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Article

Increased Diagnostic Yield of Array Comparative Genomic Hybridization for Autism Spectrum Disorder in One Institution in Taiwan

1
Department of Pediatrics, MacKay Memorial Hospital, Taipei 10449, Taiwan
2
Institute of Clinical Medicine, National Yang-Ming Chiao-Tung University, Taipei 11221, Taiwan
3
Department of Rare Disease Center, MacKay Memorial Hospital, Taipei 10449, Taiwan
4
Department of Medicine, MacKay Medical College, New Taipei City 25245, Taiwan
5
MacKay Junior College of Medicine, Nursing and Management, Taipei 10449, Taiwan
6
Department of Medical Research, Division of Genetics and Metabolism, MacKay Memorial Hospital, Taipei 10449, Taiwan
7
College of Medicine, Fu-Jen Catholic University, Taipei 24205, Taiwan
8
Gene Biodesign Co., Ltd., Taipei 10682, Taiwan
9
Departments of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei 10449, Taiwan
10
Department of Biotechnology, Asia University, Taichung 41354, Taiwan
11
School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung 40402, Taiwan
12
Institute of Clinical and Community Health Nursing, National Yang-Ming Chiao-Tung University, Taipei 11221, Taiwan
13
Department of Obstetrics and Gynecology, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei 11221, Taiwan
14
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
15
Department of Infant and Child Care, National Taipei University of Nursing and Health Sciences, Taipei 11219, Taiwan
*
Authors to whom correspondence should be addressed.
Medicina 2022, 58(1), 15; https://doi.org/10.3390/medicina58010015
Submission received: 5 November 2021 / Revised: 19 December 2021 / Accepted: 20 December 2021 / Published: 22 December 2021
(This article belongs to the Section Genetics and Molecular Medicine)

Abstract

:
Background and Objectives: Chromosomal microarray offers superior sensitivity for identification of submicroscopic copy number variants (CNVs) and is recommended for the initial genetic testing of patients with autism spectrum disorder (ASD). This study aims to determine the diagnostic yield of array comparative genomic hybridization (array-CGH) in ASD patients from a cohort of Chinese patients in Taiwan. Materials and Methods: Enrolled in this study were 80 ASD children (49 males and 31 females; 2–16 years old) followed up at Taipei MacKay Memorial Hospital between January 2010 and December 2020. The genomic DNA extracted from blood samples was analyzed by array-CGH via the Affymetrix GeneChip Genome-Wide Human single nucleotide polymorphism (SNP) and NimbleGen International Standards for Cytogenomic Arrays (ISCA) Plus Cytogenetic Arrays. The CNVs were classified into five groups: pathogenic (pathologic variant), likely pathogenic (potential pathologic variant), likely benign (potential normal genomic variant), benign (normal genomic variant), and uncertain clinical significance (variance of uncertain significance), according to the American College of Medical Genetics (ACMG) guidelines. Results: We identified 47 CNVs, 31 of which in 27 patients were clinically significant. The overall diagnostic yield was 33.8%. The most frequently clinically significant CNV was 15q11.2 deletion, which was present in 4 (5.0%) patients. Conclusions: In this study, a satisfactory diagnostic yield of array-CGH was demonstrated in a Taiwanese ASD patient cohort, supporting the clinical usefulness of array-CGH as the first-line testing of ASD in Taiwan.

1. Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder wherein patients have difficulty in communication and social interactions, stereotypical behaviors, and restricted interests. ASD has a prevalence of 1 in 161 children and is more frequent in males [1]. Its pathogenesis is multifactorial, but genetic alteration is the most important factor, with a heterogeneous change seen across the whole genome [2].
Array comparative genomic hybridization (array-CGH) and single nucleotide polymorphism (SNP) genotyping array, as chromosomal microarray analysis (CMA), are initially performed as cytogenetic diagnostic tests for ASD [3,4]. Before the development of CMA, karyotyping was the standard method to detect genetic anomalies in ASD patients. However, this could only detect large and microscopically visible chromosomal changes (>5–7 Mb), with a low diagnostic rate (3–5%) [3,5]. Fluorescence in situ hybridization (FISH) is another tool for detecting submicroscopic deletions and duplications. It could increase the diagnostic yield by 2% to 3% [3,6,7]. Nevertheless, karyotyping and FISH are not enough for evaluating the genetic etiology of ASD.
CMA can overcome the technical limitations of karyotyping and FISH as well as provide a higher resolution of the genome. The International Collaboration for Clinical Genomics, also known as the International Standard for Cytogenomic Array (ISCA) Consortium, recommends CMA as the first cytogenetic diagnostic test in non-syndromic ASD patients [3,8]. The American College of Medical Genetics (ACMG) also established the guidelines of CMA using [3,9,10]. Two studies have described the diseases related to abnormal findings in CMA. Ellison et al. reviewed 46,298 patients via CMA and found 151 disorders related to chromosomal/genetic abnormalities [3,11], with 35% of the patients having abnormal CMA findings. Riggs et al. surveyed the ISCA Consortium database and found 28,526 patients with 146 phenotypes [3,12]. Among the copy number variants (CNVs), 46% were found to be either pathogenic or likely pathogenic (1908/4125).
Many studies have described the causative role of CNVs in ASD [3,13], congenital heart diseases [3,14], epilepsy [3,15], and congenital kidney malformation [3,16]. However, the same CNVs might cause multiple diseases, and the development of disease can be attributed to many different factors. This is known as the two-hit hypothesis [3,17,18]. Due to the “two-hit hypothesis”, the clinical diagnosis, genetic counseling, and management become challenging.
In this study, we used array-CGH to evaluate ASD patients in Taiwan. The diagnostic rate was detected by array-CGH. We also analyzed the CNV characteristic and feature of these patients.

2. Materials and methods

2.1. Patients

This study assessed 80 idiopathic ASD children (49 males and 31 females) with ages ranging from 2 to 16 years. These patients were not related to each other. All of them were followed up at Taipei MacKay Memorial Hospital between January 2010 and December 2020. An autism diagnostic interview-revised (ADI-R) [19] was used to confirm the diagnosis of autism. These patients were diagnosed with idiopathic ASD, which is of unknown origin, and we excluded other potential etiologies such as neurocutaneous syndromes, other specific syndromes, and congenital or acquired infections among other common causes of autism before they had array-CGH. The intellectual level information was confirmed by the Wechsler Preschool and Primary Scale of Intelligence Fourth Edition (WPPSI-IV) and Wechsler intelligence scale for children–Fifth Edition (WISC-V) [20,21].

2.2. Array-CGH and Data Interpretation

We extracted genomic DNA from the peripheral blood according to standard protocols (Figure 1). All samples were sent to two different laboratories. The first laboratory, the National Center for Genome Medicine in Taiwan, used the Affymetrix GeneChip Genome-Wide Human SNP array 6.0 (Affymetrix, Santa Clara, CA, USA), while the second laboratory, Gene Biodesign, used the NimbleGen ISCA Plus Cytogenetic Array (Roche NimbleGen, Madison, WI, USA). The Affymetrix GeneChip Genome-Wide Human SNP array 6.0 had 50,000, 950,000, and 2,700,000 probes with resolutions ranging from 100 to 200 kb across the entire genome to detect CNVs. The Affymetrix Genotyping ConsoleTM version 3.0.1. was used to analyze the array data of 28 patients in this study. The NimbleGen ISCA Plus Cytogenetic Array contained 630,000 and 1,400,000 probes with a resolution of about 15–30 kb throughout the whole genome. The related data were represented using Nexus 6.1 (BioDiscovery, Hawthorne, CA, USA) for 12 patients in this study [3]. We handled all samples according to the manufacturers’ instructions. SPSS version 25.0 (SPSS, Inc., Chicago, IL, USA) was used to perform the statistical analysis. Statistical significance was set at p < 0.05.
According to ACMG guidelines [9,10], CNVs fall under one of the following five categories: pathogenic (pathologic variant), likely pathogenic (potential pathologic variant), likely benign (potential normal genomic variant), benign (normal genomic variant), and uncertain clinical significance (variance of uncertain significance (VOUS)). Pathogenic CNVs are those which cause recognized microdeletion and microduplication syndromes. These CNVs contain morbid Online Mendelian Inheritance in Man (OMIM) genes and large deletions or duplications (usually >3 Mb in size) involving many OMIM genes. They are also inherited from an affected parent and greater than 1 copy number amplification. However, it does not occur in MECP2 duplication where in some instances the parent is not affected [22]. Benign CNVs include those that are well-documented in the normal population or the public databases, not previously reported but inherited from a healthy parent, without any morbid OMIM genes, and duplications with no known dosage-sensitive genes. VOUS CNVs are those that cannot be classified as pathogenic or benign due to insufficient evidence. Recent literature does not recommend using “VOUS” to represent the “likely pathogenic” or “likely benign” categories [9]. Combining the “likely” categories and VOUS may be confusing for clinicians and patients receiving clinical reports. The cut-off value is <1.2 for loss (deletion) and >2.8 for gain (duplication). We compared the findings of our study with previous reports and evaluated the morbidity of the genes by using the following publicly available databases: Database of Genomic Variants (DGV), Database of Chromosomal Imbalance and Phenotype in Humans Using Ensemble Resources (DECIPHER), OMIM, PubMed, ClinVar, and the UCSC Genome Browser. All genomic coordinates are based on the February 2009 assembly of the Genome Reference Consortium build 37(GRCh37)/UCSC hg19.

