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Application of Whole-Exome Sequencing in Detecting Copy Number Variants in Patients with Developmental Delay and/or Multiple Congenital Malformations
2020; Elsevier BV; Volume: 22; Issue: 8 Linguagem: Inglês
10.1016/j.jmoldx.2020.05.007
ISSN1943-7811
AutoresÉvelin Aline Zanardo, Fabíola Paoli Monteiro, Samar N. Chehimi, Yanca Gasparini, Alexandre Torchio Dias, Larissa A. Costa, Luiza Ramos, Gil Monteiro Novo‐Filho, Marília M. Montenegro, A. M. Nascimento, João Paulo Kitajima, Fernando Kok, Leslie Domenici Kulikowski,
Tópico(s)Congenital heart defects research
ResumoOvercoming challenges for the unambiguous detection of copy number variations is essential to broaden our understanding of the role of genomic variants in the clinical phenotype. With the improvement of software and databases, whole-exome sequencing quickly can become an excellent strategy in the routine diagnosis of patients with a developmental delay and/or multiple congenital malformations. However, even after a detailed analysis of pathogenic single-nucleotide variants and indels in known disease genes, using whole-exome sequencing, some patients with suspected syndromic conditions are left without a conclusive diagnosis. These negative results could be the result of different factors including nongenetic etiologies, lack of knowledge about the genes that cause different disease phenotypes, or, in some cases, a deletion or duplication of genomic information not routinely detectable by whole-exome sequencing variant calling. Although copy number variant detection is possible using whole-exome sequencing data, such analysis presents significant challenges and cannot yet be used to replace chromosomal arrays for identification of deletions or duplications. Overcoming challenges for the unambiguous detection of copy number variations is essential to broaden our understanding of the role of genomic variants in the clinical phenotype. With the improvement of software and databases, whole-exome sequencing quickly can become an excellent strategy in the routine diagnosis of patients with a developmental delay and/or multiple congenital malformations. However, even after a detailed analysis of pathogenic single-nucleotide variants and indels in known disease genes, using whole-exome sequencing, some patients with suspected syndromic conditions are left without a conclusive diagnosis. These negative results could be the result of different factors including nongenetic etiologies, lack of knowledge about the genes that cause different disease phenotypes, or, in some cases, a deletion or duplication of genomic information not routinely detectable by whole-exome sequencing variant calling. Although copy number variant detection is possible using whole-exome sequencing data, such analysis presents significant challenges and cannot yet be used to replace chromosomal arrays for identification of deletions or duplications. Detection of copy number variations (CNVs) and single-nucleotide variants (SNVs) is essential for cytogenomic diagnosis. Nevertheless, CNV detection using only whole-exome sequencing (WES) data still presents a complex analysis and does not always yield decisive results.1Harel T. Lupski J.R. Genomic disorders 20 years on-mechanisms for clinical manifestations.Clin Genet. 2018; 93: 439-449Crossref PubMed Scopus (54) Google Scholar In standard practice, chromosomal microarray is the first-tier clinical test for CNV detection (deletions or duplications larger than approximately 1 kb), as well as uniparental disomy and regions of homozygosity (ROH) in patients with a developmental delay and multiple congenital malformations. This methodology allows quantification of the genome with a high resolution level (average resolution, approximately 10 to 100 kb), depending on the platform, types of probes, and how they are distributed in the genome.1Harel T. Lupski J.R. Genomic disorders 20 years on-mechanisms for clinical manifestations.Clin Genet. 2018; 93: 439-449Crossref PubMed Scopus (54) Google Scholar, 2Manning M. Hudgins L. 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Carvalho C.M.B. Shaw C. Jhangiani S. Boone P.M. Eldomery M.K. Karaca E. Bayram Y. Stray-Pedersen A. Muzny D. Charng W.L. Bahrambeigi V. Belmont J.W. Boerwinkle E. Beaudet A.L. Gibbs R.A. Lupski J.R. Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort.Nucleic Acids Res. 2017; 45: 1633-1648PubMed Google Scholar, 12Stark Z. Tan T.Y. Chong B. Brett G.R. Yap P. Walsh M. Yeung A. Peters H. Mordaunt D. Cowie S. Amor D.J. Savarirayan R. McGillivray G. Downie L. Ekert P.G. Theda C. James P.A. Yaplito-Lee J. Ryan M.M. Leventer R.J. Creed E. Macciocca I. Bell K.M. Oshlack A. Sadedin S. Georgeson P. Anderson C. Thorne N. Gaff C. White S.M. Melbourne Genomics Health AllianceA prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders.Genet Med. 2016; 18: 1090-1096Abstract Full Text Full Text PDF PubMed Scopus (256) Google Scholar According to the literature, there is a 22% to 60% success rate in molecular diagnosis using WES.5De Ligt J. Boone P.M. Pfundt R. Vissers L.E. Richmond T. Geoghegan J. O'Moore K. de Leeuw N. Shaw C. Brunner H.G. Lupski J.R. Veltman J.A. Hehir-Kwa J.Y. Detection of clinically relevant copy number variants with whole-exome sequencing.Hum Mutat. 2013; 34: 1439-1448Crossref PubMed Scopus (93) Google Scholar,11Gambin T. Akdemir Z.C. Yuan B. Gu S. Chiang T. Carvalho C.M.B. Shaw C. Jhangiani S. Boone P.M. Eldomery M.K. Karaca E. Bayram Y. Stray-Pedersen A. Muzny D. Charng W.L. Bahrambeigi V. Belmont J.W. Boerwinkle E. Beaudet A.L. Gibbs R.A. Lupski J.R. Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort.Nucleic Acids Res. 2017; 45: 1633-1648PubMed Google Scholar, 12Stark Z. Tan T.Y. Chong B. Brett G.R. Yap P. Walsh M. Yeung A. Peters H. Mordaunt D. Cowie S. Amor D.J. Savarirayan R. McGillivray G. Downie L. Ekert P.G. Theda C. James P.A. Yaplito-Lee J. Ryan M.M. Leventer R.J. Creed E. Macciocca I. Bell K.M. Oshlack A. Sadedin S. Georgeson P. Anderson C. Thorne N. Gaff C. White S.M. Melbourne Genomics Health AllianceA prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders.Genet Med. 2016; 18: 1090-1096Abstract Full Text Full Text PDF PubMed Scopus (256) Google Scholar, 13Sawyer S.L. Hartley T. Dyment D.A. Beaulieu C.L. Schwartzentruber J. Smith A. et al.Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care.Clin Genet. 2016; 89: 275-284Crossref PubMed Scopus (258) Google Scholar Thus, the use of WES has increased significantly since the laboratories began to offer it as a routine diagnostic test. Therefore, WES has become an attractive technique for routine diagnostic testing requested by clinicians, especially in cases with challenging phenotypic features.5De Ligt J. Boone P.M. Pfundt R. Vissers L.E. Richmond T. Geoghegan J. O'Moore K. de Leeuw N. Shaw C. Brunner H.G. Lupski J.R. Veltman J.A. Hehir-Kwa J.Y. Detection of clinically relevant copy number variants with whole-exome sequencing.Hum Mutat. 2013; 34: 1439-1448Crossref PubMed Scopus (93) Google Scholar,14Iglesias A. Anyane-Yeboa K. Wynn J. Wilson A. Truitt Cho M. Guzman E. Sisson R. Egan C. Chung W.K. The usefulness of whole-exome sequencing in routine clinical practice.Genet Med. 2014; 16: 922-931Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar,15Volk A. Conboy E. Wical B. Patterson M. Kirmani S. Whole-exome sequencing in the clinic: lessons from six consecutive cases from the clinician's perspective.Mol Syndromol. 2015; 6: 23-31Crossref PubMed Scopus (27) Google Scholar Recently, the screening of CNVs from WES data using specific software has been applied in research because this would make it possible to identify different types of pathogenic variants in a single method and it has been reported as a potential alternative for the detection of genomic abnormalities.5De Ligt J. Boone P.M. Pfundt R. Vissers L.E. Richmond T. Geoghegan J. O'Moore K. de Leeuw N. Shaw C. Brunner H.G. Lupski J.R. Veltman J.A. Hehir-Kwa J.Y. Detection of clinically relevant copy number variants with whole-exome sequencing.Hum Mutat. 2013; 34: 1439-1448Crossref PubMed Scopus (93) Google Scholar,10Hehir-Kwa J.Y. Pfundt R. Veltman J.A. Exome sequencing and whole genome sequencing for the detection of copy number variation.Expert Rev Mol Diagn. 2015; 15: 1023-1032Crossref PubMed Scopus (66) Google Scholar Because it is possible to search CNVs in WES results, this rate of variant detection may increase and therefore many laboratories are integrating this analysis. Thus, it could replace the array and deploy the WES as a first-tier genomic test, reducing the time to obtain a final diagnosis.16Bertier G. Hétu M. Joly Y. Unsolved challenges of clinical whole-exome sequencing: a systematic literature review of end-users' views.BMC Med Genomics. 