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. 2019 Sep 11;7(11):e877. doi: 10.1002/mgg3.877

Glycogen storage diseases: Twenty‐seven new variants in a cohort of 125 patients

Fernanda Sperb-Ludwig 1,2,✉, Franciele Cabral Pinheiro 1,2, Malu Bettio Soares 2, Tatiele Nalin 1, Erlane Marques Ribeiro 3, Carlos Eduardo Steiner 4, Eugênia Ribeiro Valadares 5, Gilda Porta 6, Carolina Fishinger Moura de Souza 7, Ida Vanessa Doederlein Schwartz 1,2,7,✉
PMCID: PMC6825860  PMID: 31508908

Abstract

Background

Hepatic glycogen storage diseases (GSDs) are a group of rare genetic disorders in which glycogen cannot be metabolized to glucose in the liver because of enzyme deficiencies along the glycogenolytic pathway. GSDs are well‐recognized diseases that can occur without the full spectrum, and with overlapping in symptoms.

Methods

We analyzed a cohort of 125 patients with suspected hepatic GSD through a next‐generation sequencing (NGS) gene panel in Ion Torrent platform. New variants were analyzed by pathogenicity prediction tools.

Results

Twenty‐seven new variants predicted as pathogenic were found between 63 variants identified. The most frequent GSD was type Ia (n = 53), followed by Ib (n = 23). The most frequent variants were p.Arg83Cys (39 alleles) and p.Gln347* (14 alleles) in G6PC gene, and p.Leu348Valfs (21 alleles) in SLC37A4 gene.

Conclusions

The study presents the largest cohort ever analyzed in Brazilian patients with hepatic glycogenosis. We determined the clinical utility of NGS for diagnosis. The molecular diagnosis of hepatic GSDs enables the characterization of diseases with similar clinical symptoms, avoiding hepatic biopsy and having faster results.

Keywords: glycogen storage disease, hepatic GSD, molecular diagnosis, next‐generation sequencing

Short abstract

The study presents the largest cohort ever analyzed in Brazilian patients with hepatic glycogenosis. Thirty new mutations predicted as pathogenic were found in the 65 variants identified. We determined the clinical utility of next‐generation sequence (NGS) for diagnosis. The molecular diagnosis of hepatic GSDs enables the characterization of diseases with similar clinical symptoms, avoiding hepatic biopsy and having faster results.

1. INTRODUCTION

Hepatic glycogen storage diseases (GSDs) are a group of inborn errors of metabolism that include 11 different diseases caused by defects in glycogenolytic pathway. These defects are caused by pathogenic variants that result in enzymatic deficiencies for glycogen breakdown or synthesis, or problems in proteins that regulate glycogen metabolism. The consequence is accumulation of glycogen in tissues, especially in liver (Chen & Zhong, 2013).

The general GSD frequency is 1 in 2,000–43,000 and their distribution is pan‐ethnic (Özen, 2007; Vega et al., 2016). Some forms of GSDs are underestimated due to mild symptoms, rare occurrence, or difficult diagnostic methods. Symptoms may range from neonatal to almost asymptomatic, and the age of onset, severity, morbidity, mortality, and prognosis are dependent of causal variants (Kishnani et al., 2014, 2010; Laforêt, Weinstein, & Smit, 2012; Özen, 2007; Wang et al., 2012). The main clinical symptoms are hypoglycemia and hepatomegaly, and long‐term complications are frequent (Burda & Hochuli, 2015).

Different types of GSDs can be clinically indistinguishable and need liver biopsy, an invasive method. In this aim, the molecular diagnosis using blood samples generates an accurate diagnosis and allows prognosis and genetic counseling (Choi et al., 2017; Davit‐Spraul et al., 2011). Similar diseases in clinical symptoms, metabolic routes, or genetic features are a challenge to diagnose. In this aim, next‐generation sequencing (NGS) is an important tool to determine the cause of the disease with accuracy and efficacy, allowing a more suitable treatment.

Only two previous studies have characterized 13 patients with GSD Ia and Ib in Brazilian population (Carlin, Scherrer, Tommaso, Bertuzzo, & Steiner, 2013; Reis et al., 2001).

In the present study, we describe the results of variant analysis in a cohort of 125 patients with hepatic GSD suspected diagnosis by NGS.

2. MATERIAL AND METHODS

This study was approved by the Research Ethics Committee of Hospital de Clínicas de Porto Alegre (project no. 15‐0556), and all patients and guardians provided written informed consent for participation.

Were analyzed 125 patients with clinical symptoms of hepatic GSD. Blood samples were collected in EDTA vacuum container. DNA was extracted with Easy‐DNA Purification kit (Thermo Fisher). DNA samples were quantificated in NanoDrop 1000 (Thermo Fisher) and through Qubit dsDNA HS Assay Kit (Thermo Fisher).

The gene panel amplicon was designed with Ion Ampliseq Designer software (Thermo Fisher), and included the exons and flanking 40 bp into introns of 11 genes involved in hepatic GSD (Table 1). The sequencing was performed in Ion Torrent PGM platform (Applied Biosystems), based in PCR amplification with minimal coverage of 200X. Base calling and sequence read quality assessments were performed using Torrent Suite 5.0.5. Alignment of the sequence reads to a reference human genome (GRCh37.p13) was performed using IonStates alignment.

Table 1.