3. Results

Figure 2 illustrates the diagnostic work-up of patients with ASD. A total of 47 CNVs were found in 39 ASD patients. Thirty-one patients had only one CNV and eight patients had two CNVs. Among the 47 CNVs, 32 were deletions and 15 were duplications. These CNVs were classified into the following five groups according to the clinical interpretation: 42.6% (20/47) were classified as pathogenic, 23.4% (11/47) as likely pathogenic, 27.6% (13/47) as VOUS, 0% (0/48) as likely benign, and 6.4% (3/47) as benign.
The summary of patient characteristics and CNV findings is shown in Table 1. There were 47 CNVs and 80 ASD patients. The detection rate of CNVs was 58.8%. In our study, there were 24 males and 15 females with CNVs. In male and female patients, the CNV detection rates were 62.5% and 53.1%, respectively. There were 31 clinically significant CNVs in 27 patients with a diagnostic yield of 33.8%. VOUS were detected in 13 patients (16.3%). We reviewed the detected CNVs according to the published CNV map of the human genome [23].
Among the 47 CNVs, 31 (65.9%) were clinically significant; 13 were duplications and 18 were deletions. The largest and smallest sizes of these significant CNVs were 17.59 Mb and 0.008 Mb, respectively. There were 22 (70.9%) CNVs smaller than 5 Mb that could not be routinely detected by karyotyping. Among the 22 CNVs, 16 (51.6%) were between 1 and 5 Mb, while 6 (19.3%) were <1 Mb.
Table 2 illustrates all clinically significant CNVs (31 CNVs) in our study. Deletions in chromosome band 15q11.2 were detected in 4 patients and these deletions were found mostly in our patients. The chromosome band 15q11.2 overlapped the Prader–Willi/Angelman region and involved the UBE3A, SNRPN, and CHRNA7 genes. Table 3 describes all 13 VOUS; 3 duplications and 10 deletions. Their sizes ranged from 0.012–148.290 Mb.

