The utility and advantages of exome and genome sequencing in pediatric kidney disease diagnosis

Abstract

Pediatric kidney diseases encompass a diverse group of disorders, ranging from congenital anomalies to glomerulopathies. Unlike adults, where lifestyle factors predominate, approximately 30% of pediatric cases constitute inherited kidney diseases. This high heritability underscores the critical importance of early and accurate genetic testing. While targeted gene panels have historically been the first-line diagnostic tool, they are limited by fixed gene content and an inability to detect complex structural variants. Next-generation sequencing, specifically exome sequencing and genome sequencing, has transformed the diagnostic landscape by enabling an unbiased, simultaneous analysis of all disease-associated genes. These modalities enhance diagnostic yields by identifying structural variants including copy number variants and non-coding variants often missed by conventional panels. Furthermore, they allow for the discovery of novel disease genes through periodic reanalysis. Despite these advancements, a significant diagnostic gap persists due to the limitations of short-read sequencing in complex genomic regions and the difficulty of interpreting variants of uncertain significance. To address these challenges, integrating long-read sequencing to resolve complex genomic regions and implementing systematic reanalysis protocols are recommended. This review provides a comprehensive comparison of diagnostic yields across testing modalities and discusses the future of genomic profiling in pediatric kidney diseases.

Introduction

Pediatric kidney disease represents a distinctive clinical entity, differing fundamentally from adult-onset kidney disease in both etiology and progression. While adult kidney failure is largely driven by acquired lifestyle factors such as diabetes and hypertension, pediatric cases are predominantly rooted in congenital and hereditary factors. These conditions span a broad spectrum, from congenital anomalies of the kidney and urinary tract (CAKUT) to complex glomerulopathies. Notably, genetic etiologies account for approximately 30% of all pediatric kidney disease cases [1]. Therefore, establishing a molecular diagnosis is critical for these patients to guide clinical management, monitor extra-kidney manifestations, and provide accurate family counseling, regardless of the stage of kidney impairment (Table 1) [2-5].

Table 1.

Clinical red flags for genetic testing in pediatric kidney diseases

Category	Clinical red flags (indications for genetic testing)	Reference
Age of onset	Early-onset disease (infancy to childhood, typically <19 years)	[2,3]
Family history	Multigenerational family history of kidney disease	[4,5]
	Consanguineous families
Clinical feature	Clinical suspicion of chronic kidney disease via renal ultrasound with ambiguous etiology	[4,5]
	Congenital anomalies of the kidney and urinary tract
	Extrarenal syndromic features (e.g., hearing loss, ocular abnormalities, polydactyly, developmental delay, or hepatic fibrosis)
Diagnostic uncertainty	Nephropathy of unknown origin or requirement for specific subtyping within broad clinical categories	[2,4]

With recent advancements in medical technology, such as dialysis and transplantation, markedly improving the prognosis of patients with pediatric kidney disease, the importance of early diagnosis has become increasingly emphasized [1]. While precise epidemiological data for the entire spectrum of pediatric kidney disease are limited due to variable definitions and disease heterogeneity, prevalence rates for its major subcategories provide insight into the disease burden. CAKUT represents the most common cause of pediatric kidney disease, occurring in approximately 3 to 6 per 1,000 live births [6]. Similarly, the incidence of nephrotic syndrome, a common glomerular manifestation of pediatric kidney disease, is reported at approximately 2–7 cases per 100,000 children annually [7]. Regarding severe forms of pediatric kidney disease progressing to failure, data from developed regions such as the United States and Europe indicate that the prevalence of kidney failure ranges between 55 and 82 per million age-related population and is showing an increasing trend attributed to improved patient survival on kidney replacement therapies [8,9]. Notably, significant regional disparities exist; studies suggest higher prevalence rates in populations with high consanguinity rates, underscoring the strong influence of genetic background on the epidemiology of pediatric kidney disease [8].

Despite the clear genetic basis, the high phenotypic overlap often subjects patients to a prolonged “diagnostic odyssey” characterized by serial, non-specific testing. A single clinical presentation, such as steroid-resistant nephrotic syndrome (SRNS), can be caused by mutations in over 50 different genes [10]. Consequently, establishing a definitive molecular diagnosis is clinically essential as it enables precision medicine by identifying patients who will not respond to immunosuppressive therapy, thereby sparing them from unnecessary toxicity and invasive procedures like kidney biopsies. Furthermore, a confirmed genetic diagnosis facilitates the monitoring of subtle extra-kidney manifestations in syndromic cases and provides accurate recurrence risk assessments for family planning, which is critical given that approximately 25% of patients report a positive family history [9,11].

