The population frequency of predicted pathogenic variants in commonly-affected genes in CAKUT in the general population

The population frequency of predicted pathogenic variants in commonly-affected genes in CAKUT in the general population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The population frequency of predicted pathogenic variants in commonly-affected genes in CAKUT in the general population Mary Huang, Judy Savige This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8297749/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of kidney failure in children, and renal imaging suggests that it affects one in 200 of the population, one in five of whom are thought to have a genetic cause. This study determined the population frequency of predicted pathogenic variants from the six most commonly-affected CAKUT genes. Methods HNF1B , SALL1 , EYA1 , PBX1 , GATA3 , PAX2 variants were downloaded from gnomADv.2.1.1 (n = 141,456) and the population frequency of predicted disease-causing variants calculated from the sum of structural, null (loss-of- function) and predicted pathogenic missense changes in the overall cohort and in the ancestries represented. This was compared with the population frequencies derived from ClinVar, HGMD and LOVD. Population frequencies were also determined in a replication cohort (gnomAD v.4.1, n = 807,162) using our method and ClinVar assessments. Results The population frequency of genetic causes of CAKUT lies between one in 249 (our strategy) and one in 1,263 (ClinVar assessments) in the gnomAD v.2.1.1 database. More than half the disease-causing variants were missense changes, and predicted pathogenic variants were commonest in African-Americans (one in 149) and least common in Ashkenazim (one in 864). The population frequency estimated from gnomAD v.4.1 lies between one in 372 (our strategy) and one in 1762 (with ClinVar). Discussion The ClinVar results are underestimates since assessments were not available for structural, copy number and many missense changes in gnomAD. However some of the predicted pathogenic variants identified in this study will have incomplete penetrance or be variably expressive and, therefore, result less often in clinical disease. Nevertheless, these calculations suggest that genetic causes of CAKUT are likely to be more common than the previously-reported one in 1,000. CAKUT congenital anomalies of the kidney and urinary tract kidney development reflux kidney agenesis kidney atrophy kidney cysts Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Congenital anomalies of the kidney and urinary tract (CAKUT) include a range of developmental malformations of the kidney and urinary tract. CAKUT is the commonest birth anomaly affecting 3 to 7 of every 1000 newborn ( 1 ) and is the leading cause of kidney failure in children ( 2 , 3 ). Common forms of CAKUT include ureteropelvic junction obstruction ( 4 ), kidney agenesis, dysplasia, hypoplasia, multicystic dysplastic kidneys, kidney, vesicoureteral reflux, megaureter, ectopic ureter, horseshoe kidney, duplex collecting system, and posterior urethral valves. Isolated CAKUT occurs without additional anomalies and syndromic CAKUT has further extra-renal features ( 4 ). Affected infants are typically diagnosed antenatally on ultrasound imaging but those who are asymptomatic may not be identified until adolescence or later ( 5 ). Some of those affected remain undiagnosed throughout life but may still pass on the disease-causing variant and clinical manifestations to their offspring. CAKUT results from abnormal kidney development that is secondary to genetic or environmental factors. Maternal diabetes, medications, and folate and iron deficiency all increase the risk ( 6 , 7 ). Genetic causes have been reported to only account for 20% of cases ( 8 ) so that genetic forms of CAKUT are thought to affect one in 1000 of the population. More than 150 genes are now associated with CAKUT, with many encoding transcription factors that are important in embryonic kidney development ( 9 , 10 ). The six commonest affected genes are hepatocyte nuclear factor-1B ( HNF1B ), spalt-like transcription factor 1 ( SALL1 ), eyes absent homolog 1 ( EYA1 ), pre-B cell leukemia ( PBX1 ), GATA binding protein 3 ( GATA3 ), and paired box gene 2 ( PAX2 ) ( 11 ), which are all monoallelic ( https://www.omim.org/ ). Disease-causing variants in HNF1B and PAX2 together account for at least 15% of people with CAKUT in case series ( 12 – 15 ). Pathogenic variants in most of these genes have a variable phenotype. HNF1B is reported to be the commonest cause of monogenic CAKUT (MIM137920). It encodes a transcription factor of the homeodomain-containing superfamily that is essential for the development of many organs including the kidneys and urogenital tract, brain, pancreas, liver, and parathyroids ( 11 ). Pathogenic HNF1 B variants result in HNF1B- nephropathy (formerly Renal Cysts And Diabetes syndrome), multicystic kidney disease, Focal and Segmental Glomerulosclerosis (FSGS), or a tubulopathy as well as genital anomalies and monogenic diabetes (previously Maturity Onset Diabetes of the Young)( 16 ) . PAX2 i s considered the second commonest cause of monogenic CAKUT ( 14 ). PAX2 variants are associated with renal coloboma syndrome (MIM 120330) and sometimes renal hypoplasia, multicystic dysplastic kidneys or vesicoureteral reflux ( 4 , 17 ). Pathogenic variants are also associated with Focal and Segmental Glomerulosclerosis (FSGS) in children ( 18 ) and adults ( 19 ). EYA1 is a transcription factor important in development of the kidney, branchial arches and ears ( 11 ). Variants in EYA1 result in Branchio-Oto-Renal syndrome (MIM 602588) with kidney agenesis and dysplasia in two-thirds of affected people ( 11 ), as well as ear abnormalities, and branchial fistulae and cysts ( 11 ). SALL1 Loss of Function variants result in Townes-Brocks syndrome (MIM 107480) ( 20 ) with renal hypodysplasia, ectopia, polycystic kidneys and vesicoureteral reflux together with an imperforate anus, dysplastic ears and impaired hearing and thumb abnormalities ( 11 , 20 ). GATA3 encodes a zinc-finger transcription factor expressed in the kidneys, inner ear, and parathyroids ( 11 ). Pathogenic GATA3 variants are associated with hypoparathyroidism, sensorineural hearing loss, and a solitary kidney, renal hypodysplasia or vesicoureteral reflux ( 11 ) (MIM 146255) ( 21 ). PBX1 is another transcription factor ( 11 ) and pathogenic PBX1 variants are associated with hypoplastic and cystic kidneys, reflux, abnormal male genitalia and sometime congenital cardiac anomalies ( 1 ). These are the classical clinical features associated with pathogenic variants in these genes, but the phenotypes in affected family members often differ because of incomplete penetrance and variable expression, and other genetic or environmental factors ( 22 ). Previous estimates of the population frequency of CAKUT based on clinical screening have varied from one in 56 ( 23 ), 103 ( 24 ), or 627 ( 25 , 26 ) to one in 2,400 ( 27 ). Having a more accurate population frequency is important to alert clinicians to the likelihood of encountering patients with CAKUT, the need to examine for extrarenal manifestations, and for health service planning. This study calculated the population frequency of CAKUT from the number of predicted pathogenic variants in the 6 commonest monogenic disease-causing genes in a cohort of normal people (Genome Aggregation Database, gnomADv.2.1.1). Examination of pathogenic variants for these genes in Simple ClinVar (Fig. 1 , https://simple-clinvar.broadinstitute.org/ ) indicated that variants were located throughout the genes, and included both loss-of-function (null) and missense changes. While these genes are the commonest affected in CAKUT, many others have been implicated too but often only in a single family ( 11 , 28 ). We confirmed our results in gnomAD v.4.1 which is a much larger dataset, that includes more structural and copy number variants, and where there is only 20% overlap with gnomADv.2.1.1. We then compared our population frequencies with those derived from gnomAD variants assessed as disease-causing in the ClinVar database ( https://www.ncbi.nlm.nih.gov/clinvar ), The Human Gene Mutation Database (HGMD), https://www.hgmd.cf.ac.uk/ac/index.php ), or the Leiden Open Variation Database (LOVD v.3.0, https://www.lovd.nl ). Our approach was based on the ACMG/AMP principles ( 29 ) and assessed all gnomAD variants ( Fig. 2 ) , but gnomAD has no clinical data and we may have misinterpreted some benign changes. Nevertheless population frequencies using a similar, and typically less rigorous, strategy have been published previously for many diseases including Alport syndrome ( 30 ), Gitelman syndrome ( 31 ), AD polycystic kidney disease ( 32 ), the mucopolysaccharidoses ( 33 ), Menke disease ( 34 ), and Fabry disease ( 35 ). In some of these conditions, the population frequency was confirmed using an independent biochemical or histological method ( 30 , 31 ). Methods gnomAD v.2.1.1 and v.4.1 variant databases Variants in EYA1 , GATA3 , HNF1B , PAX2, PBX1 and SALL1 were downloaded from gnomAD v2.1.1.(GRCh37/hg19, www.gnomAD.broadinstitute.org ) and v.4.1 (GRCh38/hg38). gnomAD v.2.1.1 (n = 141,456) comprises Whole Exome Sequencing (WES, n = 125,748) and Whole Genome Sequencing (WGS, n = 15,708) from clinical trials studying unrelated adults with diabetes, neuropsychiatric or cardiac disease. gnomAD v.4.1 includes unrelated adults (n = 807,162), whose DNA was, in some instance, examined for structural (n = 63,046) or copy number (n = 464,297) variants too. Both gnomAD v.2.1.1 and v.4.1 included equal numbers of men and women, and their ancestries, but no clinical data. There is less than 20% overlap between the two cohorts. gnomADv.2.1.1 was first accessed in March 2023, and reviewed in May 2024, and gnomAD v.4.1 was reviewed in November 2024. All participants in gnomAD had provided written, informed consent for their data to be shared anonymously and available publicly for further research at the time of recruitment, so that Austin Health Institutional Review Board approval was not required for this secondary use of the publicly available data. Annotation and filtering Our strategy has been described previously( 36 ). Variants in these genes were annotated using ANNOVAR ( https://annovar.openbioinformatics.org/ ). Variants in the intronic and 5’ or 3’ UTR or that were intronic, noncoding, splice region or synonymous were excluded. Other variants were filtered according to the following approach ( Fig. 2 ). Structural Variants Structural variants that were deletions and affected exons were considered pathogenic regardless of their allele counts. The number of structural variants in this subset was corrected to be equivalent to the whole cohort. Null variants Null variants including nonsense variants (except in the last exon and the last 50 nucleotides of the penultimate exon, which escape nonsense-mediated decay) (Supplementary Table S1 ) , canonical splice site, and frameshift variants were classified as pathogenic regardless of their allele counts. Missense variants Missense variants were ‘predicted pathogenic’ if they were rare (allele count < 6) found to be pathogenic using all three bioinformatic tools: SIFT4G (Sorting Intolerant From Tolerant) score ≤ 0.05, PP2 (Polymorphism Phenotyping v2, PolyPhen-2) score ≥ 0.95 ( http://genetics.bwh.harvard.edu/pph2/ ) and Mutation Taster where variants were classified as ‘disease causing’ or (D) or (A) ( https://www.mutationtaster.org/info/ documentation.html). Variants were also examined to determine if they affected an amino acid conserved (* or :).in vertebrates (chicken, mice, humans) using Clustal Omega ( https://www.ebi.ac.uk/Tools/ ) and the Ensembl reference sequences ( http://asia.ensembl.org/index.html ). The strategy for assessing missense variants was validated in two ways. The sensitivity, specificity, and positive and negative predictive values were calculated using variants that were classified as pathogenic or benign in an independent database (generally LOVD) that used clinical data in the variant evaluation (Supplementary Table S2 ). Our assessment for all these genes performed satisfactorily with high sensitivities (median 81%), specificities (median 100%), and positive (PPV, median 100%) and negative predictive values (NPV, median 73%) except for SALL1 which was tested with the only four pathogenic missense variants available and identified only one of these. Secondly, REVEL (Rare Exome Variant Ensemble Learner) scores > 0.932 or > 0.80 was used to assess missense variants in the gnomAD v.2.1.1 cohort, and the number added to the Structural and Null variants to obtain an independent assessment of the population frequency ( 37 , 38 ). Calculation of population frequency from gnomAD Variants downloaded from gnomAD that fulfilled all our criteria for disease-causing were classified as ‘predicted pathogenic’ to distinguish them from the ‘Pathogenic’ and ‘Likely pathogenic’ terms used by the ACMG/AMP criteria ( 29 ). Population frequencies of the 6 AD