4. Discussion

There were 27 ASD patients (33.8%) in our study with clinically significant CNVs detected by array-CGH. The rate of diagnosis is relatively high compared with other studies [24,25]. Our study showed that array-CGH could be the first-tier testing for idiopathic ASD patients due to a satisfactory diagnostic yield. Furthermore, array-CGH allows us to describe the breakpoints (BPs) of the CNV. It can also strengthen the genotype–phenotype correlation and identify candidate genes [4]. For example, a patient who had a 15q11.2 deletion at the Prader–Willi/Angelman region eventually developed autism and language delays due to a reported microdeletion at 15q11.2 between BP1 to BP2 [26,27]. 15q11.2 deletion was also the most common clinically significant CNV identified in our cohort.
In our study, the largest clinically significant CNV was a deletion within the chromosome band 18q21.33q23, and it was consistent with a previous report [28]. The 18q21.33q23 deletion had a size of 17.59 Mb and included 44 OMIM genes from PHLPP1 to PARD6G. According to a previous study, 18q deletion was associated to different phenotypes due to its remarkable genomic heterogeneity [29]. Therefore, we could not confirm diagnosis of 18q deletion by clinical characteristics; genomic analysis is necessary. Our patient had cognition delay, expressive language delay, gross and fine motor delay, hearing loss, delayed myelination of the brain, umbilical hernia, and ear canal stenosis, symptoms compatible with distal 18q deletion [30]. In previous studies, about 54% of the patients with 18q deletion had congenital cardiac anomalies [30,31,32]; however, our patient had a normal echocardiogram. The constitutional hemizygosity of 18q increases the risk of autism as well; 43% of 18q-deletion patients had autism [33]. Furthermore, if the TCF4, NETO1, and FBXO15 genes were in the region of hemizygosity, the risk of autism increased significantly [32]. Our patient had deletion of the NETO1 and FBXO15 genes. However, there was no shared region of deletion in the ASD patients with 18q deletion. Therefore, further studies are needed to confirm the genetic determinants of autism in 18q-deletion patients.
One patient in our cohort had a 14q21.2q22.1 deletion involving the NIN gene. Microcephalic primordial dwarfism disorder has been associated with compound heterozygous mutations of NIN gene [34]. However, our patient had only developmental delays without dysmorphic features. Ninein, a centrosomal protein involved in microtubule anchoring, is encoded by the NIN gene. Ninein plays an important role in microtubule stability due its influence in axonal development and bifurcation [35,36]. Disruptions of neocortex development and axon guidance are crucial factors for the development of ASD [37,38,39,40]. Thus, the NIN gene was associated with ASD possibly because of the function of ninein in axonal development and bifurcation.
The differences in certain aspects between Taiwan and European cohorts were noted by the CNV data from other studies [41,42,43,44]. According to previous reports, the most common detected CNVs in ASD occur in 16p11.2; however, this is seen in less than 1% of ASD patients [42,43,44,45]. In our study, the most frequently detected CNVs were 15q11.2 deletions, seen in 5.0% of ASD patients. To evaluate the differences between Taiwan ASD patients and other ASD cohorts, further studies are needed to assess larger Taiwan ASD cohorts compared with controls. On the other hand, VOUS comprised 13 out of 47 (27.7%) CNVs in our study. It is crucial to interpret VOUS in the context of parental data, but this information was not available during data collection. Due to their possible association with ASD, further investigations for VOUS are needed.
In our study, there were 41 patients without CNVs. However, aside from CNVs, other factors like damaging missense mutations, epigenetic alterations, environmental (in utero and early childhood), developmental factors and as-yet unknown different ways influence autism phenotype [46,47]. Based on research to date, a single condition or event could not play a major role in causing ASD. Even though syndromic or secondary autism caused by such as fragile X syndrome and tuberous sclerosis, none of these etiologies are specific to autism because these etiologies include variable proportion of patients with or without ASD [48]. New technologies in genomics and epigenomics research could uncover the epidemiology of ASD [49]. CMA has a higher resolution than conventional karyotyping. However, CMA may miss polyploidy, balanced translocations, inversion, low-level mosaicism, and marker chromosomes. Meanwhile, we could not exclude all genetic diseases by a benign CMA result. Thus, CMA should not replace the karyotyping.
There are more than 800 genes associated with autism according to case-control studies on population and animal models. In addition to three relevant CNVs and their association with ASD above, we also found other clinically significant CNVs in Table 2 and Table 3. These genes are associated with chromatin remodeling and transcriptional regulation, cell proliferation, and mostly synaptic architecture and functionality. According to the largest exome sequencing study of ASD to date [50], these genes were indicated. Other amygdala-expressed genes associated to the social pathophysiology of ASD were indicated by Herrero et al.’s survey [51].
There were some limitations in this study. Compared to the total number of ASD patients in other studies, the number of patients in our cohort was relatively small. Another limitation was that we did not have the parental samples, which could have helped determine the inheritance for VOUS. In addition, our cohort lacked cases of control CNV data from normal individuals. In other previous studies [28,52,53,54,55,56], there were also no control CNV data from normal individuals. However, according to Kousoulidou et al. study, 6 out of 50 mothers and 8 out of 50 fathers from a total of 100 parents (14%) who had ASD children appeared to carry 16 different rare variants associated with ASD [57]. From an analytical aspect, we also did not check the CNV findings using a second method. However, we reviewed all raw CNV data manually, and this matched the recommended quality parameters.
In our study, two kinds of different CMA testing were used. The methodological factors could influence the results due to different reference samples. We should use the same reference sample within one study [58]. Furthermore, according to Dana Hollenbeck et al. in 2017 [59], there are diagnostic clinical relevance of small (<500 kb) nonrecurrent CNVs during CMA clinical testing. It is necessary for careful clinical interpretation of these CNVs. These small, nonrecurrent CNVs can also facilitate the discovery of new genes involved in the pathogenesis of neurodevelopmental disorders and/or congenital anomalies. Our patient with CNV <500 kb, particularly <50 kb, did not have multiplex ligation-dependent probe amplification (MLPA) or FISH for confirmation. It should be modified in the future.