Traditional diagnostic approaches that rely on phenotype-driven testing (e.g., single-gene Sanger sequencing or limited gene panels) often fail to capture this complexity, leaving many patients in a prolonged “diagnostic odyssey” [12]. As the list of causative genes continues to expand, selecting the “correct” gene to test based solely on clinical symptoms has become increasingly challenging. To overcome these limitations, the advent of next-generation sequencing (NGS) has shifted the paradigm from phenotype-driven diagnosis to genotype-driven medicine. In this review, we examine the utility of comprehensive NGS methodologies, specifically exome sequencing (ES) and genome sequencing (GS), and discuss how emerging technologies like long-read sequencing (LRS) and periodic reanalysis strategies are closing the diagnostic gap for previously unsolved cases (Fig. 1).

Fig. 1.

Genetic diagnosis algorithm for pediatric kidney disease. This flowchart outlines a stepwise diagnostic approach for pediatric patients with suspected genetic kidney diseases. Based on the initial clinical evaluation, testing begins with chromosomal microarray or targeted gene panels for specific suspected genes, whereas ES/GS is preferred for genetically heterogeneous conditions. If ES/GS yields negative or inconclusive results, subsequent data reanalysis or long-read sequencing is recommended to achieve a confirmed diagnosis, which ultimately guides clinical management and genetic counseling. ES, exome sequencing; GS, genome sequencing.

Etiology and genetic landscape of pediatric kidney disease

Pediatric kidney disease is characterized by exceptional genetic heterogeneity, with etiologies ranging from single-gene Mendelian disorders to complex genomic rearrangements. To date, approximately 600 genes have been identified as causative for kidney and urological diseases, a number that continues to expand with the application of broad genomic profiling [13]. This vast genetic landscape necessitates a categorization based not only on clinical phenotype but also on the underlying molecular mechanism, which heavily influences diagnostic strategy.

The major categories of genetic pediatric kidney disease include CAKUT, cystic kidney diseases, glomerular diseases, and tubulointerstitial disorders. Among these, CAKUT is the most predominant, accounting for 30% to nearly 50% of cases in large registries such as the North American Pediatric Renal Trials and Collaborative Studies (NAPRTCS) and the European Society for Paediatric Nephrology/European Renal Association (ESPN/ERA) (Table 2) [14,15]. Unlike other categories often driven by single-nucleotide variants (SNVs), the genetic basis of CAKUT is significantly attributed to copy number variations (CNVs). Recent studies indicate that pathogenic CNVs are identified in a substantial proportion of CAKUT patients, with six specific genomic loci—1q12.1, 4p16.1–16.3, 16p11.2, 16p13.11, 17q12, and 22q11.2—accounting for approximately 65% of all genomic disorders within this category [1]. This high prevalence of structural variants (SVs) highlights a critical diagnostic nuance: traditional targeted panels focusing solely on SNVs may fail to detect these large deletions or duplications. While chromosomal microarray (CMA) addresses this gap, it lacks resolution for single-gene disorders, whereas GS offers a comprehensive all-in-one solution (Table 3) [1,16,17].

Table 2.

Summary of major genetic causes of inherited kidney diseases and their relative frequency

