Original Article

Ann Lab Med 2023; 43(3): 280-289

Published online May 1, 2023

Copyright © Korean Society for Laboratory Medicine.

Rapid Targeted Sequencing Using Dried Blood Spot Samples for Patients With Suspected Actionable Genetic Diseases

Man Jin Kim, M.D.1,2,* , Soo Yeon Kim, M.D.1,2,* , Jin Sook Lee, M.D.3 , Sanggoo Kang, M.B.A.4 , Lae-Jeong Park, Ph.D.5 , Wooyong Choi, B.S.4 , Ju Yeol Jung, M.S.4 , Taehyung Kim, M.S.4 , Sung Sup Park, M.D.6 , Jung Min Ko, M.D.2,3 , Moon-Woo Seong, M.D., Ph.D.6 , and Jong Hee Chae, M.D., Ph.D.1,2,7

1Department of Genomic Medicine, Seoul National University Hospital, Seoul, Korea; 2Rare Disease Center, Seoul National University Hospital, Seoul, Korea; 3Department of Pediatrics, Department of Genome Medicine and Science, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea; 4MedySapiens, Inc., Seoul, Korea; 5Department of Electrical Engineering, Gangneung-Wonju National University, Gangneung, Korea; 6Department of Laboratory Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea; 7Department of Pediatrics, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Seoul, Korea

Correspondence to: Moon-Woo Seong, M.D., Ph.D.
Department of Laboratory Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: +82-2-2072-4180
Fax: +82-2-747-0359

Jong Hee Chae, M.D., Ph.D.
Department of Genomic Medicine, Seoul National University Hospital, Department of Pediatrics, Seoul National University Children’s Hospital, Seoul National University College of Medicine, Rare Disease Center, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea
Tel: +82-2-2072-3622
Fax: +82-2-2072-3917

* These authors equally contributed to this study.

Received: March 18, 2022; Revised: July 9, 2022; Accepted: November 16, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: New genome sequencing technologies with enhanced diagnostic efficiency have emerged. Rapid and timely diagnosis of treatable rare genetic diseases can alter their medical management and clinical course. However, multiple factors, including ethical issues, must be considered. We designed a targeted sequencing platform to avoid ethical issues and reduce the turnaround time.
Methods: We designed an automated sequencing platform using dried blood spot samples and a NEOseq_ACTION panel comprising 254 genes associated with Mendelian diseases having curable or manageable treatment options. Retrospective validation was performed using data from 24 genetically and biochemically confirmed patients. Prospective validation was performed using data from 111 patients with suspected actionable genetic diseases.
Results: In prospective clinical validation, 13.5% patients presented with medically actionable diseases, including short- or medium-chain acyl-CoA dehydrogenase deficiencies (N=6), hyperphenylalaninemia (N=2), mucopolysaccharidosis type IVA (N=1), alpha thalassemia (N=1), 3-methylcrotonyl-CoA carboxylase 2 deficiency (N=1), propionic acidemia (N=1), glycogen storage disease, type IX(a) (N=1), congenital myasthenic syndrome (N=1), and citrullinemia, type II (N=1). Using the automated analytic pipeline, the turnaround time from blood collection to result reporting was <4 days.
Conclusions: This pilot study evaluated the possibility of rapid and timely diagnosis of treatable rare genetic diseases using a panel designed by a multidisciplinary team. The automated analytic pipeline maximized the clinical utility of rapid targeted sequencing for medically actionable genes, providing a strategy for appropriate and timely treatment of rare genetic diseases.

Keywords: Neonatal screening, High-throughput nucleotide sequencing, Metabolism, Inborn errors, Dried blood spot

Next-generation sequencing (NGS)-based approaches for clinical evaluation of infants with suspected genetic diseases have expanded substantially in recent years [1-3]. In particular, whole-genome sequencing (WGS) and whole-exome sequencing (WES) have improved the diagnosis of rare genetic diseases in critically ill neonates and infants. Compared with conventional genetic testing, rapid genomic testing (RGT) permits accurate genetic diagnosis of many patients [4-6]. Recent studies have suggested that RGT is faster and more reliable than standard WES and WGS [7-9]. NGS-based RGT enables rapid and timely diagnosis of rare genetic diseases.

Early diagnosis of incurable genetic diseases and profound disability in newborns raises several ethical issues, such as parents making premature decisions as the bonding with the baby is not fully established [10]. Moreover, RGT poses a substantial clinical and laboratory burden [8]. A successful diagnosis implementation requires changing the existing workflow by a multidisciplinary team, including intensive-care therapists, clinical geneticists, and clinical pathologists. Furthermore, the cost of rapid WGS and WES is two to four times that of standard WGS and WES [10]. The cost of analysis of the large amount of data generated by WGS or WES is inevitably higher than that of conventional gene sequencing [11]. Many related findings and variants of uncertain significance (VUSs) increase the risk of inaccurate diagnosis and the turnaround time (TAT) of result interpretation. Therefore, WGS or WES in infants and children with genetic diseases is not a viable option, especially in developing countries.