5. Conclusions

In the ASD patient cohort in Taiwan, there was a satisfying diagnostic rate by using array-CGH. Array-CGH could detect CNVs in high resolution. Comparing to the karyotyping, array-CGH could make enormous details to describe the genomic alterations in ASD patients. Therefore, array-CGH is useful for initial testing of ASD patients in Taiwan.

Author Contributions

C.-L.L. drafted the manuscript. S.-P.L., C.-P.C. and H.-Y.L. participated in the patients’ follow-up and helped in drafting the manuscript. C.-K.C., R.-Y.T. and P.-S.W. performed biochemical analyses and revised the manuscript. Y.-T.L., Y.-H.C., H.-C.C., Y.-J.C. and C.-L.C. were responsible for patient screening and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by research grants from the Ministry of Science and Technology, Executive Yuan, Taiwan (MOST-110-2314-B-195-010-MY3, MOST-110-2314-B-195-014, MOST-110-2314-B-195-029, MOST-109-2314-B-195-024, MOST-108-2314-B-195-012, and MOST-108-2314-B-195-014) and from MacKay Memorial Hospital (MMH-E-111-13, MMH-E-110-16, MMH-E-109-16, MMH-E-108-16, MMH-MM-10801, and MMH-107-82).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Mackay Memorial Hospital’s Institutional Review Board (protocol code: MMH-E-110-16, date of approval: 16 May 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All data have been presented in this manuscript.

Conflicts of Interest

The authors declare no potential conflict of interests.

