Abstract
Recent advancements in the next-generation sequencing have illuminated the occurrence of multiple genetic diagnoses (MGD). While exome sequencing has provided insights, genome sequencing (GS), the most comprehensive diagnostic tool, remains underexplored for studying MGD prevalence. We retrospectively analyzed 1487 pediatric cases from our laboratory, employing GS to investigate the incidence of single definitive genetic diagnosis (SDD) and MGD in children suspected of having a genetic disease. Of these patients, 273 received at least one definitive diagnosis, including 245 with SDD (16.5%) and 28 with MGD (1.9%). Diagnostic yield was consistent across genders and unaffected by previous testing in SDD cases. Notably, prior testing significantly increased the diagnostic yield in MGD cases to 2.7% overall and 14.4% among diagnosed cases, compared to 1.1% for those with GS as a first-tier test. Age was a significant factor in diagnostic outcome for both SDD and MGD cases with neonates showing the highest diagnostic yield of 24.5% in SDD and a notably higher yield in MGD at 4.9%, representing 16.7% of the diagnosed cases. Of the 28 MGD cases, 17 exhibited distinct phenotypes, 9 had overlapping features, and 2 presented a mix, underscoring the genetic and phenotypic heterogeneity within this group. This study is the first to exclusively use GS to assess MGD prevalence. Our findings highlight the complexity of rare diseases and emphasize the importance of comprehensive, genome-level diagnostics. Clinicians must ensure that diagnoses fully account for the observed phenotypes to inform optimal therapeutic strategies and management.
1. Introduction
Despite their diverse characteristics and geographically dispersed occurrence, rare diseases collectively exhibit a global prevalence of 3.5%-5.9%. Of these conditions, 70% have a genetic basis and another 70% are associated with pediatric onset [1]. These conditions predominantly manifest as a single genetic disorder, yet in certain cases, clinical features extend beyond the classic symptomatology attributed to the established genetic cause. This discrepancy may signify either an expanded phenotype of the original condition or a distinct, coexisting diagnosis [2]. The discernment between phenotype expansion and comorbidity grows increasingly complex when multiple diagnoses converge into a nuanced clinical presentation [3].
Over the last decade, a substantial influx of data has elucidated the incidence of multiple genetic diagnoses (MGD), largely propelled by advances in the next-generation sequencing (NGS) technologies. NGS permits simultaneous scrutiny of an extensive array of genes, the complete exome, or even the entire genome. According to data derived from exome sequencing (ES), the prevalence of MGD varies between 3.5-7.2% in diagnostic scenarios and 0.4-4.0% in overall cases, with fluctuations based on cohort size and population demographics [4]. Given the technical superiority of genome sequencing (GS) over ES in delivering enhanced diagnostic yield [5], it is plausible that rates of MGD could surpass previous estimates.
In this study, we undertook an exhaustive retrospective analysis of 1487 pediatric cases subjected to clinical diagnostic GS [6]. We aimed to determine the incidence of both isolated and multiple diagnoses within a pediatric cohort with clinical suspicion of a genetic disorder. To our knowledge, this study is the first to assess the prevalence of MGD exclusively using GS data, potentially offering a more accurate snapshot of the true incidence in the clinical setting.
2. Materials and Methods
2.1. Study Design and Participants
A retrospective analysis was performed on consecutive pediatric clinical GS cases referred to our laboratory between March 2018 and September 2022 without imposing specific inclusion or exclusion criteria. A parent or legal guardian of all patients included in this analysis provided written informed consent for research use of deidentified data. All procedures were executed within the framework of Revvity Omics.
