del	20p12.3 chr20:8,117,650-8,593,664	0.476	1	PLCB1	PLCB1	Autism, speech delay, temper tantrum, restlessness	0.00472	1.03E-04
35	F	Del^a	15q11.2q13.1 chr15:22,777,709-28,736,935	5.959	181	GABRB3, CYFIP1, UBE3A, HERC2, GABRA5	PWS/AS; UBE3A;GABRB3;MAGEL2	Hypotonic	0.01415	0
40	F	Del	17p11.2 chr17:16,858,825-20,387,706	3.529	136	LLGL1, SREBF1, COPS3, AKAP10	Smith Magenis Syndrome; Potocki-Lupski_syndrome_(17p11.2_duplication_syndrome); TNFRSF13B, RAI1, B9D1, ALDH3A2	Behavioral problem, speech delay, low hairline	0.00472	0
53	F	Del	1q24.2q31.2 chr1:168,503,684-191,056,769	22.553	360	TPR, NMNAT2, DHX9, RGL1, SMG7, PRRC2C, GLUL, CACNA1E, IVNS1ABP, RNF2, ASTN1, CACYBP, ATP1B1, VAMP4,TNR, XPR1	GORAB, TSEN15, LHX4, ACBD6, GLUL, PRRX1, NPHS2, DARS2, MYOC	Global developmental delay. Ammonia and lactate level high, EEG-normal	0.00472	0
59	M	Del	17q12 chr17:36,201,251-37,889,808	1.689	51	GGNBP2, ACACA	RCAD (renal_cysts_and_diabetes); HNF1B, PIGW	Global developmental delay (delayed walking and speech), salivation, restlessness, H/O pneumonia at 15 days of age	0.00472	0
86	M	Del^b	18p11.32 chr18:45,680-2,276,243	2.231	38	COLEC12	null	Developmental delay, complete cleft lip with cleft palate.	0.00472	0
86	M	Del^b	18q21.31q23 chr18:58,298,202-80,227,529	21.929	280	PHLPP1, KIAA1468, NEDD4L, ZNF532, NETO1, TSHZ1	CTDP1, RTTN, NEDD4L, TSHZ1, PIGN, TXNL4A, RAX, CCBE1, CTDP1	Developmental delay, complete cleft lip with cleft palate.	0.00472	0
87	F	Del	8q22.3 chr8:103,458,677-103,560,657	0.102	RIMS2	null	RIMS2	Autism, impaired language development, restlessness and hyperactivity, delayed walking	0.00472	0
107	F	Del	7q11.23 chr7:73,294,506-74,715,504	1.421	40	BAZ1B, LIMK1, STX1A, EIF4H, CLIP2	Williams Beuren Syndrome (WBS); 7q11.23 duplication syndrome; ELN	Developmental delay (unable to sit without support), feeding problem, jerky movement, hypotonic	0.00472	0
144	F	Del ^b	2q37.1q37.3 chr2:233,505,099-242,065,217	8.560	219	KIF1A, ATG4B, AGAP1, UBE2F, HDLBP	2q37 monosomy; KIF1A, COL6A3, NDUFA10, D2HGDH, CAPN10, TWIST2, HDAC4, UGT1A1, AGXT	Syndromic child, developmental delay, yet cannot sit, poor neck control, no speech, sleeping problem at night, lack of social response. After birth, 2 days eyes closed. Breathing with oxygen and needed blood transfusion. Pneumonia occurred on 28 days.	0.00472	0
159	M	Del	2q24.1 chr2:154,077,277-158,596,423	4.519	70	KCNJ3, NR4A2, ACVR1	ACVR1	Intellectual disability, ADHD, speech delay, involuntary movement, abnormal behaviour, self injuriousbehaviour, aggressive. History of epilipsy two times. At 6 months of age, patient suffered in Pneumonia, Facial appearance: Normal	0.00472	0
178	M	Del	15q11.2q13.1 chr15:22,600,363-28,760,485	6.160	188	GABRB3, CYFIP1, UBE3A, HERC2, GABRA5	PWS/AS; UBE3A, GABRB3, MAGEL2	Spastic Quadriplegia/CP, global developmental delay,specch problem, cannot sit with support, CT scan of brain: Atrophy ventricular dialation, Dysmorphic face	0.01415	0
182	F	Del	2q24.3 chr2:165,992,388-166,079,596	0.087	1	null	SCN1A	Global developmental delay (poor neck control, cannot sit, walk, speech delay), cerebral palsy, at the age of 5 months epilepsy starts, history of frequent infantile spasm, flexor spasm, sleeping problem, eating problem (only liquid food), restlessness, crying nonstop, drooling of saliva. Her elder brother deceased at the age of 8 months after suffering from pneumonia. He also had delay development like his neck was also weak, anemic during pregnancy. Facial appearance abnormal	0.00472	0
184	M	Del	17q12 chr17:34,815,552-36,249,430	1.434	76	AP2B1	PEX12	Low level autism, epilepsy, ADHD, club foot, tempur, tantrum, speech delay, self smiling, aggressive, poor response, poor peer relationship, admitted to NICU at the age of 4d due to convulsion for 2 days, again hospitalized at 15 day of age due to cold attack, sleeping problem. Family history: paternal cousin hyperactive, father had seizure during childhood	0.00472	0
5	F	Dup ^b	3p26.3p26.2 chr3:2,299,060-3,322,758	1.024	5	null	CRBN	Expressive language disorder	0.00472	0
77	F	Dup	9p24.3p12 chr9:48,828-39,297,860	39.249	619	PTPRD, MLLT3, SH3GL2, CDC37L1, SMARCA2, NOL6, KLHL9, NFIB, PSIP1, SMU1, ELAVL2, UBAP1, RUSC2, UBE2R2, CLTA, CNTFR, VCP, NPR2, TESK1, TLN1, RNF38	PIGO, IL11RA, NPR2, TPM2, GBA2, GALT, FANCG, EXOSC3, RMRP, MPDZ, GLIS3, DDX58, VLDLR, KANK1, APTX, FREM1, DOCK8, TEK;SMARCA2, GLIS3, PLAA, TYRP1, GLDC	Developmental delay, speech delay, intellectual disability, anorexia	0.00472	0
14	M	Dup^a	11q23.2q24.2 chr11:114,071,597-125,513,912	11.442	307	BCL9L, BACE1, IFT46, DDX6, C2CD2L, GRAMD1B, UBE4A, CADM1, HSPA8, HYOU1, ARHGEF12, SIK3, ARCN1, PKNOX2, PAFAH1B2	ZBTB16, NECTIN1, ARCN1, ROBO3, DPAGT1, SC5D, CLMP, CBL, KMT2A, MFRP	Developmental delay (speech delay), poor eye contact, history of seizure, cerebral palsy, facial dysmorphism present, corpus callosum agenesis	0.00472	0
15	F	Dup^b	14q32.11q32.33 chr14:90,576,836-106,816,816	16.240	626	DICER1, ITPK1, TTC7B, TRAF3, PAPOLA, TECPR2, AKT1, DYNC1H1, EVL, WARS, YY1, CDC42BPB, BCL11B, PPP2R5C, INF2, EIF5, PACS2, MTA1	TRIP11, UBR7, SLC24A4, CCDC88C, APOPT1, TECPR2, VRK1, DYNC1H1, AKT1, YY1, PACS2	High level of salivation, lack of self care, delay development	0.00472	0
2	M	Dup^a	15q11.2q13.1 chr15:23,443,797-28,289,373	4.846	147	GABRB3, UBE3A, HERC2, GABRA5	PWS/AS; UBE3A, GABRB3, MAGEL2	Autism, syndromic child	0.009434	0
101	F	Dup^a	15q11.2q13.1 chr15:23,370,969-28,371,148	5.000	157	GABRB3, UBE3A, HERC2, GABRA5	PWS/AS; UBE3A, GABRB3, MAGEL2	Autism, impaired language development, abnormal behaviour, steriotype hand movement, restrictive and repetitive activity, restlessness and hyperactivity, unable to play with peers. Lactic Acid and ammonia level high, Facial appearance: normal	0.009434	0
79	M	Dup^b	17p13.3p12 chr17:151,597-11,392,231	11.241	414	PRPF8, PITPNA, ABR, YWHAE, MNT, SMG6, TNFSF12, SENP3, RAP1GAP2, CHD3, CAMTA2, KDM6B, DLG4, NLGN2, ANKFY1, PHF23, FXR2, POLR2A, MINK1, ZBTB4, CTDNEP1, RABEP1, TNFSF12-TNFSF13, EIF4A1, PAFAH1B1,PITPNM3, NEURL4, RPL26, MYH10, NDEL1	Miller Dieker syndrome (MDS); INPP5K, KANSL1, WDR81, BHLHA9, WRAP53, DLG4, TRPV3, ACADVL, ASPA, C1QBP, MPDU1, KDM6B, SLC13A5, CTNS, AIPL1, AIPL1, MYH10, CTC1, MYH3, MYH8, SCO1	Speech delay, Hyperactive, Restlessness, Behavioural problem, history of seizure, seizure Free for >5 years, sleeping problem, facial dysmorphism present	0.00472	0
134	F	Dup^a	21q11.1q22.3 chr21:12,987,574-46,679,698	33.692	683	NRIP1, ETS2, COL6A1, ITSN1, PDXK, CCT8, SON, AGPAT3, NCAM2, BRWD1, DYRK1A, TIAM1	Down Syndrome; C21orf59, SON, HLCS, KCNE1, DYRK1A, C21orf2, COL18A1, PCNT, RSPH1, RIPK4, CBS, FTCD, SIK1, COL18A1, CRYAA, AIRE, CSTB	Global developmental delay, feeding problem, No toilet training, speech delay,vomitingtendancy, hyperactive, facial dysmorphism present (flat nose, Down's like eyes and body)	0.01415	0
136	F	Dup^a	21q11.1q22.3 chr21:12,987,574-46,679,698	33.692	683	NRIP1, ETS2, COL6A1, ITSN1, PDXK, CCT8, SON, AGPAT3, NCAM2, BRWD1, DYRK1A, TIAM1	Down Syndrome; C21orf59, SON, HLCS, KCNE1, DYRK1A, C21orf2, COL18A1, PCNT, RSPH1, RIPK4, CBS, FTCD, SIK1, COL18A1, CRYAA, AIRE, CSTB	Developmental delay, dysmorphic facial appearance, nystigmus, speech delay, low eye contact, eating problem. Family history: mother IQ is low	0.01415	0
152	F	Dup^a	21q11.1q22.3 chr21:12,987,574-46,679,698	33.692	683	NRIP1, ETS2, COL6A1, ITSN1, PDXK, CCT8, SON, AGPAT3, NCAM2, BRWD1, DYRK1A, TIAM1	Down Syndrome; C21orf59, SON, HLCS, KCNE1, DYRK1A, C21orf2, COL18A1, PCNT, RSPH1, RIPK4, CBS, FTCD, SIK1, COL18A1, CRYAA, AIRE, CSTB	Developmental delay,ventricularseptal defect (VSD) surgery. He had CHD in early neonatal period and was diagnosed as having large aortic VSD with inlet extension, large PDA, mild TR, severe pulmonary hypertension. She had history of fast breathing, feeding difficulty with failure to thrive, excesssive sweating and repeated LRTI's since neonatal period.	0.01415	0
6	F	Dup	22q11.22q11.23 chr22:22,749,561-24,600,663	1.851	114	BCR, SMARCB1, SPECC1L	22q11.2 distal deletion syndrome; SPECC1L, SMARCB1	Restlessness, low memory, torn out body cloths, CT scan-normal	0.00472	1.03E-04
^a= Each CNV validated by droplet digital PCR (ddPCR) with TaqMan assay; ^b=subtelomeric CNV; CNV, copy-number variation; CP, cerebral palsy; del, deletion; dup, duplication; F, female; M, male.