CAKUT genes were calculated using the average allele count and total number of people examined for each gene. The population frequencies for these genes were calculated assuming that each person included in gnomAD had only one genetic variant for CAKUT. Predicted pathogenic variants for CAKUT in people of different ancestries Population frequencies of predicted pathogenic variants were then calculated for people of each ancestry included in this version of gnomAD. Population frequencies of CAKUT using different databases All gnomAD variants were also examined in ClinVar, HGMD and LOVD databases for previous reports of pathogenicity. The population frequencies for gnomAD variants that were Pathogenic, Likely pathogenic or Conflicting (VUS plus Pathogenic or Likely Pathogenic) were then calculated for ClinVar, and the population frequencies for pathogenic variants in HGMD and LOVD were also calculated. Disease-causing variants in these databases were included regardless of the number of times they were found in gnomAD. Population frequency of CAKUT of gnomAD v.4.1 using our strategy and ClinVar We then repeated the population frequency using our approach and gnomAD v.4.1 including Structural variants (n = 63,046) and Copy number variants (n = 464,297) both corrected (x13, x1.7 respectively) for the whole cohort, as well as null and missense variants. In addition, we assessed the population frequency of CAKUT from gnomAD v.4.1 using variants assessed as Pathogenic or Likely pathogenic or Conflicting (P/LP/VUS) in ClinVar as described above. Statistical Analysis Results were compared using chi-squared testing ( https://www.graphpad.com/quickcalcs/ ). Results Population frequency of CAKUT from our strategy of gnomAD v.2.1.1 Overall there were 273 variants in one of the 6 CAKUT genes in 461 people from gnomADv.2.1.1 that were assessed as predicted pathogenic. This was equivalent to a population frequency for CAKUT of 461 in 114,963 or one in 249 people ( Table 1 ). Table 1 Population frequencies of Predicted pathogenic variants in CAKUT-associated genes in gnomADv.2.1.1 and the control subset HNF1B (n = 119,441 people) SALL1 (n = 120,389 people ) EYA1 (n = 120,908 people) PBX1 (n = 98,815 people ) GATA3 (n = 109,227 people ) PAX2 (n = 120,946 people ) Total (n = 114,963 people) Structural variants (n = 10,847) One variant in 3 people, 33 people corrected for whole cohort None None None None None One variant in 33 people Null variants 2 variants in 2 people One variant in 1 person 8 variants in 8 people 3 variants in 3 people 3 variants in 3 people 8 variants in 23 people 25 variants in 40 people Missense variants 27 variants in 39 people 114 variants in 186 people 50 variants in 84 people 8 variants in 9 people 17 variants in 27 people 31 in 43 people 247 variants in 388 people Total 30 variants in 74 people 115 variants in 187 people 58 variants in 92 people 11 variants in 12 people 20 variants in 30 people 39 variants in 66 people 273 variants in 461 people Population frequency One variant in 1,614 people One variant in 644 people One variant in 1,314 people One variant in 8,234 people One variant in 3,641 people One variant in 1,832 people One variant in 249 people 1 Structural variants corrected for smaller whole genome sequencing cohort x 10,847 2 Null variants including < 6 but not in the last exon or where ClinVar said benign or low confidence 3 Total Pathogenic variants positive in all, <6 Variants in the SALL1 gene were the commonest predicted pathogenic variants (one in 644) followed by variants in EYA1 (one in 1,314) or HNF1B (one in 1,614). HNF1B deletions occurred in nearly half the people with HNF1B variants ( 16 ), and the number of HNF1B structural variants after correction (n = 33) was nearly equivalent to the combined number of missense and null changes (n = 41). No pathogenic structural variants were found in the other five genes. In general, more people had missense (n = 388) than other variants (n = 73) using our strategy. When a REVEL score > 0.932 was used to assess the missense variants in gnomAD v.2.1.1, structural, null and predicted pathogenic missense variants were found in 188 people corresponding to a population frequency of one in 611 (188/114,963) (p 0.932 may have been too stringent and resulted in an underestimate for the population frequency. When a REVEL score > 0.8 was used instead predicted pathogenic variants were found in 319 people corresponding to a population frequency of one in 360 (319/114,963). Table 2 Population frequency of Predicted pathogenic variants in CAKUT-associated genes in gnomAD v.2.1.1using various databases HNF1B (n = 119,441) SALL1 (n = 120,389) EYA1 (n = 120,908) PBX1 (n = 98,816) GATA3 (n = 109,277) PAX2 (n = 120,946) Total (n = 114,963) Population frequency (n = 114,963) Our assessment 30 variants in 74 people 115 variants in 187 people 58 variants in 92 people 11 variants in 12 people 20 variants in 30 people 39 variants in 66 people 273 variants in 461 people 461/114,963 or one in 249 REVEL > 0.932 (missense only) 5 variants in 9 people 2 variants in 2 people 6 variants in 12 people None 4 variants in 5 people 8 variants in 17 people 25 variants in 45 people 45/114,963 or one in 2555 REVEL (SV and CNV, null and missense variants) 36 variants in 114 people 3 variants in 3 people 14 variants in 20 people 3 variants in 3 people 7 variants in 8 people 16 variants in 40 people 78 variants in 155 people 188/114,963 or one in 612 ClinVar 13 variants in 61 people 3 variants in 5 people 5 variants in 6 people 1 variant in one person 2 variants in 5 people 2 variants in 13 people 26 variants in 91 people 91/114,963 or one in 1,263 HGMD 27 variants in 1179 people 11 variants in 1156 people 12 variants in 430 people 1 variant in one person 1 variant in one person 6 variants in 85 people 58 variants in 2852 people 2,852/114,963 or one in 40 LOVD 6 variants in 12 people None 9 variants in 119 people None 1 variant in one person 2 variants in 131 people 18 variants in 263 people 263/114,963 or one in 437 N = total number in cohort examined for variants in this gene; SV structural variants; CNV copy number variants. When gnomAD variants were examined for those found Pathogenic or Likely pathogenic in ClinVar there were variants in 91 people corresponding to a population frequency of one in 1263 (p < 0.0001 compared with our assessment) ( Table 2 ). While ClinVar assessments are considered accurate, they were not available for all gnomAD variants so that the calculated population frequency was an underestimate. In particular there were no ClinVar assessments for structural variants. If the number of structural variants found disease-causing in our assessment were added to the ClinVar assessments, then there would be 124 variants in 114,963 or one in 923 people. ClinVar assessments in gnomADv.2.1.1 were available for 78/273 variants in HNF1B (29%), 131/786 in SALL1 (17%), 76/310 in EYA1 (25%), 6/126 in PBX1 (5%), 33/214 in GATA3 and 56/229 in PAX2 (24%) that is with a median of 21%, range 5–29% for gnomAD v.2.1.1. When variants were examined for those also found in HGMD there were 2,852 variants corresponding to a population frequency of one in 40 ( Table 2 ). When pathogenic variants in LOVD were used to examine the database there were 263 variants corresponding to a population frequency of one in 437 ( Table 2 ). When our Predicted pathogenic variants were examined in people of different ancestries, they were commonest in people of African/American ancestry (84/12,487, or one in 149) and East Asian (41/9,977, one in 243) and least common in Finnish people (15/12,562, one in 837 people) and Ashkenazim (6/5185 or one in 864) ( Table 3 ). These calculations included structural, null and missense variants. Table 3 Predicted pathogenic variants in the CAKUT-associated genes in people of differing ancestries HNF1B (n = 119,441) SALL1 (n = 120,389) EYA1 (n = 120,908) PBX1 (N = 98,815) GATA3 (n = 109,227) PAX2 (n = 120,946) Total (n = 114,963) Population frequency in this ancestry African American (n = 12,487) SV 33 0 0 0 0 0 33 84/12,487 0r one in 149 Null 0 0 1 1 0 0 2 Missense 5 22 10 0 3 9 49 Latino (n = 17,720) Null 0 0 2 1 0 4 7 48/17,720 or one in 369 Missense 3 25 6 0 4 3 41 Ashkenazim (n = 5,185) Null 0 0 0 0 1 0 1 6/5,185 or one in 864 Missense 0 2 1 0 2 0 5 East Asian (n = 9,977) Null 1 0 0 0 0 10 1 41/9,977 or one in 243 Missense 6 13 3 0 2 6 30 European (n = 64,603) Null 1 0 5 0 1 5 12 203/64,603 or one in 318 Missense 12 102 45 6 13 13 191 Finnish (n = 12,562) Null 0 1 0 0 1 0 2 15/12,562 or one in 837 Missense 1 3 3 0 3 3 13 South Asian (n = 15,308) Null 0 0 0 0 0 2 2 53/15,308 or one in 289 Missense 12 16 12 3 0 8 51 Other (n = 3,614) Null 0 0 0 0 0 2 2 11/3,614 or one in 329 Missense 0 3 4 1 0 1 9 SV structural variants Population frequency of CAKUT from our approach and ClinVar assessment of gnomAD v.4.1 gnomAD4.1 variants were evaluated using our assessment and the ClinVar assessment based on the previous results. gnomAD v.4.1 had the advantage of including structural and copy number variants in some patients. Our assessment found 733 predicted pathogenic variants in 2,168 people, corresponding to a population frequency of one in 372 people (p < 0.0001 compared with our estimate in gnomAD v.2.1.1) ( Table 4 ). Table 4 Predicted pathogenic variants in gnomAD v.4.1 in the 6 CAKUT-associated genes using our strategy and ClinVar assessments HNF1B SALL1 EYA1 PBX1 GATA3 PAX2 Total Our strategy Structural variants (n = 63,046) (x13, corrected number for whole cohort ) One LoF variant in 7 people (91 people) No variants 3 LoF variants in 12 people (156 people) No variants One LoF variant in 6 people (78 people) 2 LoF variants in 6 people (78 people) 7 LoF variants in 403 people Copy number variants (n = 464,297) (x1.7, corrected number for whole cohort) 7 variants in 154 people (268 people) No variants 6 variants in 6 people (10 people) 2 variants in 2 people (3 people) 3 variants in 5 people (9 people) 1 variant in 2 people (3 people) 19 variants in 293 people Null variants 14 variants in 31 people 21 variants in 28 people 55 variants in 313 people 11 variants in 12 people 17 variants in 32 people 49 variants in 123 people 167 variants in 539 people Missense variants 83 variants in 165 people 212 variants in 323 people 101 variants in 190 people 21 variants in 34 people 44 variants in 90 people 89 variants in 131 people 550 variants in 933 people Our assessment (n = 807,162) 105 variants in 555 people 233 variants in 351 people 156 variants in 669 people 34 variants in 49 people 64 variants in 209 people 141 variants in 335 people 733 variants in 2168 people Population frequency of our assessment 555/807,162 or One in 1454 351/807,162 or One in 2,300 669/807,162 or One in 1,207 49/807,162 or One in 16,473 209/807,162 or One in 3862 335/807,162 or One in 2,409 2,168/807,162 or One in 372 ClinVar assessment Pathogenic or Likely pathogenic variants in ClinVar (n = 807,162) 30 variants in 296 people 7 variants in 31 people 12 variants in 49 people 3 variants in 3 people 8 variants in 49 people 11 variants in 30 people 71 variants in 458 people Population frequency of Pathogenic or Likely pathogenic variants in ClinVar 296/807,162 or One in 2,726 31/807,162 or One in 26,037 49/807,162 or One in 16,472 3/807,162 or One in 269,054 49/807,162 or One in 16,473 30/807,162 or One in 26,905 458/807,162 or One in 1,762 With our assessment, the commonest affected genes were EYA1 (one in 1207), HNF1B (one in 1454) and SALL1 (one in 2300). The frequencies of EYA1 (chi squared = 0.193, p = 0.66) and HNF1B variants (chi squared = 0.257, p = 0.61) were not different from those found in gnomAD v.2.1.1 but disease-causing SALL1 variants were much less common in gnomAD 4.1 (one in 2300, chi-squared = 63.94, p < 0.0001). With the ClinVar assessment, there were 71 Pathogenic or Likely pathogenic or Conflicting (P/LP/VUS) variants in 458 people corresponding to a population frequency of one in 1,762 (p < 0.0001 compared with our estimate in gnomAD v.2.1.1). Again, there were no ClinVar assessments for structural or copy number variants. If all the structural and copy number variants included in our assessment were added to the total ClinVar variants assessed as disease-causing then the total number of variants (1154 in 807,162 people) is equivalent to a population frequency of one in 699. However ClinVar assessments were only available in gnomADv.4.1 for 162/679 variants in HNF1B (24%), 259/1774 in SALL1 (15%), 124/778 in EYA1 (16%), 3/348 in PBX1 (1%), 15/577 in GATA3 (3%) and 17/678 in PAX2 (3%). Thus, while gnomAD v.4.1 included more structural and copy number variants than gnomAD v.2.1.1 the median number of ClinVar assessments for its variants was 9%, range 1–24% compared with a median of 21%, range 5–29% for gnomAD v.2.1.1. This means that the population frequencies deduced from ClinVar assessments were underestimates and that the population frequency deduced from gnomAD v.4.1 was likely to represent a greater underestimate than from gnomAD v.2.1. Discussion These studies suggest that the population frequency of predicted pathogenic