2016; 9: 52Crossref PubMed Scopus (46) Google Scholar There is no doubt that WES is a powerful technique that has the potential to impact and improve patient diagnosis, but care is needed mainly because it is unclear whether WES is the most appropriate method to be used as the first-tier test to achieve a diagnosis of patients with suspected rare disease.15Volk A. Conboy E. Wical B. Patterson M. Kirmani S. Whole-exome sequencing in the clinic: lessons from six consecutive cases from the clinician's perspective.Mol Syndromol. 2015; 6: 23-31Crossref PubMed Scopus (27) Google Scholar, 16Bertier G. Hétu M. Joly Y. Unsolved challenges of clinical whole-exome sequencing: a systematic literature review of end-users' views.BMC Med Genomics. 2016; 9: 52Crossref PubMed Scopus (46) Google Scholar, 17Bertier G. Sénécal K. Borry P. Vears D.F. Unsolved challenges in pediatric whole-exome sequencing: a literature analysis.Crit Rev Clin Lab Sci. 2017; 54: 134-142Crossref PubMed Scopus (19) Google Scholar Therefore, we evaluated whether cases with a previous negative result for pathogenic SNVs and indel detection by WES had CNVs that could explain the phenotype in these patients, and whether the choice of WES as the first-tier test was the most appropriate for diagnosis. This study was performed at the Laboratório de Citogenômica in collaboration with Mendelics Análise Genômica. The patients were evaluated by clinicians from different specialties (pediatricians, neurologists, or geneticists) who described the phenotypic characteristics of the patients and decided that WES would be the best test in these cases for a diagnostic conclusion. This study involved 38 patients with a developmental delay and/or multiple congenital malformations and a previous negative result for SNV and indel detection. The WES data were analyzed by a CNV search and the results were confirmed by array technique. Genomic DNA was isolated from 3 mL peripheral blood from patients using a commercially available DNA isolation kit (QIAamp DNA Blood MiniKits; Qiagen, Hilden, Germany) according to the manufacturer's instructions. The quality and quantity of the DNA samples were determined using a Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA), and the integrity of the DNA was ascertained via agarose gel electrophoresis analysis. All of the genomic DNA was processed by Nextera Rapid Capture Exomes (Illumina, San Diego, CA), following the manufacturer's instructions. Sequencing was performed on an Illumina HiSeq 2500 and the candidate causal variants were mapped and called. WES was performed with a minimum median coverage of 80×. CNVs were analyzed by ExomeDepth software version 1.0.7.18Plagnol V. Curtis J. Epstein M. Mok K.Y. Stebbings E. Grigoriadou S. Wood N.W. Hambleton S. Burns S.O. Thrasher A.J. Kumararatne D. Doffinger R. Nejentsev S. A robust model for read count data in exome sequencing experiments and implications for copy number variants calling.Bioinformatics. 2012; 28: 2747-2754Crossref PubMed Scopus (0) Google Scholar The initial BAM files were realigned and the base quality scores were recalibrated. After marking the duplicates, the final set of alignment data (BAM files) required for computational CNV prediction were generated. The genome builds reference sequence used was hg19. The criteria evaluated for determining a CNV using this software included at least three consecutive altered exons in a region as a minimum cut-off number, and a score higher than 50 for reliability of a true result. The array technique was used on an Illumina platform using CytoSNP-850K, with 843,888 markers and an average probe spacing of 1.8 kb across the whole genome. In all samples, amplification, hybridization, staining, and washing were performed according to the manufacturers' protocols, and the data were extracted using an iScan scanner (Illumina). The raw data were analyzed using BlueFuse Multi software version 4.3 (Illumina) and the hg19 reference genomic sequence. Thus, the signal intensities were identified, normalized, compared with a reference data set based on prerun reference samples, and then the log2 ratios were calculated. The criteria used to determine a CNV in array analysis included the following: no predefined minimum size, the involvement of at least 10 consecutive probes sets (average minimum resolution, 18 kb) in a region with log2 ratio cut-off