2.2. Sequencing Data Generation and Analysis
GS was performed using a PCR-free methodology, as described previously [7, 8]. Briefly, sequence libraries were prepared from isolated genomic DNA using the NEXTFLEX Rapid XP kit (PerkinElmer) and sequenced on the NovaSeq 6000 (Illumina Inc.). Raw data was demultiplexed and converted to FASTQ format using the Illumina bcl2fastq2 converter (Illumina Inc.). Sequences were aligned to the human reference genome GRCh37 (hg19), and variant calling was completed using the Illumina Bio-IT DRAGEN Platform (Illumina Inc.). The average coverage of the nuclear and mitochondrial genomes was 40x and 1000x, respectively. Repeat expansion analysis for 31 disorders (Supplementary Table 1) was carried out using ExpansionHunter (default settings, Illumina Inc.) [8, 9]. Screening for spinal muscular atrophy (SMA) was performed using in-house bioinformatic tools adapted from published literature with modifications [8, 10].
Single nucleotide variants (SNVs) and small insertions and deletions (indels) were annotated by Revvity Omics’ proprietary Ordered Data Interpretation Network (ODIN), which integrates multiple databases, including HGMD, gnomAD, and ClinVar, and tags curated intronic pathogenic variants for analysis. Variant classification was performed based on American College of Medical Genetics and Genomics (ACMG) guidelines [11, 12] with additional triage of intronic and intergenic regions according to clinical information and inheritance patterns. Copy number variant (CNV) analysis was conducted using NxClinical (Bionano), which applies a read-depth strategy, and by utilizing genome maps from DGV, DECIPHER, and ClinGen for assessment [13].
2.3. Clinical GS Testing and Reporting Policy
GS serves as a diagnostic tool for individuals with suspected rare genetic disorders, offering proband-only to trio-based tests with parents. The testing encompasses sequencing of the nuclear and mitochondrial genomes, screening for repeat expansion disorders, and SMA analysis. Variants classified as pathogenic (P), likely pathogenic (LP), or uncertain significance (VUS) concomitant with the clinical phenotype, as provided by the ordering provider, were reported. Screening results for repeat expansion disorders were evaluated and reported if pertinent to the clinical phenotype, following a “rule-out” strategy [8, 14]. SMA analysis was conducted when relevant to the clinical phenotype or when consent for carrier status reporting was obtained. Both screening approaches have been standard for all cases sequenced after November 2021 and were not applied retroactively. In addition to primary diagnostic findings, patients can consent to receive secondary findings, including ACMG-recommended genes [15–18], pharmacogenomic variants (PGx), carrier status, and other diagnostic findings. Reported PGx variants include selected allele haplotypes that have been recommended by the Association for Molecular Pathology pharmacogenomics working group and are classified as Clinical Pharmacogenetics Implementation Consortium (CPIC) level A and PharmGKB level 1A (Supplementary Table 2).
Carrier status is reported for 341 genes associated with autosomal recessive (AR) and X-linked (XL) gene disorders (Supplementary Table 3). Other diagnostic findings include all pathogenic (P or LP) findings in genes associated with diseases that are unrelated to indications for testing. Most diseases in this category manifest as reduced penetrance or late onset.
2.4. Diagnostic Case Category Definition
A definitive diagnosis (DD) refers to any case with a pathogenic finding aligned with the patient’s clinical phenotype. Such cases are further delineated into three categories for analytical clarification: (1) single definitive diagnosis (SDD) that describes cases where only a single diagnostic finding was identified; (2) definitive multiple genetic diagnosis (DMGD) that denotes cases with two or more pathogenic findings associated with distinct genetic diseases concordant with the clinical phenotype; and (3) presumed multiple genetic diagnosis (PMGD) that describes cases with a pathogenic finding and a VUS associated with distinct genetic diseases that aligned with the clinical presentation. Cases categorized as DMGD and PMGD were collectively evaluated as MGD.
2.5. Statistical Analysis
Statistical analysis was performed to assess the distribution and significance of various demographic and clinical variables. Fisher’s exact test was performed to evaluate the significance of differences in diagnostic yield between sex and history of previous testing. The chi-square test was performed to evaluate the significance of differences in diagnostic yield across age groups. The statistical significance was set at , two-tailed. Tests were executed using GraphPad Prism 10.1.0, and results were reported with 95% confidence intervals where applicable.
