Posterior urethral valves (PUV) are the commonest cause of end-stage renal disease (ESRD) in children, affecting 1 in 4000 male births (Brownlee et al., 2019; Thakkar et al., 2015) and result in congenital bladder outflow obstruction. It is an exclusively male disorder, with over a third of those affected developing ESRD (i.e., requirement for dialysis or kidney transplantation) before the age of 30 years (Heikkilä et al., 2011; Sanna-Cherchi et al., 2009) and often associated with renal dysplasia, vesicoureteral reflux, and bladder dysfunction which are poor prognostic factors for renal survival (Sanna-Cherchi et al., 2009). Management involves endoscopic valve ablation to relieve the obstruction, however the majority of affected children have long-term sequelae related to ongoing bladder dysfunction (DeFoor et al., 2008).

The pathogenesis of PUV is poorly understood (Krishnan et al., 2006). Although usually sporadic, familial clustering and twin studies suggest an underlying genetic component, although Mendelian inheritance is rare (Chiaramonte et al., 2016; Frese et al., 2019; Morini et al., 2002; Schreuder et al., 2008; Weber et al., 2005). Several monogenic causes of other congenital bladder outflow obstruction disorders have been described including BNC2 in urethral stenosis (Kolvenbach et al., 2019); FLNA in syndromic urethral anomalies (Wade et al., 2021); HPSE2 and LRIG2 in urofacial syndrome (Daly et al., 2010; Stuart et al., 2008); CHRM3 in prune-belly like syndrome (Weber et al., 2011); and MYOCD in megabladder (Houweling et al., 2011). However, a monogenic etiology for isolated PUV has not been identified. Case reports of chromosomal abnormalities resulting in PUV as part of a wider syndrome (Demirkan, 2021; Houcinat et al., 2011; Tong et al., 2015) and microarray-based studies linking rare copy number variants (CNVs) with PUV (Boghossian et al., 2016; Caruana et al., 2016; Faure et al., 2016; Schierbaum et al., 2021; Verbitsky et al., 2019) suggest that structural variation, and in particular duplications (Schierbaum et al., 2021; Verbitsky et al., 2019), may be important, but no recurrent CNVs have demonstrated consistent association with PUV. The observation that isolated PUV does not usually follow a classical Mendelian inheritance pattern, and that a monogenic cause has not been identified, suggests that the underlying genetic architecture of this rare disorder is likely to be complex.

Here, we used whole-genome sequencing (WGS) data from a diverse ancestry cohort to perform a sequencing-based genome-wide association study (seqGWAS) investigating how common, low-frequency, and rare single-nucleotide and structural variation contribute to this complex disorder. The recently proposed term seqGWAS is used to distinguish this approach from the well-established genome-wide association study (GWAS) design based on sampling of common variation across the genome using high-throughput single-nucleotide polymorphism genotyping with or without imputation (McMahon et al., 2021). Our seqGWAS identified significant associations with common and rare variation that implicated TBX5 (T-Box Transcription Factor 5) and PTK7 (Protein Tyrosine Kinase 7), respectively, as well as enrichment of structural variation affecting candidate cis-regulatory elements (cCREs). In addition, we demonstrate that a well-controlled diverse ancestry sequencing-based GWAS can increase power for disease locus discovery and facilitate the fine mapping of causal variants.

We analysed WGS data from 132 unrelated male probands with PUV and 23,727 non-PUV controls (unaffected relatives without known kidney disease), recruited to the UK’s 100,000 Genomes Project (100KGP) (Smedley et al., 2021; see Figure 1 for study workflow). The available dataset (version 10) combined WGS data, clinical phenotypes standardized using Human Phenotype Ontology (HPO) codes, and comprehensive hospital clinical records for 89,139 individuals with cancer, rare disease, and their unaffected relatives. None of the cases included had received a definitive genetic diagnosis through the clinical arm of the 100KGP. Two individuals had a pathogenic and likely pathogenic variant affecting HNF1B and FOXC1, respectively, but these were not considered causal for PUV (see Appendix 1). Given the small number of recruited cases with this rare disorder, we chose to jointly analyse individuals from diverse ancestral backgrounds, thereby preserving sample size and boosting power. To mitigate confounding due to population structure whilst using this mixed-ancestry approach, we performed ancestry-matching of cases and controls using weighted principal components (Figure 1—figure supplement 1) and utilized SAIGE which employs a scalable generalized logistic mixed model (GLMM) to account for relatedness between individuals and a saddlepoint approximation to avoid inflated type I error seen with case-control imbalance (Zhou et al., 2016a). Clinical characteristics and genetic ancestry of the cases and controls are detailed in Table 1.