values of −0.41 and +0.32 for loss and gain, respectively, and to consider an ROH a minimum size of 3 Mb and 500 consecutive altered probes are required. The software generated graphic representations of CNV breakpoints and ROHs for each sample. Moreover, the bead arrays supplied the B allele frequency, which represents the proportion of B alleles in genotype. A region without evidence of CNVs should show a log2 ratio near zero, and three B allele frequency clusters of 0, 0.5, and 1, corresponding to the AA, AB, and BB genotypes, respectively. All samples were evaluated and were found to be in accordance with the quality standards. The results were analyzed according to the American College of Medical Genetics standards and guidelines,19Kearney H.M. Thorland E.C. Brown K.K. Quintero-Rivera F. South S.T. Working Group of the American College of Medical Genetics Laboratory Quality Assurance CommitteeAmerican College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants.Genet Med. 2011; 13: 680-685Abstract Full Text Full Text PDF PubMed Scopus (686) Google Scholar and were compared with the following databanks of CNVs and classified as benign, pathogenic, or variants of uncertain clinical significance, as follows: the Database of Genomic Variants (http://projects.tcag.ca/variation, last accessed April 25, 2019), the Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (http://decipher.sanger.ac.uk, last accessed April 26, 2019), the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov, last accessed May 2, 2019), the Online Mendelian Inheritance in Man (https://www.omim.org, last accessed May 2, 2019), the Ensembl Genome Browser (http://www.ensembl.org/index.html, last accessed April 26, 2019), and the University of California Santa Cruz Genome Bioinformatics database (http://genome.ucsc.edu, last accessed April 25, 2019). The genomic positions are reported according to their mapping on the hg19 genome build. The Research Ethics Committee of the Faculdade de Medicina da Universidade de São Paulo approved this study (235/15), and written informed consent for publication was obtained from the parents of the patients. Across all 38 filtered samples (noise and bias were excluded), the ExomeDepth identified 745 CNVs (534 deletions and 211 duplications), and in parallel the BlueFuse found 332 CNVs (224 deletions and 108 duplications) and 37 ROHs in the same samples. Thus, the detection rates of deletions and duplications corresponded to approximately 70% and 66% for the ExomeDepth, and 30% and 34% for the BlueFuse, respectively. Therefore, there was a disparity in CNV detection by WES and the array technique, and WES identified more deletions and duplications when compared with the array technique (Figure 1A). However, the ability to identify the variants was proportionally similar in both techniques, in which approximately 70% of the variants were deletions (Figure 1B). The size of the variants was also evaluated. The smallest and the largest CNVs detected were 70 bp and 10,994,556 bp by ExomeDepth and 1702 bp and 26,560,780 bp by BlueFuse, respectively (Table 1). Therefore, WES has the ability to detect abnormalities smaller than the array, and the array can detect larger CNVs.Table 1Size of Variants Detected by BlueFuse (Array) and by ExomeDepth (WES) SoftwareVariantsSoftware (technique)Deletion, bpDuplication, bpROH, bpSmallerBlueFuse (array)170239173,036,260ExomeDepth (WES)701688NALargerBlueFuse (array)12,446,47426,560,780243,034,523ExomeDepth (WES)10,994,5567,984,948NANA, not applicable; ROH, regions of homozygosity; WES, whole-exome sequencing. Open table in a new tab NA, not applicable; ROH, regions of homozygosity; WES, whole-exome sequencing. Among these results, it was verified that approximately 50% (556 of 1114) of the alterations were smaller than 50 kb, of which approximately 74% (411 of 56) were detected by ExomeDepth. According to Illumina, the average minimum detection resolution of CNVs for the array using BeadChip CytoSNP-850K (Illumina) is approximately 18 kb, and in smaller sizes only 59 CNVs (48 deletions and 11 duplications) were found using the array technique. However, in WES using ExomeDepth, 232 CNVs were identified (189 deletions and 43 duplications); 31% of all variants detected by ExomeDepth were CNVs smaller than 18 kb. In addition, the variant detection rate by BlueFuse according to size in relation to the total