If actionable genes are targeted, ethical approval can be waived, which reduces the TAT. Applying variant interpretation and pathogenicity prediction models can further reduce the TAT and the risk of inaccurate diagnosis. Therefore, we designed an RGT platform to specifically sequence 254 medically actionable genes associated with preventable or curable Mendelian diseases using dried blood spot (DBS) samples of patients.

Patient enrollment

Patients were prospectively enrolled based on the following criteria: (1) abnormalities in routine neonatal screening testing (NST); (2) unexplained neonatal hypotonia or neonate-onset seizures without exponential causes such as prematurity, malformations, and perinatal insults; (3) unexplained and abnormal laboratory findings, such as liver enzymes, metabolic acidosis, and hyperammonemia, suggesting hidden metabolic diseases; (4) skeletal dysplasia or joint problems suggestive of storage diseases; and (5) neurodevelopmental delay with abnormal results in metabolic screening testing. This study was approved by the Institutional Review Board of Seoul National University Hospital, Seoul, Korea (No. H-2002-069-1101). Informed consent for genetic testing was obtained from all patients and their guardians in the case of minor patients.

NEOseq_ACTION panel design

The NEOseq_ACTION panel was designed to diagnose genetic diseases in neonates or during early infancy that require a rapid and precise diagnosis to ensure prompt and efficient treatment. The NEOseq_ACTION is a comprehensive NGS panel that includes 254 genes that were selected based on consultations with clinical and metabolic geneticists. The selected genes include potentially actionable genes associated with diseases exposed by neonatal metabolic screening performed in Korea and diseases listed on the US Recommended Uniform Screening Panel Core and Secondary Conditions of Advisory Committee on Heritable Disease in Newborns and Children (

The diseases targeted in this panel can be classified as: glycogen storage diseases; adrenal insufficiency; galactosemia; inborn errors of amino acid, organic acid, and fatty acid metabolism; treatable or curable lysosomal storage diseases; immunodeficiency; hemoglobinopathy; genetic hyperbilirubinemia; congenital and genetic sensorineural hearing loss; and other genetic diseases, such as cystic fibrosis, mineral metabolism disorders, and congenital myasthenic syndromes (curable diseases: Table 1; full list: Supplemental Data Table S1).

Table 1 . Overview of the genes included in the NEOseq_ACTION panel, with associated diseases and curative medical actio

CategoryDiseaseGene symbolAction
Inborn errors of metabolism of amino acids, organic acids, and fatty acidsBiotinidase deficiencyBTDBiotin supplementation
Carnitine deficiencySLC22A5L-carnitine supplementation
Holocarboxylase synthetase deficiencyHLCSBiotin supplementation
HomocystinuriaMTHFRVitamin B6, folic acid
HyperphenylalaninemiaGCH1, PTS, QDPRTetrahydrobiopterin, L-DOPA, 5-hydroxytryptophan supplementation
Isolated methylmalonic acidemiaMMAA, MMAB, MMADHCHydroxycobalamin supplementation
Methylmalonic acidemia with homocystinuriaMMACHC, MMADHCHydroxycobalamin supplementation
N-acetylglutamate synthetase deficiencyNAGSCarglumic acid (Carbaglu) treatment
Lysosomal storage diseasesCystinosisCTNSCysteamine treatment
α-MannosidosisMAN2B1Stem cell transplantation
AspartylglucosaminuriaAGAStem cell transplantation
Other lysosomal storage diseasesARSB, GAA, GALNS, GBA, GLA, IDS, IDUA, LIPA, TPP1Enzyme replacement therapy
ImmunodeficiencyImmunodeficiencyADA, AK2, CD3D, CD3E, DCLRE1C, FOXN1, IL2RA, IL2RG, IL7R, JAK3, LCK, LIG4, MTHFD1, NFKBIA, NHEJ1, PEPD, PNP, PTPRC, RAG1, RAG2, STK4, TCN2, ZAP70Stem cell transplantation
Other diseasesAcrodermatitis enteropathicaSLC39A4Zinc supplementation
HypophosphatasiaALPLEnzyme replacement therapy
Pyridoxine-dependent epilepsyALDH7A1Pyridoxine supplementation
Thiamine metabolism dysfunction syndrome 2SLC19A3Biotin and thiamine supplementation

Retrospective validation of the NEOseq_ACTION panel

The validation set included data from 24 patients with known pathogenic single nucleotide variants (SNVs) and small insertion or deletions (indels) across 22 genes (Supplemental Data Table S2). All validation samples were confirmed through biochemical testing. In total, 99.8% and 98.96% of bases were covered by at least 1- and 20-fold coverage, respectively. The median percentage of on-target reads across samples was 78.2%. The mean depth of coverage was 142×. All 40 variants in the 24 cases were detected using the NEOseq_ACTION panel.

Sample processing, target enrichment, and sequencing

Genomic DNA was extracted from two out of three DBSs (Fig. 1) using a Chemagic 360 instrument (PerkinElmer, Baesweiler, Germany) and fragmented using a Nextera DNA library preparation kit (Illumina, San Diego, CA, USA). Libraries were prepared from 100 ng of total genomic DNA using the Nextera DNA library preparation kit, according to the manufacturer’s instructions. Paired-end 150-bp sequencing was performed on the MiSeqDx platform (Illumina). Raw sequencing data were obtained in the FASTQ format.