Clinical diagnosis	Related genomic regions or genes	Frequency (%)
		NAPRTCS [14]	ESPN/ERA [15]
Congenital anomalies of the kidney and urinary tract	1q12.1, 4p16.1-16.3, 16p11.2, 16p13.11, 17q12, 22q11.2	49.2	30.4
- Kidney dysplasia/aplasia	ACE, AGT, AGTR1, BICC1, BMP4, BMP7, CDC5L, CDKN1C, CHD1L, CHD7, CHRM3, EYA1, FGFR1, FGFR2, GATA3, GDF11, GDNF, GFRA1, GLI3, GPC3, GREM1, HNF1B, HPSE2, JAG1, KAL1, NOTCH2, OFD1, PAX2, PAX8, RET, ROBO2, SALL1, SIX1, SIX5, SOX17, SOX9, UPK3A, WNT4, WT1
- Hydronephrosis
- Vesicoureteral reflux
- Posterior urethral valves
Cystic kidney disease	PKD1, PKD2, GANAB, DNAJB11, PKHD1, DZIP1L	4	13.3
- Autosomal dominant polycystic kidney disease
- Autosomal recessive polycystic kidney disease
Tubulointerstitial kidney disease	AQP2, ATP6V0A4, ATP6V1B1, AVPR2, BSND, CLCNKA, CLCNKB, CTNS, HNF1B, KCNJ1, MAGED2, MUC1, REN, SEC61A1, SLC12A1, SLC12A3, SLC4A1, UMOD	4.3	5.1
- Bartter and Gitelman syndrome
- Nephrogenic diabetes insipidus
- Renal tubular acidosis
- Cystinosis
Glomerular disease	Non-syndromic: PLCE1, KANK2, KANK4, TRPC6, NPHS1, NPHS2, CD2AP, MYO1E, MAGI2, XPO5, NUP85, NUP93, NUP205, NUP160, PTPRO, MP2, COQ8B, TTC21B, AVIL, INF2, ACTN4, ARHGAP24, ANLN, CRB2, ARHGDIA, FAT1, NXF5, TBC1D8B	18.5	14.1
- Nephrotic syndrome	Syndromic: WT1, PAX2, EYA1, LMX1B, SMARCAL1, LMNA, WDR73, SCARB2, COL4A3, COL4A4, COL4A5, ITGB4, LAMB2, MYH9, CUBN, SGPL1, PODXL, NUP107, NUP133, MT-TL1, MT-TL2, MT-TY, COQ2, COQ6
- Alport syndrome
Nephronophthisis-related ciliopathies	NPHP1-NPHP6, TMEM67, ANKS6, TTC21B, GLIS2, CEP164, INVS, MAPKB1, NEK8, CEP83, WDR19, ZNF423, INPP5E, TMEM216, CC2D2A, AHI1, CEP41, RPGRIP1L, BBS1-BBS12, NPHP6, CEP290, MKS1, SDCCAG8, SEPT7, NPHP1, NPHP4, CEP290, IQCB1, SDCCAG8, WDR19	NR	NR
- Joubert syndrome
- Bardet–Biedl syndrome
- Senior–Løken syndrome
Atypical hemolytic uremic syndrome	C3, CD46 (MCP), CFB, CFH, CFHR1, CFHR3, CFHR4, CFHR5, CFI, DGKE, THBD, VTN	2	5.4
Unknown		22	31.7

NAPRTCS, North American Pediatric Renal Trials and Collaborative Studies; ESPN/ERA, European Society for Paediatric Nephrology/European Renal Association; NR, not reported.

Table 3.

Comparison of genetic testing methods

	Target and resolution (read length)	Variants detected	Limitations
Chromosomal microarray	Genome; ≥30 kb–400 kb	Submicroscopic deletions/duplications	Limited resolution for small SVs; cannot detect SNVs or balanced rearrangements
Targeted gene panel	Specific genes of interest; single base	SNVs/small INDELs (<1 kb)	Unable to identify variants in genes not included in the panel
Exome sequencing	Coding regions (exome); single base (100–300 bp)	SNVs/small INDELs (<1 kb) in exons, CNVs (≥3 consecutive exons)	Limited ability to detect variants in non-coding regions (introns and promoters), small CNVs, and “NGS dead zones”
Genome sequencing	Coding and non-coding regions; single base (100–300 bp)	SNVs/small INDELs (<1 kb), CNVs, SVs	Technical challenges in “NGS dead zones”
Long-read sequencing	Coding and non-coding regions; single base (10 kb–1 Mb)	SNVs/small INDELs (<1 kb), CNVs, SVs, epigenetic modification (CpG methylation)	NA

SVs, structural variants; SNVs, single nucleotide variants; INDELs, insertions/deletions; CNVs, copy number variations; NGS, next-generation sequencing; CpG, cytosine-phosphate-guanine; NA, not available.

Glomerular diseases, including SRNS and focal segmental glomerulosclerosis, represent the second most common category (14.1%–18.5%) [14,15]. This group exhibits profound locus heterogeneity, where phenotypes are often indistinguishable clinically but arise from distinct genetic defects in podocyte-associated genes (e.g., NPHS1, NPHS2, WT1) or collagen genes (COL4A3/4/5). Similarly, cystic kidney diseases (e.g., autosomal dominant polycystic kidney disease, autosomal recessive polycystic kidney disease) are linked to defects in primary cilia function. Notably, genes such as PKD1 involve complex genomic architectures that present unique sequencing challenges, often creating “blind spots” for conventional short-read sequencing (SRS) methods. Understanding this complex genetic landscape, where a single phenotype can be caused by hundreds of genes (genetic heterogeneity) and a single gene can present with diverse phenotypes (pleiotropy), is vital. It underscores why a comprehensive, genotype-driven approach is often superior to limited phenotype-driven testing in the pediatric setting.

