Sequential sequencing reveals the architecture and complexity of genomic variants in patients with Alport syndrome

Domain: biology
Published: 2026-03-23
Editorial outcome: minor_revision

Abstract
Alport Syndrome (AS) is a prevalent inherited kidney disorder mainly caused by mutations in COL4A3, COL4A4, and COL4A5 genes. To elucidate the genetic variants of AS, we implemented a sequential sequencing strategy within a Chinese cohort of 555 patients, comprising whole-exome sequencing (WES) for all participants, followed by whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and nanopore long-read sequencing (NLR-seq) for selected individuals.

We identify 431 distinct variants in 509 (91.7%) patients, with 42.2% being novel. Beyond WES, additional sequencing approaches resolve 23 patients with non-coding, copy number, or structural variants. Notably, non-coding variants account for 16.2% of detected variants and exhibit ethnic-specific mutagenesis patterns. More interestingly, NLR-seq uncovers two novel types of structural variants, namely large insertions in intronic regions and complex duplication-inversion variants. This study provides deeper insights into the genetic architecture of AS and proposes a research paradigm for improving the genetic diagnosis of inherited diseases.

Introduction
Alport Syndrome (AS) is one of the prevalent inherited kidney diseases that often leads to end-stage kidney disease (ESKD) due to the absence of a definitive cure. AS primarily arises from the basement membrane integrity disruption caused by mutations in COL4A3, COL4A4, and COL4A5 genes, responsible for encoding the α3, α4, and α5 chains of type IV collagen, respectively1.

AS is classified into three inheritance patterns: X-linked AS (XLAS) due to hemizygous or heterozygous COL4A5 variants (accounted for 80%–85%), autosomal dominant AS (ADAS) due to heterozygous COL4A3 or COL4A4 variants (~5%), and autosomal recessive AS (ARAS) due to homozygous or compound heterozygous COL4A3 or COL4A4 variants (~15%)1. The distribution of these inheritance patterns has become clearer through next-generation sequencing (NGS). A recent population-based study suggests that ADAS prevalence may be higher than previously recognized. Predicted pathogenic variants in COL4A5 affect approximately one in 2,300 individuals, while predicted pathogenic heterozygous variants in COL4A3 or COL4A4 occur in approximately one in 106 individuals2. Furthermore, digenic inheritance, which involves the concurrent presence of pathogenic variants in two of the three genes, has been progressively identified as a distinctive inheritance mode for AS3,4. Given the relative rarity of digenic cases, the true proportion of affected patients remains to be determined.

During kidney development, the α1α1α2(IV) heterotrimer is abundantly expressed in glomerular capillary bundles but is later replaced by the α3α4α5(IV) trimer, which forms a mature glomerular basement membrane (GBM)5. The α3α4α5(IV) trimer is predominantly distributed in the GBM and basement membranes of the eye, cochlea, and distal renal tubules, while the α5α5α6(IV) trimer is located in Bowman's capsule and epidermal basement membrane (EBM). These distribution patterns result in distinct immunofluorescence staining profiles across different inheritance models. In XLAS, α5(IV) expression in the GBM, Bowman's capsule, and EBM is frequently absent or mosaic. In ADAS and ARAS, the GBM shows absent or discontinuous α3(IV) and α5(IV) expression, but Bowman's capsule and EBM exhibit complete α5(IV) expression5,6. Thus, type IV collagen immunofluorescence patterns are informative for diagnosis and subclassification of AS.

Each α3(IV), α4(IV), and α5(IV) chain has three main domains: a carboxy-terminal non-collagenous domain (NC1), an amino-terminal 7S domain, and an intermediate triple-helical collagenous domain7. The collagenous domain features numerous Glycine (Gly)-Xaa-Yaa triplet repeats, with 21–26 interruptions providing structural flexibility7. The Gly-Xaa-Yaa repeats make Gly substitutions the most common missense variant type in AS8. As Gly is the smallest amino acid, its substitution by any other residue can potentially compromise triple helix formation or stability. Thus, Gly substitutions within the collagenous domain are generally pathogenic8.

Accurate variant characterization in AS patients is critical for prognostic assessment, clinical management, and family genetic counseling4. Genetic testing is thus increasingly integrated into clinical evaluation. Currently, the LOVD database reports 648, 662 and 1,701 distinct variants in COL4A3, COL4A4, and COL4A5, respectively. These variants are categorized by type into single nucleotide variants (SNVs)9,10, short insertions/deletions (INDELs)10,11, and copy number variants (CNVs)12; by genetic model into de novo13 and inherited variants; and by genomic location into coding or non-coding variants14,15. Coding variants account for the majority of reported alterations, comprising approximately 44%–49% missense variants, 21%–35% truncating variants, and 16%–21% rearrangements, whereas non-coding variants represent about 6%–14%16. Nevertheless, a substantial proportion of AS patients remain without a definitive genetic diagnosis17-19.

In clinical practice, targeted panel sequencing or whole-exome sequencing (WES) is commonly used for suspected AS cases due to cost and accessibility. Although these approaches achieve a genetic diagnostic rate of 55%–86%17-19, many variants remain undetected. Compared to WES, whole-genome sequencing (WGS) provides higher analytical validity for non-coding variants, CNVs, and short tandem repeats through uniform genomic coverage20,21. Recent advances have identified additional specific variants like deep intronic variants22, mosaicism23, and hypomorphic variants24 as contributors to AS. Nevertheless, these findings still failed to interpret all genetically unresolved cases.

Structural variants (SVs) in AS are reported less frequently than SNVs and INDELs, with only a few cases documented, like balanced translocation, inversion, and complex deletion-insertion25-27. However, complex genomic alterations may play a role in genetic disorders such as MECP2 duplication syndrome and Apert syndrome28,29. Short-read sequencing limits our ability to capture the full variant spectrum in AS. In contrast, third-generation long-read sequencing excels at detecting variants in high GC regions, low-complexity repeats, mobile elements, and complex rearrangements30. Therefore, a sequential sequencing strategy—especially one incorporating long-read sequencing—holds promise for enhancing the sensitivity and specificity of variant detection.

In this study, we apply a sequential sequencing strategy—comprising WES, WGS, RNA-seq, and nanopore long-read sequencing (NLR-seq)—to a large cohort of 555 Chinese AS patients to generate a panoramic overview of the genetic variant spectrum and its characteristics. This study provides a valuable reference for precise diagnosis and individualized intervention in AS.

Results
AS patient cohort
A total of 555 unrelated patients with kidney biopsy-proven AS were enrolled in this study, comprising 318 males (57.3%) and 237 females (42.7%). Median age at kidney biopsy was 23 (17,32) years. Detailed clinical and pathological data are presented in Table 1.

A sequential sequencing strategy was employed to thoroughly investigate COL4A3, COL4A4, and COL4A5 variants. Firstly, WES was conducted on all 555 patients to identify simple coding variants, flanking non-coding variants, and CNVs, resulting in identification of COL4A3, COL4A4, or COL4A5 variants in 486 patients. Secondly, WGS was performed on 65 of the remaining 69 WES-negative patients to detect CNVs and non-coding variants. Furthermore, bioinformatics analyses were conducted to predict transcriptional alterations using the uniform genomic sequencing data. RNA sequencing (RNA-seq) was performed on 20 prediction-negative patients with available tissue samples to validate transcript changes. Lastly, for the remaining 52 unresolved patients from aforementioned analysis, 13 were randomly selected for NLR-seq to identify SVs (Fig. 1A). Overall, a total of 431 distinct COL4A3, COL4A4, and COL4A5 variants were detected in 509 patients, including 296 males (58.2%) and 213 females (41.8%), resulting in a detection rate of 91.7%.

Within this cohort, 92 patients harbored COL4A3 variants, 98 harbored COL4A4 variants, and 298 harbored COL4A5 variants. Additionally, 21 patients harbored variants in two of the three genes. Inheritance patterns were distributed as follows: XLAS in 58.6% (40.3% male and 18.3% female), ADAS in 20.6%, ARAS in 16.7%, and digenic AS in 4.1%. We compared the clinical characteristics between patients with and without a confirmed genetic diagnosis. In summary, patients with a definitive genetic diagnosis demonstrated a higher prevalence of positive family history and proteinuria, but no significant differences in sex, incidence of ESKD, or extra-renal manifestations (Supplementary Data 1).

Across the three genes, 75.0%–81.6% of variants were coding SNVs/INDELs, 11.4%–19.1% were non-coding variants (including 1.6%–2.3% deep intronic variants in COL4A4 and COL4A5), and 5.7%–9.1% were SVs. Notably, 0.8%– 1.1% of SVs represented novel sub-types identified in this study, including complex SVs and large insertions (Fig. 1B).

To evaluate predicted functional impact, the 431 identified variants were classified into three groups: Group i) 333 (77.3%) small variants in coding regions that potentially affect protein coding; Group ii) 70 (16.2%) small variants in non-coding regions that may affect gene splicing or expression; and Group iii) 28 (6.5%) large variants, specifically SVs, which result in significant gene alterations (Table 2). Notably, 42.2% (182/431) of variants were newly detected in our cohort and absent from both the HGMD Pro31 and ClinVar32 databases. This comprised 127 variants in Group i, 27 in Group ii, and 28 in Group iii. The high proportion of novel variants indicates that a substantial number of variants observed in our AS cohort have not been previously reported.

Identification of small variants in coding regions
We identified 333 distinct small coding variants (Group i) in 415 individuals by WES, of which 127 (38.1%) were newly discovered. The variants were distributed as follows: 71 (21.3%) in COL4A3, 70 (21.0%) in COL4A4, and 192 (57.7%) in COL4A5. Missense variants were the predominant type within Group i (222/333, 66.7%), with over 80% (184/222) being Gly substitutions within the triple-helical collagenous domain. All Gly substitutions were classified as pathogenic or likely pathogenic. The distribution of substituted residues was as follows: arginine (Arg) 30.4%, valine (Val) 18.5%, glutamic acid (Glu) 17.4%, serine (Ser) 15.2%, asparagine (Asp) 13.0%, cysteine (Cys) 3.8%, and alanine (Ala) 1.6%. We also identified 38 non-glycine missense variants, 14 (36.8%) of which were novel. These variants were predominantly localized in the NC1 domain (29/38, 76.3%), with fewer in the collagenous domain (5/38, 13.2%) and 7S domain (4/38, 10.5%). Of these 38 variants, 18 (47.4%) were classified as pathogenic or likely pathogenic, and 20 (52.6%) were high-risk variants of uncertain significance (VUS). Among the non-missense variants, nonsense variants accounted for 9.9% (33/333), synonymous variants for 1.2% (4/333), and small INDELs for 22.2% (74/333). INDELs included 13 in-frame, 56 frameshift predicted to result in protein truncation, and five spanning both exonic and intronic regions (Table 2, Supplementary Data 2). Specifically, bioinformatics predicted four synonymous variants could induce aberrant splicing. One such variant, COL4A3: c.765G>A, had been previously reported to cause exon 13 skipping (p.Gly230_Thr255del)33. We conducted transcriptional validation for the remaining three and confirmed that all caused aberrant splicing (Supplementary Data 3). Consequently, these four synonymous variants were classified as likely pathogenic according to the American College of Medical Genetics (ACMG) criteria34,35.

Of the 333 variants described above, 89 were pathogenic, 222 likely pathogenic, and 22 high-risk VUS. These variants span nearly all exons and structural domains of COL4A3, COL4A4, and COL4A5 genes, and no obvious mutational hotspots were observed (Fig. 2A).

Identification of small variants in non-coding regions
To investigate small non-coding variants (Group ii), we analyzed the flanking introns adjacent to the exons using WES data. Sixty distinct non-coding variants were identified in 68 patients, comprising 43 pathogenic canonical splice sites and 17 unclassified near intronic variants (Fig. 1B, Fig. 2B, Supplementary Data 2). Among the 17 variants, eight have been previously reported to cause aberrant splicing36-41. Transcriptional validation of the remaining nine confirmed that six induced exon skipping, two resulted in exon length alterations by activating new splice donor or acceptor sites. Additionally, the COL4A5 variant c.937-10T>A generated three splicing isoforms: exon 17 skipping, and exon 17 elongation by 92 or 8 bp through activation of alternative acceptor splice sites (Supplementary Data 3).

