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High-resolution melting: Applications in genetic disorders
Clinica Chimica Acta 414 (2012) 197–201
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Invited critical review
High-resolution melting: Applications in genetic disorders Tze-Kiong Er a, b, Jan-Gowth Chang a, c, d, e, f,⁎ a
Division of Molecular Diagnostics, Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan c Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan d Center of RNA Biology and Clinical Application, China Medical University Hospital, Taichung, Taiwan e Department of Laboratory Medicine, China Medical University Hospital, Taichung, Taiwan f The Institute of Integrated Medicine, China Medical University, Taichung, Taiwan b
a r t i c l e
i n f o
Article history: Received 10 July 2012 Received in revised form 8 September 2012 Accepted 8 September 2012 Available online 18 September 2012 Keywords: High-resolution melting analysis Mutation Genetic disorders Gene scanning
a b s t r a c t High-resolution melting (HRM) analysis is a feasible and powerful method for mutation scanning of sequence variants. Denatured doubled-stranded DNA can be detected in fluorescence changes by increasing the melting temperature and wild-type and heterozygous samples can be easily differentiated in the melting plots. HRM analysis represents the next generation of mutation-scanning technology and offers considerable time and cost savings compared to other screening methods. HRM analysis is a closed-tube method, indicating that polymerase chain reaction amplification and subsequent analysis are sequentially performed in the well, making HRM analysis more convenient than other scanning methodologies. Taken together, HRM analysis can be used for high-throughput mutation screening for research, as well as for molecular diagnostic and clinical purposes. This review summarizes the effectiveness of HRM analysis in the diagnosis of autosomal recessive, dominant, and X-linked genetic disorders. Notably, we will also discuss the limitations of HRM analysis and how to overcome them. © 2012 Elsevier B.V. All rights reserved.
Contents 1. Introduction . . . . . . . . . . . . . 2. Applications of HRM analysis in genetic 3. Limitations of HRM analysis . . . . . 4. Conclusion . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . Appendix A. Supplementary data . . . . References . . . . . . . . . . . . . . . .
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1. Introduction Identifying and understanding genetic variation between populations and individuals are important aims in the field of genomics. The unique genetic profile of an individual confers susceptibility to a given trait or disease. Therefore, there is a rapidly growing interest in feasible methods for mutation screening in life science research. A key step in the search for potentially pathogenic genetic variants in disease susceptibility genes is mutation screening of coding exons and proximal intronic splice ⁎ Corresponding author at: Department of Laboratory Medicine, Kaohsiung Medical University Hospital, 100 Shih-Chuan 1st Rd., Kaohsiung, Taiwan. Tel.: +886 7 3115104; fax: +886 7 3213931, Center of RNA Biology and Clinical Application, China Medical University Hospital, No. 2, Yuh-Der Road, Taichaung, Taiwan. Tel.: +886 4 22052121 ext. 2008. E-mail addresses: [email protected], [email protected] (J.-G. Chang). 0009-8981/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.cca.2012.09.012
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consensus sequences of the entire gene in large subject series [1]. High-resolution melting (HRM) analysis represents the next generation of mutation-scanning technology and offers considerable time and cost savings compared to other screening methods. HRM analysis has recently been tested in a variety of clinical mutation-scanning and genotyping applications and is shown to be sensitive, cost-effective, and economical. In recent years, many papers describing HRM analysis were submitted by researchers who wished to rapidly and precisely screen the mutations of disease-causing genes. Notably, HRM analysis of “orphan” genetic disorders can be performed rapidly after polymerase chain reaction (PCR) with minimal added cost or processing. HRM analysis generates DNA melt curve profiles that are both specific and sensitive enough to distinguish nucleic acid species based on small sequence differences, enabling mutation scanning, methylation analysis, and genotyping [1]. HRM analysis can be
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used to detect single-base sequence variations or to discover unknown genetic mutations, for example, a single-nucleotide change or multiple-base changes, insertions, and/or deletions. Mutation scanning with HRM analysis is based on the dissociation behavior of DNA exposed to increasing temperature. The transition from double to a single strand in the presence of fluorescent dyes actively intercalating double-stranded DNA (dsDNA) leads to signal modification. The HRM analysis melting profile gives a specific sequence-related pattern allowing discrimination between wild-type (WT) sequences and homozygote–heterozygote variants [2]. Unlike other scanning methods, such as sequencing, is time-consuming and labor intensive. HRM analysis offers a faster and more convenient closed-tube method of assessing the presence of mutations and gives a result that can be further investigated if it is of interest. Besides, the closed-tube system reduces the risk of contamination. Many applications of HRM analysis have been described including small amplicon genotyping, unlabeled probe genotyping, methylation analysis, snapback primer genotyping, and sequence matching, and reviews are available [3,4]. Nevertheless, the main application of HRM analysis is gene scanning. This review will discuss and focus on the effectiveness and limitations of HRM analysis in the diagnosis of genetic disorders. 2. Applications of HRM analysis in genetic disorders HRM analysis of amplicons depends on DNA melting in the presence of saturating DNA-binding dyes. As the temperature of the solution is increased, the specific sequence of the amplicon determines the melting behavior. When the fluorescence signal is plotted against temperature, the fluorescence intensity decreases as the double stranded DNA becomes single stranded and the dye is released. The unique pattern of the melting curve, the derivative plot, or the difference plot may be used for mutation scanning. Gene scanning depends on the recognition of changes in the shape of the amplicon-melting curve that result from heterozygous sequence modification. Accordingly, the detection of human genetic disorders, especially autosomal recessive, dominant, and X-linked disorders, has been one of the largest applications of HRM analysis (Table 1). HRM analysis is a reliable gene-scanning method with greater speed than DNA sequencing. This application was further extended to be used in the diagnosis of Duchenne (DMD)/Becker muscular dystrophy (BMD). DMD and BMD are allelic X-linked recessive diseases, caused by mutations in one of the largest human genes known to date, the DMD gene, which is distributed over about 2.4 