Literature DB >> 29020723

Chromosomal Microarray Testing in 42 Korean Patients with Unexplained Developmental Delay, Intellectual Disability, Autism Spectrum Disorders, and Multiple Congenital Anomalies.

Sun Ho Lee1, Wung Joo Song2.   

Abstract

Chromosomal microarray (CMA) is a high-resolution, high-throughput method of identifying submicroscopic genomic copy number variations (CNVs). CMA has been established as the first-line diagnostic test for individuals with developmental delay (DD), intellectual disability (ID), autism spectrum disorders (ASDs), and multiple congenital anomalies (MCAs). CMA analysis was performed in 42 Korean patients who had been diagnosed with unexplained DD, ID, ASDs, and MCAs. Clinically relevant CNVs were discovered in 28 patients. Variants of unknown significance were detected in 13 patients. The diagnostic yield was high (66.7%). CMA is a superior diagnostic tool compared with conventional karyotyping and fluorescent in situ hybridization.

Entities:  

Keywords:  autism spectrum disorder; chromosomal microarray; developmental delay; intellectual disability; multiple congenital anomalies

Year:  2017        PMID: 29020723      PMCID: PMC5637341          DOI: 10.5808/GI.2017.15.3.82

Source DB:  PubMed          Journal:  Genomics Inform        ISSN: 1598-866X


Introduction

Chromosomal microarray (CMA) analysis is widely recognized and recommended as the first-tier cytogenetic diagnostic test for patients with developmental delay (DD), intellectual disability (ID), autism spectrum disorders (ASDs), or multiple congenital anomalies (MCAs) [1, 2]. DD describes persons aged 5 years or younger who have demonstrated several significant delays in the following areas: cognitive, speech, social/personal, fine/gross motor, and daily activities. ID is diagnosed at or after age 5 years when one’s intelligence quotient is deemed to be less than 70 and when permanent intellectual impairment results in a general deficit in adaptive behaviors [3]. ASD comprises a wide range of neurological and developmental disabilities, encompassing autism, Asperger syndrome, pervasive developmental disorders, and childhood disintegrative disorder [3]. MCAs refer to the presence of multiple major malformations, such as cardiac defects and missing limbs, or 3 or more minor malformations (e.g., syndactyly and club feet). Combined, these disorders are highly prevalent (DD/ID, up to 3% [4]; ASDs, 1% to 2% [5]; MCAs, 0.16% [6]) and might have a genetic etiology, such as copy number variation (CNV) and loss of heterozygosity (LOH). The prevalence of pathogenic CNVs is high in those with autism (5% to 10%) and children with both ID and MCAs (20% to 25%) [7]. CMA is a high-resolution, high-throughput technique that detects submicroscopic CNVs that are not observable with traditional cytogenetic analysis tools, including karyotyping and fluorescence in situ hybridization (FISH) [8]. Further, CMA has a higher diagnostic yield (12.2% on average) than conventional tests [1, 2]. However, as with next-generation sequencing (NGS), interpreting microarray anomalies poses challenges for clinical laboratories and clinicians in establishing the clinical significance and pathogenicity of the detected CNVs. Variability in the interpretation arises from internal databases that are limited by the number of cases that are analyzed; racial and socio-economic bias; and the complete lack of openly accessible centralized resources for sharing CMA data, patient phenotypes, and clinical interpretations between laboratories in South Korea and overseas. Moreover, disparate technology platforms with varying array designs, resolution, and coverage render the standardization of CMA and its uniform interpretation more difficult. In this study, we attempted to establish the origin of DD, ID, ASDs, and MCAs in 42 Korean patients; demonstrate the value of CMA in determining the genomic etiology of unexplained DD, ID, and MCAs; and discuss the challenges facing CMA.

Methods

Patients

The parents and guardians of the probands were informed about the microarray study and given information about the risks, benefits, and limitations of CMA testing. Informed consent was obtained by a clinical geneticist or the researchers. The study sample comprised 42 individuals (29 males, 13 females), ranging in age from newborns to 38 years, who had negative test results for metabolic disorders and other suspected disorders and did not present with any recognizable syndrome. Clinical data, including medical history, were collected from the medical records and the parents and guardians of the probands. General observations for dysmorphic features were made, and the height and head circumference were measured by the clinician or the researchers.