Subsequently, WGS was employed to interrogate deep intronic regions in 65 of the 69 WES-negative patients. This analysis identified 10 variants (seven novel) in 13 patients. In silico predictions suggested that all 10 could induce aberrant splicing (Supplementary Data 3). Minigene assays were conducted for the seven novel variants to evaluate their splicing effects. Five were confirmed to cause aberrant splicing, four of which resulted in pseudoexon inclusion. The remaining novel variant, COL4A5: c.1032+563T>G, produced two distinct aberrant splicing isoforms: a 78 bp pseudoexon inclusion and exon 19 skipping (Fig. 3A–E). In contrast, COL4A5: c.1032+768T>G and COL4A5: c.3943-409T>C, identified in patients AS514 (female) and AS515 (male), respectively, showed no aberrant splicing and were classified as benign.

Among the WGS unresolved patients with available kidney tissue, 20 underwent RNA-seq to identify splicing alterations or transcriptional aberrations that may have not been captured by WES or WGS. RNA-seq revealed aberrant COL4A5 splicing in two patients, and the variant positions were confirmed by WGS (Fig. 3F–G). Patient AS484 (male) harbored COL4A5: c.277-566A>G, which resulted in retention of a 58 bp pseudoexon in intron 4. Patient AS485 (male) carried COL4A5: c.4822-12T>C, leading to exon 52 skipping. These variants were missed in the initial WGS analysis due to low splicing-prediction scores. Upon WGS reanalysis, patient AS486 (male) was found to carry the same COL4A5: c.4822-12T>C variant as AS485. Both variants were novel and classified as likely pathogenic, resulting in the diagnosis of three additional patients (Supplementary Data 3).

In summary, we identified 70 small non-coding variants across 75 patients, including 24 pathogenic and 46 likely pathogenic variants. They were distributed across COL4A3 (11 variants), COL4A4 (10 variants), and COL4A5 (49 variants) (Table 2). Regarding the intronic positions, the 70 variants comprised 43 canonical splice sites, 21 near intronic variants, and six deep intronic variants. Similar to the coding variants, the non-coding variants were distributed across the gene loci, without evident mutational hotspots (Fig. 2B).

Mutagenesis in Chinese AS genome
To further characterize the exonic variants in AS, we compared our findings with exonic variants from HGMD pro31 and ClinVar databases32. The gene-specific variant distribution in our Chinese cohort was consistent with published datasets (Supplementary Fig. 1A). Within collagenous domains, Gly substitutions were most frequently replaced by Arg, Asp, Glu, Ser, and other residues (Supplementary Fig. 1B). Notably, two variants were relatively recurrent in our cohort: COL4A3: c.4793T>G (p.Leu1598Arg in NC1 domain), found in 3.1% (16/509) of cases, and COL4A4: c.4421C>T (p.Thr1474Met in NC1 domain), found in 2.4% (12/509).

COL4A3: c.4793T>G has been previously reported in a small Japanese ARAS cohort (detection rate is 3/24)10, and is classified as pathogenic/likely pathogenic in ClinVar. In contrast, COL4A4: c.4421C>T was identified in two patients within a cohort of 29 Chinese AS individuals42 and is annotated as VUS in ClinVar. In genome aggregation database (gnomAD), the allele frequencies are 8.475 × 10⁻⁷ in Europeans and 7.575 × 10⁻⁴ in East Asians for COL4A3: c.4793T>G, and 2.881 × 10⁻⁵ in Europeans and 1.047 × 10⁻³ in East Asians for COL4A4: c.4421C>T. Based on our pathogenicity classification, COL4A3: c.4793T>G was classified as likely pathogenic, whereas COL4A4: c.4421C>T was classified as a high-risk VUS.

We curated public chronic kidney disease (CKD)-related genome-wide association study (GWAS) data from the CKD Gen consortium and EMBL-EBI GWAS Catalog (Supplementary Data 4). COL4A3: c.4793T>G is absent from studies of hematuria, urine albumin-to-creatinine ratio (UACR), and other CKD-related traits, supporting its potential ethnic specificity. To evaluate potential founder effects, we performed Identity-by-Descent (IBD) analysis using 776,243 high-quality single nucleotide polymorphisms (SNPs) from chromosome 2. Haplotype blocks were constructed and IBD segment lengths (in centimorgans, cM) were compared between variant carriers and the remaining individuals, both chromosome-wide (Supplementary Fig. 1C) and within variant-flanking regions (Supplementary Fig. 1D). No evidence for a recent founder effect was found.

Linkage disequilibrium (LD) analysis was performed for COL4A3: c.4793T>G with two reported CKD-associated lead SNPs, rs55703767 and rs20028795243,44. Since COL4A3: c.4793T>G is located in a high-recombination region, two nearest proxy SNPs, rs10432551 and rs55761800, were selected for LD assessment (Supplementary Fig. 1E). Weak LD was observed between rs55703767 and each of the two proxy SNPs (R2 = 0.04 for rs10432551 and R2 = 0.02 for rs55761800; Fig. 4A). Owing to the low allele frequency of rs200287952 in Asians, two alternative proxy SNPs, rs4305260 and rs4390760, were used for analysis. They exhibited only moderate LD with rs55761800 (Fig. 4A). Functionally, the genomic region harboring COL4A3: c.4793T>G is enriched for regulatory elements, containing approximately twice the number of DNase I hypersensitive sites (DHSs) compared with randomly permuted protein-coding regions (Supplementary Fig. 1F), as well as multiple annotated enhancers (Supplementary Fig. 1G).

Collectively, these results highlight the etiological diversity of AS and suggest that complex selective forces acting within high-recombination regions contribute to population-specific genetic architectures.

Non-coding variants accounted for more than 10% of AS etiologies in our cohort (Fig 1B). We further investigated their mutagenesis patterns by comparing data from the HGMD pro31 and ClinVar32. We first examined the genomic locations of the three types of non-coding variants. As expected, all canonical splice sites were situated in evolutionarily conserved regions, irrespective of disease ethnicity, indicating strong negative selection. In contrast, near intronic variants displayed a bimodal distribution of phastCons scores in all three datasets, while most deep intronic variants were located in non-conserved regions (Fig. 4B). These findings suggest that the mutational functionalities of near intronic and deep intronic variants are evolutionarily linked to the sequence context.

Given the significant association of non-coding variants with sequence context and conservation, we investigated whether sequence conservation contributed to the mutagenesis. Interestingly, our findings indicate that the evolutionary context is strongly associated with the original nucleotides, yet independent of whether substitutions occur via transitions or transversions. Variants originating from purines tend to be enriched in evolutionarily conserved regions, whereas those derived from pyrimidines tend to be excluded from these regions (Fig. 4C). In conclusion, the ethnic-specific intronic mutagenesis patterns highlight the importance of comprehensive screening and detailed classification of previously undetectable variant types, particularly near intronic and deep intronic variants.

Identifications of CNVs
To identify large genomic variants (Group iii) in AS genome, we performed CNV analysis using WES data. This analysis identified pathogenic CNVs in 18 patients, including 13 copy number losses (CNLs) and five copy number gains (CNGs). CNLs include six hemizygous COL4A5 CNLs in males, one heterozygous COL4A5 CNL in a female, two heterozygous COL4A4 CNLs, two homozygous COL4A3 CNLs, and two large heterozygous CNLs spanning both COL4A3 and COL4A4. CNGs comprise four hemizygous COL4A5 duplications in males, and one heterozygous COL4A5 triplication in a female (Fig. 2C, Supplementary Data 5). All 18 CNVs were validated by high-depth targeted panel sequencing of COL4A3, COL4A4, and COL4A5 genes (Supplementary Data 6).

WGS provides more uniform genome-wide coverage, conferring superior sensitivity for detecting CNVs. Using the WGS data from 65 WES-negative patients, we obtained an average coverage depth exceeding 30X across COL4A3, COL4A4, and COL4A5, and identified three heterozygous CNLs affecting three additional patients. These included one in exons 10–12 of COL4A4 (AS505, female), one in exons 41–42 of COL4A4 (AS506, female), and one in exons 34–36 of COL4A5 (AS507, female) (Fig. 2C, Supplementary Data 5). All three CNLs were validated by high-depth targeted panel sequencing (Supplementary Data 7). As these CNLs spanned two or three exons, they could not be reliably resolved by WES, whereas WGS enabled their detection by capturing both exonic and intronic breakpoint regions (Supplementary Fig. 2).

NLR-seq improves SV detection sensitivity compared to WES and WGS
Considering the limitations of short-read sequencing in detecting SVs and complex rearrangements, we performed NLR-seq in 13 patients who remained undiagnosed after WES, WGS, and RNA-seq. The NLR-seq data were of high quality, yielding an average of 72.9 Gb per sample with a read N50 length exceeding 40.8 kb. Genome-wide coverage averaged 94.4%, with a mean sequencing depth greater than 22X, and an overall mapping rate of 99.8% across all samples (Supplementary Fig. 3). This analysis uncovered distinct SV events in COL4A3, COL4A4, and COL4A5 (Fig. 5A), enabling the genetic diagnosis of six patients. The six SVs comprised two heterozygous deletions, one heterozygous insertion, one hemizygous insertion, and two complex structural variants (CSVs) (Table 2, Supplementary Data 8).

Two heterozygous deletions were identified in COL4A4 (AS508, female) and COL4A5 (AS509, female), respectively. The exact deletion breakpoints were validated by polymerase chain reaction (PCR) and Sanger sequencing. The COL4A4 deletion spanned approximately 1.7 kb, affecting exon 18 (Fig. 5B), while the other COL4A5 deletion was 157 bp, disrupting exon 7 (Supplementary Fig. 4). Utilizing our established SV analysis pipeline, these short-span heterozygous deletions were not simultaneously detected by Lumpy and Delly. In contrast, all long-read SV calling tools consistently identified these events, highlighting the superior sensitivity of NLR-seq for SV detection.

Novel SV types identified by NLR-seq in AS
Notably, four patients harbored two novel SV types in COL4A3, COL4A4, and COL4A5, underscoring the utility of NLR-seq in resolving diagnostically challenging AS cases. These new types of SVs comprised large intronic insertions and CSVs involving duplication-inversion, neither of which has, to our knowledge, been previously described in AS.

In patient AS510 (female), a heterozygous insertion was identified in COL4A5 intron 7. SpliceAI predicted that this insertion would disrupt canonical splice junctions, and minigene assay demonstrated that the insertion led to exon 8 skipping, resulting in an in-frame deletion of p.Gly147_Lys155del. These results support the pathogenicity of this insertion (Supplementary Fig. 5).

In patient AS511 (male), a hemizygous insertion of approximately 2 kb was detected in COL4A5 intron 22 (Fig. 5C). RNA-seq revealed transcriptional disruption characterized by termination at exon 22 and reinitiation at exon 24, with an absence of splice junction reads spanning these regions (Supplementary Fig. 6A). We therefore hypothesized that this insertion functions as a neo-promoter. Sequence feature annotation identified multiple promoter-associated motifs, including TATA/CCAAT boxes, and initiator elements. In addition, the insertion contains a 44 bp tandem (T)n repeat and two LINE-1 repeat subtypes, thereby classifying this SV as a mobile element insertion (MEI). Specifically, a reversed L1HNS repeat is followed by two fragments of L1P1 repeats in the opposite orientation (Supplementary Fig. 6B). Multiple open reading frames and transcription start sites were identified within the insertion.

To further characterize its transcriptional impact, we incorporated the insertion into the corresponding locus of the reference genome and reanalyzed the RNA-seq data. To specifically capture reads originating from this MEI rather than LINE-1 repeats, we retained only those reads that were soft-clipped when mapped to other genomic loci but fully aligned to the inserted sequence as junction reads with biologically plausible intron sizes. Nanopore reads confirmed that the soft-clipped reads were not attributable to additional SVs (Supplementary Fig. 6C). The presence of two distinct pileup peaks within the insertion region suggests bi-directional promoter activity, disrupting the normal COL4A5 transcription (Fig. 5D). A dual-luciferase reporter assay confirmed weak promoter activity in both directions, with stronger activity in the reverse orientation, consistent with RNA-seq data (Supplementary Fig. 6D).