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Variation at 12q24.21 and 6p21.1 is associated with PUV

To determine the contribution of common and low-frequency variation to PUV, we carried out a seqGWAS using 19,651,224 single-nucleotide variants (SNVs) and indels with minor allele frequency (MAF) ≥0.1%. Statistically significant (p<5 × 10^–8) association was detected at two loci (Figure 2 and Table 2). The genomic inflation factor (λ) of 1.04 confirmed population stratification was well controlled in this diverse ancestry cohort, although this may represent an underestimate given the low power of the cohort (Figure 2—figure supplement 1).

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Figure 2—source data 1

Joint analysis of common and low-frequency variation (minor allele frequency [MAF] >0.1%) by gene across the exome.

Data generated using the genome-wide association study (GWAS) summary statistics as input to MAGMA. Gene names are given as ENSEMBL Gene IDs and SYMBOL. CHR, chromosome; NSNPS, number of variants annotated to that gene; NPARAM, number of parameters used in the model; N, sample size; ZSTAT, Z-value for the gene based on permutation p-value; P, p-value.

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Figure 2—source data 2

Joint analysis of common and low-frequency variation (minor allele frequency [MAF] >0.1%) by gene set.

Data generated using the genome-wide association study (GWAS) summary statistics as input to MAGMA.

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Table 2

				P value		OR (95% CI)		Case EAF		Control EAF
Lead variant	CHR:POS	Effect Allele	Closest gene	Discovery	Replication	Discovery	Replication	Discovery	Replication	Discovery	Replication
rs10774740	chr12:114228397	T	TBX5	7.81x10–12	5.17x10–3	0.4	0.8	0.19	0.31	0.37	0.36
						(0.31–0.52)	(0.68–0.93)
rs144171242	chr6:43120356	G	PTK7	2.02x10–8	7.21x10–3	7.2	2.18	0.05	0.018	0.01	0.008
						(4.08–12.70)	(1.22–3.90)

Table 2—source data 1

Replication study.

The lead variants at the top four loci with p<5 × 10^–7 were genotyped in an independent European cohort of 395 posterior urethral valves (PUV) cases and 4151 controls. p-Values in the replication cohort were calculated using a one-sided Cochran Armitage Trend test. OR, odds ratio; CI, 95% confidence interval.

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At 12q24.21, the lead intergenic variant (rs10774740) was common (MAF 0.37) and reached p=7.81 × 10^–12 (OR 0.40; 95% CI 0.31–0.52; Figure 3). A rare (MAF 0.007) variant (rs144171242) at 6p21.1, located in an intron of PTK7, was also significant at p=2.02 × 10^–8 (OR 7.20; 95% CI 4.08–12.70; Figure 4). The meanDP (mean sequencing depth) and meanGQ (mean genotype quality) at these sites were 33.43 and 133.28, for rs10774740 and 29.34 and 75.59 for rs144171242, respectively, confirming these variants were high-quality calls. Conditional analysis did not identify secondary independent signals at either locus and epistasis was not detected between the two lead variants (p=0.10).

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Due to the male nature of PUV, we also conducted a sex-specific analysis using only male controls (n=10,425) to determine whether any additional signals could be detected by removing females with potentially undetected genitourinary phenotypes. Both lead variants showed stronger evidence of association: rs10774740 at 12q24.21 (p=7.08 × 10^–12; OR 0.40; 95% CI 0.31–0.52) and rs144171242 at 6p21.1 (p=1.79 × 10^–8; OR 7.40; 95% CI 4.14–13.22). No significant associations on chromosome X were identified.

Gene and gene set analysis was carried out to assess the joint effect of common and low-frequency variants and identify potential functional pathways associated with PUV, however, no genes (Figure 2—source data 1) or pathways (Figure 2—source data 2) reached statistical significance after correction for multiple testing.