DNA preparation

Blood samples were collected at the time of consent. Genomic DNA was extracted from peripheral blood lymphocytes using the AccuPure Cell/Blood DNA Mini Kit (AccuBioMed Co, Ltd, New Taipei City, Taiwan) according to the manufacturer’s instructions. DNA concentration and quality were measured using a NanoDrop One Microvolume UV-Vis spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and a Fragment Analyzer (Advanced Analytical Technologies, Ankeny, IA, USA).

Chromosomal microarray

CMA tests were performed with Affymetrix CytoScan 750K arrays (Affymetrix, Santa Clara, CA, USA) and a GeneChip GCS3000dx V2 scanner (Affymetrix) according to the manufacturer’s instructions. The array had 750,436 markers (550,000 non-polymorphic markers and 200,436 single nucleotide polymorphism markers), based on genome build hg19. The results were analyzed with Chromosome Analysis Suite ver. 3.2.0.1252 (Affymetrix).

Interpretation

CNVs were categorized as pathogenic, benign, and variants of uncertain significance (VUS) by considering gene content, size, inheritance pattern, and previously reported microdeletion or microduplication regions. University of California Santa Cruz Genome Browser was employed to check all of the information relevant to the locations of the CNVs. dbVar (https://www.ncbi.nlm.nih.gov/dbvar) and the Database of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources (https://decipher.sanger.ac.uk/) were consulted to check clinically relevant structural variations and determine whether the observed CNVs were of clinical significance. We considered CNVs to be causative if the variant was responsible for a known syndrome; encompassed a gene(s) of known function; or occurred de novo or, if inherited, the parent was variably affected or if the involved gene(s) had been reported to be associated with ID/DD/ASDs in public databases and the scientific literature. The term VUS was used if the variants affected a gene(s) of unknown clinical significance and if, at same time, the size was substantially less than 100 kb (the average resolution of the probes used) and when the family studies were inconclusive or unavailable [9].

Results

A total of 41 clinically relevant CNVs were detected in 28 (66.7%) patients: three (10.7%) of those presenting with MCAs, 23 (82.1%) with a variable degree of DD or ID, and two (7.1%) with varying degrees of ID and ASDs; 10 (35.7%) patients had multiple rearrangements. Among 28 pathogenic cases, nine were diagnosed with microdeletion/microduplication syndromes, including 1p36 deletion syndrome, 5q14.3 deletion syndrome, 16p11.2 deletion syndrome, 16p11.2 duplication syndrome, 19p duplication syndrome, 9p duplication syndrome, 19p13.2 deletion syndrome, and 22q11.2 duplication syndrome. In this study, more pathogenic duplications were observed than deletions (22 vs. 19). Also, 12 cases (28.6%) were VUS, and two cases (4.8%) were benign CNVs (Table 1).
Table 1.