Importantly, the identification of CSVs affecting COL4A3 or COL4A4 in two patients highlights AS genomic complexity and heterogeneity. One heterozygous COL4A3 CSV spanned and disrupted at least three exons in AS512 (male) (Fig. 5E), while the other was located in COL4A4, disrupting the accurate transcription of exon 25 in AS513 (female) (Supplementary Fig. 7). The variant allele frequencies were 0.53 (8/15 reads) and 0.41 (7/17 reads), respectively. Notably, low-complexity sequences were identified at the breakpoint junctions of both CSVs. The breakpoint region in COL4A3 was localized within an intronic AluY element, while the breakpoint region in COL4A4 mapped to intron 24 and overlapped a LINE-1 (L1PA8) element.

Collectively, these findings from four patients with SVs illustrate the potential of NLR-seq in resolving undiagnosed AS patients and identifying novel classes of pathogenic variants.

Genetic variant-clinical manifestation association analysis
Although previous studies have demonstrated that clinical severity in AS is influenced by genetic variants3,45-47, large-scale Chinese cohort studies remain scarce. We therefore leveraged our cohort's genetic data to investigate correlations with patients' clinical characteristics, thereby providing comprehensive genetic and phenotypic profiles of AS.

We first examined associations between variant classes in COL4A3, COL4A4, and COL4A5 and immunofluorescence staining patterns of the α3(IV) and α5(IV) chains in GBM and EBM across different inheritance models. Among 205 XLAS male patients, 197 underwent type IV collagen staining. Of these, 34.5% showed absent α5(IV) chain staining in GBM, 38.6% displayed discontinuous expression, and 26.9% exhibited normal staining patterns. Bowman's capsule abnormalities were seen in 70.4% of patients. The severity of GBM α5(IV) chain deficiency was significantly associated with variant class (P = 9.50 × 10⁻7). More than half of patients carrying severe variants exhibited complete loss of α5(IV) expression, whereas those with mild variants predominantly showed a discontinuous expression pattern. A similar association was observed in EBM (P = 0.03) (Table 3). Among 93 XLAS female patients, 89 underwent type IV collagen staining for GBM: 29.2% exhibited discontinuous expression of α5(IV) chain, 70.8% showed normal expression, and none displayed complete absence. Bowman's capsule abnormalities were observed in 60.2% of patients. Unlike males, no significant association was observed between the α5(IV) staining patterns in GBM or EBM and variant class in females (Table 3). In autosomal AS, ARAS patients exhibited a higher frequency (57.3%) of GBM α3(IV) chain staining abnormalities (either complete absence or discontinuous expression) than those with ADAS (12.0%). Moreover, in ARAS, the severity of the GBM α3(IV) chain deficiency increased with more severe variants, whereas no such trend was observed in ADAS (Supplementary Data 9).

Subsequently, patients were stratified by inheritance pattern and sex. A Bayesian hierarchical regression model with a non-informative prior distribution was employed to evaluate the impacts of inheritance on the progression to ESKD. Based on posterior means (PM), the estimated likelihood of progression to ESKD was ranked as follows: XLAS males (PM: 0.63, 95% credible interval (CI): -0.45–1.65), ARAS (PM: 0.27, 95% CI: -0.83–1.31), digenic AS (PM: 0.09, 95% CI: -1.15–1.25), ADAS (PM: -0.39, 95% CI: -1.55–0.65), and XLAS females (PM: -1.00, 95% CI: -2.33–0.02) (Supplementary Fig. 8). To assess variant classes’ impact on renal outcomes, progression to ESKD was defined as the endpoint for XLAS males and ARAS patients, whereas impaired kidney function (eGFR < 60 mL/min/1.73 m²) was adopted for XLAS females and ADAS patients, who exhibited slower disease progression and fewer ESKD events. Among XLAS males, all non-missense variant classes were associated with increased risk of progression to ESKD versus missense variants (P = 5.25 × 10⁻¹²): non-coding variants (hazard ratio [HR] = 1.81, 95% CI: 1.06–3.09), INDELs (HR = 4.98, 95% CI: 2.89–8.58), SVs (HR = 5.11, 95% CI: 2.52–11.57), and nonsense variants (HR = 9.23, 95% CI: 3.45–24.67). In XLAS females, severe variants exhibited a non-significant trend toward impaired kidney function (P = 0.06). Among ARAS patients, an increasing burden of severe variants was associated with a graded elevation in ESKD risk, although this trend did not reach statistical significance (P = 0.21). No significant differences were observed among ADAS variant groups (P = 0.48) (Supplementary Fig. 9).

Thirdly, variants were categorized by mutated gene, zygosity, and affected protein domain. Due to limited sample sizes, patients with COL4A4 homozygous or compound heterozygous variants were not further stratified by domain. We investigated associations between genotype and extra-renal manifestations, including hearing loss, ocular lesions, and kidney cysts. The COL4A5-collagen-hemi variant exhibited the strongest effect on hearing loss (PM: 0.23, 95% CI: -0.19–0.70). Other severe genotypes, including COL4A4-hom (PM: 0.16, 95% CI: -0.30–0.74), COL4A5-NC1-hemi (PM: 0.06, 95% CI: -0.46–0.64), and COL4A3-collagen-hom (PM: 0.06, 95% CI: -0.50–0.66), demonstrated elevated probabilities of hearing loss (Fig. 6A). In XLAS males, severe variant types, such as SVs (PM: 0.06, 95% CI: -0.53–0.68) and nonsense variants (PM: 0.07, 95% CI: -0.59–0.79), were associated with a more substantial impact on hearing loss compared to missense variants (PM: -0.18, 95% CI: -0.93–0.24) or non-coding variants (PM: -0.14, 95% CI: -0.91–0.30). In contrast, the effects were attenuated in XLAS females. Among ARAS patients, hearing loss probability increased with severe variant number, whereas ADAS showed weaker genotype-phenotype associations (Supplementary Fig. 10). Cox proportional hazards modeling demonstrated that patients with hearing loss were at a significantly increased risk of progression to ESKD (HR = 1.52, 95% CI: 1.09–2.16; P = 0.015). Genotypes associated with more severe auditory phenotypes, particularly COL4A5-collagen-hemi and COL4A5-NC1-hemi, conferred an even higher risk of progression to ESKD (P = 1.6 × 10⁻⁸) (Fig. 6B). In contrast, no significant differences were observed for ocular manifestations (Supplementary Fig. 11).

Finally, using a generalized linear model (GNLM) regression with a Poisson link function, we identified significant associations between the development of kidney cysts and COL4A3-collagen-het variants (P = 2.3 × 10⁻⁴, coefficient = 0.49) as well as COL4A5-collagen-hemi variants (P = 3.3 × 10⁻⁵, coefficient = 0.30) (Fig. 6C).

In summary, hemizygous COL4A5 variants were associated with more severe hearing loss, while heterozygous COL4A3 collagen region variants and hemizygous COL4A5 collagen region variants were linked to kidney cyst development. Moreover, we identified distinct genotype-phenotype profiles across inheritance patterns: variant severity exerted the strongest effect on renal prognosis in XLAS males, showed a graded impact in ARAS, and was less evident in XLAS females and ADAS. Similarly, strong genotype-phenotype associations for hearing loss were observed in XLAS males and ARAS, but were minimal in XLAS females and ADAS.

Methods
Ethics statement
This study received approval from the Jinling Hospital Ethics Committee (2022DZKY-098-01), and informed consent was obtained from all participants and legal guardians. The study design and conduct complied with all relevant regulations regarding the use of human research participants and was conducted in accordance to the criteria set by the Declaration of Helsinki.

Patients
Patients included in this study were diagnosed with AS by kidney biopsy in the National Clinical Research Center of Kidney Diseases at Jinling Hospital from 2003 to 2020. Participants exhibited persistent glomerular hematuria or hematuria accompanied by proteinuria and satisfied the following inclusion criteria: 1) kidney biopsy revealing ultrastructural changes in the GBM, including irregular thickening and thinning, with or without splitting, lamellation or basket weave changes of the lamina densa; or segmental or diffuse thinning of the GBM with splitting, lamellation, or basket weave changes of the lamina densa; and/or 2) abnormal expression of the α3(IV) or α5(IV) chain in the glomerulus, or abnormal expression of the α5(IV) chain in the EBM. A total of 555 unrelated patients with AS who fulfilled these criteria were included in the study.

Clinical and pathological data
Clinical data were retrospectively collected from the medical records system of our center. The demographic and clinical information included sex, age at the time of kidney biopsy, family history, the presence of microscopic hematuria, proteinuria, ESKD, and additional extra-renal manifestations, including ocular lesions and hearing loss. Sex information was determined by genetic data. Pathological data included findings from light microscopy and electron microscopy, as well as immunofluorescence staining for the α3(IV) and α5(IV) chains in the glomerulus and α5(IV) chain in the EBM.

Based on kidney ultrasound results, cystic alterations were categorized into three classifications: 1) normal, indicating no cystic alterations in the kidney; 2) type I, defined as fewer than three cysts in one or both kidneys; 3) type II, characterized by three or more cysts in one or both kidneys.

Short-read exome and genome sequencing
DNA was extracted from cell pellets isolated from peripheral blood using the Chemagic DNA Blood 3k Kit (Perkin Elmer, MA). WES was conducted on the MGISEQ-2000 sequencing platform (BGI, China) following library preparation with the Agilent SureSelect Human All Exon V6 Kit (Agilent, USA), achieving an average read depth of 130X. For WGS, libraries were prepared using the TruePrep DNA Library Prep Kit for MGI (Vazyme, China) and sequenced on the DNBSEQ-T7 sequencing platform (BGI, China), with an average read depth of 30X.

Nanopore long-read sequencing
DNA was extracted from cell pellets isolated from peripheral blood as described above. Nanopore ultra-long DNA libraries were constructed with minor modifications to established protocols64. Briefly, high molecular weight DNA was purified using phenol-chloroform extraction and size-selected with the Circulomics SRE kit (PacBio, USA). Subsequently, 1.5 μg of DNA underwent repair and dA-tailing using the NEBNext Companion Module for Oxford Nanopore Technologies Ligation Sequencing (NEB, USA). Libraries were constructed with the Ligation Sequencing Kit SQK-LSK110 (ONT, UK) and loaded onto the PromethION 24 platform (ONT, UK). The average sequencing depth was 24X and the average N50 length of all libraries exceeded 40 kb.

SNV and INDEL analysis
WGS and WES data were aligned to the reference genome (GRCh38.p14) using BWA (v.0.7.17) following the trimming of adapters and low-quality bases by Trim Galore (v.0.6.10)65. The alignments were subsequently sorted based on genomic coordinates using SAMtools (v.1.15), and PCR or optical duplicates were identified and marked with Picard tools (v.2.27.0)66. SNVs and INDELs were called from the raw short-read WES and WGS data via Genome Analysis Toolkit HaplotypeCaller (v.4.0.4.0), and variant annotation was conducted using ANNOVAR (v.2020-06-07). Variants with minor allele frequencies (MAF) >0.1% were filtered according to the gnomAD and ExAC databases. The pathogenicity of missense variants was predicted in silico using SIFT, CADD, and PolyPhen-267-69. SpliceAI (v.1.3) and dbscSNV (v.1.1) were employed to identify candidate synonymous and non-coding variants that could potentially result in aberrant splicing70,71. Canonical splice sites were defined as variants occurring at the highly conserved +1, +2, -1, and -2 positions at the exon-intron boundary. Near intronic variants were defined as those located 3 to 50 bp from the canonical exon-intron boundary. Deep intronic variants were defined as those located more than 50 bp from the canonical exon-intron boundary71. In the case of synonymous, near intronic and deep intronic variants, follow-up mRNA sequencing was conducted to ascertain pathogenicity.

All detected variants underwent pathogenicity evaluation in accordance with the ACMG guidelines, using the Franklin tool (https://franklin.genoox.com/), followed by a manual review based on the Alport Syndrome-specific ACMG criteria as proposed by the Alport Variant Collaborative Group34,35. For variants initially classified as VUS, a three-tiered classification was applied following the supplementary practice guidelines established by the Association for Clinical Genomic Science (ACGS), which are grounded in the ACMG criteria72. Variants that scored 4–5 points, without any conflicting pathogenic or benign criteria, were designated as "high-risk VUS" and are reported in this study72. Other VUS were not reported unless they satisfied the criteria for high-risk VUS.