Genes and diseases diagnosed in gene panel

GSD type Gene OMIM Location Enzyme deficiency Inheritance Incidence Main clinical symptoms Mutations in HGMD
Hypoglycemia Hepatomegaly Hyperlipidemia
0 GYS2 138571 12p12.2 glycogen synthase AR   Yes No No 18 (19)
Ia G6PC 232200 17q21 Glucose‐6‐phosphatase AR 1 in 50,000–100,000 Yes Yes Yes 106 (111)
Ib SLC37A4 232220 11q23.3 Glucose‐6‐phosphate transporter AR   Yes Yes Yes 101 (110)
III AGL 232400 1p21 glycogen debranching enzyme AR 1 in 100,000 Yes Yes Yes 155 (239)
IV GBE1 232500 3p12.3 glycogen branching enzyme AR 1 to 500,000 No Yes No 50 (69)
VI PYGL 232700 14q21‐q22 liver glycogen phosphorylase AR 1 in 65,000–85,000 Yes Yes Yes 31 (43)
XIa PHKA2 306000 Xp22.2‐p22.1 phosphorylase kinase α subunit X‐linked   Yes Yes No 80 (104)
XIb PHKB 261750 16q12‐q13 phosphorylase kinase β subunit AR   Yes Yes No 18 (24)
XIc PHKG2 613027 16p12.1‐p11.2 phosphorylase kinase γ subunit AR   Yes Yes Yes 19 (31)
XI SLC2A2 612933 3q26.1‐q26.2 facilitated glucose transporter AR   Yes Yes Yes 66 (78)
XII ALDOA 611881 16q22‐q24 aldolase A AR   No Yes No 7 (8)
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The softwares Enlis Genome Research (LLC), Variant Effect Predictor (Ensembl), Ion Reporter (Thermo Fisher) and Varstation® (Varstation) were used to detect and classify variants. To determine the variants causing disease, the following were considered: ACMG guideline (Richards et al., 2015); allele frequency under 1% in the 1,000 Genomes Project (Sabeti, 2015); location in exon or borderlines; impact in the protein (missense, nonsense or splicing sites); and pathogenicity by predictors SIFT and Polyphen 2. For score of pathogenicity predictions in missense novel variants were used the softwares Polyphen 2 (Adzhubei et al., 2010), SIFT (Vaser, Adusumalli, Leng, Sikic, & Ng, 2016), PROVEAN (Choi, Sims, Murphy, Miller, & Chan, 2012), Mutation Teaster (Schwarz, Cooper, Schuelke, & Seelow, 2014), Pmut 2017 (López‐Ferrando, Gazzo, Cruz, Orozco, & Gelpí, 2017), SNP&Go (Profiti, Martelli, & Casadio, 2017), PhDSNP (Capriotti & Fariselli, 2017), Panther (Thomas et al., 2003), SNAP2 (Hecht, Bromberg, & Rost, 2015) and MutPred (Pejaver et al., 2017). For splice site variants, Genescan (Burge & Karlin, 1997) and MaxEntScan were used (Yeo & Burge, 2003).

Validations of NGS results were realized by Sanger sequencing in patients and in parents when the sample was available. The unbiased capture and deep coverage of each coding exon and adjacent intronic region of all genes in this panel ensure accuracy of variant detection.

3. RESULTS

We analyzed 125 patients with clinical suspicion of hepatic GSD. All samples were successfully sequenced. We found 63 different variants in 110 families, and 27 of those were new variants (Tables 2 and Appendix S1).

Table 2.

Variants found in 125 patients with hepatic GSDs, their references, frequencies in databanks, and ACMG classification

Gene GenBank Allele Protein Location Reference ExAC Frequence ACMG
G6PC NM_000151 c.70C > T p.Gln24* E1 Rocha, Cabral, and Vilarinho (2000)   PM2, PVS1 Probably pathogenic
c.77delC p.Ser26fs E1 Lei et al. (1995)# 0.00006 PM2, PP5, PVS1 Pathogenic
c.113A > T p.Asp38Val E1 ChevalierPorst et al. (1996) 0.000008 PM1, PM2, PP2, PP3, PP5 Probably pathogenic
c.161A > C p.Gln54Pro E1 Trioche et al. (2000)   PM1, PM2, PP2, PP3 Probably pathogenic
c.189G > C p.Trp63Cys E1 New   PM1, PM2, PP2, PP3 Probably pathogenic
c.202G > A p.Gly68Arg E1 Reis et al. (2001)   PM1, PM2, PP2, PP3 Probably pathogenic
c.231−1G > A   I1 Akanuma et al. (2000)   PM2 Uncertain
c.247C > T p.Arg83Cys E2 Lei et al. (1993) 0.0005 BS1, PM1, PP2, PP3, PP5 Uncertain
c.323C > T p.Thr108Ile E2 Trioche et al. (2000)   PM1, PM2, PP2, PP3 Probably pathogenic
c.401_402delCT p.Thr134 = fs E3 New   PM2 Uncertain
c.439A > T p.Arg147* E3 New* 0.000008 PM1, PM2, PVS1 Pathogenic
c.509G > A p.Arg170Gln E4 Barkaoui et al. (2007) 0.00001 PM1, PM2, PP2, PP3 Probably pathogenic
c.563‐3C > G   I4 Kishnani et al. (2009)   PM2 Uncertain
c.809G > T p.Gly270Val E5 Lei et al. (1995) 0.00001 PM2, PP2, PP3 Uncertain
c.969C > A p.Tyr323* E5 Calderaro et al. (2013) 0.000008 PM2, PVS1 Probably pathogenic
c.1012G > T p.Val338Phe E5 Rake et al. (2000) 0.00001 PM2, PP2, PP3 Uncertain
c.1039C > T p.Gln347* E5 Lei, Pan, Shelly, Liu, and Chou (1994) 0.0002 BS1, PP5, PVS1 Uncertain
SLC37A4 NM_001467 c.59G > A p.Gly20Asp E3 Veiga da Cunha et al. (1998) 0.00001 PM1, PM2, PP2, PP3 Probably pathogenic
c.92_94delTCT p.Phe31_Ser32del E3 New   PM2 Uncertain
c.344_345dupGG p.Leu116Glyfs E4 Galli et al. (1999) 0.00001 PM2, PVS1 Probably pathogenic
c.446G > A p.Gly149Glu E5 Galli et al. (1999) 0.00002 PM1, PM2, PP2, PP3 Probably pathogenic
c.547T > C p.Cys183Arg E5 Veiga da Cunha et al. (1998)   PM1, PM2, PP2, PP3 Probably pathogenic
c.557T > C p.Leu186Pro E5 New   PM1, PM2, PP2, PP3 Probably pathogenic
c.703_705delGTG p.Val236del E7 Hou et al. (1999)   PM2, PP5 Uncertain
c.899G > A p.Arg300His E9 Marcolongo et al. (1998) 0.00003 PM1, PM2, PP2, PP3 Probably pathogenic
c.1042_1043delCT p.Leu348fs E9 Veiga da Cunha et al. (1998)   BS1, PP5, PVS1 Uncertain
c.1179G > A p.Trp393* E10 New* 0.00002 PM2, PVS1 Probably pathogenic
AGL NM_000642 c.293 + 2T>A   I3 Hadjigeorgiou et al. (1999)   PM2 Uncertain
c.325G > T p.Val109Leu E4 New* 0.00009 BP1, PM1, PM2, PP3, Uncertain
c.744G > A p.Trp248* E6 New   PM1, PM2, PVS1 Pathogenic
c.1383G > A p.Trp461* E11 New* 0.000008 PM1, PM2, PVS1 Pathogenic
c.1481G > A p.Arg494His E12 Goldstein et al. (2010) 0.008 BP1, BS1, PM1, PP3 Probably Benign
c.1571G > A p.Arg524His E12 Lucchiari et al. (2006) 0.000008 BP1, PM1, PM2, PP3 Uncertain
c.1734A > T p.Arg578Ser E13 New   BP1, PM1, PM2, PP3 Uncertain
c.1858_1859delCT p.Leu620Valfs E14 New   PM2, PVS1 Probably pathogenic
c.2455_2458delAAAC p.Lys819 = fs E19 New   PM2, PVS1 Probably pathogenic
c.2728C > T p.Arg910* E21 Lucchiari et al. (2006) 0.000008 PM1, PM2, PVS1 Pathogenic
c.2904_2905insT p.Tyr969Leufs E22 New   PM2, PVS1 Probably pathogenic
c.3214_3215delGA p.Glu1072Aspfs E24 Goldstein et al. (2010) 0.000008 PM2, PVS1 Probably pathogenic
c.3475_3476insA p.Gln1159fs E26 New   PM2, PVS1 Probably pathogenic
c.3484C > T p.Gln1162* E26 New   PM1, PM2, PVS1 Pathogenic
c.3625C > T p.Gln1209* E27 New   PM1, PM2, PVS1 Pathogenic
c.3814_3815delG p.Arg1272 = fs E28 New   PM2, PVS1 Probably pathogenic
c.3904delA p.Lys1302fs E29 New   PM2, PVS1 Probably pathogenic
c.3980G > A p.Trp1327* E30 Lucchiari et al. (2002) 0.00002 PM1, PM2, PVS1 Pathogenic
c.4528_4529insA p.Tyr1510* E34 Shen and Chen (2002) 0.00001 PM2, PVS1 Probably pathogenic
PYGL NM_001163940 c.131G > A p.Arg44His E1 Hoogeveen et al. (2015)   PM2, PP2, PP3 Uncertain
c.697G > A p.Gly233Ser E6 New* 0.00004 PM2, PP2, PP3 Uncertain
PHKA2 NM_000292 c.133C > T p.Arg45Trp E2 Beauchamp et al. (2007)   PM1, PM2, PP2, PP3 Probably pathogenic
c.557G > A p.Arg186His E6 Burwinkel et al. (1996)   PM1, PM2, PP2, PP3, PP5 Probably pathogenic
c.883C > T p.Arg295Cys E9 Ban, Sugiyama, Goto, Mizutani, and Togari (2003)   PM1, PM2, PP2, PP3, PP5 Probably pathogenic
c.1963 + 1G>A   I18 Rodríguez‐Jimenez et al. (2017) 0.00001 PM2 Uncertain
c.2452C > T p.Gln818* E22 New   PM2, PVS1 Probably pathogenic
c.3614C > T p.Pro1205Leu E33 van den Berg et al. (1995)   PM2, PP2, PP3, PP5 Uncertain
c.3629G > A p.Gly1210Glu E33 Rudolfova, Slováčková, Trbušek, Pešková, and Kozák (2001)   PM2, PP2, PP3 Uncertain
PHKB NM_000293 c.572_576delAGATT p.Gln191Hfs E6 New* 0.000008 BS1, PVS1 Uncertain
c.1972‐2A > G   I20 New   PM2 Uncertain
c.2081C > G p.Ser694* E22 New   PM2, PVS1 Probably pathogenic
c.2181delT p.Leu728fs E22 New   PM2, PVS1 Probably pathogenic
PHKG2 NM_000294 c.454C > T p.Arg152* E6 New* 0.000008 PM1, PM2, PVS1 Pathogenic
c.502C > T p.Arg168* E6 Davit Spraul et al. (2011) 0.00001 PM1, PM2, PVS1 Pathogenic
c.835C > T p.Arg279Cys E9 New   PM1, PM2, PP2, PP3 Probably pathogenic
c.927 + 1G>A   I9 New   PM2 Uncertain
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E: Exon; I: Intron; New*: New mutation related to hepatic GSDs, however presented in data banks; #discordance between literature nomenclature and HGVS rules.