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Figure 1. Basic steps involved in all DNA extraction methods.
Figure 1. Basic steps involved in all DNA extraction methods.
Medicina 58 00015 g001
Figure 2. Diagnostic work-up of patients with autism spectrum disorder (ASD) (N = 80). Abbreviations: ASD, autism spectrum disorder; CNV, copy number variant; VOUS, variance of uncertain significance.
Figure 2. Diagnostic work-up of patients with autism spectrum disorder (ASD) (N = 80). Abbreviations: ASD, autism spectrum disorder; CNV, copy number variant; VOUS, variance of uncertain significance.
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Table 1. Summary of patient characteristics and CNV findings; CNV, copy number variant.
Table 1. Summary of patient characteristics and CNV findings; CNV, copy number variant.
Number of Patients80
Male48
Female32
Age range (median) (years)2–16 (6)
Total number of CNV47
(Detection rate %)(58.8)
Detected in male30
(Detection rate %)(62.5)
Detected in female17
(Detection rate %)(53.1)
Clinically significant CNV31
(Diagnostic yield %)(38.8)
Detected in male18
(Diagnostic yield %)(37.5)
Detected in female13
(Diagnostic yield %)(40.6)
Table 2. Clinically significant CNVs.
Table 2. Clinically significant CNVs.
Patient
Number
GenderArray CGH Result (hg18)Chromosome
Region (Genes Associated with ASD Phenotype)
Aberration
Type
Size
(Mb)
Clinical SignificanceIQAdditional
Clinical Features
1Malearr15q11.2(22,842,145 − 25,235,046) × 315q11.2
(UBE3A, SNRPN, CHRNA7)
Duplication2.393Susceptibility to ASDN/ADevelopmental delay
arr15q11.2q13.1(25,236,676 − 28,559,402) × 415q11.2q13.1
(UBE3A, SNRPN, CHRNA7)
Duplication3.323
3Malearr17p11.2(16,782,546 − 20,219,464) × 117p11.2
(RAI1)
Deletion3.437Smith-Magenis syndromeN/ADevelopmental delay and facial dysmorphism
4Malearr7q11.23(72,776,313 − 74,133,332) × 17q11.23
(AUTS2)
Deletion1.367Williams syndromeN/ADevelopmental delay
5Femalearr15q11.2q13.2(22,765,628 − 30,653,876) × 415q11.2q13.2
(UBE3A, SNRPN, CHRNA7)
Duplication7.888Susceptibility to ASDN/ADevelopmental delay
arr15q13.2q13.3(30,653,877 − 32,509,926) × 315q13.2q13.3
(CHRNA7)
Duplication1.856
7Femalearr22q11.21(18,706,001 − 21,505,417) × 322q11.21
(CRKL, FGF8, TBX1)
Duplication2.799Susceptibility to ASDN/ADevelopmental delay and facial dysmorphism
8Malearr4p15.1p12(28,451,191 − 47,062,229) × 44p15.1p12
(UGDH)
Duplication18.611Susceptibility to ASDN/ADevelopmental delay
9Femalearr22q11.23q12.1(25,695,469 − 25,903,543) × 022q11.23q12.1
(CRKL, FGF8, TBX1)
Deletion0.208Susceptibility to ASDN/ADevelopmental delay
11Malearr4p16.3(72,447 − 3,848,881) × 14p16.3
(WHS)
Deletion3.776Wolf-Hirschhorn
syndrome
33Developmental delay and facial dysmorphism
12Femalearr15q11.2q13.3(22,770,421 − 32,915,593) × 115q11.2q13.3
(UBE3A, SNRPN, CHRNA7)
Deletion10.145Angelman syndromeN/ADevelopmental delay and facial dysmorphism
13Femalearr4p16.3(68,345 − 4,044,985) × 1.04p16.3
(WHS)
Deletion3.977Wolf-Hirschhorn
syndrome
55Developmental delay and facial dysmorphism
14Femalearr22q13.33(50,967,018 − 51,197,725) × 122q13.3
(SHANK3)
Deletion0.231Susceptibility to ASDN/ADevelopmental delay and facial dysmorphism
arr4p16.3p14 (68,345 − 40,111,547) × 34p16.3p14
(WHS)
Duplication40.000
15Femalearr18p11.32p11.21(136,227 − 15,181,207) × 418p11.32
18p11.21
(SMCHD1)
Duplication15.045Susceptibility to ASD57Developmental delay and facial dysmorphism
16Malearr11q13.4q14.3(71,567,724 − 89,547,851) × 411q13.4q14.3
(SHANK2)
Duplication17.980Susceptibility to ASD34Developmental delay and facial dysmorphism
18Femalearr2q22.1q22.3(141,332,947 − 145,948,739) × 12q22.1q22.3
(TBR1)
Deletion4.161Susceptibility to ASD55Developmental delay and facial dysmorphism
21Malearr1p31.3p31.1(61,947,700 − 73,030,143) × 11p31.3p31.1
(NEGR1)
Deletion11.080Susceptibility to ASDN/ADevelopmental delay and facial dysmorphism
22Femalearr3q22.3q23(138,681,193 − 139,438,715) × 33q22.3q23
(ZBTB20)
Duplication0.758Susceptibility to ASDN/ADevelopmental delay
23Malearr10p15.3(162,270 − 468,133) × 310p15.3
(DIP2C)
Duplication0.306Susceptibility to ASD78Developmental delay
25Malearr14q21.2q22.1(45,863,061 − 50,360,747) × 014q21.2q22.1
(NIN)