million base pairs (bp) [5]. HRM analysis followed by sequencing for amplicons with altered melting profiles has been used to scan DNA for small alterations in DMD/BMD patients and female carriers [6]. Another X-linked genetic disorder, Fabry disease (FD), is associated with GLA gene mutations. To date, more than 500 mutations in the GLA gene have been associated with FD [7]. HRM analysis of the GLA gene has been demonstrated as a reliable pre-sequencing screening tool, and it can be applied to any genomic feature to identify known and unknown genetic alterations [8,9]. Recently, HRM analysis was used to develop a screen for GLA gene mutations via dry blood spots obtained from newborns afflicted with FD. This assay was designed to cover seven exons and the Chinese common intronic mutation, IVS4+919G>A of the GLA gene [10]. Additionally, the X-linked Charcot–Marie–Tooth (CMT) type 1 disease has been associated with 296 mutations in the GJB1 gene [11]. Scanning with HRM analysis was described for mutations in the GJB1 gene [12]. HRM analysis successfully detected 23 mutations that had been confirmed previously by sequence analysis. Accordingly, HRM analysis has a potential to reduce sequencing of exons of large multi-exon genes that have been discovered to be associated with CMT neuropathy. Glucose-6-phosphate dehydrogenase (G6PD) deficiency is also classified as an X-linked inherited disease. More than 173 mutations
Table 1 Applications of HRM analysis in genetic disorders. Genetic disorders
Causative genes
Reference
X-linked inherited disease Duchenne/Becker muscular dystrophy Fabry disease Charcot-Marie-Tooth disease Glucose-6-phosphate dehydrogenase deficiency Chronic granulomatous disease Familial hypophosphatemic rickets
DMD GLA GJB1 G6PD CYBB PHEX
[6] [7–10] [12] [13,14] [15] [16]
Autosomal dominant disease Marfan syndrome Noonan syndrome Myotonic dystrophy Polycystic kidney disease
FBN1 PTPN11 DMPK PKD1, PKD2
[17–19] [20] [21] [22]
Autosomal recessive disease Glutaric aciduria type II Congenital adrenal hyperplasia Wilson disease Pendred syndrome Hemochromatosis Spinal muscular atrophy Prader–Willi syndromes Angelman syndrome Beta-thalassemia Alpha-thalassemia
ETFDH CYP21A2, CYP21A1P ATP7B SLC26A4 HFE SMN1 PW71 (D15S63) SNRPN HBB HBA1, HBA2
[24] [25] [26] [27] [28,29] [30–33] [37] [37] [40–42] [43–45]
or combinations of mutations in the G6PD gene have been identified among ethnic populations [11]. Two reports [13,14] showed that HRM analysis is a useful tool for screening G6PD mutations. However, Yan et al. [14] demonstrated that G1388A and G1376T, the two most common variants accounting for 50% to 60% of G6PD deficiency mutations in Chinese population, could be differentiated by HRM analysis. On the other hand, two novel G6PD mutations were reported in this study. The author indicated that ~7% of 171 tested samples resulted in false-negative or false-positive results. Also, HRM analysis has been described as a promising tool for the diagnosis of X-linked chronic granulomatous disease, a rare disorder resulting from mutations in the CYBB gene [15]. In this report, 19 different mutations and 10 novel mutations were found, including 7 near intron–exon boundaries, predicting splicing defects; 5 substitutions within exons; 3 small deletions predicting premature termination; and 4 gross deletions of multiple exons. On the other hand, familial hypophosphatemic rickets, an X-linked dominant disease, is a rare disorder resulting from mutations in the PHEX gene. Gaucher et al. [16] used HRM analysis for screening PHEX gene mutations along with classical sequencing in a large cohort of 118 pedigree representing 56 familial cases and 62 sporadic cases. Marfan syndrome (MFS), one of the most common lethal genetic disorders in children, is an autosomal dominant disorder with an estimated incidence of 1 in 5000 live births [17,18]. MFS has been associated with approximately 926 mutations in the fibrillin-a (FBN1) gene. Mutation scanning of the FBN1 gene with direct sequencing is time-consuming and expensive because of its large size. A total of 75 PCR amplicons that covered the complete regions and splicing sites were evaluated by HRM analysis [19]. HRM analysis was also assessed by Lo et al. [20] to identify PTPN11 gene mutation in Noonan syndrome, a relatively common autosomal dominant disorder. In this study, HRM analysis differentiated all 10 known mutations and identified 9 additional mutations from 10 patients. Myotonic dystrophy, or dystrophia myotonica (DM), an autosomal dominant disease, is a chronic, slowly progressing, highly variable and multiple inherited disease. HRM analysis was used by Radvansky et al. [21] for identifying the problematic region of five consecutive Alu elements that is associated with DM type 1. In this report, the authors showed that HRM is an alternative approach for rapid-throughput screening of 1-kb Alu insertions/ deletion polymorphism in families with DM type 1. Autosomal dominant polycystic kidney disease (ADPKD) is a hereditary kidney disorder,
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genetically heterogeneous kidney disorder and involves two genes, PKD1 and PKD2. Mutations in PKD1 account for approximately 85% of ADPKD cases and are associated with a more severe disease than PKD2. HRM analysis has been demonstrated by Bataille et al. [22] for molecular diagnosis of ADPKD. In this report, the author identified 440 sequence variants and 28 pathogenic mutations (25 in PKD1 and 3 in PKD2). Other applications of HRM analysis include determining an autosomal recessive disease, multiple acyl CoA dehydrogenase deficiency (MADD) or glutaric aciduria type II. Mutations in ETFA, ETFB, and ETFDH, have been reported as the major cause of riboflavin-responsive MADD [23]. HRM analysis has been demonstrated as feasible in detecting ETFDH mutations [24]. In this study, 13 exons of ETFDH were screened by HRM analysis and a hotspot mutation, c.250G>A, was identified in the Taiwanese population with a carrier frequency, 1:125 (0.8%). HRM analysis has also been applied for screening most patients with congenital adrenal hyperplasia (CAH) caused by defects of the CYP21A2 gene converted from the CYP21A1P pseudogene [25]. CAH is also an autosomal recessive disease of steroid metabolism in human. More than 90% of CAH cases are caused by mutations of the steroid 21-hydroxylase (CYP21A2) gene, and approximately 75% of the defective CYP21A2 genes are generated through an intergenic recombination with the neighboring CYP21A1P pseudogene. In this report, the most common 11 mutation sites of the CYP21A2 gene can be identified by HRM analysis. HRM analysis has also been applied in the diagnosis of Wilson disease (WD), an autosomal recessive inherited disorder of copper metabolism, affecting many organs and tissues [26]. The genetic defect is located at the copper-transporting adenosine triphosphatase (ATPase) gene (ATP7B). To date, over 480 mutations of the ATP7B gene have been reported in WD [11]. The 21 exons of the ATP7B gene were screened by HRM analysis, 10 different hotspot mutations and 7 polymorphisms in the ATP7B gene were identified, and 1 sequence variant (p.A476T) and 1 novel SNP (p.L776L) in 50 normal Taiwanese individuals