3. Results
3.1. Cohort Characteristics
The cohort of 1487 pediatric patients included 647 females (43.5%) and 840 males (56.5%). The age distribution ranged from neonates to adolescents, with the largest group being children aged 3-11 years (44.2%, 657/1487). The case categories were almost evenly split between nontrio (47.8%, 711/1487) and trio (52.2%, 776/1487) setups. Prior genetic testing was conducted for 694 patients (46.7%), while 793 patients (53.3%) had GS as a first-tier test. The distribution of demographic information across various age groups is detailed in Table 1.
3.2. Overall Diagnostic Rate for SDD and MGD
A definitive diagnosis was made in 273 of 1487 patients, corresponding to a diagnostic yield of 18.4%. Of these, 245 were SDD cases, comprising 16.5% (245/1487) of the total cohort and 89.7% (245/273) of the diagnosed cases. The remaining 28 cases were MGD, totaling 1.9% (28/1487) of the cohort and 10.3% (28/273) of the diagnostic cases (Figure 1(a)). The MGD cases included 15 DMGD cases [1.0% (15/1487) overall, 5.5% (15/273) among diagnosed; Table 2 and Supplementary Table 4] and 13 PMGD cases [0.9% (13/1487) overall, 4.8% (13/273) among diagnosed; Table 3 and Supplementary Table 5]. Additionally, 9 cases were identified with at least one definitive diagnostic finding and one secondary finding (Supplementary Table 6).
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3.3. Variables Affecting Diagnostic Yield in SDD and MGD Cases
Diagnostic yield showed no difference between male and female patients in SDD and MGD cases. In SDD cases, the yield was 16.2% (136/840) for males and 16.8% (109/647) for females. For MGD, it was 1.9% for both (Figure 1(b)). Similarly, the diagnostic yield for SDD cases was unrelated to the previous testing: 16.3% (113/694) for those with prior testing and 16.6% (132/793) for those without. Remarkably, a statistically significant disparity emerged in the diagnostic yield among MGD cases: individuals with prior genetic testing showed notably higher diagnostic yield with 2.7% (19/694) overall and 14.4% (19/130) among diagnosed compared to those with GS as a first-tier genetic test (1.1% (9/793) overall and 6.4% (9/143) among diagnosed) (Figure 1(c)). Importantly, 17 of the 19 MGD cases with prior genetic testing failed to receive a complete diagnosis: 8 patients received a partial diagnosis, and testing was nondiagnostic in 9 patients. Subsequent GS was necessary to delineate the remaining, unexplained phenotypes. In 2 cases, GS confirmed prior results, suggesting that further studies like transcriptomic or epigenetic analyses may be needed.
Age at testing also imposed a significant impact on diagnostic yield (Figure 1(d)). Specifically, neonates and infants exhibited the highest diagnostic yields for SDD at 24.6% (15/61) and 24.5% (59/241), respectively. A similar trend was observed for MGD cases with neonates showing a markedly elevated diagnostic yield of 4.9% (3/61), accounting for 16.7% (3/18) of the diagnosed cases. This significantly exceeded the diagnostic yield in all other age groups, which fluctuated between 1.3% and 2.3%.
3.4. Complex Landscape of Definitive Diagnosed Cases: Variant Types, Inheritance Patterns, and Phenotypic Diversity
In SDD cases, 79% of diagnoses were attributed to SNVs or indels. These variants predominantly manifested in autosomal dominant (AD, 43.3%, 106/245) and AR (25.3%, 62/245) genes, while XL genes contributed to 9.8% (24/245) of the cases. CNVs accounted for 16.3% (40/245) of the diagnostic variants and included aneuploidies (5), gains (10), losses (24), and a complex rearrangement. Additionally, three cases were compound heterozygous for an SNV and CNV in an AR gene. SMA and repeat expansion disorder screening each identified 2 positive cases. Mitochondrial variants were identified in 2.4% (6/245) of cases (Figure 2(a)).