variants in the 6 commonest CAKUT genes lies in the range between one in 249 and one in 1263. However the number of people with CAKUT-associated clinical features will be less than this because of reduced penetrance and variable expressivity. According to our strategy, the commonest affected CAKUT genes in gnomAD v.2.1.1 and gnomAD v.4.1 were SALL1 , EYA1 and HNF1B and EYA1, HNF1B and SALL1 respectively. This is different from published series that have found HNF1B and SALL1 most often and may be due in part to the large number of structural and copy number variants in HNF1B and EYA1 in gnomAD v.4.1 ( 12 – 15 ). Overall, missense variants were more common than null changes in these six genes and missense variants are associated with less penetrant forms of CAKUT where affected individuals do not develop CAKUT or have all the typical features. Our assessment suggested that predicted pathogenic variants in these 6 CAKUT genes were more common in people of African/African-American ancestry in part from the correction for the smaller cohort of the three HNF1B structural variants in gnomAD v.2.1.1. CAKUT variants were also common in people of an East Asian background. Predicted pathogenic variants were least common in Ashkenazim and Finns, which may result from their relative social and geographic isolation. However, there were a number of methodological considerations in assessing the results from these studies. Only the six commonest CAKUT genes were examined whereas more than 150 have been identified. GnomAD v.2.1.1 underrepresents people with severe or early onset disease, such as those with CAKUT-associated kidney failure who were ineligible for the clinical trials that represented most of this cohort. Most gnomAD v.2.1.1 samples were tested by WES which did not detect structural and copy number changes. This was particularly important for HNF1B where half the variants are large deletions ( 16 ). Interestingly no structural changes were found in the other five genes in gnomAD v.2.1.1. While our strategy had the advantage that it provided an assessment for each variant and our criteria for pathogenicity were more stringent than those used previously in similar studies ( 31 , 32 ), its major source of inaccuracy was in evaluating missense changes. This was demonstrated by the low sensitivity for SALL1 variants (Suppl Table S2 ) and by comparison with the highly rigorous REVEL assessment. ClinVar assessments are generally accurate ( 39 ) because they are submitted from accredited testing laboratories who use sequencing from people with clinically-suspected kidney disease, and the ACMG/AMP criteria and variant rarity based on recently-available large datasets. However, ClinVar overrepresents high-penetrance variants from clinically-referred cohorts. In addition, ClinVar only includes assessments for 21% of gnomAD v.2.1.1 variants in these 6 CAKUT genes (median, range 5–29%) and not for structural or copy number changes. There were even fewer assessments for gnomAD v.4.1 (median variants assessed 9%, range 1–24%). The low number of ClinVar assessments is explained by fewer laboratories performing genetic CAKUT testing because of its mainly clinical diagnosis, the large number of affected genes, the difficulty in assessing missense changes in the CAKUT genes, and the lack of treatment. This suggests that the population frequency of the genetic forms of CAKUT is more common than one in 1263 and closer to our estimate of one in 249. The population frequencies derived from the LOVD and HGMD databases, unlike ClinVar, comprise many variants reported before the availability of ACMG/AMP guidelines and large datasets for checking variant rarity, which reduce their accuracy. In summary, the strengths of this study were the large cohorts who had undergone genetic testing, the rigorous criteria used for our variant assessment, the comparison of multiple strategies, the use of gnomAD v.4.1 as a replication cohort, and the ability to determine population frequencies in people of different ancestries. The study’s limitations were the inability to confirm variant pathogenicity with clinical data in gnomAD, the lack of ClinVar assessments for all gnomAD variants, the incompleteness of the structural and copy number analysis in ClinVar, and the potential inaccuracies in our computational assessment. Nevertheless, we have used this approach previously to estimate the population frequencies of other genetic diseases such as Alport syndrome, Fabry disease and AD Polycystic kidney disease ( 30 , 35 , 40 ). The population frequency of Alport syndrome was an underestimate because the only missense variants assessed were Gly substitutions but, even so, all the estimated population frequencies suggested that these diseases were more common than previously believed. This computational study demonstrated that predicted pathogenic variants found in the six commonest CAKUT genes in gnomAD affect between one in 249 and one in 1263 people where the one in 1263 is likely an underestimate because it is derived from ClinVar assessments which were not available for all gnomAD changes. Previous estimates suggested that CAKUT affects about one in 1000 people but our results indicate that genetic causes are more common and possibly responsible for a majority of cases. Importantly, about half the predicted pathogenic variants in the CAKUT genes are missense changes that may be associated with an incompletely penetrant or milder phenotype, so that the number of people with clinical features of CAKUT is likely to be less common than our estimate of one in 249. Future improvements in computational tools will make even more accurate estimates possible. Declarations Conflict of Interest statement The authors have no Conflicts of Interest in relation to this manuscript. References Heidet L, Morinière V, Henry C, De Tomasi L, Reilly ML, Humbert C et al (2017) Targeted Exome Sequencing Identifies PBX1 as Involved in Monogenic Congenital Anomalies of the Kidney and Urinary Tract. J Am Soc Nephrol 28(10):2901–2914 Ardissino G, Daccò V, Testa S, Bonaudo R, Claris-Appiani A, Taioli E et al (2003) Epidemiology of chronic renal failure in children: data from the ItalKid project. Pediatrics 111(4 Pt 1):e382–e387 Wühl E, van Stralen KJ, Verrina E, Bjerre A, Wanner C, Heaf JG et al (2013) Timing and outcome of renal replacement therapy in patients with congenital malformations of the kidney and urinary tract. Clin J Am Soc Nephrol 8(1):67–74 Sanna-Cherchi S, Caridi G, Weng PL, Scolari F, Perfumo F, Gharavi AG et al (2007) Genetic approaches to human renal agenesis/hypoplasia and dysplasia. Pediatr Nephrol 22(10):1675–1684 Murugapoopathy V, Gupta IR (2020) A Primer on Congenital Anomalies of the Kidneys and Urinary Tracts (CAKUT). Clin J Am Soc Nephrol. ;15(5) Groen In 't Woud S, Renkema KY, Schreuder MF, Wijers CH, van der Zanden LF, Knoers NV, et al. Maternal risk factors involved in specific congenital anomalies of the kidney and urinary tract: A case-control study. Birth Defects Res A Clin Mol Teratol. (2016) ;106(7):596–603 Macumber I, Schwartz S, Leca N (2017) Maternal obesity is associated with congenital anomalies of the kidney and urinary tract in offspring. Pediatr Nephrol 32(4):635–642 Nicolaou N, Renkema KY, Bongers EM, Giles RH, Knoers NV (2015) Genetic, environmental, and epigenetic factors involved in CAKUT. Nat Rev Nephrol 11(12):720–731 Young MD, Mitchell TJ, Vieira Braga FA, Tran MGB, Stewart BJ, Ferdinand JR et al (2018) Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361(6402):594–599 O'Brien LL, Guo Q, Bahrami-Samani E, Park JS, Hasso SM, Lee YJ et al (2018) Transcriptional regulatory control of mammalian nephron progenitors revealed by multi-factor cistromic analysis and genetic studies. PLoS Genet 14(1):e1007181 Kagan M, Pleniceanu O, Vivante A (2022) The genetic basis of congenital anomalies of the kidney and urinary tract. Pediatr Nephrol 37(10):2231–2243 Weber S, Moriniere V, Knüppel T, Charbit M, Dusek J, Ghiggeri GM et al (2006) Prevalence of mutations in renal developmental genes in children with renal hypodysplasia: results of the ESCAPE study. J Am Soc Nephrol 17(10):2864–2870 Thomas R, Sanna-Cherchi S, Warady BA, Furth SL, Kaskel FJ, Gharavi AG (2011) HNF1B and PAX2 mutations are a common cause of renal hypodysplasia in the CKiD cohort. Pediatr Nephrol 26(6):897–903 Madariaga L, Morinière V, Jeanpierre C, Bouvier R, Loget P, Martinovic J et al (2013) Severe prenatal renal anomalies associated with mutations in HNF1B or PAX2 genes. Clin J Am Soc Nephrol 8(7):1179–1187 Capone VP, Morello W, Taroni F, Montini G (2017) Genetics of Congenital Anomalies of the Kidney and Urinary Tract: The Current State of Play. Int J Mol Sci. ;18(4) Bockenhauer D, Jaureguiberry G (2016) HNF1B-associated clinical phenotypes: the kidney and beyond. Pediatr Nephrol 31(5):707–714 Sanyanusin P, Schimmenti LA, McNoe LA, Ward TA, Pierpont ME, Sullivan MJ et al (1995) Mutation of the PAX2 gene in a family with optic nerve colobomas, renal anomalies and vesicoureteral reflux. Nat Genet 9(4):358–364 Vivante A, Chacham OS, Shril S, Schreiber R, Mane SM, Pode-Shakked B et al (2019) Dominant PAX2 mutations may cause steroid-resistant nephrotic syndrome and FSGS in children. Pediatr Nephrol 34(9):1607–1613 Barua M, Stellacci E, Stella L, Weins A, Genovese G, Muto V et al (2014) Mutations in PAX2 associate with adult-onset FSGS. J Am Soc Nephrol 25(9):1942–1953 Kohlhase J, Wischermann A, Reichenbach H, Froster U, Engel W (1998) Mutations in the SALL1 putative transcription factor gene cause Townes-Brocks syndrome. Nat Genet 18(1):81–83 Van Esch H, Groenen P, Nesbit MA, Schuffenhauer S, Lichtner P, Vanderlinden G et al (2000) GATA3 haplo-insufficiency causes human HDR syndrome. Nature 406(6794):419–422 Clissold RL, Hamilton AJ, Hattersley AT, Ellard S, Bingham C (2015) HNF1B-associated renal and extra-renal disease—an expanding clinical spectrum. Nat Rev Nephrol 11(2):102–112 Melo BF, Aguiar MB, Bouzada MCF, Aguiar RL, Pereira AK, Paixão GM et al (2012) Early risk factors for neonatal mortality in CAKUT: analysis of 524 affected newborns. Pediatr Nephrol 27(6):965–972 Choi HA, Lee DJ, Shin SM, Lee YK, Ko SY, Park SW (2016) The Prenatal and Postnatal Incidence of Congenital Anomalies of the Kidneys and Urinary Tract (CAKUT) Detected by Ultrasound. Child Kidney Dis 20(1):29–32 Wiesel A, Queisser-Luft A, Clementi M, Bianca S, Stoll C (2005) Prenatal Detection of Congenital Renal Malformations by Fetal Ultrasonographic Examination: An Analysis of 709,030 Births in 12 European Countries. Eur J Med Genet 48(2):131–144 Li Z-y, Chen Y-m, Chen QL-q, Xu D-qHC-g (2019) Prevalence, types, and malformations in congenital anomalies of the kidney and urinary tract in newborns: a retrospective hospital-based study. Ital J Pediatr 45(1):50 Tain YL, Luh H, Lin CY, Hsu CN (2016) Incidence and Risks of Congenital Anomalies of Kidney and Urinary Tract in Newborns: A Population-Based Case-Control Study in Taiwan. Med (Baltim) 95(5):e2659 Savige J (2024) Tips for Testing Adults With Suspected Genetic Kidney Disease. Am J Kidney Dis 83(6):816–824 Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J et al (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17(5):405–424 Gibson J, Fieldhouse R, Chan MMY, Sadeghi-Alavijeh O, Burnett L, Izzi V et al (2021) Prevalence Estimates of Predicted Pathogenic COL4A3-COL4A5 Variants in a Population Sequencing Database and Their Implications for Alport Syndrome. J Am Soc Nephrol 32(9):2273–2290 Kondo A, Nagano C, Ishiko S, Omori T, Aoto Y, Rossanti R et al (2021) Examination of the predicted prevalence of Gitelman syndrome by ethnicity based on genome databases. Sci Rep 11(1):16099 Lanktree MB, Haghighi A, Guiard E, Iliuta I-A, Song X, Harris PC et al (2018) Prevalence Estimates of Polycystic Kidney and Liver Disease by Population Sequencing. J Am Soc Nephrol. ;29(10) Borges P, Pasqualim G, Giugliani R, Vairo F, Matte U (2020) Estimated prevalence of mucopolysaccharidoses from population-based exomes and genomes. Orphanet J Rare Dis 15(1):324 Kaler SG, Ferreira CR, Yam LS (2020) Estimated birth prevalence of Menkes disease and ATP7A-related disorders based on the Genome Aggregation Database (gnomAD). Mol Genet Metabolism Rep 24:100602 Kermond-Marino A, Weng A, Xi Zhang SK, Tran Z, Huang M, Savige J Population Frequency of Undiagnosed Fabry Disease in the General Population. Kidney International Reports Kermond-Marino A, Weng A, Xi Zhang SK, Tran Z, Huang M, Savige J (2023) Population Frequency of Undiagnosed Fabry Disease in the General Population. Kidney Int Rep 8(7):1373–1379 Pejaver V, Byrne AB, Feng BJ, Pagel KA, Mooney SD, Karchin R et al (2022) Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet 109(12):2163–2177 Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S et al (2016) REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet 99(4):877–885 Baudhuin LM, Kluge ML, Kotzer KE, Lagerstedt SA (2019) Variability in gene-based knowledge impacts variant classification: an analysis of FBN1 missense variants in ClinVar. Eur J Hum Genet 27(10):1550–1560 Varughese S, Huang M, Savige J Typical and atypical ADPKD: Predicted pathogenic genetic variants and population frequencies 2025 Supplementary Files SupplTablesS1andS2.pdf SupplementaryTableS3.xlsx SupplementaryTableS4.