Deletion4.500Deletion of the NIN geneN/ADevelopmental delay
27Femalearr2q23.3q24.1(150,619,633 − 157,576,339) × 1.32q23.3q24.1
(MBD5)
Deletion6.957Susceptibility to ASDN/ADevelopmental delay
28Malearr18q21.33q23(60,414,497 − 78,003,508) × 118q21.33q23
(NETO1, FBXO15)
Deletion17.590Susceptibility to ASDN/ADevelopmental delay
29Malearr22q11.21(18,657,470 − 21,843,336) × 122q11.21
(CRKL, FGF8, TBX1)
Deletion3.190CATCH22N/ADevelopmental delay
30MalearrXp22.31(6,450,627 − 8,141,242) × 0Xp22.31
(NLGN4)
Deletion1.690Susceptibility to ASD80Developmental delay
arrXp22.31(8,429,167 − 8,435,863) × 0.5Xp22.31
(NLGN4)
Deletion1.310
31Femalearr15q11.2(20,760,484 − 23,601,857) × 1.115q11.2
(UBE3A, SNRPN, CHRNA7)
Deletion2.840Susceptibility to ASD41Developmental delay
32Malearr15q11.2(22,748,697 − 23,188,522) × 115q11.2
(UBE3A, SNRPN, CHRNA7)
Deletion0.440Susceptibility to ASD35Developmental delay
33Malearr15q11.2q13.1(23,614,732 − 28,536,497) × 115q11.2q13.1
(UBE3A, SNRPN, CHRNA7)
Deletion4.920Angelman syndrome17Developmental delay
34Malearr9q34.3 (140,687,823 − 140,695,906) × 19q34.3
(TSC1, EHMT1)
Deletion0.008Kleefstra syndrome59Developmental delay and facial dysmorphism
36MalearrXq28(152,956,854 − 155,270,560) × 2Xq28
(MECP2)
Duplication2.310Susceptibility to ASDN/ADevelopmental delay
N/A, not available; IQ, intelligence quotient; ASD, autism spectrum disorder.
Table 3. List of variants of uncertain significance.
Table 3. List of variants of uncertain significance.
Patient
Number
GenderArray CGH Result (hg18)Chromosome
Region (Genes Associated with ASD Phenotype)
Aberration
Type
Size
(Mb)
IQAdditional
Clinical Features
2Malearr22q11.22(22,336,268 − 22,556,733) × 122q11.22
(CRKL, FGF8, TBX1)
Deletion0.22072Developmental delay
6Malearr17p13.3(1693 − 2,393,788) × 117p13.3
(MDLS)
Deletion2.39285Developmental delay
10Femalearr9p24.39p23(204,193 − 10,972,824) × 19p24.39p23
(KANK1)
Deletion10.768N/ADevelopmental delay
17Malearr16q22.1q22.2(69,098,865 − 72,591,930) × 116q22.1q22.2
(SCA4)
Deletion3.49369Developmental delay
19Malearr12p13.33p13.32(173,786 − 4,424,837) × 112p13.33p13.32
(EMG1)
Deletion4.25069Developmental delay
23Malearr20p12.3(8,085,389 − 8,589,571) × 120p12.3
(PLCB1)
Deletion0.50478Developmental delay
24MalearrXq13.1(69,228,881 − 69,240,595) × 0Xq13.1
(NLGN3)
Deletion0.012N/ADevelopmental delay
26FemalearrXp21.2(29,336,996 − 29,372,188) × 1Xp21.2
(CDKL5)
Deletion0.035N/ADevelopmental delay
36MalearrXp22.33(1 − 2,196,782) × 0Xp22.33
(NLGN4)
Deletion2.200N/ADevelopmental delay
37Femalearr8q21.2q21.13(51,301,121 − 54,915,042) × 18q21.2q21.13
(TCF4)
Deletion3.61019Developmental delay
38MalearrXq13.1q13.3(70,749,306 − 74,335,167) × 2Xq13.1q13.3
(NLGN3)
Duplication3.59072Developmental delay
39Malearr17q25.3 (77,856,839 − 78,293,128) × 2.9517q25.3
(NF1)
Duplication0.436N/ADevelopmental delay
arrXp22.31q28(6,980,000 − 155,270,000) × 1.1Xp22.31q28
(NLGN4)
Duplication148.290
N/A, not available; IQ, intelligence quotient; ASD, autism spectrum disorder.
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Lee, C.-L.; Chuang, C.-K.; Tu, R.-Y.; Chiu, H.-C.; Lo, Y.-T.; Chang, Y.-H.; Chen, Y.-J.; Chou, C.-L.; Wu, P.-S.; Chen, C.-P.; et al. Increased Diagnostic Yield of Array Comparative Genomic Hybridization for Autism Spectrum Disorder in One Institution in Taiwan. Medicina 2022, 58, 15. https://doi.org/10.3390/medicina58010015

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Lee C-L, Chuang C-K, Tu R-Y, Chiu H-C, Lo Y-T, Chang Y-H, Chen Y-J, Chou C-L, Wu P-S, Chen C-P, et al. Increased Diagnostic Yield of Array Comparative Genomic Hybridization for Autism Spectrum Disorder in One Institution in Taiwan. Medicina. 2022; 58(1):15. https://doi.org/10.3390/medicina58010015

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Lee, Chung-Lin, Chih-Kuang Chuang, Ru-Yi Tu, Huei-Ching Chiu, Yun-Ting Lo, Ya-Hui Chang, Yen-Jiun Chen, Chao-Ling Chou, Peih-Shan Wu, Chih-Ping Chen, and et al. 2022. "Increased Diagnostic Yield of Array Comparative Genomic Hybridization for Autism Spectrum Disorder in One Institution in Taiwan" Medicina 58, no. 1: 15. https://doi.org/10.3390/medicina58010015

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