were newly identified. Pendred syndrome and DFNB4 (autosomal recessive nonsyndromic congenital deafness, locus 4) are associated with autosomal recessive congenital sensorineural hearing loss and mutations in the SLC26A4 gene. HRM analysis was applied by Chen et al. [27] for identifying mutations in the SLC26A4 gene. In this report, the author used 28 primer pairs to cover all 21 SLC26A4 exons and splice junction sequences. Hemochromatosis is the most common inherited liver disease in Whites and the most common autosomal recessive genetic disorder. The three most common mutations in the HFE gene (C282Y, H63D, and S65C), account for the main form of hereditary hemochromatosis. HRM analysis has been applied in the detection of common mutations in the HFE gene in two recent studies [28,29]. Both studies showed that HRM analysis is an appealing technology for HFE gene screening. Spinal muscular atrophy (SMA) is the most common neuromuscular autosomal recessive disorder. Recently, the HRM platform has been applied in the detection of survival motor neuron 1 (SMN1) gene homozygous deletion for the diagnosis of SMA [30–33]. Dobrowolski et al. [34] developed a short amplicon that amplified a 52-bp PCR product in SMN1 exon 7 (c.840C>T) by HRM analysis. In this report, homozygous deletion of SMN1 exon 7 produced a distinctive melt profile that identified SMA patients. Intriguingly, three studies [30,32,33] used the dried blood spots as a source of DNA for the detection of SMA. It should be noted that early induction of SMN is most efficacious in the course of SMA and also a strong rescue of the SMA phenotype [34,35]. Accordingly, treatment in the immediate postnatal period, prior to the development of weakness, may be effective. Although SMA children are often asymptomatic at birth, new born screening to detect SMA has been developed, supporting the feasibility of starting treatment for asymptomatic SMA infants in the near future [36]. HRM analysis is a feasible method for use as a rapid and large-scale newborn screening technique for SMA in the near future. The use of methylation-sensitive HRM (MS-HRM) analysis has been established in the screening of the SNRPN gene for the diagnosis of
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Prader–Willi syndrome (PWS) and Angelman syndrome (AS) [37]. Methylation analyses of D15S63 (PW71) and the promoter region of the small nuclear ribonucleoprotein polypeptide N (SNRPN) gene have been recognized as clinically and scientifically valid diagnostic tests for PWS/AS [38]. In this report, 97.6% of the samples were classified into the three diagnostic categories (AS, PWS, normal) by the use of automated calling with an 80% confidence percentage threshold, and the failure rate was 0.6% using MS-HRM assay. Hemoglobinopathies resulting from mutations in the α- or β-like globin gene clusters are the most common inherited disorders in humans, with around 7% of the world population being a carrier of globin gene mutations [39]. HRM analysis was used by Shih et al. and He et al. for mutations in the HBB gene, and the results of the HRM analysis were confirmed by direct DNA sequencing [40,41]. HRM analysis was also used by Joly et al. [42] to develop a screen for β-globin haplotypes in order to predict the clinical development of patients suffering from sickle cell disease (SCD). In this report, the combination of fluorescence resonance energy transfer (FRET) and HRM assays is a reliable method for β-globin gene cluster haplotyping. HRM analysis has also been used for detecting α-hemoglobin (α-Hb) variants [43]. In this report, each Hb variant could be easily identified through the melting curve and the variants could be distinguished to be located at either HBA1 or HBA2 gene. Recently, HRM analysis has also been applied for the detection of three common nondeletional α-thalassemia disorders including Hb Quong Sze, Hb Constant Spring and Hb Westmead [44,45]. 3. Limitations of HRM analysis One of the limitations of HRM analysis is the possibility that another unexpected SNP interferes in the mutation of interest. So, we have designed amplicons that are as short as possible in order to limit the probability of such an event. In addition, the design primers should flank the exon or intron as closely as possible. In our previous study [40], we found five SNPs in the promoter region, exon 1 region, exon 2 region and intron 2 region (c.9C>T c.171C>G, c.315+16G>C, c.315+74T>G, and c.316-185C>T), respectively. These SNPs interfere with the identification of HBB gene mutations using HRM analysis. The shape of melting curves cannot be differentiated in the normalized and temperature-shifted plots. Therefore, we redesigned new primer sets that overlay these SNPs in order to block SNP interference. Finally, we successfully used the new primer set to identify HBB gene mutations. Accordingly, if a SNP is close to the exon or intron boundary, the primer can be placed over the SNP and a mismatched base with no allelic preference can be introduced at the SNP position. Therefore, direct DNA sequencing should be performed if the melting curve of an unknown sample is unique. HRM analysis provides a convenient way to detect mutations in a large multiexon gene without performing full-gene sequencing. However, HRM analysis cannot detect mutations encompassing an entire exon if the exon is too large or deletions of entire genes and exons. Therefore, we have to design more primer pairs in order to encompass the entire exon. Additionally, effective amplicon design is an important consideration for achieving robust and reproducible results in HRM analysis. Amplicon length may also influence the sensitivity of genotyping because shorter amplicons generally allow better discrimination of small sequence differences. As the amplicon size decreases, the Tm differences among the genotypes increase, thus allowing better differentiation between mutant and non-mutant samples. Notably, amplicon lengths of 100–300 bp are generally recommended for HRM analysis. Sensitivity and specificity were 100% for amplicons less than 400 bp but dropped to 96% and 99% respectively, from 400 to 1000 bp [46]. Therefore, long exons may need to be segmented into multiple amplicons for mutation screening. The influence of a variation on the melting curve shape decreases with increasing amplicon length, and amplicons with more than 500 bp often exhibit a multiphase melting behavior, disturbing the variation detection. It is difficult to interpret