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revisions Needed 29 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 10 Dec, 2025 First submitted to journal 09 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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07:06:15","extension":"pptx","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164892,"visible":true,"origin":"","legend":"","description":"","filename":"FigureICAKUT.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/e2f3059f694211043e50dbcc.pptx"},{"id":98376300,"identity":"f35b7c90-63dc-4aca-9776-9902328de01e","added_by":"auto","created_at":"2025-12-17 07:06:15","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138274,"visible":true,"origin":"","legend":"","description":"","filename":"PNEPD25011290structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/443071a503e5d0abf877daad.xml"},{"id":98440075,"identity":"83d37a5c-792b-47f7-abe8-c46b72f0bd60","added_by":"auto","created_at":"2025-12-17 17:03:16","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153961,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/54ee80eda0b81d22157d0ad9.html"},{"id":98376290,"identity":"35cffd15-6708-4d39-acb4-fd094ed98d24","added_by":"auto","created_at":"2025-12-17 07:06:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCAKUT-associated genes and distribution and type of Pathogenic and likely pathogenic variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis demonstrates that pathogenic variants according to ClinVar are distributed throughout individual genes (there are no obvious hotspots) and variants include both null and missense variants (from Simple ClinVar, https://simple-clinvar.broadinstitute.org/)\u003c/p\u003e","description":"","filename":"FigureICAKUT.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/9f6d04a5dcf2277864831367.jpg"},{"id":98439712,"identity":"ddd17f34-6d4e-49ac-b7cc-702eb0caeaa5","added_by":"auto","created_at":"2025-12-17 17:02:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":187830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStrategy for deriving population frequency of predicted pathogenic variants in CAKUT-associated genes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2CAKUT.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/b63542da9d365ee1d1ff15c5.jpg"},{"id":98376292,"identity":"0bfc0dbe-03cd-42f4-90cf-74bb76fae1a2","added_by":"auto","created_at":"2025-12-17 07:06:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95075,"visible":true,"origin":"","legend":"\u003cp\u003eNumbers of predicted pathogenic variants identified in the 6 CAKUT genes in gnomAD v.2.1.1 and v.4.1 using different strategies\u003c/p\u003e","description":"","filename":"Figure3CAKUT.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/390bd0d3e0854ef042ff2156.jpg"},{"id":98376293,"identity":"a3068997-c992-4441-bc29-df7791f7c9aa","added_by":"auto","created_at":"2025-12-17 07:06:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98670,"visible":true,"origin":"","legend":"\u003cp\u003eNumbers of predicted pathogenic variants identified in the 6 CAKUT genes in gnomAD v.2.1.1 in people of different ancestries\u003c/p\u003e","description":"","filename":"Figure4CAKUT.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/0f0845b1b2b61bc1f44aa722.jpg"},{"id":98623378,"identity":"3088d92d-787d-4ceb-aa97-f940031cc91f","added_by":"auto","created_at":"2025-12-19 17:06:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2448587,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/7bc86141-ebe8-4e01-9623-893c96896244.pdf"},{"id":98439785,"identity":"20de596d-6a2a-4251-99e1-e395b1c3a275","added_by":"auto","created_at":"2025-12-17 17:02:57","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":87014,"visible":true,"origin":"","legend":"","description":"","filename":"SupplTablesS1andS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/c2c0591a52b13c6915135888.pdf"},{"id":98376306,"identity":"3625db3b-33c0-4902-9a96-85ad3685aa59","added_by":"auto","created_at":"2025-12-17 07:06:15","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":2843384,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/36ced88650ab3903830f8298.xlsx"},{"id":98376302,"identity":"56c59c4e-c5f1-4692-803a-98fb78aada1f","added_by":"auto","created_at":"2025-12-17 07:06:15","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":2900049,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8297749/v1/d789fd8ce3b718e10826aca8.xlsx"}],"financialInterests":"","formattedTitle":"The population frequency of predicted pathogenic variants in commonly-affected genes in CAKUT in the general population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCongenital anomalies of the kidney and urinary tract (CAKUT) include a range of developmental malformations of the kidney and urinary tract. CAKUT is the commonest birth anomaly affecting 3 to 7 of every 1000 newborn (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and is the leading cause of kidney failure in children (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCommon forms of CAKUT include ureteropelvic junction obstruction (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), kidney agenesis, dysplasia, hypoplasia, multicystic dysplastic kidneys, kidney, vesicoureteral reflux, megaureter, ectopic ureter, horseshoe kidney, duplex collecting system, and posterior urethral valves. Isolated CAKUT occurs without additional anomalies and syndromic CAKUT has further extra-renal features (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Affected infants are typically diagnosed antenatally on ultrasound imaging but those who are asymptomatic may not be identified until adolescence or later (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Some of those affected remain undiagnosed throughout life but may still pass on the disease-causing variant and clinical manifestations to their offspring.\u003c/p\u003e \u003cp\u003eCAKUT results from abnormal kidney development that is secondary to genetic or environmental factors. Maternal diabetes, medications, and folate and iron deficiency all increase the risk (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Genetic causes have been reported to only account for 20% of cases (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) so that genetic forms of CAKUT are thought to affect one in 1000 of the population.\u003c/p\u003e \u003cp\u003eMore than 150 genes are now associated with CAKUT, with many encoding transcription factors that are important in embryonic kidney development (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The six commonest affected genes are hepatocyte nuclear factor-1B (\u003cem\u003eHNF1B\u003c/em\u003e), spalt-like transcription factor 1 (\u003cem\u003eSALL1\u003c/em\u003e), eyes absent homolog 1 (\u003cem\u003eEYA1\u003c/em\u003e), pre-B cell leukemia (\u003cem\u003ePBX1\u003c/em\u003e), GATA binding protein 3 (\u003cem\u003eGATA3\u003c/em\u003e), and paired box gene 2 (\u003cem\u003ePAX2\u003c/em\u003e) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), which are all monoallelic (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omim.org/\u003c/span\u003e\u003cspan address=\"https://www.omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Disease-causing variants in \u003cem\u003eHNF1B\u003c/em\u003e and \u003cem\u003ePAX2\u003c/em\u003e together account for at least 15% of people with CAKUT in case series (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Pathogenic variants in most of these genes have a variable phenotype.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHNF1B\u003c/em\u003e is reported to be the commonest cause of monogenic CAKUT (MIM137920). It encodes a transcription factor of the homeodomain-containing superfamily that is essential for the development of many organs including the kidneys and urogenital tract, brain, pancreas, liver, and parathyroids (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Pathogenic \u003cem\u003eHNF1\u003c/em\u003eB variants result in \u003cem\u003eHNF1B-\u003c/em\u003enephropathy (formerly Renal Cysts And Diabetes syndrome), multicystic kidney disease, Focal and Segmental Glomerulosclerosis (FSGS), or a tubulopathy as well as genital anomalies and monogenic diabetes (previously Maturity Onset Diabetes of the Young)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003e \u003cem\u003ePAX2 i\u003c/em\u003es considered the second commonest cause of monogenic CAKUT (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). \u003cem\u003ePAX2\u003c/em\u003e variants are associated with renal coloboma syndrome (MIM 120330) and sometimes renal hypoplasia, multicystic dysplastic kidneys or vesicoureteral reflux (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Pathogenic variants are also associated with Focal and Segmental Glomerulosclerosis (FSGS) in children (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and adults (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). \u003cem\u003eEYA1\u003c/em\u003e is a transcription factor important in development of the kidney, branchial arches and ears (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Variants in \u003cem\u003eEYA1\u003c/em\u003e result in Branchio-Oto-Renal syndrome (MIM 602588) with kidney agenesis and dysplasia in two-thirds of affected people (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), as well as ear abnormalities, and branchial fistulae and cysts (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). \u003cem\u003eSALL1\u003c/em\u003e Loss of Function variants result in Townes-Brocks syndrome (MIM 107480) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) with renal hypodysplasia, ectopia, polycystic kidneys and vesicoureteral reflux together with an imperforate anus, dysplastic ears and impaired hearing and thumb abnormalities (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). \u003cem\u003eGATA3\u003c/em\u003e encodes a zinc-finger transcription factor expressed in the kidneys, inner ear, and parathyroids (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Pathogenic \u003cem\u003eGATA3\u003c/em\u003e variants are associated with hypoparathyroidism, sensorineural hearing loss, and a solitary kidney, renal hypodysplasia or vesicoureteral reflux (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (MIM 146255) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). \u003cem\u003ePBX1\u003c/em\u003e is another transcription factor (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and pathogenic \u003cem\u003ePBX1\u003c/em\u003e variants are associated with hypoplastic and cystic kidneys, reflux, abnormal male genitalia and sometime congenital cardiac anomalies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These are the classical clinical features associated with pathogenic variants in these genes, but the phenotypes in affected family members often differ because of incomplete penetrance and variable expression, and other genetic or environmental factors (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious estimates of the population frequency of CAKUT based on clinical screening have varied from one in 56 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), 103 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), or 627 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) to one in 2,400 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Having a more accurate population frequency is important to alert clinicians to the likelihood of encountering patients with CAKUT, the need to examine for extrarenal manifestations, and for health service planning.\u003c/p\u003e \u003cp\u003eThis study calculated the population frequency of CAKUT from the number of predicted pathogenic variants in the 6 commonest monogenic disease-causing genes in a cohort of normal people (Genome Aggregation Database, gnomADv.2.1.1). Examination of pathogenic variants for these genes in Simple ClinVar (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://simple-clinvar.broadinstitute.org/\u003c/span\u003e\u003cspan address=\"https://simple-clinvar.broadinstitute.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) indicated that variants were located throughout the genes, and included both loss-of-function (null) and missense changes. While these genes are the commonest affected in CAKUT, many others have been implicated too but often only in a single family (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We confirmed our results in gnomAD v.4.1 which is a much larger dataset, that includes more structural and copy number variants, and where there is only 20% overlap with gnomADv.2.1.1. We then compared our population frequencies with those derived from gnomAD variants assessed as disease-causing in the ClinVar database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/clinvar\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/clinvar\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), The Human Gene Mutation Database (HGMD), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hgmd.cf.ac.uk/ac/index.php\u003c/span\u003e\u003cspan address=\"https://www.hgmd.cf.ac.uk/ac/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), or the Leiden Open Variation Database (LOVD v.3.