Chromosomal microarray results

Patient No.SexAgeMedical historyMicroarray resultSize (kbp)Critical gene(s) or known syndromeClassification
1M7 yID, ASDarr[hg19] 9q34.3(139,486,767-140,141,288)x3654.521THRB, GLB1, TRAF2, MAN1B1, GRIN1Pathogenic
arr[hg19] 3p24.2(24,448,650-24,453,383)x14.733THRB
2M2 yDD, dystonia, family history of hereditary spastic paraplegiaarr[hg19] 1q41q42.11(223,903,817-224,304,043)x3400.226CAPN2Pathogenic
arr[hg19] Xp21.1(32,535,197-32,544,026)x08.829DMD
3M8 yIDarr[hg19] 2q24.3(166,914,464-166,920,459)x16SCN1APathogenic
arr[hg19] 3p26.3(282,750-283,756)x11CHL1
4M9 yIDarr[hg19] 2q24.3(166,914,464-166,920,459)x16SCN1APathogenic
arr[hg19] Xp11.4(41,420,369-41,436,593)x016CASK
5M4 yDDarr[hg19] Xq22.2(103,022,505-103,032,637)x010PLP1Pathogenic
6M9 yIDarr[hg19] 8q23.3(116,594,180-116,597,777)x13.597TPRS1VUS
arr[hg19] 19p13.3(1,377,665-1,712,859)x3335.194NDUFS7
arr[hg19] 20q13.33(61,914,225-62,223,211)x3308.986KCNQ2
7M5 yDD, dystoniaarr[hg19] 5q14.3q21.3(89,128,834-105,955,784)x116,826.955q14.3 deletion syndromePathogenic
arr[hg19] Xp21.1(32,685,839-32,696,594)x010.755DMD
8M17 yMild IDarr[hg19] 2q21.2q21.3(134,274,063-135,144,454)x3870.391NoneBenign
arr[hg19] 7q11.21(64,612,879-65,162,169)x1549.29None
9M7 yID, convulsionarr[hg19] 9q21.13q21.31 (74804031-81810541)x17,006.51TRPM6Pathogenic
10M16 yDD, MCAarr[hg19] Xq28(152,927,530-153,002,877)x275.347SLC6A8Pathogenic
11M17 yID, ASDarr[hg19]16p11.2(29,567,295-30,177,916x161116p11.2 deletion syndromePathogenic
12M10 moDD, hypotonia, failure to thrivearr[hg19]16p11.2(29,657,192-30,192,347)x359816p11.2 duplication syndromePathogenic
13MNBMCANormal (balanced de novo translocation later found)-N/ABenign
14F5 yMCAarr[hg19] 1p36.33p36.23(849,466-7,637,060)x16,7881p36 deletion syndromePathogenic
15M6 yID, ASDarr[hg19] 2q14.2(121,565,880-121,569,723)x13.843GLI2VUS
16F5 yDD, mitochondrial disorderarr[hg19] 7q36.2(153,965,792-153,987,424)x121.632DPP6Pathogenic
arr[hg19] 9q34.3(139,015,355-139,425,340)x3409.985NOTCH1
arr[hg19] 12p13.33(1,953,989-2,306,966)x3352.977CACNA2D4
17M7 yID, multiple neuromuscular problemsarr[hg19] 9q34.3(139,870,881-140,169,934)x3299.053MAN1B1Pathogenic
arr[hg19] 16p13.3(549,826-1,045,161)x3495.335CCDC78
arr[hg19] Xq28(153,167,262-153,189,112)x221.85AVPR2
arr[hg19] Xp21.1(31,536,869-31,542,339)x05.47DMD
18F15 yID, seizure, brain anomalyarr[hg19] 16p11.2(32,046,521-33,814,547)x11,768.02616p11.1 deletion syndromePathogenic
19F15 yIDarr[hg19] Xp21.2(29,368,422-29,383,438)x415.016IL1RAPL1Pathogenic
20M3 yDD, cryptorchidism, dystoniaarr[hg19] 15q11.2(24,340,678-24,829,846)x3489.168PWRN1Pathogenic
21M6 yID, dystonia, long limbsarr[hg19] 15q21.1q21.3(46,413,347-53,769,553)x17356.206FBN1Pathogenic
22M3 yDD, dystoniaarr[hg19] 16p13.3(571,141-910,738)x3339.597CCDC78Pathogenic
23M14 yDD, dystoniaarr[hg19] 16p13.3(1,002,394-1,007,394)x45.0LMF1Pathogenic
arr[hg19] Xp21.1(31,536,869-31,536,878)x00.009DMD
24F31 yMild IDarr[hg19] 15q26.3(99,183,519-99,204,934)x421.415IGF1RVUS
25M6 yIDarr[hg19] 22q11.21(18,916,842-19,024,659)x4107.817PRODH (22q11.2 duplication syndrome)Pathogenic
26F27 yIDarr[hg19] 9p24.3q21.11(208,454-69,977,404)x369,768.959p duplication syndromePathogenic
arr[hg19] 19p13.3(669,306-1,725,991)x31,056.6919p duplication syndrome
27F10 yDD, epilepsyarr[hg19] Xq28(152,765,599-153,008,454)x3242.855ATP2B3, SLC6A8Pathogenic
28M38 yHistory of DD, hypotoniaarr[hg19] 7p21.2(16,438,181-16,443,839)x45.658ISPDVUS
29F7 yHistory of DD, polydactylyarr[hg19] 7q35(146,193,991-146,199,530)x15.539CNTNAP2VUS
arr[hg19] Xp11.22(53,225,023-53,240,693)x315.67KDM5C
30M5 yDD, dystoniaarr[hg19] 9q34.3(139,381,821-140,086,032)x3704.211MAN1B1,GRIN1Pathogenic
arr[hg19] Xp21.1(31,536,869-31,542,335)x05.466DMD
31M18 yIDarr[hg19] 9q34.3(139,897,180-140,086,032)x3188.852MAN1B1,GRIN1Pathogenic
32F11 yMild ID, ASDarr[hg19] 16p13.3(536,666-1,449,862)x3913.196STUB1VUS
33M4 yDD, iron-deficiency anemiaarr[hg19] 7p15.2(27,223,591-27,224,687)x11.096HOXA11VUS
arr[hg19] 16p13.3(1,129,251-1,517,728)x3388.477GNPTG
34F16 yID, low bone densityarr[hg19] 1q21.3(151,331,913-151,409,973)x378.06POGZVUS
35M15 yID, low bone densityarr[hg19] 1q21.3(151,331,913-151,409,973)x378.06POGZVUS
36F5 yDD, hypotonia, suspected Haddad syndromearr[hg19] 19p13.2p13.12(12,697,352-14,926,569)x12,229.2219p13.2 deletion syndromePathogenic
37M13 yIDarr[hg19] Xp11.22(53,428,070-53,477,879)x249.809SMC1APathogenic
38F20 yHistory of DD, convulsionarr[hg19] Xp11.22(53,428,070-53,477,882)x349.812SMC1A, HSD17B10Pathogenic
39M10 yID, ASDarr[hg19] 5q31.2q31.3(137,500,664-140,552,558) hmz3,051.894PURAVUS
40M6 yIDarr[hg19] Xq28(152,927,530-152,993,325)x265.795ABCD1VUS
41M4 yDDarr[hg19] 1p36.33(2,226,599-2,242,417)x415.818SKIPathogenic
42F21 yIDarr[hg19] Xp22.32(5,844,864-5,872,572)x127.70NLGN4XVUS

