Beyond Panel-Based Testing: Exome Analysis Increases Sensitivity for Diagnosis of Genetic Kidney Disease : Kidney360

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Original Investigations: Genetics

Beyond Panel-Based Testing: Exome Analysis Increases Sensitivity for Diagnosis of Genetic Kidney Disease

Wilson, Parker C.1; Love-Gregory, Latisha1; Corliss, Meagan2; McNulty, Samantha2; Heusel, Jonathan W.2,3; Gaut, Joseph P.1,4

Author Information
Kidney360 1(8):p 772-780, August 2020. | DOI: 10.34067/KID.0001342020



Next-generation sequencing (NGS) is as an important diagnostic tool for the characterization of kidney disease (1). Targeted gene panels aid in the diagnosis of steroid-resistant nephrotic syndrome (NS) (2), nephronophthisis (3), autosomal dominant polycystic kidney disease (4), atypical hemolytic uremic syndrome (aHUS) (5,6), and congenital anomalies of the kidney and urinary tract (7). Renal biopsy is useful for evaluating patients with suspected genetic kidney disease, but it may reveal nonspecific findings. A genetic diagnosis is critical for assessing risk in family members and may provide therapeutic options for otherwise untreatable diseases (89101112–13).

Recent efforts have explored the utility of exome sequencing in large populations of adults with CKD, however, these studies were done retrospectively on selected populations (8,14). This is an important distinction because this approach significantly differs from physician-ordered testing performed in real time in an accredited clinical laboratory. With these caveats in mind, Groopman et al. (8) reported that 9% of adults with CKD have a monogenic cause, encompassing 66 different disorders. Similarly, Lata et al. (14) identified a genetic cause for CKD in 24% of patients after selecting for individuals with CKD of unknown cause or family history of kidney disease. Interestingly, the majority of monogenic disorders were detected as singletons, which underscores the importance of a broad approach to genetic evaluation of kidney disease.

The selection of a genetic test is based on clinical assessment and family history. However, many kidney diseases have overlapping clinical and pathologic phenotypes, complicating selection of the appropriate panel-based test. Fortunately, many NGS-based targeted gene sets are built on an exome backbone. This approach allows the laboratory to use a single process for library preparation and sequencing followed by a customized informatics workflow that extracts a subset of genes for clinical reporting. In the event an initial gene set returns no pathogenic or likely pathogenic variants, additional clinically relevant gene sets can be “unmasked” and interrogated (5). Although there is an abundance of archived genetic data that may or may not have implications for the patients’ health, there are currently no guidelines on which patients should undergo additional review, which genes should be reviewed, or how frequently this process should be repeated. Furthermore, new genes are implicated in renal disease on a regular basis (151617–18).

Materials and Methods

Clinical Laboratory Workflow

This study was approved by the Washington University School of Medicine Institutional Review Board and adheres to the Declaration of Helsinki. All patients provided informed consent before clinical genetic testing. Clinical gene sets (Table 1) were ordered by licensed physicians and performed in a College of American Pathologists (CAP)–accredited and Clinical Laboratory Improvement Amendments–certified laboratory. The data represent consecutive samples submitted to Genomics and Pathology Services at Washington University in Saint Louis between December 1, 2014 and October 31, 2018. Library preparation for the NGS assay began by fragmenting DNA to approximately 200 bp by ultrasonication, followed by end repair, A-tailing, and ligation to sequencing adapters. Libraries were prepared using the Agilent SureSelect XT Clinical Research Exome reagent, and sequenced using an Illumina (San Diego, CA) HiSeq2500 in 2×101-bp paired-end configuration. Analytic sensitivity, specificity, and reproducibility were established per the CAP NGS-testing checklist (19). Alignment was performed with Novoalign (version 2.08.02) and duplicates were marked with Picard (version 1.53). Single nucleotide variants were called with samtools (version 0.1.19) and insertions and deletions up to 21 bp were called using Unified Genotyper (GATK version 1.2) and pindel (version 0.2.4). In silico functional prediction was performed with SIFT (version 5.0.2) and polyPhen-2 (version 2.2.2). Patients referred for aHUS genetic testing underwent additional multiplex ligation-dependent probe amplification to detect deletion of CFHR1-CFHR3 at Cincinnati Children’s Hospital as previously described (5).