Diagnostic yields: from targeted panels to genomic sequencing

Targeted gene panel testing

NGS-based testing can be broadly classified into targeted gene panels, ES, and GS. A targeted gene panel is a method that covers a selected set of genes that are considered to be associated with a specific phenotype (e.g., Alport syndrome, nephrotic syndrome). Historically, this testing has been favored over ES/GS because of relatively lower sequencing costs, shorter turnaround time, and a low rate of unspecific or incidental findings [18]. Numerous large-scale studies have evaluated the diagnostic yield of panel testing in pediatric kidney disease (Table 4) [19-23]. A study by Mallett et al. [19] analyzed 135 families using 10 curated multigene panels and observed the highest diagnostic yield in Alport syndrome (88%). Similarly, Tanudisastro et al. [20] reported a 100% yield for cystinosis and 64% for Alport syndrome using targeted panels. However, the limitations of panel testing become evident in genetically heterogeneous conditions. In both studies mentioned above, CAKUT exhibited markedly low yields of 9% and 12%, respectively. A primary limitation is the panel's inability to detect CNVs effectively. For instance, Mallett et al. [19] noted that although multiplex ligation-dependent probe amplification was performed, it was restricted to only eight genes, which likely contributed to the low overall diagnostic rate. Furthermore, fixed gene lists mean that reanalysis cannot detect variants in newly discovered genes without re-sequencing the patient.

Table 4.

Comparative diagnostic yields of genetic testing in pediatric kidney disease

Clinical diagnosis	Gene panel (Mallett et al. [19])	Gene panel (Tanudisastro et al. [20])	CMA and ES (Bekheirnia et al. [21])	ES and trio-ES (Rao et al. [22])	ES (Vaisitti et al. [23]
Congenital anomalies of kidney and urinary tract	9	12	42	17	21
- Kidney dysplasia/aplasia
- Hydronephrosis
- Vesicoureteral reflux
- Posterior urethral valves
Cystic kidney disease	0	25	79	61	NR
- Autosomal dominant polycystic kidney disease
- Autosomal recessive polycystic kidney disease
Tubulointerstitial kidney disease	86	50 (cystinosis 100)	NR	62	45
- Bartter and Gitelman syndrome
- Nephrogenic diabetes insipidus
- Renal tubular acidosis
- Cystinosis
Glomerular disease	35	31	NR	32	24
- Nephrotic syndrome
Nephronophthisis-related ciliopathies	36	30	NR	32	74
- Joubert syndrome
- Bardet–Biedl syndrome
- Senior–Løken syndrome
Alport syndrome	88	64	NR	55	NR
Atypical hemolytic uremic syndrome	67	23	NR	NR	NR
Unknown	NR	NR	Proteinuria 70, hematuria 67	43	NR
Total	46	35	51	42	37

Data are presented as %.

CMA, chromosomal microarray; ES, exome sequencing; NR, not reported.

ES and GS

In contrast to panels, ES targets the protein-coding regions of the genome (exons), which comprise less than 2% of the human genome but contain approximately 85% of known pathogenic variants [24]. GS covers nearly all three billion base pairs of the genome, including non-coding regions. Recent studies demonstrate that comprehensive approaches significantly outperform targeted panels. A study by Bekheirnia et al. [21] utilized a stepwise approach including CMA and ES, achieving a high overall diagnostic yield of 51%. Notably, the yield for CAKUT increased to 42%, significantly higher than the approximately 10% reported in panel studies. This suggests that incorporating broad testing like CMA and ES is critical for increasing the yield in complex categories. Similarly, Rao et al. [22] achieved a 42% diagnostic yield using ES in a large Chinese cohort. Crucially, the diagnostic yield increased when trio-ES (sequencing the patient and both parents) was employed compared to singleton testing. Trio testing clarifies the clinical significance of variants, particularly by identifying de novo mutations, raising the diagnostic rate from 40.8% to 44.8% in their cohort.