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In contrast, the MGD cases were comprised of a more complex genetic landscape. Genetic diagnoses in 46.4% (13/28) of cases were exclusively due to SNVs while 42.9% (12/28) of cases received genetic diagnoses based on a combination of both SNVs and CNVs for each diagnosis. In addition, a minority (3/28, 10.7%) of patients had diagnoses purely based on CNVs (Figures 2(b) and 2(c)). A single mode of inheritance was noted in 42.9% (12/28) of cases, and 57.1% (16/28) of patients showed multiple inheritance types (Figures 2(b) and 2(d)). Most patients had one additional diagnosis (25/28, 89.3%), and a minority had three distinct diagnoses (3/28, 10.7%) (Figure 2(e)). Furthermore, the diversity within this cohort eloquently demonstrates the phenotypic complexity of MGD cases: 17 presented with distinct features of each of their diagnoses, 9 showed traits that overlapped between their diagnoses, and 2 displayed a mix of distinct and overlapping features (Figure 2(f)). This finding underscores the necessity for a comprehensive diagnostic approach to capture the multitude of variation underlying complex genetic conditions.
3.5. Illustrative Case Presentation
Case PKIG00063 was an 11-year-old male with a known diagnosis of trisomy 21 and a marker chromosome originating from 2q11.1 to 2q12.1, initially identified by karyotype and chromosomal microarray. In addition to the classic features of Down syndrome, this individual presented with other unexplained clinical features including intrauterine growth restriction (IUGR), cerebral atrophy, cerebellar and brain stem volume loss, cataract, microphthalmia, hemiparesis, and nephrotic syndrome, including hypertension, proteinuria, electrolyte abnormalities, and renal failure. The unexplained severe and complicated clinical features indicated the possibility of an additional underlying etiology. GS was pursued, and the data not only confirmed the initial diagnosis of trisomy 21 and copy number gain of 2q11.1q12.1 but also identified uniparental isodisomy (UPD) of chromosome 5 and a homozygous likely pathogenic variant in ERCC8 [NM_000082.3:c.613G>C p.(Ala205Pro)]. Homozygous or compound heterozygous pathogenic variants in the ERCC8 gene have been associated with Cockayne syndrome type A (OMIM 216400). Since the ERCC8 gene is located on chromosome 5, we thereby confirmed a diagnosis of an AR condition due to UPD, which explained the severe neurodegeneration and abnormal ocular and renal manifestations presented in this patient. The identification of UPD5 also provides valuable information for understanding the parental reproductive risk regarding AR Cockayne syndrome.
Case PKIG00155 was a 3-year-old female with a history of complex febrile seizures, rhizomelic shortening of the proximal long bones, narrow thorax, lumbar kyphosis, brachydactyly, and macrocephaly. Clinical suspicion included hypochondroplasia/achondroplasia caused by FGFR3-related skeletal dysplasia. Previous karyotype testing and FGFR3 gene sequencing were unremarkable. GS identified a pathogenic variant in COL2A1 [NM_001844.4:c.2257G>A p.(Gly753Ser)] and a pathogenic variant in PCDH19 [NM_001184880.1:c.2571_2572delGC p.?]. Pathogenic COL2A1 variants have been associated with varying skeletal dysplasias, including achondrogenesis and hypochondrogenesis (OMIM 200610) [19], while pathogenic PCDH19 variants have been associated with a female-restricted form of sporadic infantile epileptic encephalopathy (OMIM 300088) [20]. The consolidated findings delineated two distinct genetic etiologies that contributed to this patient’s full clinical phenotype.