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data if it contains more melt domains (Supplementary data). In contrast, it is more easier to interpret if contain only one melt domain. When fragments contain more melt domains, chances that not all variants are detected increase. Therefore, it is recommended that long fragments and multiple melt domains be avoided designing HRM analysis to screen a gene. For example, in our previous study [26], five sets of primers were used to amplify exon 2 of the ATP7B gene. They ranged from 192 bp to 366 bp. In this report, we tried to use fewer primer sets to cover exon 2; however, the melting curve cannot be interpreted because of non-specific binding and multiple melt domains occur. In fact, all heterozygous mutants were identified from their melting curves. The heteroduplexes altered the shape of the melting curves, allowing the heterozygotes to be easily identified [47]. Even slight differences in the melting temperature (Tm) were visible in the difference plots. Different heterozygotes may produce melting curves so similar to each other although HRM analysis heterozygotes are excellent [48]. According to published works [49,50], the misclassification risk is about 10% using HRM analysis. Direct DNA sequencing should be performed to confirm the mutation site if different heterozygotes produce melting curves so similar to each other. In our previous study, we demonstrated HRM analysis as a valid approach to efficiently pre-screen for the presence of KRAS mutation. Our results support that heterozygotes are easily identified because heteroduplexes alter the shape of the melting curves [47]. Unfortunately, HRM analysis is unable to identify which codon is mutated, because the melting curves are too similar to each other. Although heterozygotes are easily identified by a change in the shape of the melting curves [2], many homozygous variants, insertions, and deletions are more difficult to detect and may produce only small differences in the Tm value. To solve this problem, a known genotype was added to the unknown sample before HRM analysis. If a small amount of WT DNA is added, WT samples will generate no heteroduplexes; homozygous mutant samples show some heteroduplexes; and heterozygous samples show the greatest amount of heteroduplex formation [51]. Our recent study [52] demonstrated that HRM analysis is a feasible method for detecting SMA via dry blood spots. In this report, we failed to identify the substitution of a single nucleotide in SMN1 exon 7 (c.840C>T) by HRM analysis because the homozygous CC and homozygous TT cannot be distinguished by HRM analysis. Therefore, all samples were mixed with a known SMN1/SMN2 copy number (SMN1/SMN2 = 0:3), which we may call driver. This strategy is used to differentiate between homozygous CC and homozygous TT. After mixing with the driver, the melting profile of homozygous CC becomes heteroduplex; however, the homozygous TT remains the same in the normalized and temperature-shifted difference plots. Another issue is that hemizygous X-linked variants of unknown male samples are best detected by mixing 1:1 with WT male DNA before PCR amplification. As males have only one X-chromosome, mixing is necessary to ensure heteroduplex formation. This approach may double the amount of work required compared to other methodologies. Melting of dsDNA molecules is influenced by several factors. Some of these factors are inherent to the individual molecule (length, GC content, etc.), but other factors, such as Mg2+ buffer, DNA quality and DNA concentration can influence the behavior of the melting curves [53]. For example, amplification of the α-gene cluster is technically more difficult, possibly owing to the high GC content of the α-globin gene cluster sequence. To solve the high GC content of the α-globin gene cluster sequence, we attempted to use two-round PCR to detect Hb variants in our previous study [43]. The first-round PCR was used to separate α-1 and α-2 globin genes, and then the PCR product was used as a template to perform HRM analysis. The PCR products obtained from the first-round PCR needed to be diluted prior to HRM analysis. Additionally, we also attempted to use two-round PCR for isolating the CYP21A2 gene free from the CYP21A1P gene and then used the first-round PCR products as templates for identifying mutation related to CAH [25]. On the other hand, the same approach has been used for
detecting PKD1 and PKD2 gene mutations in ADPKD patients [22]. Amplification of five specific long-range PCRs with primers designed in the PKD1 sequence differing from the homologues was performed prior to HRM analysis. Note the limitation of this approach: if the PCR product is not diluted properly, the melting curve will be divergent and difficult to interpret. The first-round PCR products should be handled properly and carefully to prevent their contamination. Recently, Next Generation Sequencing (NGS) has revolutionized genomics, providing a wide range of novel application on a high throughput, genome wide level. As mentioned earlier, HRM analysis cannot detect mutations encompassing an entire exon if the exon is too large or deletions of entire genes and exons. In contrast, entire genetic coding sequence can be identified by NGS. NGS has been applied in whole-genome sequencing, targeted resequencing, discovery of transcription factor binding sites, genome rearrangements, and noncoding RNA expression profiling [54,55]. The field of NGS development and applications is a fast-moving area of research especially in genomic studies [56]. Although NGS has become the premier tool in genetic and genomic analysis, this approach is more expensive and the volume of results produced is bioinformatically challenging. Equipment, labor, reagent, and supply costs for analysis using the HRM analysis are substantially lower than the costs associated with analysis using NGS [57]. The HRM analysis is faster and less expensive than NGS, facilitating rapid analysis of disease-causing mutations. 4. Conclusion HRM analysis is a sensitive and specific method for molecular diagnosis of genetic disorders. It does not require labeled probes, processing, or separations after PCR. It is efficient and can be used for mutation pre-screening in many genetic disorders. In the clinical laboratory, it is an ideal format for in-house testing, with minimal cost and time requirements for new assay development. As reviewed before, HRM analysis has become a feasible analytical tool to detect gene mutations in many genetic disorders. However, before implementation of HRM analysis in clinical settings, more comprehensive validation studies are needed to define the limitations of HRM analysis for mutation screening. Acknowledgments This study was supported by a grant from the Kaohsiung Medical University Hospital (KMUH-9M65, KMUH-M045, KMUH-G867, and KMUH-G754). We are also grateful to my colleagues in the Division of Molecular Diagnostic: Dr. Ta-Chih Liu, Li-Ling Hsieh, Chin-Wen Lin, Shu-Kai Lin, Li-Hsuan Wang, Liang-Yi Ke, and Dr. Yi-Ching Lin. Appendix A. Supplementary data Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.cca.2012.09.012. References [1] Nguyen-Dumont T, Calvez-Kelm FL, Forey N, et al. Description and validation of high-throughput simultaneous genotyping and mutation scanning by highresolution melting curve analysis. Hum Mutat 2009;30:884-90. [2] Graham R, Liew M, Meadows C, et al. Distinguishing different DNA heterozygotes by high-resolution melting. Clin Chem 2005;51:1295-8. [3] Erali M, Wittwer CT. High resolution melting analysis for gene scanning. Methods 2010;50:250-61. [4] Erali M, Voelkerding KV, Wittwer CT. High resolution melting applications for clinical laboratory medicine. Exp Mol Pathol 2008;85:50-8. [5] Den Dunnen JT, Grootscholten PM, Bakker E, et al. Topography of the Duchenne muscular dystrophy (DMD) gene: FIGE and cDNA analysis of 194 cases reveals 115 deletions and 13 duplication. Am J Hum Genet 1989;45:833-47.