CNV analysis
Short-read WES and WGS data were analyzed for CNVs using the CNVkit software package (v.0.9.9), which employs the depth-of-coverage method73. Annotation of the identified CNVs was performed with AnnotSV (v.2.2)74. Following filtration, the detected CNVs were manually inspected using the Integrated Genomics Viewer (IGV) software (v.2.16.2).

SV analysis
SVs from short-read data were identified using Delly (v.1.1.8) and Lumpy (v.0.2.8) after read alignment75,76. For Delly, SVs were filtered based on a quality flag of 'PASS' and required a minimum of four supporting split or discordant reads. For Lumpy, the filtration criteria included a Phred-scaled quality score greater than 100 and at least four supporting split or discordant reads. Only SVs identified by both tools were considered final. Annotation of SVs was conducted using AnnotSV.

The raw squiggle NLR-seq signal data were basecalled to fastq files using Guppy (v.6.3.9). SV analysis was performed as follows: 1) Quality control and alignment. Reads with an average quality score below seven and a length shorter than 100 were excluded prior to alignment. The human reference genome GRCh38 and its corresponding annotation were obtained from GENCODE (v.41). Minimap2 (v.2.24) was employed for sequence alignment using the 'ONT sequence-to-reference mapping' flag77; 2) SV calling. We initially utilized Picky (v.0.2.a)78, which is particularly adept at identifying sensitive duplications. LAST (v.1418) was used for mapping, and Picky heuristically selected the optimal representative alignments79. SVs supported by fewer than two reads were excluded from further analysis. Subsequently, we integrated three SV callers—Sniffles (v.2.0.7), CuteSV (v.2.0.1), and NanoSV (v.1.2.4)—as part of a standard methodology80-82. Only raw SVs marked with a 'PASS' flag with a depth of variant (DV) greater than two, and a breakpoint allele frequency (BAF) >0.1 were retained for downstream analysis. For CSV calling, we employed SVision (v.1.3.8)83, utilizing a pretrained convolutional neural network (CNN) model for the human genome. CSV filtration was conducted following manual recommendations; 3) SV merging. Jasmine (v.1.1.5) was used to obtain non-redundant SV representations and to consolidate SV breakpoints and genetic types84. SV sets from the aforementioned three approaches were initially merged within each patient based on the criteria of max_dist_linear=0.5, max_dist=200, and kd_tree_norm=2. Due to Picky's capability to identify comprehensive types of duplication and SVision's ability to detect CSVs, SV types called by these two algorithms were retained only if corroborated by other SV callers. A SV with breakpoints within 200 bp around the breakpoints from the same type of SV in the normal SV pool, as obtained from the Genome in a Bottle (GIAB) Consortium, was considered a background SV and subsequently excluded from further analysis. Following filtration, inter-sample merging was performed using Jasmine with default parameters; 4) SV annotation. SV candidates were prioritized through multi-level annotations. Gene-based annotation was performed using ANNOVAR, while alternative splice events were predicted using SpliceAI. Pathological annotation of SV candidates was informed by ClinVar, OMIM, and CGD. Population-level phenotypic annotation of SV candidates was collectively supported by DGV, the 1000 Genomes Project, dbVar, and gnomAD.

RNA-seq
RNA was extracted from frozen kidney tissues utilizing the TaKaRa MiniBEST Universal RNA Extraction Kit (TaKaRa, Japan). Subsequent RNA-seq was performed on the Illumina NovaSeq 6000 platform (BENAGEN, China) with a paired-end setting of 150 bp, following library preparation using the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, USA). After sequencing, adapters and low-quality bases were removed using Trim Galore (v.0.6.10). The alignment of the clean reads to the human reference genome GRCh38 was performed using the STAR program (v.2.7.11)85. Quantification of gene and isoform expression levels was achieved with Salmon (v.0.13.1)86. Splice junction read counts were determined using the Regtools (v.1.0.0) and LeafCutter (v.0.2.7) pipelines87. RNA-Seq expression matrices for normal kidney cortex and medulla were sourced from The Genotype-Tissue Expression (GTEx) project (v.2017-06-05_v8).

Minigene splicing assay
To create minigene constructs, we synthesized wild-type and mutant genomic fragments containing the exon proximal to the variant site along with at least 150 bp of the adjacent intronic sequences. These fragments were subsequently inserted into linearized pCAS2 vectors (vector map was presented in Supplementary Fig. 13, with the specific sequence provided in Supplementary Data 11), which had been digested by MluI and BamHI restriction enzymes. The wild-type and mutant minigene expression vectors were transfected into HEK293T cells using jetPRIME (Polyplus, France), following the manufacturer's protocol. After 24 hours, the cells were harvested, and total RNA was extracted using the FastPure Cell/Tissue Total RNA Isolation Kit V2 (Vazyme, China). Reverse transcription of 2 μg of total RNA was then performed with the HiScript II 1st Strand cDNA Synthesis Kit (Vazyme, China). The cDNA products were subsequently amplified via PCR using the primers pCAS2-RT-F (5'-GAATTCGTCCGCTGACGTCG-3') and pCAS2-RT-R (5'-CTGAGAAGGCGTGGTAGAGC-3'). The PCR products were verified through 1.5% agarose gel electrophoresis and Sanger sequencing. Wild-type and mutant genomic fragments, pCAS2 vector, and primers were all produced by GenScript (Nanjing, China).

Validation of variants
For candidate SNVs and INDELs at COL4A3, COL4A4, and COL4A5 loci, verification was conducted using Sanger sequencing. For candidate CNVs, high-depth targeted panel sequencing was employed for COL4A3, COL4A4, and COL4A5 genes, achieving an average sequencing depth >200X. Briefly, 2 μg of genomic DNA, extracted from blood, was used to construct sequencing libraries via a Target Enrichment System (MyGenostics, China). The enriched libraries underwent high-throughput sequencing on the HiSeq2000 platform. After quality control, the sequencing data were analyzed using CNVkit for CNV detection. SV candidates were manually examined using the IGV software to confirm their authenticity as genomic variants. Subsequently, the breakpoints and altered sequences of each SV were validated through PCR and Sanger sequencing. All primers used are listed in Supplementary Data 12.

Annotation of top recurrent variants by public GWAS data
Firstly, we reconstructed haplotype blocks across the AS genomes using Eagle (v.2.4.1), and subsequently performed IBD analysis on chromosome 2 using the Java software package refined-ibd (17Jan20)88,89. Next, we obtained CKD-related public GWAS data from the CKD Gen consortium and EMBL-EBI GWAS Catalog (Supplementary Data 4)90,91. Significant causal variants identified in the public GWAS summary data were converted to the hg38 reference genome, followed by haplotype block analysis incorporating our recurrent variants. Due to the low alternative allele frequencies of these causal variants, we employed proxy SNPs that met the following criteria: 1) common in different populations; 2) MAF greater than 0.01 in the Han Chinese in Beijing (CHB) population; and 3) proximity to the target variants (Supplementary Fig. 1A). For public lead variants with a MAF greater than 0.01 in the CHB population, the variant itself was used directly for LD score calculation. LD scores for SNP pairs within regions of interest were generated using data from the 1000 Genomes Project. The LD score between two SNP pairs was calculated using LDlink92. The UCSC Genome Browser facilitated the visualization of the DHSs and other genomic features. DHSs were sourced from Meuleman et al.93, and candidate cis-regulatory elements (cCREs) of human kidney tissue were obtained from the ENCODE project (https://www.encodeproject.org/). Intronic and exonic variants overlapped with DHS or regulatory elements were identified using BEDTools (v.2.31.1).

Dual-luciferase reporter assay
Firefly luciferase vector (Youbio, China) and renilla luciferase vector (Yeasen, China) were used to construct dual-luciferase reporter plasmids in this assay. The SV40-Fluc, INS_F-Fluc, and INS_R-Fluc reporter plasmids were generated by inserting the SV40 promoter, the forward MEI insertion, and the reverse MEI insertion, respectively, cloned from the patient genome at the 5' end of the firefly luciferase sequence. HEK293T cells were co-transfected with firefly luciferase reporter plasmids and renilla luciferase reporter plasmids at a ratio of 50:1. Subsequently, the transfected cells were lysed and collected 24 hours post-transfection. Luciferase activities were quantified using the Duo-Lite Luciferase Assay System (Vazyme, China) following the manufacturer's instructions and were normalized to renilla luciferase activities.

Genetic variants and clinical manifestations association analysis
Initially, based on different inheritance patterns and sex, we categorized patients into XLAS male, XLAS female, ARAS, and ADAS groups. We then analyzed the correlation between different patient categories and immunofluorescence staining of the GBM and EBM. For XLAS and ADAS patients, variants were classified into two groups based on predicted functional severity. Mild variants were defined as missense variants not occurring at the initiation codon, whereas severe variants included nonsense variants, INDELs, synonymous variants, non-coding variants, SVs, and missense variants involving the initiation codon. For ARAS patients, variant severity was categorized into three groups: Type I, in which both alleles harbor mild variants; Type II, in which one allele carries a mild variant and the other a severe variant; and Type III, in which both alleles carry severe variants. The association between immunostaining patterns and variant types was evaluated using Chi-square test, Yates' continuity correction Chi-square test, or Fisher’s exact test by SPSS (v.27).

Subsequently, we categorized variants according to zygosity and protein domains of the three collagen genes, resulting in 17 genetic types within our cohort. For robust statistical modeling, genetic types with fewer than 10 validated clinical records were excluded when analyzing each clinical trait. To explore the potential association between causal variants and the presence of kidney cysts, we employed GLM regression model to estimate the clinical impact of various genomic alterations. The null hypothesis posited no association between the clinical trait and causal genetic type. The GLM fitting was conducted using a Poisson link function, with the models implemented via the R package glmnet (v.4.1-8).