2.83% (6/212) pathogenic CNVs in this cohort found to be in the subtelomeric region (Table 3) that is within the range of previously reported (2.4–4.4%) diagnostic rate in patients with DD/ID and congenital anomaly [67, 68]. Of the six, we have found 4.8Mb deletion in the 4p16.3p16.2 (#1), 11.2Mb duplication in the 17p13.3p12 (#79) and 8.6Mb deletion in the 2q37.1q37.3 (#144) subtelomeric chromosomal regions that overlapped with the critical regions associated with the three well characterized syndrome [66], Wolf-Hirschhorn syndrome (WHS#194190), Miller-Dieker syndrome (MDS#247200) and 2q37 monosomy (#600430), respectively. Subtelomeric regions are particularly prone to genomic instability due to the repeat sequences found in these areas. Due to the high gene density, these subtelomeric loci reported to have a variety of phenotypic abnormalities particularly intellectual disability (ID)/mental retardation [69–73]. Rahman et al. [69] identified two siblings affected with ID who are both impacted by a large terminal duplication (18.82 Mb) and a deletion (3.90 Mb) CNVs. Rest 8 patients were carrying different lengths of deletion and duplication CNVs that disrupt the vital regions of the chromosome containing bona fide genes SCN1A, KANK1, DOCK8, etc. associated with different types of neurodevelopmental disorders (Table 3).