number of variants found by array presented similar results for all size ranges when compared with the detection rate by ExomeDepth, with the exception of the small- ( 1 Mb) variant groups, which presented more discrepant results for fewer and more variants, respectively (Figure 2). Thus, in a second analysis, only variants greater than 18 kb were evaluated because of the minimum limit of size detection using the array technique. The overlap between variants detected in WES versus chromosomal microarray was identified and 14.4% (74 of 513) of the CNVs detected by WES were equivalent to 18.1% (56 of 310) of the variants detected using the array technique. The WES technique showed more CNVs when compared with the array technique because some deletions or duplications were fragmented. Of these variants, approximately 15% (52 of 345) of WES deletions corresponded to approximately 19% (40 of 213) of array deletions and ROHs, and approximately 13% (22 of 168) of WES duplications were equivalent to 16.5% (16 of 97) of array duplication. To determine whether WES is an effective method for identification of pathogenic CNVs and to measure the consistency with the array technique and identify the true and relevant results for diagnosis, we classified the variants identified in all samples and both methods according to their clinical significance to establish which of them explained the patient's phenotype. Using published guidelines, the variants were classified as benign, pathogenic, or variants of uncertain clinical significance, and the cytogenomic diagnosis was concluded in 18 cases. In 44.7% (17 of 38) of the patients, at least one pathogenic CNV was identified, detected by both techniques, including deletions and duplications in different chromosomal regions. The samples presented in this set showed concordance in the genomic region for elucidation of the diagnosis, but some changes in the breakpoints (start and end) were identified. These regions showed 83.3% high genomic similarity (same genomic region with small variations in the breakpoints) when compared between the techniques (Table 2). Moreover, some CNVs identified by ExomeDepth were fragmented owing to genomic characteristics of the region or by technical limitation because WES was developed initially for the detection of SNV or indels and not large CNVs.Table 2Patients with Pathogenic CNVs Detected by Both TechniquesSamplePathogenic CNVDiagnostic conclusion/OMIMTypeChromosomal positionStart, bpEnd, bpSize, bpTechniqueSimilarity01del (×1)15q11.2-q13.322,652,33032,927,47610,275,147Array52.2%Angelman syndrome/#10583015q11.222,706,68023,086,413379,734WES15q11.2-q13.125,200,19428,632,8393,432,64615q13.129,090,86430,261,0421,170,17915q13.2-q13.330,905,97232,455,5381,549,56702del (×1)15q11.1-q13.120,612,84028,544,3597,931,520Array51.8%Prader-Willi syndrome/#17627015q11.222,318,83723,086,462767,626WES15q11.2-q13.125,200,14528,600,1773,400,03303dup (×3)16p13.1115,126,89016,293,1901,166,301Array74.4%16p13.11 duplication16p13.1115,068,58615,180,117111,532WES16p13.1115,493,28816,308,308815,02104dup (×3)10q24.31-q24.32102,934,720103,384,567403,289Array9.9%∗Low percentage of similarity between the CNVs detected by array in relation to whole exome sequencing.Split-hand/foot malformation 3/#24656010q24.32103,298,050103,384,56786,517WES06dup (×3)7q11.2372,305,67174,196,2441,890,574Array78.4%Williams-Beuren region duplication syndrome/#6097577q11.2372,643,60174,125,4421.481.842WES07del (×1)17q1234,476,39636,244,3581,767,963Array73.9%17q12 deletion syndrome/#61452717q1234,797,48436,104,8771.307.394WES09dup (×3)22q13.1-q13.3338,600,54251,211,39212,610,851Array11.9%∗Low percentage of similarity between the CNVs detected by array in relation to whole exome sequencing.22q13 duplication syndrome/#61553822q13.2-q13.3143,814,10145,316,3721.502.272WES12del (×1)22q11.2118,640,30021,463,7302,823,431Array84.7%22q11.2 deletion syndrome/#61186722q11.2118,726,98120,378,9721,651,992WES22q11.2120,723,71821,563,035839,31815del (×1)10q26.2-q26.3127,656,067135,477,8837,821,817Array79.1%10q26 deletion syndrome/#60962510q26.2127,668,398129,923,9332,255,536WES10q26.3131,506,157135,440,2463,934,08920dup (×3)5q35.2-q35.3175,893,576176,929,9741,036,399Array98.8%5q35.2-q35.3 duplication5q35.2-q35.3175,906,174176,932,1131,025,940WES22del (×1)7q11.2372,748,50074,200,0921,451,593Array98.9%Williams-Beuren