Seventy‐five patients are men. The patients included in the study are from all Brazilian regions: 63 from the southeast (SP n = 48, RJ n = 2, MG n = 10, ES n = 3), 50 from the south (RS n = 46, SC n = 4), eight from the northeast (BA n = 2, CE n = 3, PB n = 3), two from the Midwest (DF = 1, MT n = 1) and two from the north (PA n = 2) (Appendix S1).

Both pathogenic variants were identified in 118 patients confirming the molecular diagnosis of hepatic GSD. For two patients, only one variant was found (patients 84 and 85). In five patients, no variant was identified (Appendix S1). All identified variants were confirmed by Sanger sequencing and investigated in literature or databanks (Tables 2 and Appendix S1).

Eight families included in this study had multiple affected individuals. For patients 5, 11, and 12 (11 and 12 are sisters), their parents reported consanguinity (Appendix S1). These information were considered while counting alleles.

Sixty‐three alleles were identified, in which 26 are missense variants (41.2%), 16 are nonsense variants (25.3%), six are splice site variants (9.5%), 11 deletions (17.4%), three insertions (4.7%) and one duplication (1.5%) (Table 2).

Among the 125 patients analyzed, 53 were genetically diagnosed with GSD Ia (42%), 23 with GSD Ib (18%), 14 with GSD III (11%), two with GSD VI (1.6%), 16 GSD IXa (12%), six with GSD IXb (4.8%), six with GSD IXc (4.8%), and five were not diagnosed (4%) (Appendix S1).

The most frequent variants in patients were p.Arg83Cys, observed in 39 alleles (18.5%), and p.Gln347* present in 14 alleles (6.6%), both in G6PC gene, causing GSD Ia. The other frequent variant, p.Leu348Valfs in SLC37A4 gene, was observed in 10% of alleles causing GSD Ib.

Variants not described in the literature were evaluated for protein impact by nine in silico pathogenicity prediction algorithms. All new missense variants were predicted as pathogenic. In the bioinformatics analyses of new splice site variants, all were confirmed to modify the exon–intron structures in different forms importantly, showing sufficient entropy forces to perform the incorrect splicing.

4. DISCUSSION

This is one of the largest screening of variants causing the different forms of GSDs in patients, including 125 patients and describing 63 different variants, of which 27 are novel.

The GSD Ia and GSD Ib represent 60% of the analyzed patients. In other analyzed cohorts, Vega et al. (2016) reported more than three‐quarters of patients who had GSD III or GSD IXa (39% of each type), and Özen (2007) found GSD type IXa as the most common form of the disease. These data reflect the differences among populations, the existence of private pathogenic variants, and the differences in prevalence of variants in GSDs.