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Invited critical review
High-resolution melting: Applications in genetic disorders Tze-Kiong Er a, b, Jan-Gowth Chang a, c, d, e, f,⁎ a
Division of Molecular Diagnostics, Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan c Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan d Center of RNA Biology and Clinical Application, China Medical University Hospital, Taichung, Taiwan e Department of Laboratory Medicine, China Medical University Hospital, Taichung, Taiwan f The Institute of Integrated Medicine, China Medical University, Taichung, Taiwan b
a r t i c l e
i n f o
Article history: Received 10 July 2012 Received in revised form 8 September 2012 Accepted 8 September 2012 Available online 18 September 2012 Keywords: High-resolution melting analysis Mutation Genetic disorders Gene scanning
a b s t r a c t High-resolution melting (HRM) analysis is a feasible and powerful method for mutation scanning of sequence variants. Denatured doubled-stranded DNA can be detected in fluorescence changes by increasing the melting temperature and wild-type and heterozygous samples can be easily differentiated in the melting plots. HRM analysis represents the next generation of mutation-scanning technology and offers considerable time and cost savings compared to other screening methods. HRM analysis is a closed-tube method, indicating that polymerase chain reaction amplification and subsequent analysis are sequentially performed in the well, making HRM analysis more convenient than other scanning methodologies. Taken together, HRM analysis can be used for high-throughput mutation screening for research, as well as for molecular diagnostic and clinical purposes. This review summarizes the effectiveness of HRM analysis in the diagnosis of autosomal recessive, dominant, and X-linked genetic disorders. Notably, we will also discuss the limitations of HRM analysis and how to overcome them. © 2012 Elsevier B.V. All rights reserved.
Contents 1. Introduction . . . . . . . . . . . . . 2. Applications of HRM analysis in genetic 3. Limitations of HRM analysis . . . . . 4. Conclusion . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . Appendix A. Supplementary data . . . . References . . . . . . . . . . . . . . . .
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1. Introduction Identifying and understanding genetic variation between populations and individuals are important aims in the field of genomics. The unique genetic profile of an individual confers susceptibility to a given trait or disease. Therefore, there is a rapidly growing interest in feasible methods for mutation screening in life science research. A key step in the search for potentially pathogenic genetic variants in disease susceptibility genes is mutation screening of coding exons and proximal intronic splice ⁎ Corresponding author at: Department of Laboratory Medicine, Kaohsiung Medical University Hospital, 100 Shih-Chuan 1st Rd., Kaohsiung, Taiwan. Tel.: +886 7 3115104; fax: +886 7 3213931, Center of RNA Biology and Clinical Application, China Medical University Hospital, No. 2, Yuh-Der Road, Taichaung, Taiwan. Tel.: +886 4 22052121 ext. 2008. E-mail addresses: [email protected], [email protected] (J.-G. Chang). 0009-8981/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.cca.2012.09.012
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consensus sequences of the entire gene in large subject series [1]. High-resolution melting (HRM) analysis represents the next generation of mutation-scanning technology and offers considerable time and cost savings compared to other screening methods. HRM analysis has recently been tested in a variety of clinical mutation-scanning and genotyping applications and is shown to be sensitive, cost-effective, and economical. In recent years, many papers describing HRM analysis were submitted by researchers who wished to rapidly and precisely screen the mutations of disease-causing genes. Notably, HRM analysis of “orphan” genetic disorders can be performed rapidly after polymerase chain reaction (PCR) with minimal added cost or processing. HRM analysis generates DNA melt curve profiles that are both specific and sensitive enough to distinguish nucleic acid species based on small sequence differences, enabling mutation scanning, methylation analysis, and genotyping [1]. HRM analysis can be
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used to detect single-base sequence variations or to discover unknown genetic mutations, for example, a single-nucleotide change or multiple-base changes, insertions, and/or deletions. Mutation scanning with HRM analysis is based on the dissociation behavior of DNA exposed to increasing temperature. The transition from double to a single strand in the presence of fluorescent dyes actively intercalating double-stranded DNA (dsDNA) leads to signal modification. The HRM analysis melting profile gives a specific sequence-related pattern allowing discrimination between wild-type (WT) sequences and homozygote–heterozygote variants [2]. Unlike other scanning methods, such as sequencing, is time-consuming and labor intensive. HRM analysis offers a faster and more convenient closed-tube method of assessing the presence of mutations and gives a result that can be further investigated if it is of interest. Besides, the closed-tube system reduces the risk of contamination. Many applications of HRM analysis have been described including small amplicon genotyping, unlabeled probe genotyping, methylation analysis, snapback primer genotyping, and sequence matching, and reviews are available [3,4]. Nevertheless, the main application of HRM analysis is gene scanning. This review will discuss and focus on the effectiveness and limitations of HRM analysis in the diagnosis of genetic disorders. 2. Applications of HRM analysis in genetic disorders HRM analysis of amplicons depends on DNA melting in the presence of saturating DNA-binding dyes. As the temperature of the solution is increased, the specific sequence of the amplicon determines the melting behavior. When the fluorescence signal is plotted against temperature, the fluorescence intensity decreases as the double stranded DNA becomes single stranded and the dye is released. The unique pattern of the melting curve, the derivative plot, or the difference plot may be used for mutation scanning. Gene scanning depends on the recognition of changes in the shape of the amplicon-melting curve that result from heterozygous sequence modification. Accordingly, the detection of human genetic disorders, especially autosomal recessive, dominant, and X-linked disorders, has been one of the largest applications of HRM analysis (Table 1). HRM analysis is a reliable gene-scanning method with greater speed than DNA sequencing. This application was further extended to be used in the diagnosis of Duchenne (DMD)/Becker muscular dystrophy (BMD). DMD and BMD are allelic X-linked recessive diseases, caused by mutations in one of the largest human genes known to date, the DMD gene, which is distributed over about 2.4 million base pairs (bp) [5]. HRM analysis followed by sequencing for amplicons with altered melting profiles has been used to