Figure 1. Workflow for NEOseq_ACTION testing. Genomic DNA is isolated from DBSs, which are commonly used for neonatal screening. Libraries are prepared using the Nextera kit (Illumina, San Diego, CA, USA). The target DNA is enriched using the Twist Bioscience capture kit (Twist Bioscience, South San Francisco, CA, USA) and sequenced on the MiSeq platform (Illumina). Secondary analysis and variant interpretation are carried out using the MedyCVi (MedySapiens, Seoul, Korea) bioinformatics pipeline. Variant pathogenicity is predicted using the MedyCVi module, MedyPatho (MedySapiens). The time from DNA extraction to medical intervention was 3.4 days.
Abbreviations: DBS, dried blood spot; BWA, Burrows–Wheeler aligner; GATK, genome analysis tool kit; BAM, binary alignment map; VCF, variant call format.

Pipeline for variant calling

The sequencing reads were aligned to the human genome reference sequence (GRCh37) using the Burrow–Wheeler Aligner (BWA-MEM, version 0.7.5) [12]. The aligned sequence reads (SAM file) were sorted, indexed, and converted to BAM files using SAMtools (version 1.9) [13]. Duplicate sequences in the BAM files were removed using Picard tools (version 2.18.2) ( Next, local realignment and base recalibration of the BAM files were performed using the Base Recalibrator in GATK (version [14] based on the dbSNP (version 138), Mills, and 1000G gold standard indel databases. SNVs and indels were identified using the GATK Haplotype Caller tool. Called variants were annotated using SnpEff [15].

Variant interpretation and pathogenicity prediction model

Called variants were interpreted using the MedyCVi software (MedySapiens, Seoul, Korea) based on InterVar [16]—a bioinformatics tool for the interpretation of genetic variants according to the American College of Medical Genetics and Genomics and Association for Molecular Pathology guidelines [17] based on databases of disease-associated genetic variants such as ClinVar [18] and Human Genome Mutation Database (HGMD) [19]. A machine learning-based prediction model was developed to support clinical decisions in variant interpretation based on estimation of the pathogenicity of each VUS.

The MedyPatho (MedySapiens) prediction model predicts the pathogenicity of non-synonymous SNVs based on nine functional scores (PolyPhen [20], MutPred [21], Sorting Intolerant From Tolerant [22], Protein Variant Effect Analyzer [23], Variant Effect Scoring Tool [24], Likelihood Ratio Test [25], MutationAssessor [26], MutationTaster [27], Fathmm [28]), two conservation scores (GERP [29] and PhastCons [30]), and the population allele frequency from four databases (Genome Aggregation Database [31], Exome Aggregation Consortium [32], Korean Reference Genome [33], and Korean Variant Archive [34].

The prediction model was trained and evaluated using a dataset compiled from the variant databases of ClinVar and HGMD. Specifically, only missense variants were selected, and for the ClinVar database, variants with a review status labeled as “criteria provided, multiple submitters, no conflicts; criteria provided, single submitter; or reviewed by expert panel” were considered. In addition, pathogenic variants marked as disease-causing mutation or disease-associated polymorphism in the HGMD were included. The final dataset comprised 143,555 pathogenic and 86,326 benign variants.

The variants added to ClinVar since 2020 were considered as test data. Variants for which protein sequences were similar to those in the training set were excluded using the CD-HIT program [35] based on a sequence identity threshold of 80%. The prediction model was trained with 43,590 pathogenic and 26,499 benign variants and was subsequently evaluated with 8,295 pathogenic and 8,630 and benign variants.

The prediction model was built using LightGBM [36]—a tree-based gradient boosting model. Performance was evaluated in comparison with two in silico prediction tools (Rare Exome Variant Ensemble Learner [REVEL] [37] and Combined Annotation-Dependent Depletion [CADD] [38]). Fig. 2 shows the performance comparison of the pathogenicity prediction tools based on test data from ClinVar release Nov 29, 2020.

Figure 2. Performance comparison of pathogenicity prediction tools using test data from ClinVar release Nov 29, 2020.

All mutations filtered by MedyPatho (MedySapiens) were manually curated by genetic experts.

Clinical patient characteristics

To evaluate the usefulness of the NEOseq_ACTION panel for patients, prospective panel testing was conducted in 111 patients. The clinical characteristics of the patients are shown in Table 2. The mean age at symptom or sign recognition was 10.4 months (range, 1–188 months). Around three quarters of the patients presented with symptoms or showed laboratory abnormalities within a month after birth.

Table 2 . Demographics of the prospectively enrolled patients

VariableTotal patients, N (%) (N=111)
Male : Female58 : 53
Age at symptom/sign recognition
≤ 1 month83 (74.8)
1–12 months10 (9.0)
> 12 months18 (16.2)
Age at testing
≤ 1 month55 (49.5)
1–12 months23 (20.7)
> 12 months33 (29.7)
Major symptoms
NST abnormality57 (51.4)
LFT abnormality5 (4.5)
CPK elevation3 (2.7)
Acidosis2 (1.8)
Other laboratory abnormalities3 (2.7)
Neonatal hypotonia/respiratory failure14 (12.6)
Early-onset seizures3 (2.7)
Developmental delay and additional findings10 (9.0)
Joint pain/skeletal abnormality5 (4.5)
Others9 (8.1)

Abbreviations: NST, neonatal screening testing; LFT, liver function testing; CPK, creatine phosphokinase.