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.lovd.nl\u003c/span\u003e\u003cspan address=\"https://www.lovd.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur approach was based on the ACMG/AMP principles (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and assessed all gnomAD variants \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, but gnomAD has no clinical data and we may have misinterpreted some benign changes. Nevertheless population frequencies using a similar, and typically less rigorous, strategy have been published previously for many diseases including Alport syndrome (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), Gitelman syndrome (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), AD polycystic kidney disease (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), the mucopolysaccharidoses (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), Menke disease (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), and Fabry disease (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In some of these conditions, the population frequency was confirmed using an independent biochemical or histological method (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003egnomAD v.2.1.1 and v.4.1 variant databases\u003c/h2\u003e \u003cp\u003eVariants in \u003cem\u003eEYA1\u003c/em\u003e, \u003cem\u003eGATA3\u003c/em\u003e, \u003cem\u003eHNF1B\u003c/em\u003e, \u003cem\u003ePAX2, PBX1\u003c/em\u003e and \u003cem\u003eSALL1\u003c/em\u003e were downloaded from gnomAD v2.1.1.(GRCh37/hg19, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.gnomAD.broadinstitute.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.gnomAD.broadinstitute.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and v.4.1 (GRCh38/hg38). gnomAD v.2.1.1 (n\u0026thinsp;=\u0026thinsp;141,456) comprises Whole Exome Sequencing (WES, n\u0026thinsp;=\u0026thinsp;125,748) and Whole Genome Sequencing (WGS, n\u0026thinsp;=\u0026thinsp;15,708) from clinical trials studying unrelated adults with diabetes, neuropsychiatric or cardiac disease.\u003c/p\u003e \u003cp\u003egnomAD v.4.1 includes unrelated adults (n\u0026thinsp;=\u0026thinsp;807,162), whose DNA was, in some instance, examined for structural (n\u0026thinsp;=\u0026thinsp;63,046) or copy number (n\u0026thinsp;=\u0026thinsp;464,297) variants too. Both gnomAD v.2.1.1 and v.4.1 included equal numbers of men and women, and their ancestries, but no clinical data. There is less than 20% overlap between the two cohorts.\u003c/p\u003e \u003cp\u003egnomADv.2.1.1 was first accessed in March 2023, and reviewed in May 2024, and gnomAD v.4.1 was reviewed in November 2024.\u003c/p\u003e \u003cp\u003eAll participants in gnomAD had provided written, informed consent for their data to be shared anonymously and available publicly for further research at the time of recruitment, so that Austin Health Institutional Review Board approval was not required for this secondary use of the publicly available data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnnotation and filtering\u003c/h3\u003e\n\u003cp\u003eOur strategy has been described previously(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Variants in these genes were annotated using ANNOVAR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://annovar.openbioinformatics.org/\u003c/span\u003e\u003cspan address=\"https://annovar.openbioinformatics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Variants in the intronic and 5\u0026rsquo; or 3\u0026rsquo; UTR or that were intronic, noncoding, splice region or synonymous were excluded. Other variants were filtered according to the following approach \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eStructural Variants\u003c/b\u003e\u003c/div\u003e \u003cp\u003eStructural variants that were deletions and affected exons were considered pathogenic regardless of their allele counts. The number of structural variants in this subset was corrected to be equivalent to the whole cohort.\u003c/p\u003e\n\u003ch3\u003eNull variants\u003c/h3\u003e\n\u003cp\u003eNull variants including nonsense variants (except in the last exon and the last 50 nucleotides of the penultimate exon, which escape nonsense-mediated decay) \u003cb\u003e(Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e, canonical splice site, and frameshift variants were classified as pathogenic regardless of their allele counts.\u003c/p\u003e\n\u003ch3\u003eMissense variants\u003c/h3\u003e\n\u003cp\u003eMissense variants were \u0026lsquo;predicted pathogenic\u0026rsquo; if they were rare (allele count\u0026thinsp;\u0026lt;\u0026thinsp;6) found to be pathogenic using all three bioinformatic tools: SIFT4G (Sorting Intolerant From Tolerant) score \u0026le; 0.05, PP2 (Polymorphism Phenotyping v2, PolyPhen-2) score \u0026ge; 0.95 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genetics.bwh.harvard.edu/pph2/\u003c/span\u003e\u003cspan address=\"http://genetics.bwh.harvard.edu/pph2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Mutation Taster where variants were classified as \u0026lsquo;disease causing\u0026rsquo; or (D) or (A) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mutationtaster.org/info/\u003c/span\u003e\u003cspan address=\"https://www.mutationtaster.org/info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003edocumentation.html). Variants were also examined to determine if they affected an amino acid conserved (* or :).in vertebrates (chicken, mice, humans) using Clustal Omega (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/Tools/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/Tools/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Ensembl reference sequences (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://asia.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"http://asia.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strategy for assessing missense variants was validated in two ways. The sensitivity, specificity, and positive and negative predictive values were calculated using variants that were classified as pathogenic or benign in an independent database (generally LOVD) that used clinical data in the variant evaluation \u003cb\u003e(Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/b\u003e Our assessment for all these genes performed satisfactorily with high sensitivities (median 81%), specificities (median 100%), and positive (PPV, median 100%) and negative predictive values (NPV, median 73%) except for \u003cem\u003eSALL1\u003c/em\u003e which was tested with the only four pathogenic missense variants available and identified only one of these. Secondly, REVEL (Rare Exome Variant Ensemble Learner) scores\u0026thinsp;\u0026gt;\u0026thinsp;0.932 or \u0026gt;\u0026thinsp;0.80 was used to assess missense variants in the gnomAD v.2.1.1 cohort, and the number added to the Structural and Null variants to obtain an independent assessment of the population frequency (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of population frequency from gnomAD\u003c/h2\u003e \u003cp\u003eVariants downloaded from gnomAD that fulfilled all our criteria for disease-causing were classified as \u0026lsquo;predicted pathogenic\u0026rsquo; to distinguish them from the \u0026lsquo;Pathogenic\u0026rsquo; and \u0026lsquo;Likely pathogenic\u0026rsquo; terms used by the ACMG/AMP criteria (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Population frequencies of the 6 AD CAKUT genes were calculated using the average allele count and total number of people examined for each gene. The population frequencies for these genes were calculated assuming that each person included in gnomAD had only one genetic variant for CAKUT.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredicted pathogenic variants for CAKUT in people of different ancestries\u003c/h3\u003e\n\u003cp\u003ePopulation frequencies of predicted pathogenic variants were then calculated for people of each ancestry included in this version of gnomAD.\u003c/p\u003e\n\u003ch3\u003ePopulation frequencies of CAKUT using different databases\u003c/h3\u003e\n\u003cp\u003eAll gnomAD variants were also examined in ClinVar, HGMD and LOVD databases for previous reports of pathogenicity. The population frequencies for gnomAD variants that were Pathogenic, Likely pathogenic or Conflicting (VUS plus Pathogenic or Likely Pathogenic) were then calculated for ClinVar, and the population frequencies for pathogenic variants in HGMD and LOVD were also calculated. Disease-causing variants in these databases were included regardless of the number of times they were found in gnomAD.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePopulation frequency of CAKUT of gnomAD v.4.1 using our strategy and ClinVar\u003c/h2\u003e \u003cp\u003eWe then repeated the population frequency using our approach and gnomAD v.4.1 including Structural variants (n\u0026thinsp;=\u0026thinsp;63,046) and Copy number variants (n\u0026thinsp;=\u0026thinsp;464,297) both corrected (x13, x1.7 respectively) for the whole cohort, as well as null and missense variants. In addition, we assessed the population frequency of CAKUT from gnomAD v.4.1 using variants assessed as Pathogenic or Likely pathogenic or Conflicting (P/LP/VUS) in ClinVar as described above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eResults were compared using chi-squared testing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/quickcalcs/\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/quickcalcs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePopulation frequency of CAKUT from our strategy of gnomAD v.2.1.1\u003c/h2\u003e \u003cp\u003eOverall there were 273 variants in one of the 6 CAKUT genes in 461 people from gnomADv.2.1.1 that were assessed as predicted pathogenic. This was equivalent to a population frequency for CAKUT of 461 in 114,963 or one in 249 people \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation frequencies of Predicted pathogenic variants in CAKUT-associated genes in gnomADv.2.1.1 and the control subset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1B\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;119,441 people)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSALL1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120,389 people )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEYA1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120,908 people)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePBX1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;98,815 people )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGATA3\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;109,227 people )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePAX2\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120,946 people )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;114,963 people)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStructural variants (n\u0026thinsp;=\u0026thinsp;10,847)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne variant in 3 people, 33 people corrected for whole cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne variant in 33 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNull variants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 variants in 2 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOne variant in 1 person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 variants in 8 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 variants in 3 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 variants in 3 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 variants in 23 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25 variants in 40 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMissense variants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 variants in 39 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 variants in 186 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 variants in 84 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 variants in 9 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 variants in 27 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31 in 43 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e247 variants in 388 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 variants in 74 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 variants in 187 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 variants in 92 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 variants in 12 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 variants in 30 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39 variants in 66 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e273 variants in 461 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne variant in 1,614 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOne variant in 644 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOne variant in 1,314 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOne variant in 8,234 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOne variant in 3,641 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOne variant in 1,832 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne variant in 249 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e1\u003c/sup\u003eStructural variants corrected for smaller whole genome sequencing cohort x 10,847\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e2\u003c/sup\u003eNull variants including\u0026thinsp;\u0026lt;\u0026thinsp;6 but not in the last exon or where ClinVar said benign or low confidence\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e3\u003c/sup\u003eTotal Pathogenic variants positive in all, \u0026lt;6\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVariants in the \u003cem\u003eSALL1\u003c/em\u003e gene were the commonest predicted pathogenic variants (one in 644) followed by variants in \u003cem\u003eEYA1\u003c/em\u003e (one in 1,314) or \u003cem\u003eHNF1B\u003c/em\u003e (one in 1,614). \u003cem\u003eHNF1B\u003c/em\u003e deletions occurred in nearly half the people with \u003cem\u003eHNF1B\u003c/em\u003e variants (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and the number of \u003cem\u003eHNF1B\u003c/em\u003e structural variants after correction (n\u0026thinsp;=\u0026thinsp;33) was nearly equivalent to the combined number of missense and null changes (n\u0026thinsp;=\u0026thinsp;41). No pathogenic structural variants were found in the other five genes. In general, more people had missense (n\u0026thinsp;=\u0026thinsp;388) than other variants (n\u0026thinsp;=\u0026thinsp;73) using our strategy.