scan DNA for small alterations in DMD/BMD patients and female carriers [6]. Another X-linked genetic disorder, Fabry disease (FD), is associated with GLA gene mutations. To date, more than 500 mutations in the GLA gene have been associated with FD [7]. HRM analysis of the GLA gene has been demonstrated as a reliable pre-sequencing screening tool, and it can be applied to any genomic feature to identify known and unknown genetic alterations [8,9]. Recently, HRM analysis was used to develop a screen for GLA gene mutations via dry blood spots obtained from newborns afflicted with FD. This assay was designed to cover seven exons and the Chinese common intronic mutation, IVS4+919G>A of the GLA gene [10]. Additionally, the X-linked Charcot–Marie–Tooth (CMT) type 1 disease has been associated with 296 mutations in the GJB1 gene [11]. Scanning with HRM analysis was described for mutations in the GJB1 gene [12]. HRM analysis successfully detected 23 mutations that had been confirmed previously by sequence analysis. Accordingly, HRM analysis has a potential to reduce sequencing of exons of large multi-exon genes that have been discovered to be associated with CMT neuropathy. Glucose-6-phosphate dehydrogenase (G6PD) deficiency is also classified as an X-linked inherited disease. More than 173 mutations
Table 1 Applications of HRM analysis in genetic disorders. Genetic disorders
Causative genes
Reference
X-linked inherited disease Duchenne/Becker muscular dystrophy Fabry disease Charcot-Marie-Tooth disease Glucose-6-phosphate dehydrogenase deficiency Chronic granulomatous disease Familial hypophosphatemic rickets
DMD GLA GJB1 G6PD CYBB PHEX
[6] [7–10] [12] [13,14] [15] [16]
Autosomal dominant disease Marfan syndrome Noonan syndrome Myotonic dystrophy Polycystic kidney disease
FBN1 PTPN11 DMPK PKD1, PKD2
[17–19] [20] [21] [22]
Autosomal recessive disease Glutaric aciduria type II Congenital adrenal hyperplasia Wilson disease Pendred syndrome Hemochromatosis Spinal muscular atrophy Prader–Willi syndromes Angelman syndrome Beta-thalassemia Alpha-thalassemia
ETFDH CYP21A2, CYP21A1P ATP7B SLC26A4 HFE SMN1 PW71 (D15S63) SNRPN HBB HBA1, HBA2
[24] [25] [26] [27] [28,29] [30–33] [37] [37] [40–42] [43–45]
or combinations of mutations in the G6PD gene have been identified among ethnic populations [11]. Two reports [13,14] showed that HRM analysis is a useful tool for screening G6PD mutations. However, Yan et al. [14] demonstrated that G1388A and G1376T, the two most common variants accounting for 50% to 60% of G6PD deficiency mutations in Chinese population, could be differentiated by HRM analysis. On the other hand, two novel G6PD mutations were reported in this study. The author indicated that ~7% of 171 tested samples resulted in false-negative or false-positive results. Also, HRM analysis has been described as a promising tool for the diagnosis of X-linked chronic granulomatous disease, a rare disorder resulting from mutations in the CYBB gene [15]. In this report, 19 different mutations and 10 novel mutations were found, including 7 near intron–exon boundaries, predicting splicing defects; 5 substitutions within exons; 3 small deletions predicting premature termination; and 4 gross deletions of multiple exons. On the other hand, familial hypophosphatemic rickets, an X-linked dominant disease, is a rare disorder resulting from mutations in the PHEX gene. Gaucher et al. [16] used HRM analysis for screening PHEX gene mutations along with classical sequencing in a large cohort of 118 pedigree representing 56 familial cases and 62 sporadic cases. Marfan syndrome (MFS), one of the most common lethal genetic disorders in children, is an autosomal dominant disorder with an estimated incidence of 1 in 5000 live births [17,18]. MFS has been associated with approximately 926 mutations in the fibrillin-a (FBN1) gene. Mutation scanning of the FBN1 gene with direct sequencing is time-consuming and expensive because of its large size. A total of 75 PCR amplicons that covered the complete regions and splicing sites were evaluated by HRM analysis [19]. HRM analysis was also assessed by Lo et al. [20] to identify PTPN11 gene mutation in Noonan syndrome, a relatively common autosomal dominant disorder. In this study, HRM analysis differentiated all 10 known mutations and identified 9 additional mutations from 10 patients. Myotonic dystrophy, or dystrophia myotonica (DM), an autosomal dominant disease, is a chronic, slowly progressing, highly variable and multiple inherited disease. HRM analysis was used by Radvansky et al. [21] for identifying the problematic region of five consecutive Alu elements that is associated with DM type 1. In this report, the authors showed that HRM is an alternative approach for rapid-throughput screening of 1-kb Alu insertions/ deletion polymorphism in families with DM type 1. Autosomal dominant polycystic kidney disease (ADPKD) is a hereditary kidney disorder,
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genetically heterogeneous kidney disorder and involves two genes, PKD1 and PKD2. Mutations in PKD1 account for approximately 85% of ADPKD cases and are associated with a more severe disease than PKD2. HRM analysis has been demonstrated by Bataille et al. [22] for molecular diagnosis of ADPKD. In this report, the author identified 440 sequence variants and 28 pathogenic mutations (25 in PKD1 and 3 in PKD2). Other applications of HRM analysis include determining an autosomal recessive disease, multiple acyl CoA dehydrogenase deficiency (MADD) or glutaric aciduria type II. Mutations in ETFA, ETFB, and ETFDH, have been reported as the major cause of riboflavin-responsive MADD [23]. HRM analysis has been demonstrated as feasible in detecting ETFDH mutations [24]. In this study, 13 exons of ETFDH were screened by HRM analysis and a hotspot mutation, c.250G>A, was identified in the Taiwanese population with a carrier frequency, 1:125 (0.8%). HRM analysis has also been applied for screening most patients with congenital adrenal hyperplasia (CAH) caused by defects of the CYP21A2 gene converted from the CYP21A1P pseudogene [25]. CAH is also an autosomal recessive disease of steroid metabolism in human. More than 90% of CAH cases are caused by mutations of the steroid 21-hydroxylase (CYP21A2) gene, and approximately 75% of the defective CYP21A2 genes are generated through an intergenic recombination with the neighboring CYP21A1P pseudogene. In this report, the most common 11 mutation sites of the CYP21A2 gene can be identified by HRM analysis. HRM analysis has also been applied in the diagnosis of Wilson disease (WD), an autosomal recessive inherited disorder of copper metabolism, affecting many organs and tissues [26]. The genetic defect is located at the copper-transporting adenosine triphosphatase (ATPase) gene (ATP7B). To date, over 480 mutations of the ATP7B gene have been reported in WD [11]. The 21 exons of the ATP7B gene were screened by HRM analysis, 10 different hotspot mutations and 7 polymorphisms in the ATP7B gene were identified, and 1 sequence variant (p.A476T) and 1 novel SNP (p.L776L) in 50 normal Taiwanese individuals were newly identified. Pendred syndrome and DFNB4 (autosomal recessive nonsyndromic congenital deafness, locus 