12q24.21 and 6p21.1 replicate in an independent cohort

We next carried out a replication study in an independent European cohort consisting of 395 individuals with PUV: 333 from Poland and Germany, recruited through the CaRE for LUTO (Cause and Risk Evaluation for Lower Urinary Tract Obstruction) Study, and 62 from the UK; 4151 male individuals recruited to the cancer arm of the 100KGP were used as controls. The ancestry of a subset of cases (n=204) for whom genome-wide genotyping data was available and the control cohort (n=4151) was confirmed as European (Figure 5). The UK PUV patients and the 100KGP cancer control cohort had not been included in the discovery analyses.

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The lead variants at the top four loci with p<5 × 10^–7 were tested for replication. Association at both genome-wide significant lead variants was replicated although with smaller effect sizes (Table 2): rs10774740 (p=5.17 × 10^–3; OR 0.80; 95% CI 0.68–0.93) and rs144171242 (p=7.21 × 10^–3; OR 2.18; 95% CI 1.22–3.90). Two further loci with suggestive evidence of association (10q11.2; rs1471950716; p=1.45 × 10^–7 and 14q21.1; rs199975325; p=2.52 × 10^–7) did not replicate (Table 2—source data 1).

To confirm this signal was not being driven by case-control imbalance, we repeated the analysis with 500 male controls (a case:control ratio of 1:1.3). rs10774740 at the 12q24.21 locus remained significant (p=9.9 × 10^–3; OR 0.77; 95% CI 0.63–0.94). rs144171242 at the 6p21.1 locus did not reach statistical significance, but this is likely due to insufficient power (p=0.06; OR 2.24; 95% CI 0.93–5.36). For a rare variant such as rs144171242 (MAF 0.007), a replication study with 500 controls is only powered to detect association with variants that have a large effect size (OR >3.5).

Diverse ancestry analysis increases power for discovery

To ascertain whether the observed associations were being driven by a specific ancestry group, we next repeated the seqGWAS using a subgroup of genetically defined European individuals (88 cases and 17,993 controls) and 16,938,500 variants with MAF ≥0.1%. The 12q24.21 locus remained genome-wide significant (Figure 6 and Figure 6—figure supplement 1), however the lead variant (rs2555009) in the region showed weaker association (p=4.02 × 10^–8; OR 0.43; 95% CI 0.12–0.73) than rs10774740, the lead variant in the diverse ancestry analysis (Figure 6—source data 1). Interestingly, the two variants were not in strong linkage disequilibrium (EUR LD; r²=0.55). The lead variant at 6p21.1 from the diverse ancestry analysis did not reach genome-wide significance in the European-only study (rs144171242; p=3.60 × 10^–5; OR 5.90; 95% CI 2.88–12.11) suggesting either that this signal may be being driven partly by non-Europeans or the result of loss of power due to reduced sample size. p-Values and effect sizes were strongly correlated between the diverse ancestry and European-only seqGWAS (Figure 6—figure supplement 2), demonstrating that inclusion of individuals from diverse backgrounds to increase sample size can be an effective way to boost power and discover new disease loci, even in a small cohort.

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Comparison of diverse ancestry and European genome-wide association study (GWAS) association statistics.

The lead variants at the top four loci with p<5 × 10^–7 are shown. OR, odds ratio; CI, 95% confidence interval.

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As the numbers of African, South Asian, and admixed ancestry individuals were too small to reliably carry out subgroup association analyses and subsequent meta-analysis, we instead compared ancestry-specific allele frequencies, effect sizes, and directions for the two lead variants. rs10774740 (T) had a higher allele frequency in individuals of African ancestry (MAF 0.74) compared with European (MAF 0.37) and South Asian (MAF 0.35) populations (Figure 6—figure supplement 3), however the effect size and direction were similar between the groups (Figure 6—figure supplement 4). rs144171242 (G) was present at a lower allele frequency in South Asian (MAF 0.002) compared with European (MAF 0.008) individuals and was not seen in the African ancestry group (Figure 6—figure supplement 3). The effect size of this rare variant was higher in the South Asian than European population (Figure 6—figure supplement 4), which may explain why it only reached genome-wide significance after inclusion of South Asian individuals. Finally, comparison with population allele frequencies from gnomAD (Karczewski et al., 2020) demonstrated that although there is large variation in the allele frequency of rs10774740 between ancestries this is away from, not towards, the case allele frequency and suggests that the detected associations are not being driven by differences in allele frequency between populations (Figure 6—figure supplement 3).

rs10774740 is associated with prostate cancer and female genitourinary phenotypes