Table 1. - Clinical gene sets
Clinical Gene Sets
Atypical hemolytic uremic syndrome, thrombotic microangiopathy, and C3 glomerulopathy gene set (13 genes and CFHR3-CFHR1 deletion status)
 ADAMTS13, C3, CD46, CFB, CFH, CFHR1, CFHR2, CFHR3, CFHR4, CFHR5, CFI, DGKE, and THBD; CFHR3-CFHR1 deletion by multiplex ligation-dependent probe amplification
Alport syndrome gene set (3 genes)
 COL4A3, COL4A4, and COL4A5
Cystic disease and nephronophthisis gene set (23 genes)
Nephrotic syndrome and FSGS gene set (34 genes)

Variant Interpretation and Reporting

Variant classifications were based on standards and guidelines published by the American College of Medical Genetics and Genomics (ACMG) and the Association of Molecular Pathology (20). Variant calls were reported using Human Genome Variation Society nomenclature ( and variant attributes were examined using population databases, including the Exome Aggregation Consortium (ExAC version 0.3.1), Genome Aggregation Database (gnomAD version 2.1.0), the National Center for Biotechnology Information’s short genetic variations database (dbSNP version 135), the FH aHUS mutation database (, ClinVar (, the Human Gene Mutation Database public resource ( access 6/3/2018), and an internally curated clinical-grade database of variants and interpretations housed in the Clinical Genomics Workspace (PierianDx, St. Louis, MO) (20,21). All results were reviewed by a pathologist with subspecialty boards in molecular genetics from the American Board of Pathology, or a clinical laboratory geneticist certified in clinical molecular genetics and clinical cytogenetics from the American Board of Medical Genetics and Genomics before release to the patient’s medical record. High-risk APOL1 genotype was defined as the presence of a G1G1, G1G2, or G2G2 genotype (22). Diagnostic yield was defined as the presence of a pathogenic or likely pathogenic variant, APOL1 high-risk genotype, or CFHR3-CFHR1 homozygous deletion in patients referred for aHUS testing.

Retrospective Review of Extended Renal Gene Set

We performed retrospective exome reanalysis of the variant call files for all patients using a curated list of genes implicated in renal disease (extended gene set in Supplemental Table 1) (8,14). Additional filtering criteria based on minor allele frequency in the gnomAD exome database, location of the variant within the gene, annotation based on comparison with clinical databases, and predicted in silico effects were used to enrich the data set for variants more likely to be causative of disease (Supplemental Figure 1). Selected variants outside of the clinically validated gene lists were confirmed by Sanger sequencing before returning results to the patient record.


Patient Characteristics and Diagnostic Yield of Initial Panel-Based NGS Testing for Suspected Genetic Kidney Disease

In total, 324 patients received clinical, panel-based NGS testing for suspected genetic kidney disease between December 1, 2014 and October 31, 2018 (Table 2). Among patients with available clinical history, 78 had ESKD and 48 had undergone kidney transplant at the time of referral (additional clinical information is provided in Supplemental Table 2).

Table 2. - Patient demographics
Gene Set N Age in Years (SD) Gender Race and Ethnicity
aHUS 224 36.0 (19.1) 81 M, 143 F W, 139; B, 34; AS, 7; HIS, 21; OTH, 23
NS/FSGS 56 15.9 (15.1) 31 M, 25 F W, 24; B, 11; AS, 1; HIS, 5; OTH, 15
NPHP 26 20.4 (22.7) 13 M, 13 F W, 11; B, 6; HIS, 1; OTH, 8
Alport 13 29.9 (24.0) 5 M, 8 F W,8; B, 1; OTH, 4
Custom 5 40.2 (17.9) 3 M, 2 F W, 3; B, 2
Total 324 31.1 (20.6) 133 M, 191 F W, 185; B, 53; AS, 8; HIS, 27; OTH, 50
aHUS, atypical hemolytic uremic syndrome; M, male; F, female; W, white; B, black; AS, Asian; HIS, Hispanic; OTH, other; NS, nephrotic syndrome; NPHP, cystic renal disease and nephronophthisis.