Identifying overlapping genes and pathways across disorders is critical to improve the understanding of their potential shared genetic etiology. Gene Ontology and KEGG pathway enrichment analysis of the impacted genes within the CNV breakpoints of all pathogenic deletions and duplication identified “ubiquitin-like protein transferase activity (GO:0019787)”,“vesicle-mediated transport in synapse (GO:0099003)”, “dependent protein catabolic process (GO:0030163)”, “regulation of neuron death (GO:1901214)” and synaptic signaling (GO:0099536)” pathways to be highly significant (Figure 5A and 5B). Aberrations in autophagy (a major cellular catabolic process) related signaling and mutations in autophagy related genes have been implicated in several neurodevelopmental disorders including autism, Tuberous sclerosis, Fragile X syndrome and Neurofibromatosis type 1 [74, 75]. Loss of function in the ubiquitin ligase gene HERC2 has been associated with severe neurodevelopmental phenotype [75]. Mutations in presynaptic genes have been liked to various neurodevelopmental disorders including autism, intellectual disability and epilepsy [76] and synaptic signaling has been identified as one of the principle molecular pathways affected in neurodevelopmental disorders [77]. Perturbations in the apoptotic signaling pathway have also been identified in various NDDs including autism, Fragile X syndrome, and schizophrenia [78, 79].