Seventy patients were clinically asymptomatic, and 57 patients were referred to the outpatient clinic because of initial NST abnormality. Patients with NST abnormality were directly enrolled without further investigation, although most of them had simultaneous metabolic screening or NST repetition. Thirteen patients visited the clinic for incidentally detected laboratory abnormalities, such as elevated liver enzymes, creatinine phosphokinase (CPK), or unexplained acidosis. The patients had no perinatal history and showed normal development.

Forty-one patients had one or more clinical symptoms. Neonatal hypotonia and unexplained respiratory failures were the most frequent presentations. However, NST could not provide reliable data because the patients often required mechanical ventilation and total parenteral nutrition because of frequent infections. Among 10 patients with developmental delay, four showed abnormal metabolic screening results, and another four had additional features, including coarse face, skeletal abnormalities, or extensive Mongolian spots suggestive of storage diseases.

Evaluation and validation of the NEOseq_ACTION panel

The average time from patient visit to result reporting was 8.3 days, and the average time from sample preparation to result reporting was 3.4 days. As the MiSeq Reagent Kit v2 Nano was used for less than three samples, sequencing was performed even if only one sample was submitted. We identified significant variants in 10 genes in 15 patients; detailed information is provided in Table 3. Among them, 11 patients were asymptomatic (10 with NST abnormality, and one with abnormal liver function). Four symptomatic patients (neonatal hypotonia, early-onset seizures, skeletal abnormalities, or abdominal distension) were genetically confirmed. Suspected compound heterozygous or homozygous variants were detected in 13 patients (genes related to autosomal recessive diseases), and two hemizygous variants were detected in two patients. Among the 15 patients with suspected recessive diseases, both alleles were initially classified as pathogenic or likely pathogenic in nine patients (P2, P5, P14, P41, P45, P46, P72, P74, and P83). The other patients carried one pathogenic or likely pathogenic allele and one VUS (P6, P10, P25, P36, P40, and P111). Parental testing was performed in four families, and further biochemical testing was performed for all except two patients. Therefore, the final classification was changed for four patients (P6, P25, P36, and P40). The second variant in P10 and P111 remained of unknown significance after retesting. However, the patients started treatment as soon as the biochemical testing results provided strong evidence for the final diagnosis. As P10 and P111 showed a mildly elevated serum phenylalanine level when compared with actual patients with phenylketonuria on repeated testing, their second variants were considered not significant.

Table 3 . Significant variants with pathogenicity classification detected using the NEOseq_ACTION panel