Case PKIG02101 was a 7-day-old male that presented on the first day of life with severe hyperammonemia, lactic acidosis, seizures, arrhythmia, respiratory failure, apnea, neonatal jaundice, hyperkalemia, hypomagnesemia, foot deformity, anuria, oliguria, abnormal kidney function, abnormal phosphorus metabolism, abnormal liver function, and hypotension. GS was performed, and two findings associated with submitted clinical manifestations were identified: a pathogenic 600.5 kb contiguous deletion of Xp11.4, including the SRPX, RPGR, OTC, TSPAN7, and MID1IP1 genes, associated with severe ornithine transcarbamylase (OTC) deficiency in males [21, 22] and a likely pathogenic variant in RORB [NM_006914.4:c.286G>T p.(Glu96Ter)], associated with pediatric-onset idiopathic generalized and absence epilepsy. Although routine GS with a turnaround time of about 4 weeks was requested, the results were delivered to the ordering provider in 7 days. These timely results provided a survival opportunity for this critically ill infant, potentially averting severe complications through interventions like liver transplant [23]. Cascade parental studies will support recurrence risk assessment and provide appropriate medical and prophylactic management.
Case PKIG01849 was a 2-month-old male evaluated for hypertonia of the upper extremities; growth restriction, including IUGR and small for gestational age; failure to thrive; and microcephaly with prenatal onset. He had low-set, posteriorly rotated ears and abdominal issues, including poor feeding and gastroesophageal reflux. Other concerns included underdevelopment of the inferior vermis, cryptorchidism, and aortic root dilation. Family history revealed both parents were obligate carriers of phenylketonuria (PKU). Prenatal genetic testing disclosed that the baby carried the PAH [NM_000277.1:c.527G>T p.(Arg176Leu)] and [NM_000277.1:c.898G>T p.(Ala300Ser)] pathogenic variants in trans. However, PKU does not explain all of the clinical features present in this patient. Trio GS identified a de novo KAT6A [NM_006766.3:c.3385C>T p.(Arg1129Ter)] pathogenic variant in addition to the PAH findings. The KAT6A gene, located on chromosome 11, encodes a lysine acetyltransferase that regulates gene transcription and expression. Patients with KAT6A pathogenic variants have Arboleda-Tham syndrome (OMIM 616268), an AD disease characterized by microcephaly, hypertonia, cardiac anomalies, gastrointestinal reflux, feeding difficulties, failure to thrive, and developmental delay [24, 25]. The nature of the KAT6A variant and the clinical presentation of this patient align well with Arboleda-Tham syndrome. The new molecular finding provides an additional, distinct clinical diagnosis and guides medical management and prophylactic strategies, allowing this family to start appropriate treatment in the early stages, which may result in better outcomes.
Case PKIG01041 was a 4-year-old female referred by a genetic clinic for neurological complaints, including partially empty sella, developmental delay, right-sided weakness, and hypotonia. Family history was noncontributory. Trio GS was performed, and two findings were identified. The first was a de novo likely pathogenic variant in CUL3 (NM_003590.4:c.264+1G>C p.?). CUL3 is associated with a neurodevelopmental disorder (OMIM 619239) characterized by autism spectrum disorder, developmental delay, and cognitive developmental impairment with variable severity and age of onset [26–28]. In addition, patients with CUL3 pathogenic variants may have seizures, congenital heart defects, dysmorphic facial features, and abnormal brain imaging [29]. The second finding in this patient was a de novo 22q11.2 duplication. Chromosome 22q11.2 duplication syndrome (OMIM 608363) is a well-documented pathogenic finding with variable expressivity and incomplete penetrance. The clinical features include psychomotor developmental delay, heart defects, velopharyngeal insufficiency, and muscular hypotonia. Both findings in this patient explained the delayed neurodevelopment and provided molecular and medical evidence for her individualized education program (IEP) and the severity of the symptoms. In addition, the 22q11.2 microduplication syndrome diagnosis supports access to a comprehensive cardiac evaluation, which according to the submitted clinical information had not been conducted before GS.