Analysis of “critical-exon”method[38, 49] to identify constraint candidate genes have found high number of CEGs within pathogenic variant (7.5) compared with VOUS (0.19) (p=0.0002). More CEGs per pathogenic CNV was also previously reported[80] and reflective of length bias and gene density of pathogenic variants. CEG analysis of short focal CNVs identified 18 unique CEGs that are highly expressed in brain and have low burden of non-synonymous variants. The role of these genes within the context of neurodevelopmental disorders and our cohort, we have conducted comprehensive literature search. For example, in our cohort, one autism patient (#20) was carrying a 476 kb pathogenic deletion disrupting PLCB1 gene. Girirajan et al. found an enrichment of microdeletions and duplications involving the PLCB1 gene in individuals with autism [81]. Rest 17 patients were carrying VOUS. Of the 17, we were trying to find out a common genetic breakpoint shared by multiple patients with same clinical condition to identify candidate gene for the disrupted locus. In this cohort, we have found 2 autism patients (#119 and #121) that harbored common deletion breakpoint disrupting 4 genes SLC39A13, PSMC3, SPI1 and RAPSN including one critical exon gene, PSMC3. Autosomal recessive mutations in the SLC39A13 gene is associated with a well defined disease Ehlers-Danlos syndrome, spondylodysplastic type, 3 (OMIM# 612350). Mutation in the SPI1 gene was previously reported in acute lymphoblastic leukemia [82]. Autosomal recessive mutation in another gene, RAPSN, is associated with two other well developed diseases, Fetal akinesia deformation sequence 2 (#601592) and Myasthenic syndrome, congenital, 11, associated with acetylcholine receptor deficiency (#616326). The rest one gene of the common breakpoint is PSMC3. PSMC3 encodes the 26S regulatory subunit 6A also known as the 26S proteasome AAA-ATPase subunit (Rpt5) of the 19S proteasome complex responsible for recognition, unfolding and translocation of substrates into the 20S proteolytic cavity of the proteasome [83]. This is suggestive of PSMC3 plays an essential role in the ubiquitin–proteasome system (UPS) that includes morphogenesis, dendritic spine structure, synaptic activity, and the regulation of synaptic strength in neurons [84–87]. Recently, Ariane Kröll-Hermi et al. demonstrate that homozygous single nucleotide variant in the PSMC3 cause neurosensory syndrome combining deafness, cataract, autism/neurodevelopmental delay due to proteotoxic stress [88]. Further, we have found one 217kb deletion [89] (ClinVar_VCV000151005) and one 173kb duplication [90] (Decipher_412053) CNVs in two previously reported NDD patients in which PSMC3 gene was also disrupted (Figure 6c, 6d and Table S7). Although our patients did not show syndromic appearance at the age of 1.6 and 3.1 years except ASD we hypothesized on the basis of previous studies in UPS [84–87] and decipher [66] data that heterozygous mutation in the PSMC3 (size ~7.7kb) gene might be associated with NDDs without causing neurosensory syndrome.