Case No.Gene symbolTranscriptNucleotide changeAmino acid changeZygosityVariant classification by MedyPathoSegregationClinical validationFinal classificationACMG evidences*
P2GALNSNM_000512.4c.319G >Ap.Ala107ThrHetLPTransSerum galactose-6-sulfatase 0.37 nmol/17 hr/mg protein (ref, 18.6–61.8)LPPM2, PM3, PP3, PP5
GALNSNM_000512.4c.451C >Ap.Pro151ThrHetLPLPPM1, PM2, PM5, PP3, PP5
P5ACADSNM_000017.2c.1031A >Gp.Glu344GlyHetLPNot doneSerum C4 1.995 μmol/L (cut off, 0.41)LPPM1, PM2, PP3, PP5
ACADSNM_000017.2c.164C >Tp.Pro55LeuHetLPLPPS3, PM2, PP5
P6PCCANM_000282.3c.1846-2A >Gp?HetLPTransUrine propionylglycine 80.0 (ref, not detected)LPPVS1, PM2
PCCANM_000282.3c.938G >Cp.Arg313ProHetVUSLPPM2, PM3, PM5, PP3
P10PAHNM_000277.1c.975C >Gp.Tyr325*HetPNot doneSerum phenylalanine 163 μmol/L (ref, 13–91)PPVS1, PM2, PP5
PAHNM_000277.1c.158G >Ap.Arg53HisHetVUSVUSBS2, PM5
P14SLC25A13NM_014252.3c.852_855delTATGp.Met285Profs*2HetPNot doneSerum citrulline 378 μmol/L (ref, 8–36)PPVS1, PM2, PP5
SLC25A13NM_014252.3c.1177+1G >Ap?HetPPPVS1, PM2, PP5
P25ACADMNM_000016.4c.1189T >Ap.Try397AsnHetLPTransSerum C6 0.145 μmol/L (cut off < 0.1), C8 0.26 μmol/L (cut off < 0.18), C10:1 0.16 μmol/L (cut off < 0.13)LPPM2, PM5, PP2, PP3, PP4, PP5
ACADMNM_000016.4c.1231G >Tp.Val411LeuHetVUSLPPM2, PM3, PP2, PP4
P36ACADMNM_000016.4c.1189T >Ap.Tyr397AsnHetLPNot doneSerum C6 0.64 μmol/L (cut off < 0.1), C8 1.59 μmol/L (cut off < 0.12), C10:1 0.45 μmol/L (cut off < 0.08)LPPM2, PM5, PP2, PP3, PP4, PP5
ACADMNM_000016.4c.91C >Tp.Arg31CysHetVUSLPPM2, PM5, PP2, PP4, PP5
P40MCCC2NM_022132.4c.838G >Tp.Asp280TyrHetLPNot doneUrine 3-hydroxyisovaleric acid 1,731.1 mmol/mol Cr (ref, < 18)LPPM2, PP2, PP3, PP4, PP5
MCCC2NM_022132.4c.1342G >Ap.Gly448ArgHetVUSLPPM2, PP2, PP3, PP4, PP5
P41PREPLNM_006036.4c.1979_1980delp.Leu660Glnfs*9HetLPTransRepetitive nerve stimulation test, insignificantPPVS1, PM2, PM3
PREPLNM_006036.4c.1747-9_1747delp?HetLPPPVS1, PM2, PM3
P45HBA2NM_000517.4c.427T >Cp.*143Qext*31HemLPNot doneNot doneLPPM2, PM3, PM4
P46PHKA2NM_000292.2c.1246-2A >Gp?HemLPNot doneGOT/GPT 59/49 IU/L (ref, 1–40/1–40)LPPVS1, PM2
P72ACADSNM_000017.2c.164C >Tc.Pro55LeuHomLPNot doneSerum C4 0.805 μmol/L (ref, < 0.41)LPPS3, PM2, PP5
P74ACADSNM_000017.2c.1031A >Gp.Glu344GlyHetLPNot doneSerum C4 0.905 μmol/L (ref, < 0.41)LPPM1, PM2, PP3, PP5
ACADSNM_000017.2c.1130C >Tp.Pro377LeuHetLPLPPM1, PM2, PP3, PP5
P83ACADSNM_000017.2c.1031A >Gp.Glu344GlyHomLPNot doneSerum C4 1.965 μmol/L (ref, < 0.41)LPPM1, PM2, PP3, PP5
P111PAHNM_000277.1c.1068C >Gp.Tyr356*HetPNot doneSerum phenylalanine 173 μmol/L (ref, 25–74)PPVS1, PM2, PP5
PAHNM_000277.1c.158G >Ap.Arg53HisHetVUSVUSBS2, PM5

*All variants were assessed based on the ACMG or Association for Molecular Pathology guidelines [17]; Initial classification using MedyPatho was changed at final classification; Given the high homology between HBA1 and HBA2 and that common deletions account for 85% of pathogenic variants in alpha thalassemia, Sanger sequencing and multiplex ligation probe amplification were performed to confirm the variant. This patient had a heterozygous common–SEA deletion; thus, the zygosity of the c.427T>C variant was determined to be hemizygous.

Abbreviations: ACMG, American College of Medical Genetics and Genomics; Het, heterozygous; Hem, hemizygous; Hom, homozygous; LP, likely pathogenic; P, pathogenic; VUS, variant of unknown significance; ref, reference range.

Representative cases

All positive results in 15 cases affected the treatment decisions (Table 4). Two patients received a final diagnosis, although they had received appropriate treatment based on clinical suspicion. Eleven patients started medication or changed their daily diet. All families received counseling based on the patients’ results. The average time from testing to medical decision making was 20.8 days (2–68 days). Below, we describe some representative cases.

Table 4 . Final diagnosis and medical decision for positive cases

Case No.Age at symptom onset (months)Major complaintFinal diagnosisTime to result reporting (days)Time to medical decision (days)Treatment plan
P240Joint painMucopolysaccharidosis IVA512Enzyme replacement therapy
P50NST abnormalitySCAD deficiency318Avoid fasting, frequent meals
P68SeizurePropionic acidemia3NAL-carnitine, low-isoleucine/valine diet
P100NST abnormalityHyperphenylalaninemia311Avoid unnecessary treatment, further testing
P140NST abnormalityCitrullinemia, type II2NALow-carbohydrate and high-protein diet
P25149LFT abnormalityMCAD deficiency268Low-fat diet, avoid fasting
P360NST abnormalityMCAD deficiency519Low-fat diet, avoid fasting
P400NST abnormality3-Methylcrotonyl-CoA carboxylase 2 deficiency2NAL-carnitine, avoid fasting
P411Neonatal hypotoniaCongenital myasthenic syndrome22Acetylcholine esterase inhibitor, salbutamol
P450NST abnormality (incidental finding*)Alpha thalassemia1022Waiting for stem cell transplantation
P4621Abdominal distensionGlycogen storage disease IXa11022Corn starch, low-fat diet, avoid fasting
P720NST abnormalitySCAD deficiency428Avoid fasting, frequent meals
P740NST abnormalitySCAD deficiency917Avoid fasting, frequent meals
P830NST abnormalitySCAD deficiency424Avoid fasting, frequent meals
P1110NST abnormalityHyperphenylalaninemia18Avoid unnecessary treatment

*The patient was enrolled because of NST abnormality (elevated C18, C20–C24) and was diagnosed as having alpha thalassemia, unrelated to the initial NST findings. We concluded that the initial NST finding and diagnosis were incidental; Medical action was started before the genetic diagnosis in P6, P14, and P40 based on their symptoms or NST findings.