We employed Bayesian hierarchical regression to assess the effects of various inheritance patterns on kidney prognosis, as well as the influence of different genetic types on hearing and eyesight vision. In the absence of a matched control cohort, we used a non-informative prior, incorporating age as a confounding variable within the regression models. A binomial model served as the link function in the logistic regression analysis. The implementation was conducted using the R packages rstan (v.2.32.6) and brms (v.2.22.0). Additionally, we applied the Cox proportional hazards model and log-rank test to evaluate the risk of hearing loss and genetic variants on the development of ESKD.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Di and colleagues report the results of genetic analysis of an Alport syndrome cohort. As is customary in this field, they used WES as a first-tier test. As expected, WES did identify the causal variant in the majority of cases, but in those that remain undiagnosed they used WGS, which added modestly to the number of diagnosed cases. For a very small number that remained undiagnosed, they performed long-read sequencing, which revealed a few structural changes. They supplemented their work with RNA-seq in a small subset of cases with variants that could impact splicing. Finally, they argue that the variants that are more common in Chinese were introduced by a Chinese-specific DNA context.\nWhile this work adds a number of novel variants to the list of causes of Alport syndrome, it fails to represent a significant advance beyond what has become commonplace in genetic disorders.\n1- The authors acknowledge that WES would have diagnosed >95% of all molecularly confirmed cases of Alport syndrome. The use of WGS in cases that are negative by WES is already standard practice. The identification of novel variants in these studies is expected regardless of the study population and the higher percentage is expected in non-European populations.\n2- The use of long read sequencing in cases that remain negative after short read sequencing is extensively investigated and published in the literature.\n3- Similarly, RNA-seq to confirm non-canonical splicing variants is not only extensively investigated and published, but has also evolved already from a research tool to a clinical diagnostic tool.\n4- The entire section on “mutagenesis” is highly problematic. The authors seem to go to extraordinary lengths to explain the obvious. In every single population, the study of a particular disease will likely reveal novel variants. One need not invoke complex evolutionary and DNA structural factors to explain this unless there is something unusual about these variants, which is clearly not the case here. More importantly, the analysis is highly flawed at times. For instance, how could the authors attribute “recurrent” variants to these complex factors without even confirming that they are not simply founder in nature? There is absolutely no analysis of the surrounding haplotype to address this fundamental prerequisite.\n5- The contribution of splicing variants, CNVs and SVs to the pathogenesis of Alport syndrome is fully consistent with what is already known from the literature.\n6- The “sequential” approach by the authors is contradictory. If they do have a panel specific for Alport syndrome, why did they start with WES especially since they fail throughout the manuscript to even comment on potential hits in genes other than the three known Alport syndrome genes?\n7- The “digenic” inheritance claimed by the authors in nearly 5% of the cohort is mentioned rather casually with no evidence to back it. As the authors know, the field has moved beyond anecdotes of digenicity that littered the literature in the past and there now exist very strict guidelines for claiming digenicity.\n8- The authors should comment on why they used MGI platform for WES/WGS but Illumina platform for RNA-seq and whether their failure to detect some CNVs and SVs by WGS may be related to the known difference between MGI and Illumina.\n9- The claim that some splicing variants, including those proven to exert their pathogenicity through aberrant splicing, impact DHS is extraordinary. The authors seem to confuse the known colocalization of DHS and other classes of regulatory variants (promoters and enhancers) with splicing variants even though the biology of the latter is very distinct from the former.\n10- The claim that this is the first study to attempt using nanopore sequencing in Alport syndrome is false and appears symptomatic of widespread failure by the authors to review the literature adequately in several areas of the manuscript.\nMinor:\n1- Add “respectively” to “COL4A3, COL4A4 and COL4A5 genes, responsible for encoding the α3, α4, and α5 chains of type IV collagen”.\n2- The choice of disorders with significant SVs contribution as examples is strange. In addition, the authors did not show in their work a contribution of SVs beyond what is already expected for most genetic disorders.\n3- “diagnosis approaches” should be “diagnostic approaches”.\n4- This sentence is unclear “As the genetic variants in AS are almost from COL4A3, COL4A4 and COL4A5 genes”.\n5- “variants privately owned by our AS patients” should be “…harbored by…”.\n6- What do authors mean by “chimerically mutated”?\n7- The authors present an extremely complex explanation of the intronic insertion in AS511 claiming the role of a neo-promoter even though RNA analysis clearly indicates very simple skipping of the adjacent exon 23.\n8- What do authors mean by “biopathology”?\n9- Please provide justification for your definition of non-canonical splicing variants as “variants within 8 bp of the donor splice site or 100 bp of the acceptor splice site except for canonical splice sites”.", "reviewer_2": "The authors present a comprehensive workflow aimed at improving the genetic diagnostic yield for Alport syndrome, proposing it as a model that could be applied to other genetic disorders. A stepwise approach was employed on a cohort of 555 patients: whole-exome sequencing (WES) was performed on the entire cohort, followed by whole-genome sequencing (WGS) on the subset of WES-negative patients (n=65), and finally, long-read sequencing on a fraction of patients negative for both WES and WGS (n=13). In 20 cases where WGS did not identify pathogenic variants and no splicing-affecting variants were predicted, RNA sequencing was performed, leading to the detection of aberrant transcripts in three additional patients. Overall, the authors achieved an impressive diagnostic rate of 92.4% (513/555).\nThe study identifies numerous novel Alport syndrome variants, some of which appear to be specific to the Chinese population. The pathogenicity of several of these variants was confirmed using RNA-seq or minigene assays. Notably, two novel structural variants—large intronic insertions and complex duplication-inversion events—were detected exclusively by Nanopore long-read sequencing, according to the authors.\nThis work is of high relevance to the field of genetics, as it highlights the power of emerging technologies in improving the molecular diagnosis of a rare disorder and contributes to refining the mutational spectrum of the three genes associated with Alport syndrome. While exome sequencing, and to a lesser extent genome sequencing, are becoming standard tools in clinical genetics, a significant proportion of patients still lack a molecular diagnosis. The integrative approach proposed in this study may help to bridge this gap.\nThe methodology appears robust and thoughtfully designed. However, the manuscript is at times difficult to follow. Some concepts are introduced too briefly, while others are unnecessarily repeated. Additionally, a few sentences lack clarity (see examples below). A more concise and structured presentation would significantly improve the readability and overall quality of the manuscript.\nMajor Points to Revise:\nDespite the high diagnostic yield, the authors should further discuss potential explanations for the remaining undiagnosed cases, such as mosaicism, involvement of additional genes, or phenocopies.\nIt should be clarified whether the 555 patients are unrelated or if the cohort includes family members.\nGenotype-phenotype correlations should be explored and discussed, especially in comparison to previously published data. Do any findings deviate from what is typically seen in the literature?\nAn investigation into whether the undiagnosed patients differ clinically or familiarly (e.g., sporadic vs familial cases) from those with identified variants could provide valuable insights for future research.\nThe authors should explain why the two structural variants were only detectable by Nanopore long-read sequencing and provide supporting evidence for this claim.\nLines 80–82: The sentence “Intriguingly, complex genomic alterations that have been widely reported to cause genetic disorders in diseases such as Branchio-oto-renal syndrome and Apert syndrome” lacks clarity and may be incomplete. Similar issues are found in other parts of the manuscript.\nSome minor Comments:\nLines 67–68: The phrase “prefer exome sequencing, such as targeted panel sequencing or whole-exome sequencing (WES), for patients with suspected AS” is confusing. Did the authors mean to refer more broadly to “next-generation sequencing”?\nLines 124–126: The sum of percentages given is 100.1%, which should be corrected to total 100%.\nLines 127–130: Why is COL4A3 not mentioned? Additionally, the distinction between \"non-coding region variants\" and \"deep intronic variants\" should be clarified.\nLines 130–131: The sentence is unclear and need rewording for clarity.\nLine 159: Whenever possible, variants should also be reported at the protein level (p.), especially for collagen genes, where protein changes are highly informative.\nIn the section discussing CNVs in the COL4A5 gene (located on the X chromosome), it should be specified whether the analyzed samples were from male or female patients.\nThe pathogenicity of missense variants that do not involve glycine residues should be more thoroughly addressed. How were these variants classified as pathogenic? Were segregation studies performed? Are they located in collagenous or non-collagenous domains? Additional information on these aspects would strengthen the manuscript. Including background discussion in the introduction would also be helpful.", "reviewer_3": "Di, et al. presents a comprehensive, large-scale genomic analysis of 555 Chinese patients with biopsy-confirmed Alport Syndrome (AS) using a sequential sequencing strategy that includes whole-exome sequencing (WES), whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and nanopore long-read sequencing (NLR-seq). The authors report a high diagnostic yield of 92.4% and identify 437 variants across COL4A3, COL4A4, and COL4A5, of which 58.6% are novel. The study underscores the genetic complexity of AS, highlighting the significant contribution of non-coding and structural variants (SVs), including complex rearrangements detectable only through long-read sequencing. Ethnic-specific variants are also emphasized, and the authors advocate for the integration of comprehensive sequencing technologies to enhance diagnostic precision in genetically heterogeneous disorders such as AS.\nMajor comments\n1. It was reported that more than half (184/339) of the missense variants are Glycine substitutions.\na. It would be good to cover the pathogenicity of glycine substitions in the Gly-X-Y motif and associated mechanism within the introduction as it may not be known to readers.\nb. What about the remaining missense variants? What evidence is there that these are pathogenic versus variants of uncertain significance? Furthermore, overall ~54% of variants are novel (i.e. never been reported before) so again what evidence is there that these missense variants are pathogenic instead of rare missense variants in the population.\nc. Related to (b), in the results text a breakdown of the 155 remaining variants as previously reported, novel pathogenic and novel VUS.\nd. More details for the novel pathogenic missense variants on how ACMG criteria was applied and what codes were used for this determination.\n2. Please comment on the correlation between patients with pathogenic variants in COL4 and their respective COL4 IF staining.\n3. Is there any precedence to claim di-genic inheritance? The more likely explanation is that there is a variant of uncertain significance in another COL4 gene. At the very least this should be in the discussion.\n4. Please perform the analysis comparing against ClinVar, which a free to use mutation database instead of only comparing against HGMD Pro, which readers/researchers may not have access.\n5. The manuscript reports on two high frequency variants in Chinese AS patients\na. Both of these variants are in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/variation/550745/, https://www.ncbi.nlm.nih.gov/clinvar/variation/334704/). It's important to be transparent in the main manuscript text that one of these variants is reported as a VUS and is also reflected this way in Supplementary Table 1.\nb. Please report the allele frequency in gnomAD and comment based on this the likelihood these variants can be casual in dominant inheritance.\nc. Related to (b), in Supplementary Table 1 both these variants are reported in AD, AR and di-genic. Please comment on the likelihood of this variant being causal in all three scenarios and whether there is precedence among other collagen disease causing genes (e.g. COL6 myopathies)\n6. The caption of Figure 4 could be futher improved. There are no details at all on what each panel is showing such as the data source (Sanger vs RNA-seq) and the type of variant (deep intronic vs splice region). Are these from mini-gene assays or RNA extracted from patient samples?\n7. Table 2 is extremely difficult to interpret as it does not distinguish between pathogenic and non-pathogenic variants. This becomes very confusing when trying to interpret the small variants in non-coding section.\n8. For CNVs please be explicit with the copy number losses and gains. For the losses are they heterozgyous or homozygous deletions, while for gains are these copy number 3 gains?\na. It is not clear in the results text how the 18 CNVs detected from WES were validated? Detection from WES has a high false positive rate so it needs to be explicit how these were validated as the text in this section says that only WGS detected CNV were validated. It should be clear in Table 2, which of these have been validated.\n9. The diagnostic yield of 92.4% appears to be quite high. Does this include VUS and other variants that are still speculative? Also, has segregation been performed where possible/appropriate?\nMinor comments\n1. The inheritance pattern of Alport syndrome is neither mentioned in the abstract or the introduction of the manuscript. This is absolutely critical to give readers context. Furthermore, is the affected status of the parents of the sequenced patients known especially for the suspected autosomal dominant cases.\n2. Non-canonical variants based on the definition in the methods \"as variants within 8 bp of the donor splice site or 100 bp of the acceptor splice site.\" This is technically incorrect and these variants should be called splice region variants.\na. The 100 bp from acceptor splice site seem arbitary, how was this distance chosen?