In this study, we have found a 20 year old girl (#106) born in a consanguineous marriage with healthy parents and was delivered at preterm after an eventful pregnancy (IUGR). She had the history of delayed development. She had intellectual disability with dismorphic features of elongated face, long fingers (Table S1). Her mental and physical conditions were progressively worsening from the age of 15 years. At the age of 18 years, she developed dyskinesia and swallowing difficulty. From this age, she was unable to walk independently or talk clearly. Her MRI finding was normal at this age. Analyzing the overlapping region of fourteen previously reported deletions (length 0.19 to 4.91 Mb)[55, 90–94] and three duplications (length 3.31 to 12.63 Mb ) [90], our patient (#106) contains a shorter 2.01Mb duplication with a 38.4kb (chr19:35,700,296-35,738,700) common overlapping region among the CNVs disrupting 2 genes, ZBTB32 and KMT2B (Figure 7 and Table S8). Further, CEG and GenomeArc analysis also identified UBA2, USF2, SCN1B, KMT2B, COX6B1, LGI4 and ZNF599. SCN1B is the most interesting gene known to be associated with Atrial fibrillation, familial, 13 (#615377), Brugada syndrome 5 (#612838), developmental and epileptic encephalopathy 52 (#617350) and epilepsy with febrile seizures plus, type 1 (#604233) when get abnormal due to pathogenic mutations. Our patients had no history of seizures or epilepsy or any cardiac problems that indicate that SCN1B duplication might not be associated with our patient’s clinical condition. KMT2B is another interesting gene within this breakpoint and pathogenic mutations are associated with Dystonia 28, childhood-onset (#617284) which core phenotypes are described as limb-onset childhood dystonia that tends to spread progressively, resulting in generalized dystonia with craniocervical involvement. Co-occurring signs such as distinct facial dysmorphism and intellectual disability are most common [54, 55]. There is a distinct group of KMT2B patients presenting with a neurodevelopmental disorder in the absence of dystonia or related movement disorder [52, 54, 56]. We have also found some Decipher [90] patients carrying duplication CNV containing KMT2B gene whose common phenotype was GDD in the absence of dystonia. Most of the clinical conditions of our patient (#106) in the form of global developmental delay, intrauterine growth retardation, intellectual disability, facial dysmorphism, dyskinesia, swallowing problem, walking and talking problem match with the KMT2B related disorder. That’s why we hypothesize that KMT2B gene duplication might be associated with the KMT2B related disorder. Although KMT2B haploinsufficiency due to frameshift (small insertion/deletion), nonsense, splice-site, missense and large deletion mutations are the primary cause of disease mechanism [54–56] but it is also reported that the penetrance for KMT2Brelated disease is high with almost complete penetrance for protein-truncating variants and chromosomal deletions, and reduced penetrance for missense variants [54, 56]. A report from China showed that DYT-KMT2B and KMT2B-related neurodevelopmental disease without dystonia can occur even within the same family[95]. From the current knowledge of KMT2B related studies we hypothesize that patients with KMT2B duplication variants had reduced penetrance for KMT2B related disorders than those with chromosomal deletions and loss of function mutations[54, 56].Within the common overlapping region, ZBTB32 is another new candidate gene in our cohort with no previous report of association with dystonia or neurodevelopmental disorder patients. In our patient we have also found another 891Kb VOUS which disrupt the most important gene FTL. Pathogenic mutations in this gene are associated with neurodegeneration with brain iron accumulation 3; NBIA3 (OMIM#606159) or Hyperferritinemia with or without cataract; hrftc (OMIM#600886). NBIA3 is characterized by progressive iron accumulation in the basal ganglia and other regions of the brain, resulting in extrapyramidal movements, such as Parkinsonism, dystonia and dyskinesia. Age at onset is variable 13 to 63 years[96, 97]. No cavitation of the basal ganglia or any evidence of iron deposition was found in the MRI report of our patient at the age of 18 years (report normal finding). Although our patient has no confirmed pathology findings of NBIA3 or cataract, it is not possible to exclude FTL duplication association with the clinical conditions of our patient due to variable age of onset of the disease.