\u003c/p\u003e \u003cp\u003eWhen a REVEL score\u0026thinsp;\u0026gt;\u0026thinsp;0.932 was used to assess the missense variants in gnomAD v.2.1.1, structural, null and predicted pathogenic missense variants were found in 188 people corresponding to a population frequency of one in 611 (188/114,963) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 compared with our assessment) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. There is no recommended REVEL cut off score, and levels\u0026thinsp;\u0026gt;\u0026thinsp;0.932 may have been too stringent and resulted in an underestimate for the population frequency. When a REVEL score\u0026thinsp;\u0026gt;\u0026thinsp;0.8 was used instead predicted pathogenic variants were found in 319 people corresponding to a population frequency of one in 360 (319/114,963).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation frequency of Predicted pathogenic variants in CAKUT-associated genes in gnomAD v.2.1.1using various databases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1B\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;119,441)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSALL1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;120,389)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEYA1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;120,908)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePBX1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;98,816)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGATA3\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;109,277)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePAX2\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;120,946)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;114,963)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePopulation frequency (n\u0026thinsp;=\u0026thinsp;114,963)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOur assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 variants in 74 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 variants in 187 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 variants in 92 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 variants in 12 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 variants in 30 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39 variants in 66 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e273 variants in 461 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e461/114,963\u003c/p\u003e \u003cp\u003eor one in 249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREVEL\u0026thinsp;\u0026gt;\u0026thinsp;0.932\u003c/p\u003e \u003cp\u003e(missense only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 variants in 9 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 variants in 2 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 variants in 12 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 variants in 5 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 variants in 17 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25 variants in 45 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45/114,963\u003c/p\u003e \u003cp\u003eor one in 2555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREVEL (SV and CNV, null and missense variants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 variants in 114 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 variants in 3 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 variants in 20 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 variants in 3 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 variants in 8 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 variants in 40 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e78 variants in 155 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e188/114,963 or one in 612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinVar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 variants in 61 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 variants in 5 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 variants in 6 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 variant in one person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 variants in 5 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 variants in 13 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26 variants in 91 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91/114,963\u003c/p\u003e \u003cp\u003eor one in 1,263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 variants in 1179 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 variants in 1156 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 variants in 430 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 variant in one person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 variant in one person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 variants in 85 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58 variants in 2852 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,852/114,963 or one in 40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 variants in 12 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 variants in 119 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 variant in one person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 variants in 131 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18 variants in 263 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e263/114,963\u003c/p\u003e \u003cp\u003eor one in 437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eN\u0026thinsp;=\u0026thinsp;total number in cohort examined for variants in this gene; SV structural variants; CNV copy number variants.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen gnomAD variants were examined for those found Pathogenic or Likely pathogenic in ClinVar there were variants in 91 people corresponding to a population frequency of one in 1263 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 compared with our assessment) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e While ClinVar assessments are considered accurate, they were not available for all gnomAD variants so that the calculated population frequency was an underestimate. In particular there were no ClinVar assessments for structural variants. If the number of structural variants found disease-causing in our assessment were added to the ClinVar assessments, then there would be 124 variants in 114,963 or one in 923 people.\u003c/p\u003e \u003cp\u003eClinVar assessments in gnomADv.2.1.1 were available for 78/273 variants in \u003cem\u003eHNF1B\u003c/em\u003e (29%), 131/786 in \u003cem\u003eSALL1\u003c/em\u003e (17%), 76/310 in \u003cem\u003eEYA1\u003c/em\u003e (25%), 6/126 in \u003cem\u003ePBX1\u003c/em\u003e (5%), 33/214 in \u003cem\u003eGATA3\u003c/em\u003e and 56/229 in \u003cem\u003ePAX2\u003c/em\u003e (24%) that is with a median of 21%, range 5\u0026ndash;29% for gnomAD v.2.1.1.\u003c/p\u003e \u003cp\u003eWhen variants were examined for those also found in HGMD there were 2,852 variants corresponding to a population frequency of one in 40 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e When pathogenic variants in LOVD were used to examine the database there were 263 variants corresponding to a population frequency of one in 437 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhen our Predicted pathogenic variants were examined in people of different ancestries, they were commonest in people of African/American ancestry (84/12,487, or one in 149) and East Asian (41/9,977, one in 243) and least common in Finnish people (15/12,562, one in 837 people) and Ashkenazim (6/5185 or one in 864) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e These calculations included structural, null and missense variants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted pathogenic variants in the CAKUT-associated genes in people of differing ancestries\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHNF1B\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;119,441)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSALL1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120,389)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eEYA1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120,908)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ePBX1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;98,815)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eGATA3\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;109,227)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePAX2\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120,946)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;114,963)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePopulation frequency in this ancestry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAfrican American (n\u0026thinsp;=\u0026thinsp;12,487)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e84/12,487\u003c/p\u003e \u003cp\u003e0r one in 149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLatino (n\u0026thinsp;=\u0026thinsp;17,720)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e48/17,720\u003c/p\u003e \u003cp\u003eor one in 369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAshkenazim (n\u0026thinsp;=\u0026thinsp;5,185)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6/5,185\u003c/p\u003e \u003cp\u003eor one in 864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEast Asian (n\u0026thinsp;=\u0026thinsp;9,977)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e41/9,977\u003c/p\u003e \u003cp\u003eor one in 243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEuropean (n\u0026thinsp;=\u0026thinsp;64,603)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e203/64,603\u003c/p\u003e \u003cp\u003eor one in 318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFinnish (n\u0026thinsp;=\u0026thinsp;12,562)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15/12,562\u003c/p\u003e \u003cp\u003eor one in 837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSouth Asian (n\u0026thinsp;=\u0026thinsp;15,308)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e53/15,308\u003c/p\u003e \u003cp\u003eor one in 289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOther (n\u0026thinsp;=\u0026thinsp;3,614)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11/3,614\u003c/p\u003e \u003cp\u003eor one in 329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eSV structural variants\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePopulation frequency of CAKUT from our approach and ClinVar assessment of gnomAD v.4.1\u003c/h2\u003e \u003cp\u003egnomAD4.1 variants were evaluated using our assessment and the ClinVar assessment based on the previous results. gnomAD v.4.1 had the advantage of including structural and copy number variants in some patients. Our assessment found 733 predicted pathogenic variants in 2,168 people, corresponding to a population frequency of one in 372 people (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 compared with our estimate in gnomAD v.2.1.1) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted pathogenic variants in gnomAD v.4.1 in the 6 CAKUT-associated genes using our strategy and ClinVar