4) are associated with autosomal recessive congenital sensorineural hearing loss and mutations in the SLC26A4 gene. HRM analysis was applied by Chen et al. [27] for identifying mutations in the SLC26A4 gene. In this report, the author used 28 primer pairs to cover all 21 SLC26A4 exons and splice junction sequences. Hemochromatosis is the most common inherited liver disease in Whites and the most common autosomal recessive genetic disorder. The three most common mutations in the HFE gene (C282Y, H63D, and S65C), account for the main form of hereditary hemochromatosis. HRM analysis has been applied in the detection of common mutations in the HFE gene in two recent studies [28,29]. Both studies showed that HRM analysis is an appealing technology for HFE gene screening. Spinal muscular atrophy (SMA) is the most common neuromuscular autosomal recessive disorder. Recently, the HRM platform has been applied in the detection of survival motor neuron 1 (SMN1) gene homozygous deletion for the diagnosis of SMA [30–33]. Dobrowolski et al. [34] developed a short amplicon that amplified a 52-bp PCR product in SMN1 exon 7 (c.840C>T) by HRM analysis. In this report, homozygous deletion of SMN1 exon 7 produced a distinctive melt profile that identified SMA patients. Intriguingly, three studies [30,32,33] used the dried blood spots as a source of DNA for the detection of SMA. It should be noted that early induction of SMN is most efficacious in the course of SMA and also a strong rescue of the SMA phenotype [34,35]. Accordingly, treatment in the immediate postnatal period, prior to the development of weakness, may be effective. Although SMA children are often asymptomatic at birth, new born screening to detect SMA has been developed, supporting the feasibility of starting treatment for asymptomatic SMA infants in the near future [36]. HRM analysis is a feasible method for use as a rapid and large-scale newborn screening technique for SMA in the near future. The use of methylation-sensitive HRM (MS-HRM) analysis has been established in the screening of the SNRPN gene for the diagnosis of
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Prader–Willi syndrome (PWS) and Angelman syndrome (AS) [37]. Methylation analyses of D15S63 (PW71) and the promoter region of the small nuclear ribonucleoprotein polypeptide N (SNRPN) gene have been recognized as clinically and scientifically valid diagnostic tests for PWS/AS [38]. In this report, 97.6% of the samples were classified into the three diagnostic categories (AS, PWS, normal) by the use of automated calling with an 80% confidence percentage threshold, and the failure rate was 0.6% using MS-HRM assay. Hemoglobinopathies resulting from mutations in the α- or β-like globin gene clusters are the most common inherited disorders in humans, with around 7% of the world population being a carrier of globin gene mutations [39]. HRM analysis was used by Shih et al. and He et al. for mutations in the HBB gene, and the results of the HRM analysis were confirmed by direct DNA sequencing [40,41]. HRM analysis was also used by Joly et al. [42] to develop a screen for β-globin haplotypes in order to predict the clinical development of patients suffering from sickle cell disease (SCD). In this report, the combination of fluorescence resonance energy transfer (FRET) and HRM assays is a reliable method for β-globin gene cluster haplotyping. HRM analysis has also been used for detecting α-hemoglobin (α-Hb) variants [43]. In this report, each Hb variant could be easily identified through the melting curve and the variants could be distinguished to be located at either HBA1 or HBA2 gene. Recently, HRM analysis has also been applied for the detection of three common nondeletional α-thalassemia disorders including Hb Quong Sze, Hb Constant Spring and Hb Westmead [44,45]. 3. Limitations of HRM analysis One of the limitations of HRM analysis is the possibility that another unexpected SNP interferes in the mutation of interest. So, we have designed amplicons that are as short as possible in order to limit the probability of such an event. In addition, the design primers should flank the exon or intron as closely as possible. In our previous study [40], we found five SNPs in the promoter region, exon 1 region, exon 2 region and intron 2 region (c.9C>T c.171C>G, c.315+16G>C, c.315+74T>G, and c.316-185C>T), respectively. These SNPs interfere with the identification of HBB gene mutations using HRM analysis. The shape of melting curves cannot be differentiated in the normalized and temperature-shifted plots. Therefore, we redesigned new primer sets that overlay these SNPs in order to block SNP interference. Finally, we successfully used the new primer set to identify HBB gene mutations. Accordingly, if a SNP is close to the exon or intron boundary, the primer can be placed over the SNP and a mismatched base with no allelic preference can be introduced at the SNP position. Therefore, direct DNA sequencing should be performed if the melting curve of an unknown sample is unique. HRM analysis provides a convenient way to detect mutations in a large multiexon gene without performing full-gene sequencing. However, HRM analysis cannot detect mutations encompassing an entire exon if the exon is too large or deletions of entire genes and exons. Therefore, we have to design more primer pairs in order to encompass the entire exon. Additionally, effective amplicon design is an important consideration for achieving robust and reproducible results in HRM analysis. Amplicon length may also influence the sensitivity of genotyping because shorter amplicons generally allow better discrimination of small sequence differences. As the amplicon size decreases, the Tm differences among the genotypes increase, thus allowing better differentiation between mutant and non-mutant samples. Notably, amplicon lengths of 100–300 bp are generally recommended for HRM analysis. Sensitivity and specificity were 100% for amplicons less than 400 bp but dropped to 96% and 99% respectively, from 400 to 1000 bp [46]. Therefore, long exons may need to be segmented into multiple amplicons for mutation screening. The influence of a variation on the melting curve shape decreases with increasing amplicon length, and amplicons with more than 500 bp often exhibit a multiphase melting behavior, disturbing the variation detection. It is difficult to interpret