\nb. Whether a splice site is canonical is more to do with if it uses GT for the donor and AG for teh acceptor. Once validated these are actual novel splice sites, if they use the canonical donor/acceptor sequence.\n3. Comment in the manuscript for COL4A5 (on ChrX) what the distribution of pathogenic variants among males and females.\n4. Comment in the manuscript whether abnormal COL4 IF is predictive of COL4 mutations and whether sometimes it can be a secondary effect. Also, were there patients samples that were abnormal for more than one COL4 staining.\n5. Why were the two heterozgyous deletions detected by NLR-seq missed by WGS/WES? For WGS there should have been split read evidence or was it too low coverage in this region to detect?\n6. More details in the methods on how the DHS analysis was performed.\n7. The github repository README has little information on the scripts and how they were used to generate some of the data/figures presented.\na. https://github.com/YOUZhen93/Alport_Syndome_project/blob/main/pheno-geno/cysts_associations.R#L15 in the code makes reference to a data file not in the repo so this R script will not work."}, "author_response": "We appreciate the reviewer’s recognition of the value our study brings by identifying novel variants associated with Alport Syndrome in a large, biopsy-confirmed cohort. We understand the concerns regarding the novelty and perceived significance of our findings, and we would like to take this opportunity to address these points.\nAs the reviewer correctly note, our study employs a well-established, multi-tiered approach to genetic diagnosis. While these methods are indeed widely used, we would like to emphasize that our study’s key contribution lies not in the individual techniques, but in the comprehensive and systematic application of these technologies to a large and phenotypically well-defined cohort of Chinese patients with Alport Syndrome.\nOne of the most significant aspects of our study is the generation of the first comprehensive genetic landscape of Alport Syndrome in the Chinese population. By incorporating whole-exome sequencing, whole-genome sequencing, long-read sequencing, and RNA-seq, we have provided new insights into the genetic architecture of this disease, particularly in non-European populations, where the genetic spectrum of Alport Syndrome is still underexplored. Moreover, our study highlights the contribution of novel variant types, including deep intronic variants and structural variants, which are frequently overlooked in previous population studies. These variants represent a significant expansion of the known genetic causes of Alport Syndrome and underscore the importance of considering more possibilities when investigating rare diseases.\nWe also stress that our findings have immediate clinical relevance, providing novel insights into the genetic diagnosis of Alport Syndrome that could be directly applicable to patient care in the Chinese and broader East Asian populations.\nWe respectfully provide our detailed, point-by-point responses below.\n1- The authors acknowledge that WES would have diagnosed >95% of all molecularly confirmed cases of Alport syndrome. The use of WGS in cases that are negative by WES is already standard practice. The identification of novel variants in these studies is expected regardless of the study population and the higher percentage is expected in non-European populations.\nWe sincerely appreciate the reviewer’s insightful comment and the opportunity to further clarify the novelty and significance of our study. We agree with the reviewer that the individual techniques employed—such as whole-exome sequencing (WES), whole-genome sequencing (WGS), and RNA-seq—are well-established and have been extensively described in the literature. However, we respectfully emphasize that the key advance of our study lies in the comprehensive, integrated, and systematic application of these methods to a large, biopsy-proven cohort, which achieved a significantly higher diagnostic yield and revealed the complete genetic landscape of Alport Syndrome.\nWe agree that WES has a high diagnostic yield and that proceeding to WGS for WES-negative cases represents a widely accepted diagnostic strategy. We also acknowledge that novel variants are more frequently detected in non-European populations. However, our study goes beyond simply implementing this standard pipeline. Our cohort of 555 biopsy-proven Chinese patients represents one of the largest and most phenotypically well-defined Alport Syndrome cohorts to date. While a higher incidence of novel variants is indeed expected in non-European populations—largely due to the predominance of prior studies in European cohorts—systematic quantification and characterization of these variants have not previously been performed in a Chinese cohort of this magnitude.\nOur study thus provides the first comprehensive delineation of the genetic architecture of Alport Syndrome in the Chinese population. Based on WES results, we identified numerous novel variants that have expanded the global understanding of genetic diversity across different ethnic groups. Furthermore, we demonstrate that non-canonical variant types (such as deep intronic variants, copy number variants, and structural variants) make a substantially greater contribution to the disease than previously inferred from European-centric studies. Importantly, the novelty of our findings extends beyond single nucleotide variants or small indels. As detailed in our manuscript and expanded upon in the responses below, we identified several previously unreported structural variant types associated with Alport Syndrome, thereby expanding the known genetic spectrum of the disorder.\nTaken together, our work not only defines the population-specific features of Alport Syndrome but also provides data with direct relevance and immediate clinical utility for genetic diagnosis and counseling.\n2-The use of long read sequencing in cases that remain negative after short read sequencing is extensively investigated and published in the literature.\nWe agree with the reviewer that long-read sequencing (e.g., PacBio and Oxford Nanopore Technologies platforms) represent an emerging and powerful approach for investigating unsolved genetic disorders. Its utility in resolving complex genomic regions and detecting structural variants (SVs) is well established in the broader field of genomics.\nHowever, its application to Alport Syndrome has thus far remained limited and unsystematic. To the best of our knowledge, this study is among the first to systematically apply long-read sequencing (Oxford Nanopore Technologies) to biopsy-confirmed Alport Syndrome cases that yielded negative results from short-read whole-exome sequencing (WES) and whole-genome sequencing (WGS). This targeted implementation proved critical, as it enabled the identification of previously undetected deletions missed by both WES and short-read WGS (two cases). More importantly, it led to the discovery of novel complex structural variants in four cases—variants that would have been virtually impossible to detect or accurately phase using short-read technologies. Characterizing these structural variants represents a novel contribution to the understanding of Alport Syndrome.\nTherefore, the significance of our study lies not only in the adoption of long-read sequencing, but in demonstrating its unique diagnostic value and revealing a previously unrecognized mutational mechanism in a disease whose genetic spectrum was largely defined by short-read sequencing.\n3- Similarly, RNA-seq to confirm non-canonical splicing variants is not only extensively investigated and published, but has also evolved already from a research tool to a clinical diagnostic tool.\nWe thank the reviewer for this important comment. We fully recognized that RNA-seq is increasingly being used in diagnostic settings to assess splicing defects. The novelty of our study lies in the integrated analysis strategy we employed. Rather than using RNA-seq in isolation, we incorporated it into a structural diagnostic workflow.\nFirst, near intronic or deep intronic variants with potential splicing effects were identified through whole-genome sequencing (WGS), and their pathogenicity was subsequently confirmed via RNA-seq or minigene assays. Next, in addition to this conventional DNA-to-RNA validation approach, we also utilized RNA-seq prospectively as a discovery tool. In two cases, aberrant transcripts were first detected by RNA-seq, which then guided a focused re-analysis of the WGS data. This secondary analysis revealed the causative deep intronic variants that had been overlooked by standard bioinformatics filters owing to their low predictive scores.\nThis reverse paradigm—from RNA back to DNA—illustrates the strength of our integrated analytical framework. Thus, this systematic workflow not only enabled validation of variant pathogenicity but also established a model for comprehensive diagnostic strategies in genetically unresolved cases.\n4- The entire section on “mutagenesis” is highly problematic. The authors seem to go to extraordinary lengths to explain the obvious. In every single population, the study of a particular disease will likely reveal novel variants. One need not invoke complex evolutionary and DNA structural factors to explain this unless there is something unusual about these variants, which is clearly not the case here. More importantly, the analysis is highly flawed at times. For instance, how could the authors attribute “recurrent” variants to these complex factors without even confirming that they are not simply founder in nature? There is absolutely no analysis of the surrounding haplotype to address this fundamental prerequisite.\nWe thank the reviewer for this thoughtful comment and appreciate the concern regarding this part of our results. We fully respect the reviewer’s perspective and would like to take this opportunity to clarify our rationale for retaining the evolutionary and DNA element analysis in the Chinese Alport Syndrome cohort.\n(1) Rationale for evolutionary analysis. Evolutionary analysis has long been recognized as an informative approach for evaluating the potential impact of disease-associated variants and for providing valuable insights into disease etiology (PMID: 33408383, 21850043 and 24759409)1-3. As a powerful and systematic method, it helps prioritize candidate variants and interpret how they arise and contribute to disease, given that genetic disorders themselves are shaped by evolutionary processes and demographic events such as selection, genetic drift and population bottlenecks1. Such analysis is widely applied in studies of various human complex and rare diseases (PMID: 22604720, 24443550, 38336875 and 32750315)4-7.\n(2) Rationale for DNA functional element analysis. DNA functional elements are inherently linked to sequence context. For example, comparative genomic analyses have facilitated the identification of functional important regulatory elements (e.g., cis-regulatory elements), which exhibit distinct distribution patterns depending on the genomic sequence environments (PMID: 17086198, 38092918 and 37104580)8-10. Moreover, annotations of DNA functional elements at the tissue or single-cell level, such as those generated by the ENCODE project, have significantly advanced our understanding of developmental biology and disease mechanisms when integrated with GWAS candidate loci and evolutionarily constrained regions (PMID: 22955616 and 22955986)11,12.\n(3) Relevance to Alport Syndrome and population genetics. To date, relatively few studies have examined Alport Syndrome from the perspective of DNA sequence evolution and functional element distribution. Furthermore, the majority of the pathogenic variants recorded in databases such as ClinVar and HGMD are derived from populations of European ancestry. Considering the above points, we believe it is both reasonable and informative to perform such analyses in our Chinese cohort and related public datasets.\nWe also sincerely appreciate the reviewer’s critique of our analytical approach. We wish to emphasize that our intention was not to infer direct causal relationships between the identified variants and their surrounding genomic contexts.\nRegarding the reviewer’s mention of “founder in nature”, we think the reviewer means the “founder effect” underlying the two recurrent variants. We would like to clarify that, although these variants occur at relatively high frequencies in our cohort, they remain rare in the East Asian (EAS) population (COL4A4: c.4421C>T (rs201615111), allele frequency = 0.001047; COL4A3: c.4793T>G (rs752452590), allele frequency = 0.0007575 in the gnomAD v4 EAS). We have now included this information in the manuscript (Page 13, Lines 274–278). Importantly, all patients included in our study are unrelated (noted on Page 7, Line 139 and Page 32, Line 675). While the possibility is relatively low, given the limited power of WES for haplotype block identification and identical-by-descent (IBD) analyses, we cannot completely rule out the possibility that these recurrent variants may have arisen from a founder effect or random genetic drift. Nonetheless, we reiterate that we did not attribute the origin of these two variants solely to their local genomic context. Interestingly, we observed that the evolutionary features and functional element landscapes surrounding these two variants differ markedly: one is located in a population-conserved region, while the other resides in a high-recombination region enriched with functional elements.\nWe have also systematically re-evaluated the pathogenicity of all detected variants. In summary, the majority remain classified as pathogenic or likely pathogenic, while 22 variants (including COL4A4: c.4421C>T) were reclassified as high-risk variants of uncertain significance (VUS). The criteria for defining high-risk VUS are listed at the end of this response for reference. In the revised manuscript, we have adopted a more conservative approach to VUS interpretation. Accordingly, the analysis of COL4A4: c.4421C>T has been removed from the main text to ensure accuracy and conservatism in describing variant pathogenicity.\nConsistent with the reviewer’s suggestion, we have also merged the analyses of exonic and intronic mutagenesis into a single section to improve conciseness and have removed the single nucleotide variant signature and DNase I hypersensitive site colocalization analyses for intronic variants, as well as the cis-regulatory element colocalization analysis for exonic variants (Pages 12–15, Lines 260–320).\nCriteria for defining high-risk VUS:\nWe first utilized the Franklin platform (https://franklin.genoox.com/) to interpret all variants following the American College of Medical Genetics (ACMG) guidelines. This interpretation was further refined by a manual review based on the Alport Syndrome-specific ACMG criteria recommended by the Alport Variant Collaborative Group13. For variants initially classified as VUS, a three-tiered classification was performed in accordance with the supplementary practice guidelines established by the Association for Clinical Genomic Science (ACGS) in 2024, which are based on the ACMG criteria14. Variants scoring 4–5 points in the absence of conflicting evidence were designated as “high-risk VUS” and retained in the analysis. All other VUS were excluded unless they met these stricter criteria.\n5- The contribution of splicing variants, CNVs and SVs to the pathogenesis of Alport syndrome is fully consistent with what is already known from the literature.