GSD type Ia is the most widely distributed. The most frequent pathogenic variant found in this work was p.Arg83Cys, present in 18.5% of all patients and 39% of alleles in GSD Ia patients. This is one of the most important variant found around the world in patients with GSD Ia (Chou & Mansfield, 2008; Matern, Seydewitz, Bali, Lang, & Chen, 2002). This variant in G6PC is in the active center of the enzyme G6Pase and presented no detectable activity in transient expression assays (Lei, Shelly, Pan, Sidbury, & Chou, 1993). p.Arg83Cys is present in 50% of alleles in French and Tunisian patients (Barkaoui et al., 2007; Trioche et al., 2000), 80% of Sicilian and 100% of alleles in Ashkenazi Jewish patients (Ekstein et al., 2004; Stroppiano et al., 1999). This variant is found in genomAD in a frequency of 0.0005 (Lek et al., 2016) and appears to be in a hotspot since two other variants are observed in the same position (p.Arg83His and p.Arg83=). There are another eight variants in amino acids 80, 81, and 82, six of them being pathogenic. In the same gene, the variant p.Gln347* was in 6.9% of all alleles or in 15% of alleles in GSD Ia. They both represent approximately 54% of variants found in GSD Ia patients.

The second most frequently found variant among all patients was p.Leu348Valfs in SLC37A4 gene, present in 10% of patients, and 47.7% of alleles (21/44 alleles) in GSD Ib. This variant was present in 39% of Serbians patients (Skakic et al., 2018) and 31% of White patients reviewed in Chou, Jun, and Mansfield (2010).

Twenty‐seven novel variants were identified among the 125 patients, observed mainly among patients with GSD type III and type IX. AGL, that causes GSD III, is one of the largest genes, and has the highest number of variants reported in HGMD – The Human Gene Mutation Database (Stenson et al., 2003), which proves its heterogeneity. The increased number of variants in type IX patients can be justified by their lower characterization.

Some of the novel variants have already been detected in database projects involving the search for variants in a large number of individuals but never related to patients. We investigate the variants in “The Exome Aggregation Consortium” – ExAC – composed of 60,706 unrelated individuals, and the Online Archive of Brazilian Mutations – AbraOM – composed of 609 elderly individuals, as in other databases (Lek et al., 2016; Naslavsky et al., 2017; Sabeti, 2015). Seven of 27 novel variants were in ExAC, all in very low frequencies (Table 2).

Seven different types of GSDs were found. Only types 0, IV, XI and XII were not observed among the 125 patients. The type IX represented 22.4% of the patients (12.8% of type IXa), since GSD type IX had never been described in Brazilian patients.

In two patients, only one variant was found (patients 84 and 85) instead of two. Both patients presented variants in AGL. This gene has the highest number of gaps in coverage of NGS and is one of the biggest genes in panel, with 36 exons. However, the gaps were analyzed by Sanger sequencing and no variant was found. The results obtained from these patients are contradictory, since the variants found in both cases are described for GSD type III; however both patients presented inconsistent clinical findings. One of them has Down syndrome and liver histology similar to GSD III, but no biochemical results are compatible with the disease and the patient does not present any clinical symptoms. The other patient presented hypoglycemia from birth, however, currently associated with hyponatremia and metabolic acidosis. The liver biopsy was inconclusive and not suggestive of GSD. Therefore, it is possible that variants or technical artifacts are eliminating the amplification of the mutated allele or the variants are in regulatory regions, not covered by the panel, but the absence of disease is a possibility (Hedell, Dufva, Ansell, Mostad, & Hedman, 2015; Inokuchi et al., 2016).

In five patients with no identified variants, the clinical suspicions are mild or inconclusive, once they did not have clear clinical indications or laboratory findings supporting the diagnosis of GSD, besides hypoglycemia and/or hepatomegaly. The NGS was a diagnostic exclusion test; therefore, it was an expected result. These patients probably do not have GSD, once hepatomegaly and hypoglycemia are difficult to distinguish from other metabolic storage disorders without more clinical findings. Another possibility is the presence of variants in nontargeted deep intronic and regulatory regions (Wang et al., 2013).

Relationships of synergistic heterogeneity should be considered for GSDs, since the disease‐causing deficient enzymes share metabolic pathways, however it was not observed in the present study (Vockley, Rinaldo, Bennett, Matern, & Vladutiu, 2000).

The patient 120 is possibly a GSD patient because he presented clinical symptoms such as hypoglycemia and ketonuria but no other clinical signs, however only synonymous variants were found in GYS2 (that causes GSD type 0), which does not justify the disease.

This variety of results reflects the profile of an extremely large country with an interesting and important mix of people from all over the world. The presence of immigrants from the most diverse origins, such as Africans, Asians, and Amerindians justifies the variability of alleles found in a highly mixed population. Genetic analyses indicate that Latin Americans trace their ancestry mainly in the intermixing of Native Americans, Europeans, and Sub‐Saharan Africans. Historically, Latin America has a continuous, differential, and diverse intra‐ and intercontinental migration events, and presents higher prevalence of metabolic diseases (Adhikari, Chacón‐Duque, Mendoza‐Revilla, Fuentes‐Guajardo, & Ruiz‐Linares, 2017; Chacón‐Duque et al., 2018; Giolo et al., 2012; Quinto‐Sánches et al., 2017; Resque et al., 2016).

Among the advantages of NGS diagnosis, patients undiagnosed by traditional means were investigated and correctly diagnosed in the present study. This method is especially promising for mixed populations with high level of heterogeneity. This method also allows the identification of unexpected diagnoses in the supposed typical phenotypes. Rare genetic diseases can be a diagnostic challenge, sometimes an odyssey. The NGS technologies can provide a fast diagnosis, advantages for treatment management, in reproductive choices, genetic counseling, and fertility services (Schofield et al., 2017). The GSD traditional diagnosis methods involve liver biopsy, an invasive and risked method, that can be avoided with a well‐established molecular method (Bali, Chen, Austin, & Goldstein, 2016; Lévesque et al., 2016).

This diagnosis is an important advancement for patients with nontypical forms of disease, especially for those who need agile actions, since it evaluates 11 genes at the same time.

This was an important step to increase the knowledge about the genetics of the different types of hepatic GSDs in Brazilian patients, since they have a genetically heterogeneous origin, and it is reflected in the variability of types and variants (Vega et al., 2016; Wang et al., 2013). The evaluation by NGS also allows to detect cases of synergic heterogeneity that cannot be perceived by Sanger sequencing.

Differentiated therapeutic management among GSD justifies the population characterization of patients. If NGS analyses are not available or expensive, the molecular diagnosis should be conducted first through the search for the pathogenic variants p.Arg83Cys and p.Gln347* in G6PC in case of GSD Ia or p.Leu348Valfs in SLC37A4 for GSD Ib. Sanger sequencing approach is the most cost‐effective to solve up to 40% of the cases. However, for the cases without prevalent mutations or without suspected type of GSD, NGS is the most effective solution.

This study emphasizes that molecular genetic analysis is a reliable and convenient alternative to the assay of enzymatic activity in a fresh liver biopsy specimen for the diagnosis of GSDs. This type of study is an important tool for the estimation of disease progression, since different types of GSDs present variations in their clinical course and treatment, besides serving as a basis for genetic counseling and prenatal diagnosis.