Abbreviations: NST, neonatal screening testing; LFT, liver function testing; SCAD, short-chain acyl-CoA dehydrogenase; MCAD, medium-chain acyl-CoA dehydrogenase; NA, not applicable.

P2 was a 3.5-year-old girl with no perinatal history. At 12 months, her growth was retarded. At 2 years, she complained of bilateral knee pain, which was followed by gross deformation of several joints. She underwent some tests and received regular rehabilitation, but without clinical improvement. She was referred to our clinic 2 years after the onset of the symptoms and underwent simultaneous NEOseq_ACTION panel sequencing and enzyme testing on suspicion of mucopolysaccharidosis (MPS) type IVA. Compound heterozygous variants in GALNS were identified in 5 days, and glucose 6-sulfatase activity was 0.37 nmol/17 hr/mg protein (reference range [ref], 18.6–61.8). The patient was confirmed to have MPS IVA and started enzyme replacement therapy 3 weeks after the first visit.

P14 was a 3-week-old boy whose NST results indicated low arginine (92.6 μmol/L; ref, 120–600) and high citrulline (172.6 μmol/L; ref, 8–47) levels. The patient was admitted to the neonatal intensive care unit and started a diet with special milk and carglumic acid for 2 weeks. The patient was referred for prompt diagnosis, and panel sequencing demonstrated pathogenic compound heterozygous variants in SLC25A13. He was diagnosed as having citrullinemia type II and started oral arginine therapy. He is currently 13 months old and shows normal development.

P41 was a 4-month-old boy who presented poor suckling and respiratory difficulty after birth. He was lethargic and depended on the nasogastric tube. He could make eye contact with his family, but not control his head. Panel testing indicated pathogenic compound heterozygous variants in PREPL associated with congenital myasthenic syndrome. We attempted repetitive nerve stimulation tests; however, the results were unreliable because of extremely low muscle mass and immature myelination. He started on an acetylcholine esterase inhibitor and salbutamol and showed remarkable clinical improvement. A month after starting the treatment, he could roll and control his head. He started to crawl at 6 months and could stand without support at 13 months. He has speech and eats ordinary meals orally.

P111 was a 1-month-old boy who showed NST abnormality indicating an elevated phenylalanine level (240 μmol/L; ref, 38–137). He underwent NST thrice because of abnormal results at different hospitals. The panel test indicated a pathogenic variant and a VUS in PAH. As detailed metabolic testing indicated slightly increased phenylalanine (100 µmol/L; ref, 25–74) and normal tyrosine (89 μmol/L; ref, 13–91) levels, we concluded that the patient had hyperphenylalaninemia, rather than phenylketonuria. The patient continued on breast milk and showed normal development until the last follow-up.

In this proof-of-concept study, we demonstrated the performance of rapid targeted sequencing using a NEOseq_ACTION panel. Actionable genes were identified in 15 patients (13.5%). For actionable genetic diseases, early therapeutic intervention can allow neonates and infants to have a normal life or can minimize the consequences of the disease. We used the NEOseq_ACTION panel to confirm clinically or biochemically suspected diseases. The panel allowed for the diagnosis of various curable or treatable diseases from a few DBSs obtained during routine NST or for critically ill neonates in intensive care unit settings.

Obstacles to the application of rapid WES and WGS in neonates and young infants, including insufficient sequencing depth, limited bioinformatics, and lack of data interpretation experts, have been reported [8]. Multiple factors have to be considered, such as genetic and clinical heterogeneity, including the appearance of formes frustes or incomplete presentation of classical phenotypes in neonates and young infants, and comorbidity because of the fragility of infants. Therefore, a targeted approach covering 100–300 disease-associated genes is cost-effective for rapid detection of medically actionable diseases. A short TAT is essential for the clinical effectiveness of rapid genome sequencing, particularly in neonates.

VUS interpretation can be a bottleneck in reducing the TAT [39]. In this study, rare VUSs with a population frequency of ≤0.6% appeared 25 times per patient (data not shown). As most inborn errors of metabolism are autosomal recessive genetic diseases, clinical geneticists struggle with many VUSs. Therefore, VUSs are the major cause of a prolonged TAT. The initial classification of four variants in four families (P6, P25, P36, and P40) by MedyPatho was changed in the final classification; two VUSs in P6 and P25 were reclassified as likely pathogenic variants based on the results of segregation testing, and two VUSs in P36 and P40 were reclassified as likely pathogenic variants according to biochemical testing results. MedyPatho has shown clinically acceptable accuracy in the classification of clinically relevant variants. Variant classifiers will become more sophisticated as they include accumulating data from specialized panels, such as NEOseq_ACTION, thereby reducing the burden of variant interpretation and the time required for manual curation. Therefore, automatic variant classifiers, such as MedyPatho, are essential to reduce the TAT.