\nWe agree with the reviewer that the known spectrum of Alport Syndrome variants includes splicing variants, copy number variants (CNVs), and structural variants (SVs). Previous studies have typically reported these variant types in isolation. The key advancement of our study lies in the comprehensive integration of all variant classes within a single, large cohort, enabling precise quantification of their relative contributions to the overall diagnostic yield.\nOur analysis provides robust, quantitative data on the prevalence of non-coding variants (accounting for 11.4–19.1% across the three genes) and SVs (accounting for 5.7–9.1%) in a major population. More importantly, by applying long-read sequencing, we identified that complex SVs account for a subset of previously unsolved cases and encompass novel SV types not previously described.\nIn summary, while these variant classes are known, our work delineates a comprehensive genetic landscape of Alport Syndrome, quantifies the diagnostic yield attributed to each layer of sequencing, and expands recognized SV spectrum in Alport Syndrome. We believe this integrative analysis provides a valuable benchmark for the field and establishes a scalable diagnostic framework applicable to other Mendelian disorders.\n6- The “sequential” approach by the authors is contradictory. If they do have a panel specific for Alport syndrome, why did they start with WES especially since they fail throughout the manuscript to even comment on potential hits in genes other than the three known Alport syndrome genes?\nWe thank the reviewer for highlighting this point regarding our sequential approach and appreciate the opportunity to clarify our rationale. When designing this study, we chose whole-exome sequencing (WES) as the initial step precisely because of its broader analytical scope. A key consideration was WES’s ability to detect causative variants in genes beyond the major Alport Syndrome genes that could produce a similar phenotype. For example, genes associated with familial Focal Segmental Glomerulosclerosis (FSGS), such as WT115 and INF216, can manifest with an Alport-like phenotype. We specifically used WES data to screen for variants in these and other FSGS-related genes. However, our analysis did not identify any pathogenic or likely pathogenic variants in these genes among the undiagnosed patients. Given that our primary focus was to characterize the spectrum of variants within the three Alport Syndrome genes (COL4A3, COL4A4, and COL4A5), and in light of the absence of positive findings in FSGS-related genes, we did not include these negative results in the initial submission.\nWe agree that this aspect merits discussion. Accordingly, we have now added relevant content in the revised Discussion (Page 30, Lines 644–649) explicitly describing our use of WES data to screen for FSGS-related genes and noting the absence of positive findings in our cohort.\n7- The “digenic” inheritance claimed by the authors in nearly 5% of the cohort is mentioned rather casually with no evidence to back it. As the authors know, the field has moved beyond anecdotes of digenicity that littered the literature in the past and there now exist very strict guidelines for claiming digenicity.\nWe thank the reviewer for raising this critical point and sincerely appreciate the opportunity to clarify this aspect of our work. Digenic inheritance is indeed a genetic model that has gained increasing attention in Alport Syndrome17. We acknowledge that claims of such inheritance require rigorous evidence and adherence to established guidelines.\nAccording to the 2022 Guidelines for Genetic Testing and Management of Alport Syndrome18, digenic Alport Syndrome is characterized by the presence of two pathogenic or likely pathogenic variants or variants of uncertain significance (VUS) that is likely to be pathogenic in two different genes among COL4A3, COL4A4, and COL4A5. It is recommended that all clinically significant variants be reported. Based on these guidelines, we initially categorized patients with two such variants in our cohort as having digenic inheritance.\nTo respond to the reviewer’s valuable comment, we have carefully re-evaluated all 23 patients previously categorized under this model. We fully agree with the need for stringent criteria and recognize that 10 of these patients carry a (likely) pathogenic variant alongside a VUS.\nTo address this, we conducted a more stringent reassessment of variant pathogenicity. First, we utilized the Franklin platform (https://franklin.genoox.com/) to interpret all variants in accordance with the American College of Medical Genetics (ACMG) guidelines. This was complemented by a manual review based on the Alport-specific ACMG criteria recommended by the Alport Variant Collaborative Group13. For variants initially classified as VUS, we applied a three-tiered classification system in line with the supplementary practice guidelines established by the Association for Clinical Genomic Science (ACGS) in 2024, based on the ACMG criteria14. Only variants scoring 4–5 points, without conflicting evidence, were classified as “high-risk VUS” and retained in the analysis; other VUS were excluded unless they met these stricter criteria. We have updated the Methods section to reflect these enhanced variant assessment criteria (Page 35, Lines 731–740).\nAfter this comprehensive reassessment, we determined that 8 of the 10 patients carried one high-risk VUS and one (likely) pathogenic variant. Following the aforementioned guidelines18, these 8 cases remain consistent with the digenic model. The detailed ACMG classification evidence for these variants has been added to Supplementary Table 2 for full transparency.\nFor the remaining two patients, both of whom carried the COL4A4: c.4715C>T variant (which did not meet the threshold for high-risk VUS), we have reclassified their inheritance pattern as X-linked (because the other variant they both harbored was a likely pathogenic COL4A5 variant). Following this refined analysis, our cohort now includes 21 patients with digenic Alport Syndrome.\nWe are deeply grateful for the reviewer’s insightful comment, which has significantly improved the accuracy and validity of our genetic interpretation.\n8- The authors should comment on why they used MGI platform for WES/WGS but Illumina platform for RNA-seq and whether their failure to detect some CNVs and SVs by WGS may be related to the known difference between MGI and Illumina.\nWe sincerely thank the reviewer for raising this important point regarding the use of different sequencing platforms. We appreciate the opportunity to clarify our rationale for platform selection and to address whether the failure to detect certain copy number variants (CNVs) and structural variant (SVs) by whole-genome sequencing (WGS) could be related to differences between MGI and Illumina technologies.\nFirstly, the MGI WGS platform has been successfully applied in genetic studies across various diseases, including colorectal cancer mutation analysis (including SVs), preimplantation genetic testing, and human pangenome studies19-21, demonstrating its capability to generate reliable data and its increasing recognition within the research community. Secondly, to respond to the reviewer’s comment, we performed additional Illumina WGS on two samples (AS511, which harbors a hemizygous mobile element insertion in a COL4A5 intron, and AS509, which harbors a heterozygous deletion in COL4A4) to directly compare SV detection between the two platforms using the same bioinformatic pipeline.\nFor the hemizygous insertion in AS511 genome, both platforms yielded comparable read depths at the locus (Illumina: 16 reads; MGI: 24 reads). After downsampling the MGI data to match Illumina coverage (14 reads), soft-clipped reads indicative of the insertion was observed in both datasets (Response Figure. 1). Notably, the first 16 bp of the insertion sequence (AAAAACTTGACTTTCT) are identical to the reference sequence downstream of the insertion site (Response Figure. 2). Consequently, some Illumina reads were mapped to the reference sequence by chance and marked as soft-clipped when sequenced at the poly-T region of the insertion. This variant was not called by Lumpy or Delly in either MGI or Illumina data.\nResponse Figure 1. IGV screenshot of hemizygous insertion in COL4A5 from patient AS511 (left) and downsample MGI aligned reads to match the depth with Illumina (right).\nResponse Figure 2. IGV screen shot of hemizygous insertion in COL4A5 from patient AS511; red rectangle highlights the 16-bp sequence at the beginning of the insertion are the same with the 3’.\nFor the heterozygous deletion in AS509 genome, both platforms provided over 30X coverage at this locus (Illumina: 50 reads; MGI: 68 reads). Visualization in IGV browser revealed limited split reads support this SV in both datasets (Response Figure. 3). Discordant read pairs have limited sensitivity for SV detection. Therefore, this variant was called as deletion with imprecise coordinates by Delly on both MGI and Illumina platforms:\nSV Platform Chrom POS QUAL Flag INFO FORMAT type\nGT:0/1; MGI Chr2 227099204 DEL 438 PASS IMPRECISE;END=227100870;PE=11; DV=12; DR=8\nGT:0/1; Illumina Chr2 227099175 DEL 403 PASS IMPRECISE;END=227100866;PE=8; DV=8; DR=18\nResponse Figure 3. IGV screenshot of heterozygous deletion in COL4A4 from patient AS509; upper panel is Illumina platform and lower panel is MGI platform.\nLumpy did not detect this event in either dataset. Based on our SV filtering strategies, this variant was excluded in the final next-generation sequencing (NGS) SV callset. Notably, all third-generation sequencing (TGS)-based SV calling pipelines (Sniffles, cuteSV, nanoSV, and Picky) successfully identified this deletion.\nIn summary, our side-by-side comparison suggests that the MGI platform performs comparably to Illumina platform in SV detection. We found no evidence to indicate that previously undetected CNVs and SVs were attributable to platform-specific technical limitations.\n9- The claim that some splicing variants, including those proven to exert their pathogenicity through aberrant splicing, impact DHS is extraordinary. The authors seem to confuse the known colocalization of DHS and other classes of regulatory variants (promoters and enhancers) with splicing variants even though the biology of the latter is very distinct from the former.\nWe sincerely thank the reviewer for this insightful comment regarding the relationship between splicing variants and DNase I hypersensitive sites (DHS).\nIn our study, we did not intend to suggest that splicing variants contribute to Alport Syndrome pathogenesis by directly affecting the DHS regions. Instead, we aimed to investigate whether certain categories of splicing variants, particularly non-canonical splicing variants and deep intronic variants, might overlap with functional genomic annotations, including DHS. As the reviewer correctly points out, DHS marks regions of open chromatin and are considered indicators of active regulatory elements such as enhancers and promoters. Additionally, they reflect chromatin accessibility for transcriptional machinery, including within gene bodies22-25. Previous studies have shown that DHS within gene bodies can interact with promoters and other regulatory elements, and have been linked to alternative splicing and intron retention events26,27. Inspired by these findings, we examined whether the splicing variants in our cohort co-localized with DHS in our initial manuscript.\nTo improve clarity and avoid potential misinterpretation of the data, we have shortened the mutagenesis analysis section and removed the analysis of cis-regulatory element co-localization with intronic variants, while retaining the sequence conservation analysis of the three types of splicing variants (Pages 14–15, Lines 301–320). In our study, we emphasized that sequence conservation differed between splicing variant types: near intronic variants and deep intronic variants showed lower evolutionary conservation, and intronic nucleotide substitutions (adenine and guanine) were significantly correlated with sequence conservation.\n10- The claim that this is the first study to attempt using nanopore sequencing in Alport syndrome is false and appears symptomatic of widespread failure by the authors to review the literature adequately in several areas of the manuscript.\nWe thank the reviewer for this valuable comment. We fully understand the importance of being cautious with any claims regarding the first study in a particular field.\nIn our literature review prior to revision submission, we conducted an exhaustive search of PubMed (Response Figure. 4) and Web of Science (Response Figure. 5) using keywords such as “Alport Syndrome” AND “nanopore sequencing”, “COL4A3” OR “COL4A4” OR “COL4A5” AND “nanopore sequencing”, and “long-read sequencing” AND “Alport Syndrome” (the last search was conducted on November 25, 2025). Our search did not reveal any published studies specifically applying nanopore sequencing to the detection of Alport Syndrome-related genes (COL4A3, COL4A4, and COL4A5). While nanopore sequencing has been used for other inherited kidney diseases (e.g., autosomal dominant polycystic kidney disease28), its application in Alport Syndrome variants appears to be unexplored.\nResponse Figure 4. Screenshots of the Response Figure 5. Screenshots of the search results from PubMed using the search results from Web of Science using the following keywords: “Alport Syndrome” AND following keywords: “Alport Syndrome” AND “nanopore sequencing”; (“COL4A3” OR “nanopore sequencing”; (“COL4A3” OR “COL4A4” OR “COL4A5”) AND “nanopore “COL4A4” OR “COL4A5”) AND “nanopore sequencing”; “long-read sequencing” AND sequencing”; “long-read sequencing” AND “Alport Syndrome”. “Alport Syndrome”.\nTo address this, we have revised the manuscript to clarify the scope of our claim, replacing “the first study” with “the first report of the application of NLR-seq to COL4A3, COL4A4, and COL4A5 genotyping in AS” (Page 26, Lines 567–568). We would greatly appreciate it if the reviewer could provide any specific references we may have overlooked, and we would gladly revise our text accordingly. Our intention was not to overlook existing literature, but rather to highlight a novel methodology in the context of this specific disease.\nMinor:\n1- Add “respectively” to “COL4A3, COL4A4 and COL4A5 genes, responsible for encoding the α3, α4, and α5 chains of type IV collagen”.\nWe have added the word “respectively” as suggested by the reviewer in the revised manuscript (Page 3, Line 49), “...COL4A3, COL4A4 and COL4A5 genes, responsible for encoding the α3, α4, and α5 chains of type IV collagen, respectively”.\n2- The choice of disorders with significant SVs contribution as examples is strange. In addition, the authors did not show in their work a contribution of SVs beyond what is already expected for most genetic disorders.\nWe sincerely thank the reviewer for this thoughtful feedback. In response, we have revised the relevant section to include more representative examples (Page 6, Lines 119–121). In this study, we identified six SVs, including one mobile element insertion and two complex SVs with inversion-duplication structures. To the best of our knowledge, these variants have not been commonly reported in Alport Syndrome and other genetic disorders. We acknowledge that the pathogenicity of these SVs primarily involves disruption of normal protein structure. Nevertheless, we believe these findings broaden the current understanding of Alport Syndrome etiology and highlight the value of long-read sequencing technologies in identifying clinically relevant SVs, including those in non-coding regions that may affect functional elements.