The discovery of a significant number of new mutations reinforces the allelic variability of different GSDs and proves that the diagnosis of GSDs in Brazil can be challenging, showing the validity of NGS gene panel use for diagnosis.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Supporting information

  

Click here for additional data file. (43.2KB, xlsx)

ACKNOWLEDGMENTS

The authors thank Ana Cecilia Meireles, Louise Pinto, Guilherme Macedo, Hector Yuri Conti Wanderley, Mireille Gomes, Diego Miguel, and Irene Miura for their assistance with patient diagnosis and follow‐up. Financial support for this study was provided by CNPq, CAPES, and FIPE‐HCPA. I. Schwartz is a National Council of Scientific and Technological Development (CNPq) research productivity fellow.

Sperb Ludwig F, Pinheiro FC, Bettio Soares M, et al. Glycogen storage diseases: Twenty‐seven new variants in a cohort of 125 patients. Mol Genet Genomic Med. 2019;7:e877 10.1002/mgg3.877

Contributor Information

Fernanda Sperb-Ludwig, Email: fsperb@hcpa.edu.br.

Ida Vanessa Doederlein Schwartz, Email: ischwartz@hcpa.edu.br.

REFERENCES

  1. Adhikari, K. , Chacón‐Duque, J. C. , Mendoza‐Revilla, J. , Fuentes‐Guajardo, M. , & Ruiz‐Linares, A. (2017). The genetic diversity of the Americas. Annual Review of Genomics and Human Genetics, 18, 277–296. 10.1146/annurev-genom-083115-022331 [DOI] [PubMed] [Google Scholar]
  2. Adzhubei, I. A. , Schmidt, S. , Peshkin, L. , Ramensky, V. E. , Gerasimova, A. , Bork, P. , … Sunyaev, S. R. (2010). A method and server for predicting damaging missense mutations. Nature Methods, 7(4), 248–249. 10.1038/nmeth0410-248 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Akanuma, J. , Nishigaki, T. , Fujii, K. , Matsubara, Y. , Inui, K. , Takahashi, K. , … Narisawa, K. (2000). Glycogen storage disease type Ia: Molecular diagnosis of 51 Japanese patients and characterization of slicing mutations by analysis of ectopically transcribed mRNA from lymphoblastoid cells. American Journal of Medical Genetics, 91, 107–112. [DOI] [PubMed] [Google Scholar]
  4. Bali, D. S. , Chen, Y. T. , Austin, S. , & Goldstein, J. L. (2016). Glycogen storage disease type I. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK1312/#_NBK1312_pubdet_
  5. Ban, K. , Sugiyama, K. , Goto, K. , Mizutani, F. , & Togari, H. (2003). Detection of PHKA2 gene mutation in four Japanese patients with hepatic phosphorylase kinase deficiency. Tohoku Journal of Experimental Medicine, 200, 47–53. 10.1620/tjem.200.47 [DOI] [PubMed] [Google Scholar]
  6. Barkaoui, E. , Cherif, W. , Tebib, N. , Charfeddine, C. , Ben Rhouma, F. , Azzouz, H. , … Ben Dridi, M. F. (2007). Mutation spectrum of glycogen storage disease type Ia in Tunisia: Implication for molecular diagnosis. Journal of Inherited Metabolic Disease, 30(6), 989–989. 10.1007/s10545-007-0737-1 [DOI] [PubMed] [Google Scholar]
  7. Beauchamp, N. J. , Dalton, A. , Ramaswami, U. , Niinikoski, H. , Mention, K. , Kenny, P. , … Sharrard, M. (2007). Glycogen storage disease type IX: High variability in clinical phenotype. Molecular Genetics and Metabolism, 92, 88–99. 10.1016/j.ymgme.2007.06.007 [DOI] [PubMed] [Google Scholar]
  8. Burda, P. , & Hochuli, M. (2015). Hepatic glycogen storage disorders: What have we learned in recent years? Current Opinion in Clinical Nutrition and Metabolic Care, 18(4), 415–421. 10.1097/MCO.0000000000000181 [DOI] [PubMed] [Google Scholar]
  9. Burge, C. , & Karlin, S. (1997). Prediction of complete gene structures in human genomic DNA. Journal of Molecular Biology, 268, 78–94. 10.1006/jmbi.1997.0951 [DOI] [PubMed] [Google Scholar]
  10. Burwinkel, B. , Shin, Y. S. , Bakker, H. D. , Deutsch, J. , Lozano, M. J. , Maire, I. , & Kilimann, M. W. (1996). Mutation hotspots in the PHKA2 gene in X‐linked liver glycogenosis due to phosphorylase kinase deficiency with atypical activity in blood cells (XLG2). Human Molecular Genetics, 5, 653–658. 10.1093/hmg/5.5.653 [DOI] [PubMed] [Google Scholar]
  11. Calderaro, J. , Labrune, P. , Morcrette, G. , Rebouissou, S. , Franco, D. , Prévot, S. , … Zucman‐Rossi, J. (2013). Molecular characterization of hepatocellular adenomas developed in patients with glycogen storage disease type I. Journal of Hepatology, 58(2), 350–357. 10.1016/j.jhep.2012.09.030 [DOI] [PubMed] [Google Scholar]
  12. Capriotti, E. , & Fariselli, P. (2017). PhD‐SNPg: A webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Research, 45(W1), W247–W252. 10.1093/nar/gkx369 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Carlin, M. P. , Scherrer, D. Z. , Tommaso, A. M. A. D. , Bertuzzo, C. S. , & Steiner, C. E. (2013). Determining mutations in G6PC and SLC37A4 genes in a sample of Brazilian patients with glycogen storage disease types Ia and Ib. Genetics and Molecular Biology, 36(4), 502–506. 10.1590/S1415-47572013000400007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Chacón‐Duque, J.‐C. , Adhikari, K. , Fuentes‐Guajardo, M. , Mendoza‐Revilla, J. , Acuña‐Alonzo, V. , Barquera, R. , … Ruiz‐Linares, A. (2018). Latin Americans show wide‐spread Converso ancestry and imprint of local Native ancestry on physical appearance. Nature Communications, 9 10.1038/s41467-018-07748-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chen, Z. , & Zhong, C. (2013). Decoding Alzheimer's disease from perturbed cerebral glucose metabolism: Implications for diagnostic and therapeutic strategies. Progress in Neurobiology, 108, 21–43. 10.1016/j.pneurobio.2013.06.004 [DOI] [PubMed] [Google Scholar]