assessments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHNF1B\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSALL1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEYA1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePBX1\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGATA3\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePAX2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOur strategy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStructural variants (n\u0026thinsp;=\u0026thinsp;63,046)\u003c/b\u003e (x13, corrected number for whole cohort )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOne LoF variant in 7 people (91 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNo variants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3 LoF variants in 12 people (156 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eNo variants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOne LoF variant in 6 people (78 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2 LoF variants in 6 people (78 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e7 LoF variants in 403 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCopy number variants (n\u0026thinsp;=\u0026thinsp;464,297)\u003c/b\u003e (x1.7, corrected number for whole cohort)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7 variants in 154 people (268 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNo variants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6 variants in 6 people (10 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2 variants in 2 people (3 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3 variants in 5 people (9 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1 variant in 2 people (3 people)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e19 variants in 293 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNull variants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14 variants in 31 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e21 variants in 28 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e55 variants in 313 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11 variants in 12 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e17 variants in 32 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e49 variants in 123 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e167 variants in 539 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMissense variants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e83 variants in 165 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e212 variants in 323 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e101 variants in 190 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e21 variants in 34 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e44 variants in 90 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e89 variants in 131 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e550 variants in 933 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOur assessment (n\u0026thinsp;=\u0026thinsp;807,162)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e105 variants in 555 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e233 variants in 351 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e156 variants in 669 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e34 variants in 49 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e64 variants in 209 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e141 variants in 335 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e733 variants in 2168 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation frequency of our assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e555/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 1454\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e351/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 2,300\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e669/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 1,207\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e49/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 16,473\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e209/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 3862\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e335/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 2,409\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2,168/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 372\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinVar assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathogenic or Likely pathogenic variants in ClinVar (n\u0026thinsp;=\u0026thinsp;807,162)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e30 variants in 296 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7 variants in 31 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e12 variants in 49 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3 variants in 3 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e8 variants in 49 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e11 variants in 30 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e71 variants in 458 people\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation frequency of Pathogenic or Likely pathogenic variants in ClinVar\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e296/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 2,726\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e31/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 26,037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e49/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 16,472\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 269,054\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e49/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 16,473\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e30/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 26,905\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e458/807,162\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eor One in 1,762\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWith our assessment, the commonest affected genes were \u003cem\u003eEYA1\u003c/em\u003e (one in 1207), \u003cem\u003eHNF1B\u003c/em\u003e (one in 1454) and \u003cem\u003eSALL1\u003c/em\u003e (one in 2300). The frequencies of \u003cem\u003eEYA1\u003c/em\u003e (chi squared\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;=\u0026thinsp;0.66) and \u003cem\u003eHNF1B\u003c/em\u003e variants (chi squared\u0026thinsp;=\u0026thinsp;0.257, p\u0026thinsp;=\u0026thinsp;0.61) were not different from those found in gnomAD v.2.1.1 but disease-causing \u003cem\u003eSALL1\u003c/em\u003e variants were much less common in gnomAD 4.1 (one in 2300, chi-squared\u0026thinsp;=\u0026thinsp;63.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eWith the ClinVar assessment, there were 71 Pathogenic or Likely pathogenic or Conflicting (P/LP/VUS) variants in 458 people corresponding to a population frequency of one in 1,762 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 compared with our estimate in gnomAD v.2.1.1). Again, there were no ClinVar assessments for structural or copy number variants. If all the structural and copy number variants included in our assessment were added to the total ClinVar variants assessed as disease-causing then the total number of variants (1154 in 807,162 people) is equivalent to a population frequency of one in 699.\u003c/p\u003e \u003cp\u003eHowever ClinVar assessments were only available in gnomADv.4.1 for 162/679 variants in \u003cem\u003eHNF1B\u003c/em\u003e (24%), 259/1774 in \u003cem\u003eSALL1\u003c/em\u003e (15%), 124/778 in \u003cem\u003eEYA1\u003c/em\u003e (16%), 3/348 in \u003cem\u003ePBX1\u003c/em\u003e (1%), 15/577 in \u003cem\u003eGATA3\u003c/em\u003e (3%) and 17/678 in \u003cem\u003ePAX2\u003c/em\u003e (3%). Thus, while gnomAD v.4.1 included more structural and copy number variants than gnomAD v.2.1.1 the median number of ClinVar assessments for its variants was 9%, range 1\u0026ndash;24% compared with a median of 21%, range 5\u0026ndash;29% for gnomAD v.2.1.1. This means that the population frequencies deduced from ClinVar assessments were underestimates and that the population frequency deduced from gnomAD v.4.1 was likely to represent a greater underestimate than from gnomAD v.2.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThese studies suggest that the population frequency of predicted pathogenic variants in the 6 commonest CAKUT genes lies in the range between one in 249 and one in 1263. However the number of people with CAKUT-associated clinical features will be less than this because of reduced penetrance and variable expressivity.\u003c/p\u003e \u003cp\u003eAccording to our strategy, the commonest affected CAKUT genes in gnomAD v.2.1.1 and gnomAD v.4.1 were \u003cem\u003eSALL1\u003c/em\u003e, \u003cem\u003eEYA1\u003c/em\u003e and \u003cem\u003eHNF1B and EYA1, HNF1B\u003c/em\u003e and \u003cem\u003eSALL1\u003c/em\u003e respectively. This is different from published series that have found \u003cem\u003eHNF1B\u003c/em\u003e and \u003cem\u003eSALL1\u003c/em\u003e most often and may be due in part to the large number of structural and copy number variants in \u003cem\u003eHNF1B\u003c/em\u003e and \u003cem\u003eEYA1\u003c/em\u003e in gnomAD v.4.1 (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Overall, missense variants were more common than null changes in these six genes and missense variants are associated with less penetrant forms of CAKUT where affected individuals do not develop CAKUT or have all the typical features.\u003c/p\u003e \u003cp\u003eOur assessment suggested that predicted pathogenic variants in these 6 CAKUT genes were more common in people of African/African-American ancestry in part from the correction for the smaller cohort of the three \u003cem\u003eHNF1B\u003c/em\u003e structural variants in gnomAD v.2.1.1. CAKUT variants were also common in people of an East Asian background. Predicted pathogenic variants were least common in Ashkenazim and Finns, which may result from their relative social and geographic isolation.\u003c/p\u003e \u003cp\u003eHowever, there were a number of methodological considerations in assessing the results from these studies. Only the six commonest CAKUT genes were examined whereas more than 150 have been identified. GnomAD v.2.1.1 underrepresents people with severe or early onset disease, such as those with CAKUT-associated kidney failure who were ineligible for the clinical trials that represented most of this cohort. Most gnomAD v.2.1.1 samples were tested by WES which did not detect structural and copy number changes. This was particularly important for \u003cem\u003eHNF1B\u003c/em\u003e where half the variants are large deletions (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Interestingly no structural changes were found in the other five genes in gnomAD v.2.1.1.\u003c/p\u003e \u003cp\u003eWhile our strategy had the advantage that it provided an assessment for each variant and our criteria for pathogenicity were more stringent than those used previously in similar studies (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), its major source of inaccuracy was in evaluating missense changes. This was demonstrated by the low sensitivity for \u003cem\u003eSALL1\u003c/em\u003e variants \u003cb\u003e(Suppl Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e and by comparison with the highly rigorous REVEL assessment.\u003c/p\u003e \u003cp\u003eClinVar assessments are generally accurate (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) because they are submitted from accredited testing laboratories who use sequencing from people with clinically-suspected kidney disease, and the ACMG/AMP criteria and variant rarity based on recently-available large datasets. However, ClinVar overrepresents high-penetrance variants from clinically-referred cohorts. In addition, ClinVar only includes assessments for 21% of gnomAD v.2.1.1 variants in these 6 CAKUT genes (median, range 5\u0026ndash;29%) and not for structural or copy number changes. There were even fewer assessments for gnomAD v.4.1 (median variants assessed 9%, range 1\u0026ndash;24%). The low number of ClinVar assessments is explained by fewer laboratories performing genetic CAKUT testing because of its mainly clinical diagnosis, the large number of affected genes, the difficulty in assessing missense changes in the CAKUT genes, and the lack of treatment. This suggests that the population frequency of the genetic forms of CAKUT is more common than one in 1263 and closer to our estimate of one in 249. The population frequencies derived from the LOVD and HGMD databases, unlike ClinVar, comprise many variants reported before the availability of ACMG/AMP guidelines and large datasets for checking variant rarity, which reduce their accuracy.