Advantages of comprehensive genomic profiling

While targeted gene panels have been a traditional first-line test, ES offers significant advantages by eliminating the risks associated with panel selection. A primary benefit of ES is its comprehensive coverage of the protein-coding regions of approximately 20,000 genes, eliminating the need for physicians to select a specific disease panel (Table 3). This is particularly valuable in pediatric nephrology, where phenotypic overlap between conditions can make selecting the correct panel challenging. For instance, genes such as COL4A3, COL4A4, and COL4A5 are classically associated with Alport syndrome but are also frequently implicated in cases clinically diagnosed as SRNS or focal segmental glomerulosclerosis. A clinician ordering a specific “Nephrotic Syndrome” panel might miss these variants if the panel design is strictly limited to podocyte-related genes, whereas ES captures the full spectrum of glomerular, tubular, and ciliopathy genes simultaneously, ensuring a diagnosis is not missed due to clinical ambiguity.

ES can be superior to panels when "isolated" kidney symptoms can mask an underlying syndromic disorder. For instance, a clinician evaluating a child with apparent isolated SRNS might opt for a targeted “Glomerular Disease” panel to minimize costs. However, this approach can miss syndromic causes if the extra-kidney features are subtle. A notable example is Nail-patella Syndrome, caused by variants in LMX1B. While LMX1B is included in broad comprehensive kidney panels, it is frequently omitted from narrower, phenotype-specific SRNS panels, or clinicians may inadvertently select a restricted panel that excludes syndromic genes. In a study by Landini et al. [25], ES identified LMX1B variants in patients initially diagnosed with isolated kidney disease; this finding prompted a “reverse phenotyping” re-examination, which revealed subtle nail dysplasia that had been overlooked during the initial physical assessment. By capturing the full spectrum of disease genes simultaneously, ES ensures that such diagnoses are not missed due to the limitations of a focused panel design or the challenge of recognizing mild syndromic presentations. Furthermore, ES enables “future-proofing” through reanalysis; as new gene-disease associations are discovered, existing ES data can be re-interrogated without the need for re-sequencing, a capability that targeted panels lack. Additionally, while panels often require separate testing for CNVs, ES allows for the detection of CNVs, provided they span at least three consecutive exons.

Despite the advantages of ES, it has limitations that GS can overcome. ES relies on capture probes to target coding regions, which can result in variable coverage and reduced sensitivity for CNVs smaller than three exons. GS, in contrast, covers the entire genome including non-coding regions (introns and promoters) and provides uniform coverage free from capture bias. This allows GS to detect variants in regulatory regions and identify complex SVs and small CNVs with much higher resolution than ES (Table 3) [1,18,26]. A compelling example of this utility is reported in a case of a 6-year-old girl with SRNS [27]. Initial targeted testing identified only a single heterozygous pathogenic variant in NPHS2, which causes autosomal recessive SRNS. Because a second variant was not found, the diagnosis remained unconfirmed. Subsequent whole genome sequencing (WGS) identified a deep intronic variant (c.794+851T>G) that created a cryptic splice site, leading to the inclusion of a pseudo-exon. This variant was located deep within an intron—a region completely ignored by standard ES and panels. This case illustrates that while ES is a powerful tool, GS is essential for detecting deep intronic variants, small deletions, and structural anomalies that lie beyond the reach of exome capture.

Addressing the undiagnosed: limitations and future directions

Despite the power of ES/GS, approximately two-thirds of patients remain undiagnosed. The diagnostic yield of ES for rare diseases generally plateaus at 30%–35%, increasing by about 10% when WGS is performed [28,29]. Several factors contribute to this "missing heritability," ranging from the interpretative challenges of variants of uncertain significance (VUS) to the existence of undiscovered disease genes and technological blind spots in "dead zones."

Navigating uncertainty: the challenge of VUS

One of the most substantial hurdles in the diagnostic workflow is the interpretation of VUS. A VUS result leaves both the clinician and the family in a state of ambiguity, as there is insufficient evidence to classify the variant as clearly pathogenic or benign. In such cases, genetic counseling is paramount; clinicians must explain that a VUS is not a definitive diagnosis and should not be used as the sole basis for irreversible medical decisions. The immediate “next step” involves resolving this uncertainty through segregation analysis, testing parents and affected siblings to see if the variant tracks with the disease (suggesting pathogenicity) or appears de novo. Furthermore, “reverse phenotyping” is essential, where the clinician re-evaluates the patient for subtle, previously overlooked features associated with the specific gene in question.

The value of periodic reanalysis: reclassification and novel discovery

Genetic diagnosis is not a static event but a dynamic process. A “negative” or “inconclusive” result today may become a definitive diagnosis tomorrow as scientific knowledge accumulates. Consequently, periodic reanalysis of existing sequencing data has emerged as a cost-effective strategy to improve diagnostic yield without the need for generating new sequencing data. Studies indicate that reanalyzing exome data every 1–3 years yields a new diagnosis in approximately 10% to 15% of previously undiagnosed cases [30]. This diagnostic gain is primarily driven by two mechanisms: the reclassification of variants and the discovery of novel disease genes.