  16. Chevalier‐Porst, F. , Bozon, D. , Bonardot, A. M. , Bruni, N. , Mithieux, G. , Mathieu, M. , & Maire, I. (1996). Mutation analysis in 24 French patients with glycogen storage disease type 1a. Journal of Medical Genetics, 33, 358–360. 10.1136/jmg.33.5.358 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Choi, R. , Park, H. D. , Ko, J. M. , Lee, J. , Lee, D. H. , Hong, S. J. , … Choe, Y. H. (2017). Novel SLC37A4 mutations in Korean patients with glycogen storage disease Ib. Annals of Laboratory Medicine, 37(3), 261–266. 10.3343/alm.2017.37.3.261 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Choi, Y. , Sims, G. E. , Murphy, S. , Miller, J. R. , & Chan, A. P. (2012). Predicting the functional effect of amino acid substitutions and indels. PLoS ONE, 7(10), e46688 10.1371/journal.pone.0046688 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chou, J. Y. , Jun, H. S. , & Mansfield, B. C. (2010). Glycogen storage disease type I and G6Pase‐β deficiency: Etiology and therapy. Nature Reviews Endocrinology, 6(12), 676 10.1038/nrendo.2010.189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Chou, J. Y. , & Mansfield, B. C. (2008). Mutations in the glucose‐6‐phosphatase‐α (G6PC) gene that cause type Ia glycogen storage disease. Human Mutation, 29(7), 921–930. 10.1002/humu.20772 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Davit‐Spraul, A. , Piraud, M. , Dobbelaere, D. , Valayannopoulos, V. , Labrune, P. , Habes, D. , … Baussan, C. (2011). Liver glycogen storage diseases due to phosphorylase system deficiencies: Diagnosis thanks to non‐invasive blood enzymatic and molecular studies. Molecular Genetics and Metabolism, 104(1), 137–143. 10.1016/j.ymgme.2011.05.010 [DOI] [PubMed] [Google Scholar]
  22. Ekstein, J. , Rubin, B. Y. , Anderson, S. L. , Weinstein, D. A. , Bach, G. , Abeliovich, D. , … Risch, N. (2004). Mutation frequencies for glycogen storage disease Ia in the Ashkenazi Jewish population. American Journal of Medical Genetics. Part A, 129(2), 162–164. 10.1002/ajmg.a.30232 [DOI] [PubMed] [Google Scholar]
  23. Galli, L. , Orrico, A. , Marcolongo, P. , Fulceri, R. , Burchell, A. , Melis, D. , … Sorrentino, V. (1999). Mutations in the glucose‐6‐phosphate transporter (G6PT) gene in patients with glycogen storage diseases type 1b and 1c. FEBS Letters, 459, 255–258. 10.1016/S0014-5793(99)01248-X [DOI] [PubMed] [Google Scholar]
  24. Giolo, S. R. , Soler, J. M. , Greenway, S. C. , Almeida, M. A. , De Andrade, M. , Seidman, J. G. , … Pereira, A. C. (2012). Brazilian urban population genetic structure reveals a high degree of admixture. European Journal of Human Genetics, 20(1), 111 10.1038/ejhg.2011.144 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Goldstein, J. L. , Austin, S. L. , Boyette, K. , Kanaly, A. , Veerapandiyan, A. , Rehder, C. , … Bali, D. S. (2010). Molecular analysis of the AGL gene: Identification of 25 novel mutations and evidence of genetic heterogeneity in patients with glycogen storage disease type III. Genetics in Medicine, 12(7), 424 10.1097/GIM.0b013e3181d94eaa [DOI] [PubMed] [Google Scholar]
  26. Hadjigeorgiou, G. M. , Comi, G. P. , Bordoni, A. , Shen, J. , Chen, Y. T. , Salani, S. , … Rodolico, C. (1999). Novel donor splice site mutations of AGL gene in glycogen storage disease type IIIa. Journal of Inherited Metabolic Disease, 22(6), 762–763. 10.1023/a:1005572906807 [DOI] [PubMed] [Google Scholar]
  27. Hecht, M. , Bromberg, Y. , & Rost, B. (2015). Better prediction of functional effects for sequence variant. BMC Genomics, 16(S8), S1 10.1186/1471-2164-16-s8-s1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hedell, R. , Dufva, C. , Ansell, R. , Mostad, P. , & Hedman, J. (2015). Enhanced low‐template DNA analysis conditions and investigation of allele dropout patterns. Forensic Science International: Genetics, 14, 61–75. 10.1016/j.fsigen.2014.09.008 [DOI] [PubMed] [Google Scholar]
  29. Hoogeveen, I. J. , van der Ende, R. M. , van Spronsen, F. J. , de Boer, F. , Heiner‐Fokkema, M. R. , & Derks, T. G. (2015). Normoglycemic ketonemia as biochemical presentation in ketotic glycogen storage disease. Journal of Inherited Metabolic Disease Report, 28, 41–47. 10.1007/8904_2015_511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hou, D.‐C. , Kure, S. , Suzuki, Y. , Hasegawa, Y. , Hara, Y. , Inoue, T. , … Narisawa, K. (1999). Glycogen storage disease type Ib: Structural and mutational analysis of the microsomal glucose‐6‐phosphate transporter gene. American Journal of Medical Genetics, 86(3), 253–257. [DOI] [PubMed] [Google Scholar]
  31. Inokuchi, S. , Kitayama, T. , Fujii, K. , Nakahara, H. , Nakanishi, H. , Saito, K. , … Sekiguchi, K. (2016). Estimating allele dropout probabilities by logistic regression: Assessments using Applied Biosystems 3500xL and 3130xl Genetic Analyzers with various commercially available human identification kits. Legal Medicine, 19, 77–82. 10.1016/j.legalmed.2015.07.006 [DOI] [PubMed] [Google Scholar]
  32. Kishnani, P. S. , Austin, S. L. , Abdenur, J. E. , Arn, P. , Bali, D. S. , Boney, A. , … Watson, M. S. (2014). Diagnosis and management of glycogen storage disease type I: A practice guideline of the American College of Medical Genetics and Genomics. Genetics in Medicine, 16, e1 10.1038/gim.2014.128 [DOI] [PubMed] [Google Scholar]
  33. Kishnani, P. S. , Austin, S. L. , Arn, P. , Bali, D. S. , Boney, A. , Case, L. E. , … Watson, M. S. (2010). Glycogen storage disease type III diagnosis and management guidelines. Genetics in Medicine, 12(7), 446–463. 10.1097/GIM.0b013e3181e655b6 [DOI] [PubMed] [Google Scholar]
  34. Kishnani, P. S. , Chuang, T. P. , Bali, D. , Koeberl, D. , Austin, S. , Weinstein, D. A. , & Chen, Y. T. (2009). Chromosomal and genetic alterations in human hepatocellular adenomas associated with type Ia glycogen storage disease. Human molecular genetics, 18(24), 4781–4790. [DOI] [PubMed] [Google Scholar]