\u003c/p\u003e \u003cp\u003eIn summary, the strengths of this study were the large cohorts who had undergone genetic testing, the rigorous criteria used for our variant assessment, the comparison of multiple strategies, the use of gnomAD v.4.1 as a replication cohort, and the ability to determine population frequencies in people of different ancestries. The study\u0026rsquo;s limitations were the inability to confirm variant pathogenicity with clinical data in gnomAD, the lack of ClinVar assessments for all gnomAD variants, the incompleteness of the structural and copy number analysis in ClinVar, and the potential inaccuracies in our computational assessment. Nevertheless, we have used this approach previously to estimate the population frequencies of other genetic diseases such as Alport syndrome, Fabry disease and AD Polycystic kidney disease (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The population frequency of Alport syndrome was an underestimate because the only missense variants assessed were Gly substitutions but, even so, all the estimated population frequencies suggested that these diseases were more common than previously believed.\u003c/p\u003e \u003cp\u003eThis computational study demonstrated that predicted pathogenic variants found in the six commonest CAKUT genes in gnomAD affect between one in 249 and one in 1263 people where the one in 1263 is likely an underestimate because it is derived from ClinVar assessments which were not available for all gnomAD changes. Previous estimates suggested that CAKUT affects about one in 1000 people but our results indicate that genetic causes are more common and possibly responsible for a majority of cases. Importantly, about half the predicted pathogenic variants in the CAKUT genes are missense changes that may be associated with an incompletely penetrant or milder phenotype, so that the number of people with clinical features of CAKUT is likely to be less common than our estimate of one in 249. Future improvements in computational tools will make even more accurate estimates possible.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest statement\u003c/h2\u003e \u003cp\u003eThe authors have no Conflicts of Interest in relation to this manuscript.\u003c/p\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHeidet L, Morini\u0026egrave;re V, Henry C, De Tomasi L, Reilly ML, Humbert C et al (2017) Targeted Exome Sequencing Identifies PBX1 as Involved in Monogenic Congenital Anomalies of the Kidney and Urinary Tract. J Am Soc Nephrol 28(10):2901\u0026ndash;2914\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArdissino G, Dacc\u0026ograve; V, Testa S, Bonaudo R, Claris-Appiani A, Taioli E et al (2003) Epidemiology of chronic renal failure in children: data from the ItalKid project. Pediatrics 111(4 Pt 1):e382\u0026ndash;e387\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW\u0026uuml;hl E, van Stralen KJ, Verrina E, Bjerre A, Wanner C, Heaf JG et al (2013) Timing and outcome of renal replacement therapy in patients with congenital malformations of the kidney and urinary tract. Clin J Am Soc Nephrol 8(1):67\u0026ndash;74\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanna-Cherchi S, Caridi G, Weng PL, Scolari F, Perfumo F, Gharavi AG et al (2007) Genetic approaches to human renal agenesis/hypoplasia and dysplasia. 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Pediatr Nephrol 31(5):707\u0026ndash;714\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanyanusin P, Schimmenti LA, McNoe LA, Ward TA, Pierpont ME, Sullivan MJ et al (1995) Mutation of the PAX2 gene in a family with optic nerve colobomas, renal anomalies and vesicoureteral reflux. Nat Genet 9(4):358\u0026ndash;364\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVivante A, Chacham OS, Shril S, Schreiber R, Mane SM, Pode-Shakked B et al (2019) Dominant PAX2 mutations may cause steroid-resistant nephrotic syndrome and FSGS in children. Pediatr Nephrol 34(9):1607\u0026ndash;1613\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarua M, Stellacci E, Stella L, Weins A, Genovese G, Muto V et al (2014) Mutations in PAX2 associate with adult-onset FSGS. J Am Soc Nephrol 25(9):1942\u0026ndash;1953\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohlhase J, Wischermann A, Reichenbach H, Froster U, Engel W (1998) Mutations in the SALL1 putative transcription factor gene cause Townes-Brocks syndrome. Nat Genet 18(1):81\u0026ndash;83\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Esch H, Groenen P, Nesbit MA, Schuffenhauer S, Lichtner P, Vanderlinden G et al (2000) GATA3 haplo-insufficiency causes human HDR syndrome. Nature 406(6794):419\u0026ndash;422\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClissold RL, Hamilton AJ, Hattersley AT, Ellard S, Bingham C (2015) HNF1B-associated renal and extra-renal disease\u0026mdash;an expanding clinical spectrum. Nat Rev Nephrol 11(2):102\u0026ndash;112\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelo BF, Aguiar MB, Bouzada MCF, Aguiar RL, Pereira AK, Paix\u0026atilde;o GM et al (2012) Early risk factors for neonatal mortality in CAKUT: analysis of 524 affected newborns. Pediatr Nephrol 27(6):965\u0026ndash;972\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi HA, Lee DJ, Shin SM, Lee YK, Ko SY, Park SW (2016) The Prenatal and Postnatal Incidence of Congenital Anomalies of the Kidneys and Urinary Tract (CAKUT) Detected by Ultrasound. Child Kidney Dis 20(1):29\u0026ndash;32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiesel A, Queisser-Luft A, Clementi M, Bianca S, Stoll C (2005) Prenatal Detection of Congenital Renal Malformations by Fetal Ultrasonographic Examination: An Analysis of 709,030 Births in 12 European Countries. Eur J Med Genet 48(2):131\u0026ndash;144\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z-y, Chen Y-m, Chen QL-q, Xu D-qHC-g (2019) Prevalence, types, and malformations in congenital anomalies of the kidney and urinary tract in newborns: a retrospective hospital-based study. Ital J Pediatr 45(1):50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTain YL, Luh H, Lin CY, Hsu CN (2016) Incidence and Risks of Congenital Anomalies of Kidney and Urinary Tract in Newborns: A Population-Based Case-Control Study in Taiwan. Med (Baltim) 95(5):e2659\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavige J (2024) Tips for Testing Adults With Suspected Genetic Kidney Disease. Am J Kidney Dis 83(6):816\u0026ndash;824\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J et al (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17(5):405\u0026ndash;424\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibson J, Fieldhouse R, Chan MMY, Sadeghi-Alavijeh O, Burnett L, Izzi V et al (2021) Prevalence Estimates of Predicted Pathogenic COL4A3-COL4A5 Variants in a Population Sequencing Database and Their Implications for Alport Syndrome. J Am Soc Nephrol 32(9):2273\u0026ndash;2290\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKondo A, Nagano C, Ishiko S, Omori T, Aoto Y, Rossanti R et al (2021) Examination of the predicted prevalence of Gitelman syndrome by ethnicity based on genome databases. Sci Rep 11(1):16099\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanktree MB, Haghighi A, Guiard E, Iliuta I-A, Song X, Harris PC et al (2018) Prevalence Estimates of Polycystic Kidney and Liver Disease by Population Sequencing. J Am Soc Nephrol. ;29(10)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorges P, Pasqualim G, Giugliani R, Vairo F, Matte U (2020) Estimated prevalence of mucopolysaccharidoses from population-based exomes and genomes. Orphanet J Rare Dis 15(1):324\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaler SG, Ferreira CR, Yam LS (2020) Estimated birth prevalence of Menkes disease and ATP7A-related disorders based on the Genome Aggregation Database (gnomAD). Mol Genet Metabolism Rep 24:100602\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKermond-Marino A, Weng A, Xi Zhang SK, Tran Z, Huang M, Savige J Population Frequency of Undiagnosed Fabry Disease in the General Population. Kidney International Reports\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKermond-Marino A, Weng A, Xi Zhang SK, Tran Z, Huang M, Savige J (2023) Population Frequency of Undiagnosed Fabry Disease in the General Population. Kidney Int Rep 8(7):1373\u0026ndash;1379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePejaver V, Byrne AB, Feng BJ, Pagel KA, Mooney SD, Karchin R et al (2022) Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria. Am J Hum Genet 109(12):2163\u0026ndash;2177\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIoannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S et al (2016) REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet 99(4):877\u0026ndash;885\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaudhuin LM, Kluge ML, Kotzer KE, Lagerstedt SA (2019) Variability in gene-based knowledge impacts variant classification: an analysis of FBN1 missense variants in ClinVar. Eur J Hum Genet 27(10):1550\u0026ndash;1560\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarughese S, Huang M, Savige J Typical and atypical ADPKD: Predicted pathogenic genetic variants and population frequencies 2025\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pediatric-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pnep","sideBox":"Learn more about [Pediatric Nephrology](http://link.springer.com/journal/467)","snPcode":"467","submissionUrl":"https://www.editorialmanager.com/pnep/default2.aspx","title":"Pediatric Nephrology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CAKUT, congenital anomalies of the kidney and urinary tract, kidney development, reflux, kidney agenesis, kidney atrophy, kidney cysts","lastPublishedDoi":"10.21203/rs.3.rs-8297749/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8297749/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCongenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of kidney failure in children, and renal imaging suggests that it affects one in 200 of the population, one in five of whom are thought to have a genetic cause. This study determined the population frequency of predicted pathogenic variants from the six most commonly-affected CAKUT genes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e \u003cem\u003eHNF1B\u003c/em\u003e, \u003cem\u003eSALL1\u003c/em\u003e, \u003cem\u003eEYA1\u003c/em\u003e, \u003cem\u003ePBX1\u003c/em\u003e, \u003cem\u003eGATA3\u003c/em\u003e, \u003cem\u003ePAX2\u003c/em\u003e variants were downloaded from gnomADv.2.1.1 (n\u0026thinsp;=\u0026thinsp;141,456) and the population frequency of predicted disease-causing variants calculated from the sum of structural, null (loss-of- function) and predicted pathogenic missense changes in the overall cohort and in the ancestries represented. This was compared with the population frequencies derived from ClinVar, HGMD and LOVD. Population frequencies were also determined in a replication cohort (gnomAD v.4.1, n\u0026thinsp;=\u0026thinsp;807,162) using our method and ClinVar assessments.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe population frequency of genetic causes of CAKUT lies between one in 249 (our strategy) and one in 1,263 (ClinVar assessments) in the gnomAD v.2.1.1 database. More than half the disease-causing variants were missense changes, and predicted pathogenic variants were commonest in African-Americans (one in 149) and least common in Ashkenazim (one in 864). The population frequency estimated from gnomAD v.4.1 lies between one in 372 (our strategy) and one in 1762 (with ClinVar).\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eThe ClinVar results are underestimates since assessments were not available for structural, copy number and many missense changes in gnomAD. However some of the predicted pathogenic variants identified in this study will have incomplete penetrance or be variably expressive and, therefore, result less often in clinical disease. Nevertheless, these calculations suggest that genetic causes of CAKUT are likely to be more common than the previously-reported one in 1,000.\u003c/p\u003e","manuscriptTitle":"The population frequency of predicted pathogenic variants in commonly-affected genes in CAKUT in the general population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 07:06:04","doi":"10.21203/rs.3.rs-8297749/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-12-29T13:41:36+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-10T19:35:34+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T18:18:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T17:32:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pediatric Nephrology","date":"2025-12-09T17:05:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pediatric-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pnep","sideBox":"Learn more about [Pediatric Nephrology](http://link.springer.com/journal/467)","snPcode":"467","submissionUrl":"https://www.editorialmanager.com/pnep/default2.aspx","title":"Pediatric Nephrology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d5579c38-44e4-4a83-afb1-a7640d311b4e","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T13:59:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 07:06:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8297749","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8297749","identity":"rs-8297749","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}