The initial panel-based tests returned a clinically significant pathogenic or likely pathogenic variant in 42 patients (13%). An additional 101 (31%) patients had a variant of uncertain significance (VUS) with no pathogenic or likely pathogenic variant. The proportion of patients with a pathogenic or likely pathogenic variant was lowest in the aHUS group (6%) and significantly higher in the NS/FSGS (14%), cystic disease and nephronophthisis (46%), and Alport syndrome (30%) groups (Figure 1).

Figure 1.:
Expanding the gene panel increases sensitivity for detection of a clinically-significant variant. Clinical next-generation sequencing (NGS) for renal disorders: physician-ordered, panel-based clinical testing for suspected genetic kidney disease was performed on 324 consecutive patients for the following indications: (1) atypical hemolytic uremic syndrome (aHUS), thrombotic microangiopathy, and C3 glomerulopathy; (2) nephrotic syndrome and FSGS; (3) nephronophthisis and cystic kidney disease; (4) Alport syndrome; and (5) other. Clinical reports were generated for the initial gene panel and submitted to the patient medical record. Retrospective exome analysis identified additional clinically significant pathogenic and likely pathogenic variants defined by American College of Medical Genetics and Genomics criteria. P, pathogenic; LP, likely pathogenic; VUS, variants of uncertain significance. *Risk, homozygous deletion of CFHR3-CFHR1 are displayed as gene (assignment: number of patients) with “/” representing the presence of multiple variants (20).

Genetic Variants in Patients Referred for aHUS Testing

aHUS is characterized by thrombotic microangiopathy and mutations in complement pathway genes. In total, 224 patients were referred for aHUS panel-based testing. A total of 18 patients had a clinically significant pathogenic or likely pathogenic variant detected in the initial aHUS gene set (Table 3). The genes most commonly affected were CFH (n=6), C3 (n=4), and CD46 (n=4). A total of 24 patients had homozygous loss of CFHR3-CFHR1 detected by multiplex ligation-dependent probe amplification. We identified two novel variants not previously reported in aHUS. CFH p.D1119E was identified in a 9-month-old boy with anemia, thrombocytopenia, bloody diarrhea, and an Escherichia coli–positive stool sample. Similar variants (p.D1119G and p.D1119N) have been reported in aHUS (23,24), and the p.D1119G variant impairs the ability of CFH to bind C3b and C3d (25,26). ADAMTS13 p.Q725* was identified in a 30-year-old man with isolated thrombocytopenia. Heterozygous nonsense variants in ADAMTS13 have been reported in parents of patients with congenital thrombocytopenic purpura and are associated with decreased ADAMTS13 activity (27).