  35. Laforêt, P. , Weinstein, D. A. , & Smit, G. P. A. (2012). The glycogen storage diseases and related disorders In Inborn metabolic diseases (pp. 115–139). Berlin: Heidelberg, Springer. [Google Scholar]
  36. Lei, K.‐J. , Chen, Y.‐T. , Chen, H. , Wong, L.‐J.‐C. , Liu, J.‐L. , McConkie‐Rosell, A. , … Chou, J. Y. (1995). Genetic basis of glycogen storage disease type 1a: Prevalent mutations at the glucose‐6‐phosphatase locus. American Journal of Human Genetics, 57, 766–771. [PMC free article] [PubMed] [Google Scholar]
  37. Lei, K.‐J. , Pan, C.‐J. , Shelly, L. L. , Liu, J.‐L. , & Chou, J. Y. (1994). Identification of mutations in the gene for glucose‐6‐phosphatase, the enzyme deficient in glycogen storage disease type 1a. Journal of Clinical Investigation, 93, 1994–1999. 10.1172/JCI117192 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Lei, K.‐J. , Shelly, L. L. , Pan, C.‐J. , Sidbury, J. B. , & Chou, J. Y. (1993). Mutations in the glucose‐6‐phosphatase gene that cause glycogen storage disease type 1a. Science, 262, 580–583. 10.1126/science.8211187 [DOI] [PubMed] [Google Scholar]
  39. Lek, M. , Karczewski, K. J. , Minikel, E. V. , Samocha, K. E. , Banks, E. , Fennell, T. , & Tukiainen, T. (2016). Analysis of protein‐coding genetic variation in 60,706 humans. Nature, 536, 285–291. 10.1038/nature19057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lévesque, S. , Auray‐Blais, C. , Gravel, E. , Boutin, M. , Dempsey‐Nunez, L. , Jacques, P.‐E. , … Kishnani, P. (2016). Diagnosis of late‐onset Pompe disease and other muscle disorders by next‐generation sequencing. Orphanet Journal of Rare Diseases, 11(1), 8 10.1186/s13023-016-0390-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. López‐Ferrando, V. , Gazzo, A. , de la Cruz, X. , Orozco, M. , & Gelpí, J. L. (2017). PMut: A web‐based tool for the annotation of pathological varians on proteins 2017 update. Nucleic Acids Research, 45(W1), W222–W228. 10.1093/nar/gkx313 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lucchiari, S. , Donati, M. A. , Parini, R. , Melis, D. , Gatti, R. , Bresolin, N. , & Comi, G. P. (2002). Molecular characterisation of GSD III subjects and identification of six novel mutations in AGL. Human mutation, 20(6), 480–480. [DOI] [PubMed] [Google Scholar]
  43. Lucchiari, S. , Pagliarani, S. , Salani, S. , Filocamo, M. , Di Rocco, M. , Melis, D. , … Comi, G. P. (2006). Hepatic and neuromuscular forms of glycogenosis type III: Nine mutations in AGL. Human Mutation, 27(6), 600–601. 10.1002/humu.9426 [DOI] [PubMed] [Google Scholar]
  44. Marcolongo, P. , Barone, V. , Priori, G. , Pirola, B. , Giglio, S. , Biasucci, G. , … Sorrentino, V. (1998). Structure and mutation analysis of the glycogen storage disease type 1b gene. FEBS Letters, 436, 247–250. 10.1016/S0014-5793(98)01129-6 [DOI] [PubMed] [Google Scholar]
  45. Matern, D. , Seydewitz, H. , Bali, D. , Lang, C. , & Chen, Y. T. (2002). Glycogen storage disease type I: Diagnosis and phenotype/genotype correlation. European Journal of Pediatrics, 161(1), S10–S19. 10.1007/BF02679989 [DOI] [PubMed] [Google Scholar]
  46. Naslavsky, M. S. , Yamamoto, G. L. , de Almeida, T. F. , Ezquina, S. A. M. , Sunaga, D. Y. , Pho, N. , … Zatz, M. (2017). Exomic variants of an elderly cohort of Brazilians in the ABraOM database. Human Mutation, 38(7), 751–763. 10.1002/humu.23220 [DOI] [PubMed] [Google Scholar]
  47. Özen, H. (2007). Glycogen storage diseases: New perspectives. World Journal of Gastroenterology, 13(18), 2541 10.3748/wjg.v13.i18.2541 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Pejaver, V. , Urresti, J. , Lugo‐Martinez, J. , Pagel, K. A. , Lin, G. N. , Nam, H. , … Radivojac, P. (2017). MutPred2: inferring the molecular and phenotypic impact of amino acid variants. bioRxiv. 10.1101/134981 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Profiti, G. , Martelli, P. L. , & Casadio, R. (2017). The Bologna Annotation Resource (BAR 3.0): Improving protein functional annotation. Nucleic Acids Research, 45(W1), W285–W290. 10.1093/nar/gkx330 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Quinto‐Sánchez, M. , Cintas, C. , Silva de Cerqueira, C. C. , Ramallo, V. , Acuña‐Alonzo, V. , Adhikari, K. , … González‐José, R. (2017). Socioeconomic status is not related with facial fluctuating asymmetry: Evidence from Latin‐American populations. PLoS ONE, 12 (1), e0169287 10.1371/journal.pone.0169287 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Rake, J. P. , ten Berge, A. M. , Verlind, E. , Visser, G. , Verlind, E. , Niezen‐Koning, K. E. , … Scheffer, H. (2000). Glycogen storage disease type 1a: Recent experience with mutation analysis, a summary of mutations reported in the literature and a newly developed diagnostic flowchart. European Journal of Pediatrics, 159, 322–330. 10.1007/s004310051281 [DOI] [PubMed] [Google Scholar]
  52. Reis, F. C. , Caldas, H. C. , Norato, D. Y. , Schwartz, I. V. D. , Giugliani, R. , Burin, M. G. , & Sartorato, E. L. (2001). Glycogen storage disease type Ia: Molecular study in Brazilian patients. Journal of Human Genetics, 46(3), 146 10.1007/s100380170102 [DOI] [PubMed] [Google Scholar]
  53. Resque, R. , Gusmão, L. , Geppert, M. , Roewer, L. , Palha, T. , Alvarez, L. , … Santos, S. (2016). Male lineages in Brazil: Intercontinental admixture and stratification of the European background. PLoS ONE, 11(4), e0152573 10.1371/journal.pone.0152573 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Richards, S. , Aziz, N. , Bale, S. , Bick, D. , Das, S. , Gastier‐Foster, J. , … Rehm, H. L. (2015). 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. Genetics in Medicine, 17(5), 405 10.1038/gim.2015.30 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Rocha, H. , Cabral, A. , & Vilarinho, L. (2000). Identification of a novel mutation (Q24X) in the glucose‐6‐phosphatase gene of a Portuguese patient with GSD‐Ia. Human Mutation, 16, 449 [DOI] [PubMed] [Google Scholar]