We conducted a prospective study in patients with suspected actionable diseases. More than half of the patients were asymptomatic neonates with NST abnormalities. Although the standard option was to repeat the testing, the patients may have had several unnecessary tests, diet modification, and extra medical expenses. Moreover, we could take immediate, appropriate medical action for confirmed cases. Rapid screening and not missing any treatable diseases are critical to achieve qualitative practice, and the panel provided valuable information to clinicians for patients in the intensive care unit.

This study demonstrated that simultaneous biochemical or functional validation reduced the uncertainty of VUSs. Diseases recently covered in NST panels, such as genetic hearing loss and critical arrhythmias, may require regular check-ups for confirmative diagnosis if patients harbor a VUS. Although numerous ethical and social issues are to be considered, these diseases can be diagnosed and appropriately treated before symptom onset. Accumulating data on VUSs and long-term phenotypes may provide valuable clues on pathogenicity and can be utilized for the advancement of automated analysis programs. For example, P10, referred for NST abnormality, carried missense variants c.975C>G and c.158G>A in PAH. The c.975C>G variant was classified as pathogenic and c.158G>A as a VUS. P10 was diagnosed as having hyperphenylalaninemia with one pathogenic missense variant because serial biochemical testing consistently indicated a mild elevation of the serum phenylalanine level. The c.158G>A variant had no clinical significance. P40 had two variants in MCCC2: c.838G>T was classified as likely pathogenic and c.1342G>A as a VUS. His urine 3-hydroxyisovaleric acid level was 173.1.1 mmol/mol Cr, which is 10 times higher than the normal level, and thus, he was confirmed to have 3-methylcrotonyl-CoA carboxylase 2 deficiency with reclassification of the VUS.

This study demonstrated the importance of collaboration among field experts during panel design. Targeted RGT does not simply require a collection of genes of interest. A multidisciplinary team including clinical experts in pediatrics, clinical geneticists, clinical pathologists, and bioinformaticians should participate in gene selection for confirmative diagnosis. In particular, the details of medical actions should be clearly defined. Such a combined effort can maximize the clinical utility of targeted RGT of medically actionable genes. Moreover, predefining clear medical actions avoids ethical issues.

One of the limitations of this study was the relatively low detection rate. However, considering the benefits of detecting and managing actionable diseases at the presymptomatic stage, our diagnostic yield was acceptable.

NEOseq_ACTION panel sequencing requires only a small amount of DNA, e.g., from DBSs, and the TAT from blood collection to result reporting using the automated analytic pipeline was less than 4 days. The cost of NEOseq_ACTION sequencing is substantially lower than that of rapid WGS or WES, and because automated variant classification systems are embedded in bioinformatics pipelines, fewer experts are required. NEOseq_ACTION can be particularly advantageous in regions where resources and clinical geneticists are scarce, but rare genetic diseases are prevalent.

In conclusion, this prospective pilot study evaluated the clinical feasibility of rapid and timely diagnosis of treatable rare genetic diseases using a NEOseq_ACTION panel. Targeted RGT using the NEOseq_ACTION panel in acutely unwell neonates with suspected genetic diseases represents an example of the clinical application of genomic medicine. Considering the fragility of neonates in medical and ethical terms, the NEOseq_ACTION panel may provide a valuable opportunity for appropriate treatment of presymptomatic or early-stage diseases, even for NST in asymptomatic neonates.

Chae JH contributed to study conception and design; Chae JH, Kim SY, Lee JS, and Ko JM were involved in clinical evaluation; Kim MJ interpreted the results; Kim MJ and Kim SY drafted the manuscript; and Chae JH and Seong MW supervised the study. All authors read and approved the final manuscript.

This study was supported by the Technology Development Program (S2782642) funded by the Ministry of SMEs and Startups, Korea, and MedySapiens Inc. 