Interrogation of the NHGR/EBI GWAS Catalog (MacArthur et al., 2017) revealed that the risk allele rs10774740 (G) is associated with prostate cancer aggressiveness (Berndt et al., 2015; p=3 × 10^–10; OR 1.14; 95% CI 1.09–1.18). PheWAS data from the UK Biobank demonstrated that the protective allele rs10774740 (T) also has a protective effect in female genitourinary phenotypes (Figure 7): urinary incontinence (p=8.3 × 10^–12; OR 0.90; 95% CI 0.87–0.92), female stress incontinence (p=7.9 × 10^–10; OR 0.89; 95% CI 0.85–0.92), genital prolapse (p=1.1 × 10^–9; OR 0.92; CI 0.89–0.94) and symptoms involving the female genital tract (p=1.7 × 10^–8; OR 0.90; 95% CI 0.87–0.94). No significant phenotypic associations affecting other organ systems were seen. These data therefore provide independent validation of a role for TBX5 in urogenital phenotypes. No known GWAS or PheWAS associations were identified for rs144171242, which is likely too rare to be genotyped or imputed in previous GWAS.

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TBX5 and PTK7 proteins are detected in the developing urinary tract

To determine whether TBX5 and PTK7 proteins can be detected during urinary tract development, immunohistochemistry was undertaken in a 7-week gestation normal human embryo (Figure 8). At this stage of development, the urogenital sinus is a tube composed of epithelia that will differentiate into urothelial cells of the proximal urethra and the urinary bladder. Uroplakin 1B, a water-proofing protein, was detected in urogenital sinus epithelia (Figure 8B). PTK7 was detected in epithelia lining the urogenital sinus, and intensely in stromal-like cells surrounding the mesonephric ducts (Figure 8C). TBX5 was detected in a nuclear pattern in a subset of epithelial cells lining the urogenital sinus (Figure 8D). Omission of primary antibodies resulted in absent signals, as expected (Figure 8E). Immunostaining of a second human embryo at the same stage of gestation produced similar results, with a prominent signal for PTK7 in stromal cells surrounding the mesonephric duct and a subset of nuclei in the epithelium of the urogenital sinus stained for TBX5 (Figure 8—figure supplement 1).

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Monogenic causes of PUV are uncommon

Having identified two novel gene associations through single-variant association testing, we next aimed to determine whether there was any gene-based enrichment of rare coding variation in this PUV cohort. Single-variant association tests can be underpowered when variants are rare and collapsing variant data into specific regions or genes can increase power and aid gene discovery.

We therefore aggregated rare (gnomAD allele frequency [AF]<0.1% [Karczewski et al., 2020]), predicted deleterious [protein-truncating, or CADD ≥20 [Rentzsch et al., 2019]] SNVs and small indels by gene, comparing the burden between cases and controls on an exome-wide basis. No significant enrichment was detected in any of the 19,161 protein-coding genes analysed after correction for multiple testing (Figure 9 and Figure 9—figure supplement 1). The median number of variants tested per gene was 41 (IQR 47). None of the genes previously associated with congenital bladder outflow obstruction showed evidence of enrichment: BNC2 (congenital lower urinary tract obstruction, MIM 618612); FLNA (frontometaphyseal dysplasia, MIM 305620); HPSE2 and LRIG2 (urofacial syndrome, MIM 236730/615112); CHRM3 (prune-belly syndrome, MIM 100100); MYOCD (megabladder, MIM 618719). Despite the limited power of this cohort, the lack of gene-based enrichment seen is consistent with an absence of proven Mendelian causes of non-syndromic PUV.

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Exome-wide rare coding variant analysis by gene.

p-Values generated using SAIGE-GENE and the SKAT-O rare variant test.

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Structural variation affecting cis-regulatory elements is enriched

Large, rare CNVs have been identified in patients with PUV using conventional microarrays (Boghossian et al., 2016; Caruana et al., 2016; Faure et al., 2016; Verbitsky et al., 2019), however high-coverage WGS enables detection of smaller structural variants (SVs) with superior resolution (Gross et al., 2019; Zhou et al., 2016c), and allows the identification of balanced rearrangements including inversions. We therefore aimed to test for association with different types of SVs, by comparing the burden of rare (MAF <0.1%), autosomal SVs on an exome-wide and cis-regulatory element basis.