ID, intellectual disability; ASD, autism spectrum disorders; DD, developmental delay; VUS, variantsof uncertain significance; MCA, multiple congenital anomalies.

Discussion

Genomic CNVs can contribute to the etiologies of DD, ID, ASDs, and MCAs. With its proven diagnostic yield, CMA has been recommended by a number of professional societies as part of the standard assessment for individuals in whom the underlying cause is unclear [10, 11]. Evaluating genomic variants identified by CMA is time-consuming and difficult. Variations in their interpretat ion are not uncommon due to platform differences (e.g., resolution and array design), lack of standardization, and differences in the resources that are available to and the expertise of the bioinformatics team and clinicians [12]. At the time of writing, South Korea’s National Health Insurance (NHI) does not recognize CMA as a medically necessary test nor does it subsidize the cost of CMA testing. NHI allows only multiplex ligation-dependent probe amplification tests for four microdeletion syndromes: Angelman/Prader-Willi syndrome, DiGeorge syndrome, Miller-Dieker syndrome, and Williams syndrome [13]. Although subsidies for NGS genetic testing have received government approval this year, NGS tests are limited to a small number of pre-approved diagnostic gene panels. With NGS in the early stages of clinical application in this country due to government restrictions and due to insufficient human bioinformatics resources and lack of a central system for a nationwide interlaboratory QA program and data sharing, there is currently no affordable high-resolution, high-throughput diagnostic genetic test for CNVs available to the public in South Korea. One major limitation of this study is that due to the costs of CMA, we were unable to investigate the families of the probands to confirm the clinical significance of some detected CNVs. Other limitations were associated with the sensitivity and specificity of the chosen CMA testing platform; low-level mosaicism and balanced rearrangements might not be detected [14]. For example, in patient 26, FISH revealed a derivative chromosome 15 resulting from a translocation between chromosomes 9 and 15. This study illustrates the ability of CMA to greatly improve the diagnostic yield (66.7%) for patients with unexplained DD, ID, ASDs, and MCAs. However, it is also important to note that the clinical impact of CMA beyond the diagnosis is very limited. It is unlikely that any genomic genetic test will lead to better clinical outcomes for adults with DD, ID, ASDs, and MCAs, because the genetic diagnosis often does not lead to an intervention strategy for adult patients. Nevertheless, the diagnosis will influence the parents’ reproductive planning, assist genetic counselors in assessing recurrence risks and providing guidance to the patients’ families, and ultimately help reduce childhood morbidity and mortality. Therefore, we recommend the introduction and recognition of CMA as the first-tier diagnostic genetic test in South Korea.
  11 in total