  1. Meng L, Pammi M, Saronwala A, Magoulas P, Ghazi AR, Vetrini F, et al. Use of exome sequencing for infants in intensive care units: ascertainment of severe single-gene disorders and effect on medical management. JAMA Pediatr 2017;171:e173438.
  2. Petrikin JE, Willig LK, Smith LD, Kingsmore SF. Rapid whole genome sequencing and precision neonatology. Semin Perinatol 2015;39:623-31.
    Pubmed KoreaMed CrossRef
  3. Willig LK, Petrikin JE, Smith LD, Saunders CJ, Thiffault I, Miller NA, et al. Whole-genome sequencing for identification of Mendelian disorders in critically ill infants: a retrospective analysis of diagnostic and clinical findings. Lancet Respir Med 2015;3:377-87.
    Pubmed CrossRef
  4. Anazi S, Maddirevula S, Faqeih E, Alsedairy H, Alzahrani F, Shamseldin HE, et al. Clinical genomics expands the morbid genome of intellectual disability and offers a high diagnostic yield. Mol Psychiatry 2017;22:615-24.
    Pubmed CrossRef
  5. Stark Z, Tan TY, Chong B, Brett GR, Yap P, Walsh M, et al. A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders. Genet Med 2016;18:1090-6.
    Pubmed CrossRef
  6. Vissers LELM, van Nimwegen KJM, Schieving JH, Kamsteeg EJ, Kleefstra T, Yntema HG, et al. A clinical utility study of exome sequencing versus conventional genetic testing in pediatric neurology. Genet Med 2017;19:1055-63.
    Pubmed KoreaMed CrossRef
  7. Farnaes L, Hildreth A, Sweeney NM, Clark MM, Chowdhury S, Nahas S, et al. Rapid whole-genome sequencing decreases infant morbidity and cost of hospitalization. NPJ Genom Med 2018;3:10.
    Pubmed KoreaMed CrossRef
  8. Stark Z, Lunke S, Brett GR, Tan NB, Stapleton R, Kumble S, et al. Meeting the challenges of implementing rapid genomic testing in acute pediatric care. Genet Med 2018;20:1554-63.
    Pubmed CrossRef
  9. van Diemen CC, Kerstjens-Frederikse WS, Bergman KA, de Koning TJ, Sikkema-Raddatz B, van der Velde JK, et al. Rapid targeted genomics in critically ill newborns. Pediatrics 2017;140:e20162854.
    Pubmed CrossRef
  10. Gyngell C, Newson AJ, Wilkinson D, Stark Z, Savulescu J. Rapid challenges: ethics and genomic neonatal intensive care. Pediatrics 2019;143:S14-21.
    Pubmed KoreaMed CrossRef
  11. Mardis ER. The \$1,000 genome, the \$100,000 analysis? Genome Med 2010;2:84.
    Pubmed KoreaMed CrossRef
  12. Li H and Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754-60.
    Pubmed KoreaMed CrossRef
  13. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25:2078-9.
    Pubmed KoreaMed CrossRef
  14. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-303.
    Pubmed KoreaMed CrossRef
  15. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 2012;6:80-92.
    Pubmed KoreaMed CrossRef
  16. Li Q and Wang K. InterVar: clinical interpretation of genetic variants by the 2015 ACMG-AMP guidelines. Am J Hum Genet 2017;100:267-80.
    Pubmed KoreaMed CrossRef
  17. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. 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. Genet Med 2015;17:405-24.
    Pubmed KoreaMed CrossRef
  18. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res 2016;44:D862-8.
    Pubmed KoreaMed CrossRef
  19. Stenson PD, Mort M, Ball EV, Chapman M, Evans K, Azevedo L, et al. The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting. Hum Genet 2020;139:1197-207.
    Pubmed KoreaMed CrossRef
  20. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods 2010;7:248-9.
    Pubmed KoreaMed CrossRef
  21. Li B, Krishnan VG, Mort ME, Xin F, Kamati KK, Cooper DN, et al. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 2009;25:2744-50.
    Pubmed KoreaMed CrossRef
  22. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009;4:1073-81.
    Pubmed CrossRef
  23. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels. PLoS One 2012;7:e46688.
    Pubmed KoreaMed CrossRef
  24. Carter H, Douville C, Stenson PD, Cooper DN, Karchin R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 2013;14(S3):S3.
    Pubmed KoreaMed CrossRef
  25. Chun S and Fay JC. Identification of deleterious mutations within three human genomes. Genome Res 2009;19:1553-61.
    Pubmed KoreaMed CrossRef
  26. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 2011;39:e118.
    Pubmed KoreaMed CrossRef
  27. Schwarz JM, Rödelsperger C, Schuelke M, Seelow D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods 2010;7:575-6.
    Pubmed CrossRef
  28. Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GL, Edwards KJ, et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat 2013;34:57-65.
    Pubmed KoreaMed CrossRef
  29. Cooper GM, Stone EA, Asimenos G, Green ED, Batzoglou S, et al; NISC Comparative Sequencing Program. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res 2005;15:901-13.
    Pubmed KoreaMed CrossRef
  30. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 2005;15:1034-50.
    Pubmed KoreaMed CrossRef
  31. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 2020;581:434-43.
    Pubmed KoreaMed CrossRef
  32. Karczewski KJ, Weisburd B, Thomas B, Solomonson M, Ruderfer DM, Kavanagh D, et al. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res 2017;45:D840-5.
    Pubmed KoreaMed CrossRef
  33. Jung KS, Hong KW, Jo HY, Choi J, Ban HJ, Cho SB, et al. KRGDB: the large-scale variant database of 1722 Koreans based on whole genome sequencing. Database (Oxford) 2020;2020:baz146.
    Pubmed KoreaMed CrossRef
  34. Lee S, Seo J, Park J, Nam JY, Choi A, Ignatius JS, et al. Korean Variant Archive (KOVA): a reference database of genetic variations in the Korean population. Sci Rep 2017;7:4287.
    Pubmed KoreaMed CrossRef
  35. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012;28:3150-2.
    Pubmed KoreaMed CrossRef
  36. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 2017;30:3146-54.
  37. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 2016;99:877-85.
    Pubmed KoreaMed CrossRef
  38. Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res 2019;47:D886-94.
    Pubmed KoreaMed CrossRef
  39. Navarrete R, Leal F, Vega AI, Morais-López A, Garcia-Silva MT, Martín-Hernández E, et al. Value of genetic analysis for confirming inborn errors of metabolism detected through the Spanish neonatal screening program. Eur J Hum Genet 2019;27:556-62.
    Pubmed KoreaMed CrossRef