4. Discussion
The inherent difficulty in diagnosing rare genetic disorders is often underestimated due to the synergy of numerous genetic factors and diverse clinical symptoms complicating the diagnostic process [30–34]. To tackle this challenge, an exhaustive and methodological approach is required to uncover their multifaceted genetic basis and consequential clinical implications. We conducted an extensive retrospective analysis of 1487 pediatric cases, making this the first study to explore the prevalence of MGD using GS, the most advanced diagnostic assay currently available.
With a definitive diagnostic yield of 18.4%, we observed that MGD cases constituted 10.3% of the diagnostic cohort and 1.9% of all cases. Although the incidence of MGD in our cohort is higher than previous ES studies on dual diagnoses, this finding may still be under-representative given GS’s superior diagnostic yield. Comparing diagnostic rates between laboratories can be challenging due to differences in sequencing technology, how MGD is defined, variant evaluation protocols, and reporting criteria. Technical advantage of genome sequencing over exome sequencing in improving the resolution of copy number variant detection has been discussed in previous literatures [8, 35, 36]. Specifically, GS in patient PKIG01887 (Table 3 and Supplementary Table 5), whose previous ES was nondiagnostic, identified additional variants in two genes. These variants include a single exon deletion and a missense variant. While we would expect at least the missense variant to be identified by ES, technical differences between ES and GS sample preparation can produce discordant sequencing results. ES requires PCR amplification directed by an extensive probe set, which can introduce amplification bias or allele dropout. The PCR-free simplicity of GS provides the most comprehensive exome via even, consistent coverage of disease-associated intergenic regions of the genome. Moreover, the definition of MGD requires careful consideration. In our analysis, cases with terminal losses and gains suggestive of derivative chromosomes from parental balanced translocations were counted as a single diagnosis. However, both DMGD and PMGD were grouped as MGD. In PMGD cases, a single P/LP variant coexists with a VUS that accounts for additional, distinct phenotypes not explained by the P/LP variant. Although the clinical significance of the variant was uncertain, strong clinical relevance justified inclusion in our diagnostic yield. This approach is supported by the potential for reclassification of the VUS as our understanding of gene-disease relationships evolves. Despite these nuances, the notable prevalence of MGD underscores the importance of multidisciplinary investigation, especially when a confirmed, single genetic diagnosis does not explain the entirety of the clinical presentation. In addition to the MGD cases, 9 cases feature at least one definitive diagnostic and one secondary finding. Although not classified as multiple diagnoses, these cases carry significant clinical and ethical implications, particularly in the areas of long-term healthcare and reproductive planning.
An in-depth review, exemplified by case PKIG00063, enriches our understanding of the complex landscape of pediatric genetic disorders and further highlights the crucial role of GS in deciphering intricate genetic architectures beyond initial impressions. The detection of uniparental isodisomy and the likely pathogenic variant in the ERCC8 gene in addition to the previously identified trisomy 21 and copy number gain of 2q11.1q12.1 highlight the challenges in achieving nuanced diagnostic accuracy, thereby refining the quality of genetic counseling and family planning recommendations. Similarly, in case PKIG01276 (Table 2 and Supplementary Table 4), an initial microarray identified a 17p12 duplication associated with Charcot-Marie-Tooth disease but did not explain the patient’s intellectual disability and global developmental delay. Subsequent GS revealed an additional pathogenic SNV in POGZ, associated with White-Sutton syndrome, and a 15q11.2 deletion overlooked by the initial microarray. Although ACMG guidelines now recommend GS or ES for pediatric patients with congenital anomalies or intellectual disability [37], microarrays persist due to reimbursement challenges. If the 15q11.2 deletion had been initially identified, would the genetic clinic have proceeded with GS that revealed White-Sutton syndrome? These instances are not isolated occurrences. In 19 MGD cases with prior testing, 17 had inconclusive or partial results. Luckily, these cases all exhibited phenotypes that led physicians to explore additional underlying causes. This emphasizes the necessity for a thorough and inclusive genetic testing approach, even with an initial diagnosis. Specifically, clinicians should scrutinize whether initial diagnostic findings comprehensively account for the observed phenotypes to ensure optimal therapeutic strategies and management.