First, as large-scale population databases like gnomAD and ClinVar expand, the interpretation of specific variants evolves. Many variants previously labeled as VUS due to insufficient data can be reclassified as benign (if found frequently in healthy populations) or pathogenic (if accumulated evidence supports disease causality). This continuous refinement allows clinicians to provide definitive answers to families who were previously left in ambiguity.

Second, reanalysis allows for the identification of causative variants in novel disease genes, genes that were not associated with human disease at the time of the initial test. Standard clinical pipelines typically filter analysis to known disease-associated genes (e.g., OMIM genes) to focus on actionable findings. However, for undiagnosed patients, expanding the analysis to the "research exome" is crucial. This process often relies on international collaboration through matchmaking platforms such as GeneMatcher, which connect clinicians and researchers worldwide who have identified candidates with variants in the same novel gene. By pooling patients with similar phenotypes, researchers can provide the statistical evidence needed to establish a new gene-disease association. This research-driven approach continuously feeds back into clinical practice, expanding the list of testable genes for future patients.

Overcoming NGS dead zones with LRS

Beyond interpretative challenges, technical limitations also hinder diagnosis. The vast majority of current clinical sequencing utilizes SRS technology, which produces reads of 150–300 base pairs. SRS has inherent difficulties in analyzing complex genomic regions, often referred to as "NGS dead zones" [17]. These are areas characterized by complex repetitive sequences or regions of high homology with pseudogenes. A prime example in nephrology is the PKD1 gene, responsible for autosomal dominant polycystic kidney disease. PKD1 shares high sequence homology with six pseudogenes located on the same chromosome, making it challenging for short reads to be mapped uniquely. This ambiguity often leads to sequencing gaps, leaving potentially causative variants undetected [31].

To overcome these limitations, LRS is emerging as a powerful diagnostic tool. LRS sequences original DNA fragments ranging from 10 kb up to megabases in size. This extended read length enables the unambiguous mapping of reads in repetitive or homologous regions, effectively resolving the "dead zones" [17]. Beyond resolving homology, LRS facilitates phasing—determining whether two variants are on the same (cis) or opposite (trans) chromosomes, which is critical for autosomal recessive conditions [32]. Furthermore, LRS can reliably detect complex SVs and epigenetic modifications like CpG methylation (Table 3) [33]. Recent studies have demonstrated an added diagnostic yield of 7%–17% when LRS is applied to rare disease cohorts [34,35].

Multi-omics integration

Finally, the integration of multi-omics, particularly transcriptome sequencing (RNA-seq), enables the functional validation of genomic findings. RNA-seq can detect splicing defects or aberrant gene expression caused by deep intronic variants that GS may identify but cannot functionally characterize. Although currently limited by tissue accessibility, RNA-seq has demonstrated an incremental diagnostic yield of 7.5% to 18% in unsolved rare disease cohorts [36-38].

Conclusions

The integration of genomic medicine has redefined the diagnostic standard for pediatric kidney disease. The paradigm shift from phenotype-driven targeted panels to genotype-driven comprehensive profiling—specifically ES/GS—addresses the exceptional genetic heterogeneity of pediatric kidney disease. By overcoming the limitations of fixed gene lists and enabling the detection of complex variants such as CNVs, these technologies provide a robust platform for both current diagnosis and future reanalysis. Emerging tools like LRS further complement this workflow, promising to resolve remaining technical blind spots. The value of a definitive molecular diagnosis extends beyond the diagnosis itself. It directly alters clinical decision-making by preventing unnecessary immunosuppression, guiding transplant donor selection, and enabling accurate risk assessment for family planning. Moreover, a genetic diagnosis facilitates a holistic approach to patient care, prompting the evaluation of potential extra-kidney manifestations in syndromic cases that might otherwise be overlooked.

Ultimately, comprehensive genomic profiling should no longer be viewed as a supplementary tool or a last resort. Given the high heritability of pediatric kidney disease and the profound impact of diagnosis on management, we advocate for the early implementation of ES and GS in the clinical workflow. As interpretation capabilities evolve, genomic medicine will continue to close the gap for undiagnosed patients, paving the way for truly personalized therapeutic strategies.

Funding Statement

None.

Data availability statement

Data sharing is not applicable as no new data were created or analyzed in this study.