  56. Rodríguez‐Jiménez, C. , Santos‐Simarro, F. , Campos‐Barros, Á. , Camarena, C. , Lledín, D. , Vallespín, E. , … Rodríguez‐Nóvoa, S. (2017). A new variant in PHKA2 is associated with glycogen storage disease type IXa. Molecular Genetics and Metabolism Reports, 31(10), 52–55. 10.1016/j.ymgmr.2017.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Rudolfová, J. , Slováčková, R. , Trbušek, M. , Pešková, Št'astná. S. , & Kozák, L. (2001). Identification of three novel mutations in the PHKA2 gene in Czech patients with X‐linked liver glycogenosis. Journal of Inherited Metabolic Disease, 24(1), 85–87. 10.1023/a:1005635629149 [DOI] [PubMed] [Google Scholar]
  58. Sabeti, P. C. (2015). A global reference for human genetic variation. Nature, 526(7571), 68–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Schofield, D. , Khurshid Alam, K. , Douglas, L. , Shrestha, R. , MacArthur, D. G. , Davis, M. , … O’Grady, G. L. (2017). Cost‐effectiveness of massively parallel sequencing for diagnosis of paediatric muscle diseases. Genomic Medicine, 2(1), 4 10.1038/s41525-017-0006-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Schwarz, J. M. , Cooper, D. N. , Schuelke, M. , & Seelow, D. (2014). MutationTaster2: Mutation prediction for the deep‐sequencing age. Nature Methods, 11(4), 361–362. 10.1038/nmeth.2890 [DOI] [PubMed] [Google Scholar]
  61. Shen, J. , & Chen, Y. (2002). Molecular characterization of glycogen storage disease type III. Current Molecular Medicine, 2(2), 167–175. 10.2174/1566524024605752 [DOI] [PubMed] [Google Scholar]
  62. Skakic, A. , Djordjevic, M. , Sarajlija, A. , Klaassen, K. , Tosic, N. , Kecman, B. , & Stojiljkovic, M. (2018). Genetic characterization of GSD I in Serbian population revealed unexpectedly high incidence of GSD Ib and 3 novel SLC37A4 variants. Clinical Genetics, 93(2), 350–355. 10.1111/cge.13093 [DOI] [PubMed] [Google Scholar]
  63. Stenson, P. D. , Ball, E. V. , Mort, M. , Phillips, A. D. , Shiel, J. A. , Thomas, N. S. , & Cooper, D. N. (2003). The Human Gene Mutation Database (HGMD®): 2003 update. Human Mutation, 21, 577–581. 10.1002/humu.10212 [DOI] [PubMed] [Google Scholar]
  64. Stroppiano, M. , Regis, S. , DiRocco, M. , Caroli, F. , Gandullia, P. , & Gatti, R. (1999). Mutations in the glucose‐6‐phosphatase gene of 53 Italian patients with glycogen storage disease type Ia. Journal of Inherited Metabolic Disease, 22(1), 43–49. 10.1023/a:1005495131118 [DOI] [PubMed] [Google Scholar]
  65. Thomas, P. D. , Campbell, M. J. , Kejariwal, A. , Mi, H. , Karlak, B. , Daverman, R. , & Narechania, A. (2003). PANTHER: A library of protein families and subfamilies indexed by function. Genome Research, 13(9), 2129–2141. 10.1101/gr.772403 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Trioche, P. , Francoual, J. , Chalas, J. , Capel, L. , Lindenbaum, A. , Odièvre, M. , & Labrune, P. (2000). Genetic heterogeneity of glycogen storage disease type Ia in France: A study of 48 patients. Human Mutation, 16(5), 444 [DOI] [PubMed] [Google Scholar]
  67. van den Berg, I. E. , van Beurden, E. A. , Malingre, H. E. , van Amstel, H. K. , Poll‐The, B. T. , Smeitink, J. A. , & Berger, R. (1995). X‐linked liver phosphorylase kinase deficiency is associated with mutations in the human liver phosphorylase kinase alpha subunit. American Journal of Medical Genetics, 56(2), 381. [PMC free article] [PubMed] [Google Scholar]
  68. Vaser, R. , Adusumalli, S. , Leng, S. K. , Sikic, M. , & Ng, P. (2016). SIFT missense predictions for genomes. Nature Protocols, 11, 1–9. 10.1038/nprot.2015.123 [DOI] [PubMed] [Google Scholar]
  69. Vega, A. I. , Medrano, C. , Navarrete, R. , Desviat, L. R. , Merinero, B. , Rodríguez‐Pombo, P. , … Pérez, B. (2016). Molecular diagnosis of glycogen storage disease and disorders with overlapping clinical symptoms by massive parallel sequencing. Genetics in Medicine, 18(10), 1037–1043. 10.1038/gim.2015.217 [DOI] [PubMed] [Google Scholar]
  70. Veiga‐da‐Cunha, M. , Gerin, I. , Chen, Y.‐T. , de Barsy, T. , de Lonlay, P. , Dionisi‐Vici, C. , … Van Schaftingen, E. (1998). A gene on chromosome 11q23 coding for a putative glucose‐6‐phosphate translocase is mutated in glycogen‐storage disease types Ib and Ic. American Journal of Human Genetics, 63, 976–983. 10.1086/302068 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Vockley, J. , Rinaldo, P. , Bennett, M. J. , Matern, D. , & Vladutiu, G. D. (2000). Synergistic heterozygosity: Disease resulting from multiple partial defects in one or more metabolic pathways. Molecular Genetics and Metabolism, 71, 10–18. 10.1006/mgme.2000.3066 [DOI] [PubMed] [Google Scholar]
  72. Wang, D. Q. , Carreras, C. T. , Fiske, L. M. , Austin, S. , Boree, D. , Kishnani, P. S. , & Weinstein, D. A. (2012). Characterization and pathogenesis of anemia in glycogen storage disease type Ia and Ib. Genetics in Medicine, 14(9), 795 10.1038/gim.2012.41 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Wang, J. , Cui, H. , Lee, N.‐C. , Hwu, W.‐L. , Chien, Y.‐H. , Craigen, W. J. , … Zhang, V. W. (2013). Clinical application of massively parallel sequencing in the molecular diagnosis of glycogen storage diseases of genetically heterogeneous origin. Genetics in Medicine, 15(2), 106–114. 10.1038/gim.2012.104 [DOI] [PubMed] [Google Scholar]
  74. Yeo, G. , & Burge, C. B. (2003). Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. Journal of Computational Biology, 11(2–3), 377–394. 10.1089/1066527041410418 [DOI] [PubMed] [Google Scholar]

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