Table 3. - Pathogenic and likely pathogenic variants identified in physician-ordered gene set
Patient Gene Variant Genomic Coordinates (hg37) ACMG Novel Gene Set Family History a
4 C3 p.R161W chr19:g.6718128G>A LP Yes aHUS No
66 C3 p.R1042L chr19:g.6694471C>A LP Yes aHUS No
123 C3 p.K65Q chr19:g.6719296T>G P Yes aHUS No
295 C3 p.K65Q chr19:g.6719296T>G P Yes aHUS No
2 CD46 p.C35Y chr1:g.207930365G>A LP Yes aHUS No
9 CD46 c.287-2A>G chr1:g.207930883A>G P Yes aHUS No
23 CD46 c.1127+2T>G chr1:g.207959029T>G P Yes aHUS No
35 CD46 c.287-2A>G chr1:g.207930883A>G P Yes aHUS No
313 CFB p.K323E chr6:g.31916220A>G LP Yes aHUS No
113 CFH p.D1119E chr1:g.196714993C>G LP No aHUS No
166 CFH p.R1210C chr1:g.196716375C>T LP Yes aHUS No
234 CFH p.R53H chr1:g.196642207G>A LP Yes aHUS No
298 CFH p.T956M chr1:g.196709833C>T LP Yes aHUS No
267 CFH c.3493+1G>A chr1:g.196715130G>A P Yes aHUS No
18 CFH c.619+1G>A chr1:g.196646798G>A P Yes aHUS No
71 CFI p.I370N chr4:g.110670413A>T LP Yes aHUS No
304 CFI p.R474* chr4:g.110667387G>A P Yes aHUS No
85 DGKE p.W322*,p.W322* chr17:g.54926134G>A P Yes aHUS No
102 COL4A3 p.G695R chr2:g.228142227G>A LP Yes AS Yes
280 COL4A4 p.G478E chr2:g.227954610C>T LP Yes AS Yes
271 COL4A5 c.231+1G>A chrX:g.107802384G>A P Yes AS Yes
264 COL4A5 c.4315+1G>A chrX:g.107929360G>A P Yes NPHP No
188 NPHP4 p.K424Rfs*7 chr1:g.5993238delT LP Yes NPHP Yes
244 PKD1 p.G3326D chr16:g.2149718C>T LP Yes NPHP Yes
252 PKD1 p.G1433R chr16:g.2160661C>T LP Yes NPHP Yes
250 PKD1 c.9712+1G>A chr16:g.2150166C>T P Yes NPHP Yes
273 PKD1 p.Q1203* chr16:g.2161561G>A P Yes NPHP No
176 PKD1 p.V2569Rfs*43 chr16:g.2143887_2143888insG P Yes NPHP No
190 PKD1 p.A3082Cfs*96 chr16:g.2152218_2152219delAT P Yes NPHP Yes
225 PKD1 p.L3834Cfs*111 chr16:g.2141819delG P No NPHP Yes
245 PKD1 p.Q2900* chr16:g.2153360G>A P Yes NPHP No
248 PKD1 p.L3999Wfs*40 chr16:g.2140892delA P No NPHP No
207 PKHD1 p.V1741M chr6:g.51889387C>T LP Yes NPHP No
233 RPGRIP1L p.D596Efs*5 chr16:g.53686809_53686823delinsAC LP No NPHP No
102 COL4A3 p.G695R chr2:g.228142227G>A LP Yes NS/FSGS Yes
98 INF2 p.R177C chr14:g.105169653C>T LP Yes NS/FSGS Yes
161 INF2 p.R177C chr14:g.105169653C>T LP Yes NS/FSGS No
277 NPHS1 p.R743C chr19:g.36334481G>A LP Yes NS/FSGS No
24 NPHS1 p.T847Rfs*57 chr19:g.36333146_36333149delTTAG P Yes NS/FSGS No
213 NPHS1 p.L16Gfs*3 chr19:g.36342694_36342695insCC P No NS/FSGS No
193 NPHS2 p.R138Q chr1:g.179530462C>T P Yes NS/FSGS Yes
238 NPHS2 p.R291W chr1:g.179521740G>A P Yes NS/FSGS No
216 WT1 p.H445R chr11:g.32414217T>C P Yes NS/FSGS No
Variant pathogenicity assignments based upon American College of Medical Genetics and Genomics guidelines (20). ACMG, American College of Medical Genetics and Genomics; chr, chromosome; LP, likely pathogenic; aHUS, atypical hemolytic uremic syndrome; P, pathogenic; AS, Alport syndrome; NPHP, cystic renal disease and nephronophthisis; del, deletion; ins, insertion; NS, nephrotic syndrome.
aFirst or second-degree relative with CKD or kidney transplant.

Exome reanalysis identified a previously unknown pathogenic or likely pathogenic variant in 12 patients initially referred for panel-based aHUS testing (Table 4). These data resulted in a new genetic diagnosis in eight patients and confirmed the genetic cause of an existing clinical diagnosis of autosomal dominant polycystic kidney disease (n=3) or Laurence–Moon–Biedl syndrome (n=1) in four patients. Two patients that received a new genetic diagnosis were newborns under evaluation for potential neonatal aHUS. In the first patient, exome reanalysis identified a previously reported homozygous nonsense mutation in REN (p.43*), which leads to absent renin expression at the protein and RNA level (28). In the second patient, exome reanalysis identified previously reported compound heterozygous mutations in SLC26A1 (p.S358L, p.T185M) associated with ARF and calcium oxalate nephrolithiasis (29).