syndrome/#1940507q11.2372,643,60174,171,1951,527,595WESdup (×4)15q11.1-q13.220,071,67330,657,95210,586,279Array47.3%15q11-q13 duplication syndrome/#608636dup (×3)15q11.222,706,01123,265,556559,546WES15q11.2-q13.125,200,19428,566,5813,366,38815q13.1-q13.228,600,10230,437,5101,837,40924del (×1)10q26.12-q26.3123,031,410135,477,88312,446,474Array67.3%10q26 deletion syndrome/#60962510q26.13-q26.2123,233,899129,923,9336,690,035WES10q26.3133,747,955135,440,2461,692,29226del (×1)8p22-p21.115,837,97727,852,48812,014,512Array91.5%8p22-p21 deletion8p22-p21.116,850,57927,845,13410,994,556WES28del (×1)19q13.33-q13.4150,000,85453,353,2303,352,377Array99.2%19q13.33-q13.41 deletion19q13.3350,017,13750,464,338447,202WES19q13.33-q13.4150,474,92353,352,4682,877,54631del (×1)15q11.2-q13.122,652,33028,544,3595,892,030Array89.7%Angelman syndrome/#10583015q11.222,833,52323,265,046431,524WES15q11.2-q13.123,684,68928,538,1704,853,48233dup (×2)Xp22.33-p21.360,81426,621,59426,560,781Array29.8%∗Low percentage of similarity between the CNVs detected by array in relation to whole exome sequencing.Xp22.33-p21.3 duplicationXp22.29,652,01911,317,0951,665,077WESXp22.214,549,28117,095,5322,546,252Xp22.13-p22.1217,705,83120,236,9562,531,126Xp22.1123,682,63724,861,7961,179,16034del (×1)6p23-p22.313,715,30318,385,8894,670,587Array71.6%6p23-p22.3 deletion6p2313,791,01914,135,469344,451WES6p22.315,368,89618,369,0673,000,172CNV, copy number variation; del, deletion; dup, duplication; OMIM, Online Mendelian Inheritance in Man; WES, whole-exome sequencing.∗ Low percentage of similarity between the CNVs detected by array in relation to whole exome sequencing. Open table in a new tab CNV, copy number variation; del, deletion; dup, duplication; OMIM, Online Mendelian Inheritance in Man; WES, whole-exome sequencing. One patient showed a pathogenic CNV detected by BlueFuse but was missed by ExomeDepth. This patient presented one deletion on chromosome 1q21.1 that was approximately 2 Mb in size (starting at 143,343,508 pb and ending at 145,395,440 pb) and related to 1q21.1 deletion syndrome (Online Mendelian Inheritance in Man no. 612474). In addition, only the array technique showed cases with ROH (8%; 3 of 38) because that contributed to correct diagnostic elucidation. These cases showed uniparental disomy, genomic imprinting, or the possible occurrence of recessive disease due to SNV (Table 3).Table 3Patients with ROH Detected by Array TechniqueSamplePathogenic ROHDiagnostic conclusionChromosomal positionStart, bpEnd, bpSize, bp112p25.3-q37.314,238243,048,760243,034,523Uniparental isodisomy/whole chromosome 21314q11.2-q21.119,327,82338,438,51519,155,693Uniparental disomy/Kagami-Ogata syndrome366p25.3-q27108,666170,980,171170,871,506ROH with SNV on HACE1 geneROH, region of heterozygosity; SNV, single-nucleotide variant. Open table in a new tab ROH, region of heterozygosity; SNV, single-nucleotide variant. Pathogenic CNVs were not found in the other 17 samples (44.7%). The SNV found by WES and classified as benign or variants of uncertain clinical significance will be reanalyzed because new information is constantly being added to the databases, such as the characterization or establishment of new genes. Although WES is an excellent technique for SNV and indels detection, it will not always lead to the diagnosis of patients with a developmental delay and multiple congenital malformations. The reasons for an unsolved diagnosis after a WES test are incomplete coverage of the genome (eg, variants present in intronic or repetitive regions), or even abnormalities that are not detectable by WES (CNVs, chromosome rearrangements, or epigenomic abnormalities).13Sawyer S.L. Hartley T. Dyment D.A. Beaulieu C.L. Schwartzentruber J. Smith A. et al.Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care.Clin Genet. 2016; 89: 275-284Crossref PubMed Scopus (258) Google Scholar,16Bertier G. Hétu M. Joly Y. Unsolved challenges of clinical whole-exome sequencing: a systematic literature review of end-users' views.BMC Med Genomics. 2016; 9: 52Crossref PubMed Scopus (46) Google Scholar Because it is possible to analyze CNVs in WES data, the integration of this detection to SNV and indel identification would improve the diagnostic efficiency and then promote a more attractive cost benefit of WES.5De Ligt J. Boone P.M. Pfundt R. Vissers L.E. Richmond T. Geoghegan J. O'Moore K.
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