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Journal:  Neurology       Date:  2011-09-28       Impact factor: 9.910

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Authors: 
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Authors:  David T Miller; Margaret P Adam; Swaroop Aradhya; Leslie G Biesecker; Arthur R Brothman; Nigel P Carter; Deanna M Church; John A Crolla; Evan E Eichler; Charles J Epstein; W Andrew Faucett; Lars Feuk; Jan M Friedman; Ada Hamosh; Laird Jackson; Erin B Kaminsky; Klaas Kok; Ian D Krantz; Robert M Kuhn; Charles Lee; James M Ostell; Carla Rosenberg; Stephen W Scherer; Nancy B Spinner; Dimitri J Stavropoulos; James H Tepperberg; Erik C Thorland; Joris R Vermeesch; Darrel J Waggoner; Michael S Watson; Christa Lese Martin; David H Ledbetter
Journal:  Am J Hum Genet       Date:  2010-05-14       Impact factor: 11.025

4.  Array-CGH detection of micro rearrangements in mentally retarded individuals: clinical significance of imbalances present both in affected children and normal parents.

Authors:  C Rosenberg; J Knijnenburg; E Bakker; A M Vianna-Morgante; W Sloos; P A Otto; M Kriek; K Hansson; A C V Krepischi-Santos; H Fiegler; N P Carter; E K Bijlsma; A van Haeringen; K Szuhai; H J Tanke
Journal:  J Med Genet       Date:  2005-06-24       Impact factor: 6.318

5.  The utility of chromosomal microarray analysis in developmental and behavioral pediatrics.

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6.  Epidemiology of multiple congenital anomalies in Europe: a EUROCAT population-based registry study.

Authors:  Elisa Calzolari; Ingeborg Barisic; Maria Loane; Joan Morris; Diana Wellesley; Helen Dolk; Marie-Claude Addor; Larraitz Arriola; Fabrizio Bianchi; Amanda J Neville; Judith L S Budd; Kari Klungsoyr; Babak Khoshnood; Bob McDonnell; Vera Nelen; Annette Queisser-Luft; Judith Rankin; Anke Rissmann; Catherine Rounding; David Tucker; Christine Verellen-Dumoulin; Hermien de Walle; Ester Garne
Journal:  Birth Defects Res A Clin Mol Teratol       Date:  2014-04-11

7.  An evidence-based approach to establish the functional and clinical significance of copy number variants in intellectual and developmental disabilities.

Authors:  Erin B Kaminsky; Vineith Kaul; Justin Paschall; Deanna M Church; Brian Bunke; Dawn Kunig; Daniel Moreno-De-Luca; Andres Moreno-De-Luca; Jennifer G Mulle; Stephen T Warren; Gabriele Richard; John G Compton; Amy E Fuller; Troy J Gliem; Shuwen Huang; Morag N Collinson; Sarah J Beal; Todd Ackley; Diane L Pickering; Denae M Golden; Emily Aston; Heidi Whitby; Shashirekha Shetty; Michael R Rossi; M Katharine Rudd; Sarah T South; Arthur R Brothman; Warren G Sanger; Ramaswamy K Iyer; John A Crolla; Erik C Thorland; Swaroop Aradhya; David H Ledbetter; Christa L Martin
Journal:  Genet Med       Date:  2011-09       Impact factor: 8.822

8.  Comparison of chromosome analysis and chromosomal microarray analysis: what is the value of chromosome analysis in today's genomic array era?

Authors:  Weimin Bi; Caroline Borgan; Amber N Pursley; Patricia Hixson; Chad A Shaw; Carlos A Bacino; Seema R Lalani; Ankita Patel; Pawel Stankiewicz; James R Lupski; Arthur L Beaudet; Sau Wai Cheung
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9.  Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.

Authors:  Sue Richards; Nazneen Aziz; Sherri Bale; David Bick; Soma Das; Julie Gastier-Foster; Wayne W Grody; Madhuri Hegde; Elaine Lyon; Elaine Spector; Karl Voelkerding; Heidi L Rehm
Journal:  Genet Med       Date:  2015-03-05       Impact factor: 8.822

10.  Clinical genetics evaluation in identifying the etiology of autism spectrum disorders: 2013 guideline revisions.

Authors:  G Bradley Schaefer; Nancy J Mendelsohn
Journal:  Genet Med       Date:  2013-03-21       Impact factor: 8.822

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