Table 4. - Pathogenic and likely pathogenic variants identified in exome reanalysis
Patient Age (yr) Gene Variant Genomic Coordinates (hg37) Clinical Diagnosis/Indication Initial Ordered Gene Set Family History a
138 75 ADAMTS13 p.D187H b chr9:g.136291338G>C aHUS aHUS No
302 30 ADAMTS13 p.Q725* b chr9:g.136307803C>T Thrombocytopenia aHUS No
318 19 COL4A4 p.R989fs chr2:g.227917021_227917022delCT ESKD and thrombotic microangiopathy aHUS No
p.R989fs chr2:g.227917021_227917022delCT
79 26 EYA1 p.Y226* chr1:g.248813510A>G ESKD due to membranoproliferative GN aHUS No
231 19 PKD1 p.R3277C chr16:g.2149956G>A ESKD due to C3 glomerulopathy and cystic kidneys. aHUS No
152 74 PKD1 p.A2375V c chr16:g.2156891G>A ADPKD rule out aHUS aHUS No
198 33 PKD2 p.R213* c chr4:g.88940651C>T ADPKD rule out aHUS aHUS Yes
219 58 PKD2 p.R213* c chr4:g.88940651C>T ADPKD rule out aHUS aHUS Yes
195 <1 REN p.R43* chr1:g.204131263G>A Congenital ARF aHUS No
p.R43* chr1:g.204131263G>A
69 <1 SLC26A1 p.S358L chr4:g.983654G>A Neonatal aHUS aHUS No
p.T185M chr4:g.984938G>A
52 35 USH2A p.R4935* c chr1:g.215814065G>A Laurence–Moon–Biedl syndrome aHUS No
206 9 WT1 p.R462W chr11:g.32413566G>A aHUS aHUS No
119 17 LAMB2 p.C1058fs chr3:g.49161980_49161981delCA Gross hematuria AS Yes
NPHS2 p.R138Q chr1:g.179530462C>T
264 15 TRPC6 p.N125S chr11:g.101375326T>C Collapsing FSGS and thin basement membranes AS No
212 <1 ANKS6 p.L434fs chr9:g.101542538delA Congenital renal dysplasia, heart abnormalities, and hepatosplenomegaly NPHP No
c.1973-3C>G chr9:g.101530535G>C
268 <1 PBX1 p.Q48* chr1:g.164529201C>T Congenital renal dysplasia NPHP No
168 3 WDR19 p.L92* chr4:g.39191386T>G Congenital absence left kidney and enlarged, dysplastic right kidney NPHP No
p.N273D chr4:g.39207283A>G
96 9 COL4A5 c.2042–2A>T chrX:107845113A>T Nephrotic syndrome NS/FSGS Yes
197 37 PAX2 p.G107R chr10:102510464G>C Adult-onset FSGS NS/FSGS Yes
281 53 UMOD p.H177_R185del chr16:g.20360068_20360094del FSGS NS/FSGS Yes
67 64 EYA1 p.L513P chr8:g.72127681A>G Congenital renal dysplasia and hearing loss GRHPR No
134 10 BBS10 p.Y559* chr12:g.76740088G>T Cystic renal disease NS/FSGS, NPHP No
CEP104 p.Q335* chr1:g.3753972G>A
Variant pathogenicity assignments based upon American College of Medical Genetics and Genomics guidelines (20). chr, chromosome; aHUS, atypical hemolytic uremic syndrome; ADPKD, autosomal dominant polycystic kidney disease; AS, Alport syndrome; NPHP, cystic renal disease and nephronophthisis; NS, nephrotic syndrome.
aFirst or second-degree relative with CKD or kidney transplant.
bVariants were identified in patients that underwent testing before the addition of ADAMTS13.
cVariants are associated with an established clinical diagnosis.

Exome reanalysis detected an additional seven patients with a high-risk APOL1 genotype in the absence of a pathogenic, likely pathogenic, or VUS. APOL1 high-risk genotype confers increased risk for ESKD and FSGS, but has not been previously associated with aHUS (30). All seven patients were black and the three patients with available clinical history had ESKD.