We first focused our analysis on rare SVs that were potentially gene-disrupting by extracting those that intersected with at least one exon. Although we observed an increased burden of all SV types in cases compared with controls, this only reached statistical significance for inversions (p=2.1 × 10^–3) when corrected for the multiple SV comparisons performed (Table 3). No difference in SV size between the cohorts was seen. Furthermore, exome-wide gene-based burden analysis did not detect any gene-level enrichment of rare SVs overall or when stratified by type, indicating that, in this cohort at least, rare structural variation does not appear to affect any single gene recurrently.

Table 3

		EXON		cCRE
		PUV	Controls	PUV	Controls
CNV	n (%)	109 (82.6)	17,961 (75.7)	111 (84.1)	18,773 (79.1)
	OR (CI)	1.52 (0.96–2.50)		1.39 (0.87–2.34)
	Fisher’s exact p	0.07		0.2
	Median size (kb) (IQR)	104 (183)	94 (158)	80 (165)	80 (128)
	p (Wilcoxon)	0.75		0.75
DEL	n (%)	117 (88.6)	19,987 (84.2)	132 (100)	23,031 (97.1)
	OR (CI)	1.46 (0.85–2.69)		Inf
	Fisher’s exact p	0.19		0.04
	Median size (kb) (IQR)	1.4 (4.2)	1.8 (4.6)	1.5 (4.1)	1.9 (4.5)
	p (Wilcoxon)	0.18		4.1×10–4
DUP	n (%)	59 (44.7)	8,476 (35.7)	104 (78.8)	16,004 (67.5)
	OR (CI)	1.45 (1.01–2.08)		1.79 (1.17–2.83)
	Fisher’s exact p	0.04		5.0×10–3
	Median size (kb) (IQR)	3.7 (5.7)	3.0 (5.6)	2.0 (5.3)	2.3 (5.3)
	p (Wilcoxon)	0.16		0.49
INV	n (%)	66 (50.0)	8,736 (36.8)	81 (61.4)	11,171 (47.1)
	OR (CI)	1.72 (1.20–2.45)		1.79 (1.24–2.59)
	Fisher’s exact p	2.1×10–3		1.2×10–3
	Median size (kb) (IQR)	253 (1931)	261 (1642)	129 (459)	94 (779)
	p (Wilcoxon)	0.44		0.12

Given the tightly controlled transcriptional networks that govern embryogenesis, we hypothesized that regulatory regions may be preferentially affected by rare structural variation. To investigate this, we identified rare (MAF <0.1%), autosomal SVs that intersected with 926,535 genome-wide cCREs curated by ENCODE (Moore et al., 2020). A significant enrichment of cCRE-intersecting SVs was observed for inversions (61.4% vs. 47.1%, p=1.2 × 10^–3) and duplications (78.8% vs. 67.5%, p=5.0 × 10^–3) (Table 3). While the median size of inversions was larger in cases, this was not statistically significant (129 kb vs. 94 kb, p=0.12).

To further characterize this enrichment, we repeated the burden analysis stratifying by cCRE subtype (distal enhancer-like signature [dELS], proximal enhancer-like signature [pELS], promoter-like signature [PLS], CTCF-only and DNase-H3K4me3) and demonstrated a consistent signal across all cCREs for inversions (Figure 10), most significantly affecting CTCF-only elements (49.2% vs. 31.7%, p=3.1 × 10^–5, Figure 10—source data 1). These elements act as chromatin loop anchors suggesting that inversions affecting these regions may potentially alter long-range regulatory mechanisms mediated by chromatin conformation. Duplications affecting pELS elements were also significantly enriched in cases (29.5% vs. 16.8%, p=2.7 × 10^–4).

Figure 10

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Figure 10—source data 1

Structural variant cCRE analysis.

The burden of rare autosomal structural variants intersecting with each cis-regulatory element type was compared between 132 cases and 23,727 controls. cCRE, candidate cis-regulatory element; CNV, copy number variant; DEL, deletion; DUP, duplication; INV, inversion; dELS, distal enhancer-like signature; pELS, proximal enhancer-like signature; PLS, promoter-like signature; OR, odds ratio; CI, 95% confidence interval.