In our cohort, a series of overlapping rare clinically relevant variants have been identified in multiple patients. For example, a 96.8 Kb deletion was found in two unrelated ASD patients (#8 and #127) at 6p21.33 that disrupts major histocompatibility complex (MHC) class I gene MICA that was not previously associated with broader neurodevelopmental disorder patients. Another three unrelated patients (#13, #15 and #207) carrying around 48Kb duplication at 15q13.3 that disrupt the previously reported [98–100] broader NDD genes CHRNA7 and OTUD7A. We have also found a 381Kb duplication in two unrelated ASD patients (#34 and #104) at 8q21.2 that disrupts Carbonic anhydrases II, CA2 gene associated with the disease osteopetrosis, autosomal recessive 3; OPTB3 (OMIM# 259730). We have found two siblings (#42, #43) affected with variable NDD phenotypes and one unrelated patient (#152) carrying 233 kb to 340 kb duplication at 3p26.3 disrupting previously reported [101, 102] broader NDD gene CNTN6 gene. We have found another two unrelated ASD patients carrying around 130Kb duplication CNV disrupting previously reported [103] NDD gene NPHP1. Three unrelated patients (#57, #159 and #200) carrying 442Kb to 691Kb duplication CNVs with a 236Kb overlapping region disrupting two constraint genes NPY4R and GPRIN2. In addition, we have found two unrelated ASD patients (#65 and #99) carrying 51Kb and 59Kb duplication at 4p16.3 disrupting Alpha-L-iduronidase, IDUA gene.

In this paper, we have shown the utility of whole genome microarray as a first tier diagnostic technology for neurodevelopmental disorders patients. Without a proper genetic test, the clinical complexity alone may not enough to identify the cause and often lead to a diagnostic odyssey. The price of microarray is getting cheaper and in near future developing countries will be able to implement such technology within their healthcare setting. To resolve diagnosis of NDD cases, we highly recommend to use whole genome microarray test in developing countries that eventually will lead to precision diagnosis for 10-20% of NDD cases and will enable the detection of novel variants and genes from underrepresented populations.