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data if it contains more melt domains (Supplementary data). In contrast, it is more easier to interpret if contain only one melt domain. When fragments contain more melt domains, chances that not all variants are detected increase. Therefore, it is recommended that long fragments and multiple melt domains be avoided designing HRM analysis to screen a gene. For example, in our previous study [26], five sets of primers were used to amplify exon 2 of the ATP7B gene. They ranged from 192 bp to 366 bp. In this report, we tried to use fewer primer sets to cover exon 2; however, the melting curve cannot be interpreted because of non-specific binding and multiple melt domains occur. In fact, all heterozygous mutants were identified from their melting curves. The heteroduplexes altered the shape of the melting curves, allowing the heterozygotes to be easily identified [47]. Even slight differences in the melting temperature (Tm) were visible in the difference plots. Different heterozygotes may produce melting curves so similar to each other although HRM analysis heterozygotes are excellent [48]. According to published works [49,50], the misclassification risk is about 10% using HRM analysis. Direct DNA sequencing should be performed to confirm the mutation site if different heterozygotes produce melting curves so similar to each other. In our previous study, we demonstrated HRM analysis as a valid approach to efficiently pre-screen for the presence of KRAS mutation. Our results support that heterozygotes are easily identified because heteroduplexes alter the shape of the melting curves [47]. Unfortunately, HRM analysis is unable to identify which codon is mutated, because the melting curves are too similar to each other. Although heterozygotes are easily identified by a change in the shape of the melting curves [2], many homozygous variants, insertions, and deletions are more difficult to detect and may produce only small differences in the Tm value. To solve this problem, a known genotype was added to the unknown sample before HRM analysis. If a small amount of WT DNA is added, WT samples will generate no heteroduplexes; homozygous mutant samples show some heteroduplexes; and heterozygous samples show the greatest amount of heteroduplex formation [51]. Our recent study [52] demonstrated that HRM analysis is a feasible method for detecting SMA via dry blood spots. In this report, we failed to identify the substitution of a single nucleotide in SMN1 exon 7 (c.840C>T) by HRM analysis because the homozygous CC and homozygous TT cannot be distinguished by HRM analysis. Therefore, all samples were mixed with a known SMN1/SMN2 copy number (SMN1/SMN2 = 0:3), which we may call driver. This strategy is used to differentiate between homozygous CC and homozygous TT. After mixing with the driver, the melting profile of homozygous CC becomes heteroduplex; however, the homozygous TT remains the same in the normalized and temperature-shifted difference plots. Another issue is that hemizygous X-linked variants of unknown male samples are best detected by mixing 1:1 with WT male DNA before PCR amplification. As males have only one X-chromosome, mixing is necessary to ensure heteroduplex formation. This approach may double the amount of work required compared to other methodologies. Melting of dsDNA molecules is influenced by several factors. Some of these factors are inherent to the individual molecule (length, GC content, etc.), but other factors, such as Mg2+ buffer, DNA quality and DNA concentration can influence the behavior of the melting curves [53]. For example, amplification of the α-gene cluster is technically more difficult, possibly owing to the high GC content of the α-globin gene cluster sequence. To solve the high GC content of the α-globin gene cluster sequence, we attempted to use two-round PCR to detect Hb variants in our previous study [43]. The first-round PCR was used to separate α-1 and α-2 globin genes, and then the PCR product was used as a template to perform HRM analysis. The PCR products obtained from the first-round PCR needed to be diluted prior to HRM analysis. Additionally, we also attempted to use two-round PCR for isolating the CYP21A2 gene free from the CYP21A1P gene and then used the first-round PCR products as templates for identifying mutation related to CAH [25]. On the other hand, the same approach has been used for
detecting PKD1 and PKD2 gene mutations in ADPKD patients [22]. Amplification of five specific long-range PCRs with primers designed in the PKD1 sequence differing from the homologues was performed prior to HRM analysis. Note the limitation of this approach: if the PCR product is not diluted properly, the melting curve will be divergent and difficult to interpret. The first-round PCR products should be handled properly and carefully to prevent their contamination. Recently, Next Generation Sequencing (NGS) has revolutionized genomics, providing a wide range of novel application on a high throughput, genome wide level. As mentioned earlier, HRM analysis cannot detect mutations encompassing an entire exon if the exon is too large or deletions of entire genes and exons. In contrast, entire genetic coding sequence can be identified by NGS. NGS has been applied in whole-genome sequencing, targeted resequencing, discovery of transcription factor binding sites, genome rearrangements, and noncoding RNA expression profiling [54,55]. The field of NGS development and applications is a fast-moving area of research especially in genomic studies [56]. Although NGS has become the premier tool in genetic and genomic analysis, this approach is more expensive and the volume of results produced is bioinformatically challenging. Equipment, labor, reagent, and supply costs for analysis using the HRM analysis are substantially lower than the costs associated with analysis using NGS [57]. The HRM analysis is faster and less expensive than NGS, facilitating rapid analysis of disease-causing mutations. 4. Conclusion HRM analysis is a sensitive and specific method for molecular diagnosis of genetic disorders. It does not require labeled probes, processing, or separations after PCR. It is efficient and can be used for mutation pre-screening in many genetic disorders. In the clinical laboratory, it is an ideal format for in-house testing, with minimal cost and time requirements for new assay development. As reviewed before, HRM analysis has become a feasible analytical tool to detect gene mutations in many genetic disorders. However, before implementation of HRM analysis in clinical settings, more comprehensive validation studies are needed to define the limitations of HRM analysis for mutation screening. Acknowledgments This study was supported by a grant from the Kaohsiung Medical University Hospital (KMUH-9M65, KMUH-M045, KMUH-G867, and KMUH-G754). We are also grateful to my colleagues in the Division of Molecular Diagnostic: Dr. Ta-Chih Liu, Li-Ling Hsieh, Chin-Wen Lin, Shu-Kai Lin, Li-Hsuan Wang, Liang-Yi Ke, and Dr. Yi-Ching Lin. Appendix A. Supplementary data Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.cca.2012.09.012. References [1] Nguyen-Dumont T, Calvez-Kelm FL, Forey N, et al. Description and validation of high-throughput simultaneous genotyping and mutation scanning by highresolution melting curve analysis. Hum Mutat 2009;30:884-90. [2] Graham R, Liew M, Meadows C, et al. Distinguishing different DNA heterozygotes by high-resolution melting. Clin Chem 2005;51:1295-8. [3] Erali M, Wittwer CT. High resolution melting analysis for gene scanning. Methods 2010;50:250-61. [4] Erali M, Voelkerding KV, Wittwer CT. High resolution melting applications for clinical laboratory medicine. Exp Mol Pathol 2008;85:50-8. [5] Den Dunnen JT, Grootscholten PM, Bakker E, et al. Topography of the Duchenne muscular dystrophy (DMD) gene: FIGE and cDNA analysis of 194 cases reveals 115 deletions and 13 duplication. Am J Hum Genet 1989;45:833-47.
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