The observed diversity in phenotypic presentation of those with MGD—distinctive in 17, overlapping in 9, and mixed in 2—adds another layer of diagnostic complexity. This heterogeneity directly challenges traditional diagnostic approaches that employ targeted gene panels for phenotype-based hypotheses [2, 4, 38]. For instance, in PKIG00155, karyotyping and FGFR3 gene sequencing returned inconclusive results. Comprehensive GS, on the other hand, revealed crucial variants in COL2A1 and PCDH19 that clarified the patient’s complex clinical picture. Such diagnostic limitations are exacerbated in cases with overlapping or extended phenotypes; clinicians may halt further investigation once a diagnosis is obtained through panel or single-gene testing, potentially resulting in missed diagnoses [3]. Therefore, our findings underscore the imperative for versatile and robust diagnostic methodologies to effectively navigate the intricate landscape of multiple genetic diagnoses.
In alignment with existing research on the clinical utility of GS in neonatal intensive care units, our study emphasizes its effectiveness in diagnosing MGDs. Our data demonstrate an overall diagnostic yield of 30% in infants including 25% in SDD and 5% in MGD. This is consistent with the previous studies on pediatric and acutely ill infants, with a diagnostic yield ranging from 34% to 43% [8, 39–41]. Our data first demonstrated that neonates diagnosed with MGD accounted for a remarkable 4.9% of the total infant cohort and 16.7% of all diagnosed infant cases. Intriguingly, this is substantially higher than in older age groups, where diagnostic yields ranged between 1% and 2% of the corresponding age cohort. This elevated incidence in neonates could be attributable to increased vulnerability to multisystemic genetic disorders [42, 43], stressing the urgent need to broaden the use of GS in neonatal settings for the accurate and timely diagnosis of MGDs.
Of note, we acknowledge certain limitations in this study. Our GS techniques are not optimized for detection of balanced or complex structural variants, which have known pathological implications. Second, in instances where external genetic testing had been previously conducted, we were unable to perform methodological comparisons or ascertain the reasons for discrepancies in findings. Finally, we did not include reanalysis of existing data, even though current literature indicates that such reanalysis could identify additional diagnostic findings [44]. Consequently, the actual MGD yield may exceed the number stated in this study.
5. Conclusions
Our findings and presented case studies extend our understanding of the prevalence of MGD among pediatric patients with suspected Mendelian disorders. They not only illuminate the complex terrain of rare disease but also accentuate the vital role of a comprehensive, genome-level diagnostic approach.
Data Availability
Access to deidentified data that are not provided may be requested via the corresponding author.
Disclosure
This research was conducted as part of the employment responsibilities of the authors at Revvity Omics.
Conflicts of Interest
Drs. Liu, Pan, Colasanto, Collins, and Hedge are salaried employees of Revvity Omics. Drs. Collins and Hedge have stock options from Revvity Omics. Dr. Guo is a former employee of Revvity Omics. No other disclosures were reported.
Acknowledgments
The authors thank all the study participants and their families. This study would not have been possible without their generous contribution.
Supplementary Materials
Table S1: genes/loci associated with known repeat expansion disorders. Table S2: genes included in the pharmacogenetics secondary finding category. Table S3: genes included in the carrier status secondary findings category. Table S4: GS cases with phenotypic features explained by definitive multiple genetic diagnoses. Table S5: GS cases with phenotypic features explained by presumed multiple genetic diagnoses. Table S6: GS cases with a definitive genetic diagnosis and a reportable secondary finding. (Supplementary Materials)