Neurodevelopmental Disorders (NDD); Chromosomal Microarray Analysis (CMA); Copy number variation (CNV); Autism Diagnostic Observation Schedule-Second Edition (ADOS-2); Variant of Uncertain Significance (VOUS); Critical Exon Gene (CEG); Autism Spectrum Disorder (ASD); Intellectual Disability (ID); Attention Deficit Hyperactive Disorders (ADHD); Developmental Delay (DD); Droplet Digital PCR (ddPCR); Gene set enrichment analysis (GSEA)

Ethics approval and consent to participate

The study was conducted according to the Declaration of Helsinki and was approved by the Institutional Ethical Review Committee (IERC) of Holy Family Red Crescent Medical College and Hospital, and all samples were collected with written informed consent.

Consent for publication

Written informed consent for publication was obtained from parents or legal guardians for all individuals involved in the study.

Availability of data and materials

Patient’s phenotypic data is contained within the supplementary material. Genomic data can be shared for any collaborative research that involves NeuroGen Healthcare. Please request this via the corresponding author

Competing interests

The authors declare that they have no competing interests

Funding

This work was supported, in whole or in part, by internal grant awards from Startup Bangladesh–iDEA project, Genetics and Genomic Medicine Centre, NeuroGen Healthcare; Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) – College of Medicine; Sandooq Al Watan Research & Development Grant (SWARD-F2018-002); AlMahmeed Collaborative Research Awards (ALM1801, ALM20-0074); Dr. Nasna Nassir was supported by MBRU Post-Doctoral Fellow Award (MBRU-PD-2020-02).

Authors’ Contributions

Conceptualization, MU, HA, MMR; Preparation of draft manuscript by MU, HA, MMR, ShaS, SS, MB, MI, AR, MMH, ML, NJ, AM, MOF, TBE, NS, GK, SIK, RKK, KF, MH, MAHM, NK, ZBA, NN, AHMNN, KMFU; The figures were prepared by MU, HA, SH, MAR,; Microarray was done by HA, AR, NJD; Analysis, MU, GB, AI, HA, NN, MIH, AR, MAR, NJD; Interpretation of variants, MU, HA, AI; Comprehensive literature search was conducted by MU, HA, NJD, AR, NN, ZBA, SH, TBE, SS. All authors have read and agreed to the published version of the manuscript.

Acknowledgements

We thank all the patients and participants of this study. We graciously thank Mohammed Salah Uddin Danis, Managing Director, NeuroGen Healthcare, Dhaka, Bangladesh for helping us at NeuroGen conducting these experiments.

Authors’ information

1. Genetics and Genomic Medicine Centre, NeuroGen Healthcare, Dhaka, Bangladesh 2. Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh 3. Department of Paediatric Neurology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh 4. Department of Child Neurology, NeuroGen Children’s Healthcare, Dhaka, Bangladesh 5. Department of Paediatric Neuroscience, Dhaka Shishu Hospital, Dhaka, Bangladesh 6. Department of Neurology, National Institute of Neurosciences and Hospital, Dhaka, Bangladesh 7. Centre for Precision Therapeutics, NeuroGen Healthcare, Dhaka, Bangladesh 8. International Horticulture, Leibniz University Hannover, Section Plant Biotechnology 9. Department of Medicine, Dhaka Medical College, Dhaka, Bangladesh 10. Department of Paediatric Neurology, National Institute of Neuroscience and Hospital, Dhaka, Bangladesh 11. Department of Biology and Biochemistry, University of Houston 12. Cellular Intelligence Lab, GenomeArc Inc., Toronto, ON, Canada 13. Department of Child Neurology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh 14. Department of Obstetrics & Gynaecology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh 15. Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh 16. Department of Biomedical Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh 17. Department of Paediatrics, BIRDEM General Hospital 18. College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE 19. Department of Biochemistry, Holy Family Red Crescent Medical College, Dhaka, Bangladesh 20. Cellular Intelligence (Ci) Lab, GenomeArc Inc., Toronto, ON, Canada

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Detection of Clinically Relevant Copy Number Variations and Genes in a Bangladeshi Cohort of Neurodevelopmental Disorders

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Abstract

Figures

Introduction

Methods And Materials

Cohort description

Ethics statement

ADOS‑2

DNA extraction and quantitation

Whole-genome microarray

Frequency determination

CNV classification criteria

Droplet Digital PCR

Candidate gene and pathway analysis

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

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