Genetic Variants in Patients Referred for NS, FSGS, and Alport Syndrome Testing

Genetic mutations in glomerular basement membrane proteins can lead to NS, FSGS, and Alport syndrome. In total, 56 patients were referred for the NS/FSGS gene set and 13 patients were referred for the Alport gene set. Initial panel-based NGS testing for the NS/FSGS and Alport gene sets identified 12 patients with a pathogenic or likely pathogenic variant (Table 3). The genes most commonly affected were NPHS1 (n=3), COL4A5 (n=3), and NPHS2 (n=2). We identified a previously unreported pathogenic NPHS1 frameshift variant (p.L16Gfs*3) in a 1-month-old girl with NS that is expected to result in loss of function.

Exome reanalysis identified previously unknown pathogenic or likely pathogenic variants that resulted in a new genetic diagnosis in five patients (Table 4). Two of these patients were diagnosed with autosomal dominant conditions that have important implications for family members. The first patient had a strong family history of kidney disease and a previously reported variant in UMOD (p.H175_R185del) that causes autosomal dominant tubulointerstitial kidney disease. The second patient was a 37-year-old man with ESKD and biopsy specimen–proven FSGS that was presumed secondary to obesity. He had a strong family history of kidney disease and a novel heterozygous variant in PAX2 (p.G76R) that is located at the same position as a variant previously described in the context of renal coloboma syndrome (31).

Genetic Variants in Patients Referred for Cystic Kidney Disease and Nephronophthisis Testing

Cystic kidney disease and nephronophthisis are a spectrum of disorders that can have phenotypic overlap with congenital anomalies of the kidney and urinary tract. In total, 26 patients were referred for the cystic kidney disease and nephronophthisis gene set. Initial panel-based testing identified 13 patients with a pathogenic or likely pathogenic variant (Table 3). The majority of these patients had pathogenic or likely pathogenic variants in PKD1 (n=9).

Exome reanalysis identified three patients with previously unknown pathogenic or likely pathogenic variants resulting in a new genetic diagnosis. The first patient was a 1-month-old girl with congenital echogenic kidneys and compound heterozygosity for a novel frameshift (p.L434fs) and previously reported splicing variant (c.1973-3C>G) in ANKS6 (32). The second patient was a 5-month-old boy with bilateral renal dysplasia and a heterozygous nonsense variant in PBX1 (p.Q48*). The third patient was a 3-year-old boy with congenital absence of the left kidney and compound heterozygosity for a novel frameshift variant (p.L92*) and previously reported missense variant (p.N273D) in WDR19 (33).


In this study, we report our 4-year experience with NGS panel-based testing for suspected genetic kidney disease in a clinically validated laboratory. The initial physician-ordered clinical gene sets resulted in a genetic diagnosis in 64 of 324 patients. These results include pathogenic (n=23) and likely pathogenic (n=19) variants in addition to CFHR3-CFHR1 homozygous deletion in patients with aHUS (n=22). Retrospective exome reanalysis of kidney disease–related genes resulted in a clinically meaningful genetic diagnosis in an additional 26 patients. The retrospective results included additional pathogenic or likely pathogenic variants in kidney disease genes (n=18) and eight patients with a high-risk APOL1 genotype. Our exome reanalysis indicates that affected patients are likely to have pathogenic variants in genes that rarely cause kidney disease, because no two patients had a variant in the same gene. These data highlight the difficulty in designing a disease-focused gene set for renal disorders that often have overlapping clinical and pathologic phenotypes.

In light of these findings, we are in the process of reviewing our testing paradigm that reflexes to an expanded panel in cases where pathogenic or likely pathogenic variants are not identified in the initial, clinically indicated gene set. Furthermore, as new genes are added to our clinical gene sets, all patients that were tested before the addition of the new gene will be interrogated for pathogenic variants. Although there are currently no consensus guidelines, we believe that good laboratory practices should include retrospective review of negative cases on a semiannual to yearly basis