\nWe also appreciate the reviewer’s point regarding the general contribution of SVs to genetic disorders, and we hope that the novel SVs presented in this study provide valuable insights beyond conventionally recognized variant types.\n3- “diagnosis approaches” should be “diagnostic approaches”.\nWe thank the reviewer for this suggestion. We have revised the sentence in the manuscript accordingly (Page 6, Lines 122–123).\n4- This sentence is unclear “As the genetic variants in AS are almost from COL4A3, COL4A4 and COL4A5 genes”.\nWe apologize for not making this clear. The original sentence “As the genetic variants in AS are almost from COL4A3, COL4A4 and COL4A5 genes, we focused on the three genes’ variants by applying a sequential sequencing in the cohort in this research” has been revised to “We adopted a sequential sequencing approach in this study to deeply analyze the variants in the COL4A3, COL4A4, and COL4A5 genes.” in the manuscript (Page 7, Lines 143–144). The revised sentence directly states our methodology and specific objectives, and removes the ambiguity in the original phrasing. We thank the reviewer for this suggestion to improve the manuscript’s readability.\n5- “variants privately owned by our AS patients” should be “…harbored by…”.\nWe thank the reviewer for this suggestion. We have revised the sentence as suggested (Page 9, Line 181).\n6- What do authors mean by “chimerically mutated”?\nWe thank the reviewer for pointing this out. In Reference 54 (PMID: 30968591) of the initial manuscript, a patient was reported to carry both the COL4A4: c.4421C>T variant and the COL4A5 intronic variant (c.1339+3A>T). We used this terminology to describe this situation. We acknowledge that this terminology is non-standard and may cause confusion. To improve clarity, we removed this in the revised manuscript (Page 13, Line 273).\n7- The authors present an extremely complex explanation of the intronic insertion in AS511 claiming the role of a neo-promoter even though RNA analysis clearly indicates very simple skipping of the adjacent exon 23.\nWe sincerely thank the reviewer for this thoughtful comment regarding our interpretation of the intronic insertion identified in sample AS511. We appreciate the opportunity to clarify our reasoning and provide additional supporting evidence.\nUpon re-examining the RNA-Seq data, we did not observe any junction reads spanning from exon 22 to exon 24 (as shown in Supplementary Fig. 6A and reproduced below as Response Figure. 6 for convenience). If exon 23 was simply skipped, such junction reads would be expected. However, this was not the case. Instead, transcription appeared to reinitiate at exon 24, leading us to hypothesize that the mobile element insertion (MEI) might function as a neo-promoter. To experimentally evaluate this hypothesis, we performed a dual-luciferase reporter assay using the cloned MEI sequence. The results confirmed that this element exhibits bidirectional promoter activity (Response Figure. 7). This new figure has been incorporated into the revised manuscript as Supplementary Fig. 6D.\nFurthermore, to improve clarity, we have revised the Results section (Pages 18–19, Lines 377–405) to provide a clearer description of the data and its interpretation.\nResponse Figure 6. Sashimi plot of COL4A5 quantification and alternative splicing events showing the loss of exon 23, with no splicing junction reads detected between exon 22 and exon 24.\nResponse Figure 7. Dual-luciferase reporter assay proves the MEI functions as a weak bi-directional promoter.\n8- What do authors mean by “biopathology”?\nWe appreciate the reviewer’s insightful comment regarding terminology usage. We have removed the term “biopathology” from the revised manuscript (Page 24, Lines 509 and 512) to enhance clarity and ensure consistency with standard scientific terminology.\n9- Please provide justification for your definition of non-canonical splicing variants as “variants within 8 bp of the donor splice site or 100 bp of the acceptor splice site except for canonical splice sites”.\nWe sincerely appreciate the reviewer’s valuable comment regarding our definition of non-canonical splicing variants in the initial submission. We acknowledge that our previous classification criteria lacked sufficient rigor. We have adopted the refined classification framework for non-coding variants described by Jaganathan et al. (Cell, 2019; PMID: 30661751)29. This scheme, grounded in GENCODE canonical annotations, provides a more robust and widely accepted basis. Accordingly, following Jaganathan et al., we have reclassified the non-coding variants in our study as follows:\nCanonical splice site variants: variants occurring at the highly conserved +1, +2, -1, and -2 positions of the exon-intron boundary.\nNear intronic variants: variants located within 3 to 50 base pairs (bp) from the canonical exon-intron boundary.\nDeep intronic variants: variants located more than 50 bp away from the canonical exon-intron boundary.\nWe have revised the definition accordingly in the “SNV and INDEL analysis” section of the “Methods” (Page 34, Lines 724–728) to incorporate this more rigorous classification.\nFurthermore, we have systematically updated all related descriptions and terminology throughout the manuscript to ensure consistency with the revised definition. We are grateful to the reviewer for this constructive suggestion, which has significantly improved the accuracy and clarity of our work."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the authors’ response to my comments. While extensive, the reply did not provide substantive new data and largely reiterated the authors’ views on what they consider important. I will not repeat my full initial review but highlight the following key concerns:\n1. The authors acknowledge that exome, RNA-seq, and long-read sequencing are established tools in the analysis of monogenic diseases, including kidney disorders. Their claim of novelty rests on describing a sequential testing strategy as “integrative.” This characterization is misleading, as the field has already adopted such approaches, particularly through initiatives such as the GREGoR Consortium.\n2. The evolutionary interpretation of Chinese variants remains speculative. Before proposing mechanisms for “recurrence,” the authors should exclude the more likely explanation of founder effect. Their assertion that exomes cannot resolve haplotypes is incorrect; published work demonstrates haplotype inference from exome data, and alternative assays such as WGS or genotyping could readily address this.\n3. The so-called “reverse paradigm” of RNA-seq is overstated. None of the cases in this study were diagnosed by RNA-seq, and even if they were, this approach is not novel (see PMID: 40975062).\n4. While expanding genetic studies to non-European populations is important, the identification of novel variants in a well-characterized disorder does not justify publication in Nature Communications. Such findings are more appropriate for a specialized nephrology journal.\n5. The authors misinterpret the concept of “digenic” Alport syndrome. The most recent ERKNet/ERA/ESPN guideline explicitly graded the evidence for digenicity in Alport syndrome as “D,” the lowest level of support.\n6. The authors strong worded defense of MGI technology is unwarranted. My original question remains unanswered, why did they choose MGI for some assays and Illumina for others without any justification?\n7. See PMID: 38776926 for an example of using long read sequencing in Alport syndrome.", "reviewer_2": "Overall, the manuscript has improved; however, some of the newly introduced conclusions are not fully supported by the presented data.\nIn the Abstract, the authors state that “Further genotype–phenotype correlation analysis revealed that hearing loss was correlated with COL4A5 hemizygous variants, and kidney cysts were associated with COL4A3 heterozygous variants.” This interpretation is likely influenced by the higher prevalence of hearing loss in males with X-linked Alport syndrome. Moreover, the section entitled “Association analysis of genetic variants and clinical manifestations” primarily highlights differences between X-linked Alport syndrome and the autosomal recessive and autosomal dominant forms, rather than identifying gene- or inheritance-specific genotype–phenotype correlations or confirming previously established associations.\nGenotype–phenotype correlation analyses should therefore be conducted within appropriately defined subcohorts, where sample size allows (e.g., analyzing males and females separately in X-linked Alport syndrome, and stratifying autosomal dominant and autosomal recessive cases).\nFor these reasons, I recommend removing the above-mentioned sentence from the Abstract and rephrase the paragraph lines 420-491.\nThe English language throughout the manuscript also requires improvement. Several sentences are unclear or grammatically incorrect. As an example, lines 157–158 state: “Of the 509 patients, 92, 98 and 298 were detected single gene variant in COL4A3, COL4A4 and COL4A5, respectively, and 21 were detected variants in two genes.” This sentence, as written, is difficult to understand and should be revised for clarity.\nI strongly recommend professional language editing, together with improving the overall conciseness of the manuscript.", "reviewer_3": "The authors have done a comprehensive job in answering each of my comments and have noted where it is not feasible to address.\nIf they haven't already, they may want to consider submitting the novel variants discovered in this study and their interpretation into ClinVar. This is only a suggestion but this will definitely help advance the field of Alport syndrome genetics especially in East Asian patients.\nThe github repository now has sufficient information."}, "author_response": "We sincerely thank the reviewer for the positive and encouraging feedback. We have carefully checked the entire manuscript to minimize the inconsistencies and correct any remaining linguistic errors. Below, we provide our point-by-point responses to each specific comment.\nLines 30-31: The sentence \"Additional sequencing approaches other than WES was needed\" contains a subject-verb agreement error. It should be replaced with: \"Additional sequencing approaches other than WES were needed.\"\nWe thank the reviewer for this suggestion. To comply with the word limit of Nature Communications, we have combined the two sentences into the following revised version: \"Beyond WES, additional sequencing approaches resolve 23 patients with non-coding, copy number, or structural variants.\" (Page 2, Lines 27–28).\nLines 128-135: Numerical Inconsistency: The authors state that WES was conducted on 555 patients, identifying variants in 486 of them. They then report that WGS was performed on the remaining 65 patients. However, the calculation suggests there should be 69 patients. Please clarify why only 65 patients were included in the WGS analysis or correct the figures accordingly.\nWe thank the reviewer for carefully pointing out this numerical inconsistency and for providing us the opportunity to clarify the reason.\nIn the initial submission (NCOMMS-25-41898), we identified variants in 490 patients by WES based on our primary interpretation framework. In the revised submission (NCOMMS-25-41898A), we applied more stringent criteria for variant classification in accordance with the ACMG guidelines, which resulted in a slight adjustment in the number of genetically diagnosed patients, from 490 to 486. Specifically, six non-Gly missense variants (affecting seven patients) did not meet the high-risk VUS threshold and were therefore excluded from reporting. This re-evaluation affected the molecular status of 3 patients and led to 4 patients being reclassified as genetically undiagnosed. Consequently, the number of WES-negative patients was updated from 65 to 69 in the revised manuscript. Due to limitations in sample availability, WGS was not performed in these four newly reclassified WES-negative patients.\nTo improve clarity and ensure numerical consistency, we have revised the relevant sentences in the manuscript as follows:\n“Firstly, WES was conducted on all 555 patients to identify simple coding variants, flanking non-coding variants, and CNVs, resulting in identification of COL4A3, COL4A4, or COL4A5 variants in 486 patients. Secondly, WGS was performed on 65 of the remaining 69 WES-negative patients to detect CNVs and non-coding variants.” (Page 6, Lines 118–122).\nLines 134-136: The phrasing \"Twenty of prediction-negative patients... were conducted RNA sequencing\" is grammatically incorrect. I suggest replacing it with: \"RNA sequencing (RNA-seq) was performed on twenty prediction-negative patients with available tissue samples to validate transcript changes.\"\nWe thank the reviewer for this suggestion. We have revised the sentence in the manuscript as suggested (Page 7, Lines 123–125).\nLines 138-140: in the following sentence patients’ number is changed “Taken together, a total of 431 distinct COL4A3, COL4A4, and COL4A5 variants were detected in 509 patients, including 296 males (58.2%) and 213 females (41.8%), resulting in a detection rate of 91.7%.” Please clarify and correct accordingly.\nWe thank the reviewer for this comment regarding the changes of patient numbers.\nAs detailed in our response to Point #2, we re-evaluated all variants according to the ACMG guidelines with more stringent criteria for assessing variant pathogenicity. This resulted in a slight adjustment in the number of genetically diagnosed patients by WES, from 490 to 486. Specifically, six non-Gly missense variants (affecting seven patients) did not meet the high-risk VUS threshold and were therefore excluded from reporting. This re-evaluation affected the molecular status of 3 patients and led to 4 patients being reclassified as genetically undiagnosed. Due to limitations in sample availability, WGS was not performed in these four newly reclassified WES-negative patients. Consequently, the total number of diagnosed individuals was updated from 513 to 509 in the revised manuscript. The patients’ numbers and the detection ratio are correct according to our revised manuscript.\nWe hope that this explanation adequately clarifies the numerical adjustment. We appreciate the reviewer’s meticulous attention to detail, which has helped us ensure the accuracy and consistency of our data.\nLines 460-461 and 532-533: The authors use the term \"intractable AS cases\". In a clinical context, \"intractable\" usually refers to a condition that is resistant to treatment. However, in this sentence, it appears the authors may be referring to \"unsolved,\" \"genetically undiagnosed,\" or \"clinically suspicious but molecularly negative\" cases. Please clarify the intended meaning and replace \"intractable\" with a more appropriate descriptor (e.g., \"genetically unresolved”)\nWe sincerely thank the reviewer for this valuable comment regarding terminology. We have replaced the term \"intractable\" with \"genetically unresolved\" throughout the manuscript to ensure greater precision and clarity (Page 5, Lines 94–95; Page 23, Lines 485 and 490; Page 24, Line 517)."}]

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