There is a wide spectrum of genetic testing options that range from single gene to panel-based and whole-exome sequencing for both somatic and constitutional disorders (34,35). Nephrology was a late adopter of clinical NGS testing and, as a result, relatively little is known about the optimal approach to diagnosis of genetic kidney disease. We can draw from the experience of other specialties to better understand how different modalities apply to diagnosis, prognosis, and precision medicine (34). To date, disease-focused panels have been the preferred approach for clinical applications; however, there are challenges to this approach that include development and validation of separate panels for each disease process, constant updating of existing panels, and adherence to changing medical guidelines. These challenges have raised the question of whether clinical testing should use a more comprehensive approach like whole-exome or whole-genome sequencing (11,34). However, the benefits of a comprehensive approach have to be weighed against their increased cost and turnaround time, in addition to the increased likelihood of discovering VUS that may or may not be related to the patient’s disease process. Currently, the majority of clinical laboratories rely on panel-based approaches for the diagnosis of genetic kidney disease. An important caveat is that laboratories often use an “exome backbone” for their clinical panels. This approach simplifies laboratory workflow by employing a single library preparation, sequencing, and bioinformatics process for all patient samples, and is made possible by the decreasing cost of exome sequencing. The end result is that there is a large amount of unanalyzed genetic data that may contain clinically meaningful information. A simple compromise might be to establish a workflow where the remaining genetic data is unmasked and analyzed when the first disease-focused panel is negative.

Remaining challenges in the diagnosis of genetic kidney disease include difficulty in establishing the pathogenicity of VUS and the detection of structural variants, which represent a minority of inherited kidney disease (2). The difficulty with interpreting VUS will be partly resolved as clinical databases increase in size to include new patient populations and pathogenic variants.

However, there is a role for use of in silico computational prediction tools for assessing variant pathogenicity. In our study, we used multiple in silico tools to predict the functional consequence of variants encountered in exome reanalysis. By limiting our investigation to variants with a predicted damaging or deleterious effect, we were able to increase the likelihood that variants were assigned a likely pathogenic or pathogenic assertion under ACMG criteria (36). One of the drawbacks of our approach is that we likely missed clinically significant variants with a benign functional prediction. In silico analysis alone is insufficient to assign pathogenicity and is lacking in sensitivity and specificity (35,37). A potential solution could be to design clinically validated functional assays to provide evidence for pathogenicity.

Recently, at least one group has published a case series describing a comprehensive panel-based approach (approximately 200 genes) in a series of 127 patients (38). Diagnostic yield ranged from 30% to 54%, depending on disease category, and was higher in younger patients (0–14 years), which comprised the majority of their population. An important finding was that among the 54 patients with a genetic diagnosis, 13 patients had a change in clinical diagnosis due to genetic testing. These data raise the possibility that had these patients initially received a narrow, disease-focused panel; it is possible that their genetic diagnosis would have been missed. Together with our data, these findings argue for a comprehensive approach and highlight the benefits of clinical NGS for kidney disease. NGS-based testing will continue to be an important component of the diagnostic workup in patients with a suspected genetic component to their kidney disease and this study demonstrates the utility of variant reanalysis to improve diagnostic yield .


J. Gaut reports personal fees from BioLegend, grants from National Institutes of Health, and grants from Mid America Transplant Foundation, outside the submitted work. All remaining authors have nothing to disclose.



Supplemental Material

This article contains supplemental material online at

Supplemental Table 1. Extended renal gene set.

Supplemental Table 2. Patient demographics and clinical history.

Supplemental Figure 1. Filtering strategy.


The results presented in this paper have not been published previously in whole or part, except in abstract format.

Author Contributions

M. Corliss, J. Gaut, L. Love-Gregory, S. McNulty, and P. Wilson reviewed and edited the manuscript and were responsible for data curation, investigation, and methodology; J. Gaut, L. Love-Gregory, S. McNulty, and P. Wilson were responsible for formal analysis and software; J. Gaut, L. Love-Gregory, and P. Wilson conceptualized the study; J. Heusel, J. Gaut, L. Love-Gregory, and P. Wilson provided supervision; L. Love-Gregory and P. Wilson were responsible for visualization; P. Wilson wrote the original draft; and all authors were responsible for project administration, resources, and validation.

See related editorial, “Genetic Analysis in Kidney Disease: Advancing Clinical Diagnosis and Research Discovery” on pages .


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