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Using a seqGWAS we have identified the first robust genetic associations of PUV, implicating TBX5 and PTK7 in its underlying pathogenesis. We detected an enrichment of rare structural variation affecting cCREs and demonstrate that monogenic causes of PUV are not a common feature, at least within this cohort. We also show that a well-controlled diverse ancestry WGS approach can increase power for disease locus discovery in rare disease and facilitate the fine mapping of causal variants.

The majority of genetic association studies are carried out in individuals of European ancestry, however with WGS allowing unbiased variant detection combined with the use of appropriate statistical methodology to mitigate confounding by population structure, it is widely recognized that increasing ancestral diversity in genetic studies is scientifically and ethically necessary (Fatumo et al., 2022; Peterson et al., 2019). On this basis we opted to combine individuals regardless of ancestral background, including a greater proportion of non-White individuals than is present in the UK (87% individuals are recorded as ‘White’ in the 2011 UK census [General Register Office for Scotland, Northern Ireland Statistics and Research Agency, Office for National Statistics, Census Division, 2020]), suggesting mitigation of the Euro-centric bias present in most previous genomic studies, at least with respect to the UK population. We used a generalized mixed model association test with saddlepoint approximation to maximize the signal from the resulting diverse ancestry, case-control imbalanced dataset.

We identified a significant protective effect of the common variant rs10774740 (T), highlighting that common variants can contribute to an individual’s risk of a rare disease, as is increasingly being recognized (Niemi et al., 2018). The effect size and direction were consistent between African, European, and South Asian ancestries, despite differences in allele frequency between the population groups. Using experimentally determined chromatin interaction data from mesendoderm cells, we mapped this locus to the promoter of the transcription factor TBX5, rare variants of which cause autosomal-dominant Holt-Oram syndrome (MIM 142900), characterized by congenital cardiac septal defects and upper-limb anomalies (Basson et al., 1997; Li et al., 1997).

The association of the risk allele (G) with prostate cancer aggressiveness in men and genital prolapse and urinary incontinence in women raises the intriguing possibility that TBX5, which shows moderate expression in the adult bladder, is also associated with lower urinary tract phenotypes in adults. These findings are consistent with the observation that variation in genes associated with other developmental anomalies have been linked to malignancy in the same organ (Su et al., 2012; Sun et al., 2017), highlighting the shared molecular pathways driving both embryogenesis and cancer.

We also identified an association of the rare variant rs144171242 with PUV, located in an intron of PTK7 and predicted to have regulatory activity in mesendoderm cells. Given its rarity, this association would not have been detected using a conventional array-based GWAS approach, highlighting the utility of seqGWAS. The inclusion of South Asian individuals, in whom the effect size of rs144171242 is larger, increased our power to detect association which was not genome-wide significant in the European-only analysis. PTK7 (protein tyrosine kinase 7) is an evolutionarily conserved transmembrane receptor required for vertebrate embryonic patterning and morphogenesis, and a key regulator of planar cell polarity (PCP) via the non-canonical Wnt pathway (Berger et al., 2017). The PCP pathway is critical for determining the orientation of cells in the plane of an epithelium, regulating a process called convergent extension whereby cells intercalate by converging in one axis and elongating in the perpendicular axis. Altered expression of PTK7 was initially observed in cancer (Dunn and Tolwinski, 2016; Mossie et al., 1995), but rare missense variants in PTK7 have since been linked to neural tube defects (Lei et al., 2019; Wang et al., 2015) and scoliosis (Hayes et al., 2014) in both humans and animal models, confirming a role in embryonic development.

Aberrations of mesonephric duct and urogenital sinus maturation have been implicated in the pathogenesis of PUV (Krishnan et al., 2006). Our observations that TBX5 and PTK7 are present at the appropriate timepoint during normal human embryogenesis, in or around the mesonephric ducts and urogenital sinus, suggest that altered regulation of expression of either may perturb the normal development of these structures. Further functional studies will however be necessary to provide definitive evidence of a role for these proteins in urinary tract development. Interestingly, mesoderm-specific conditional deletion of Ptk7 in mice has been shown to affect convergent extension and tubular morphogenesis of the mesonephric duct at E18.5, leading to a shortened duct with increased diameter and reduced coiling, resulting in male sterility (Xu et al., 2018; Xu et al., 2016). Whether similar disruption in mesonephric duct morphogenesis is seen at E14 (corresponding to development of the urethra) remains to be seen but may provide further insights into the biological mechanisms underpinning PUV.

PUV is a male-limited phenotype, although urethral abnormalities have been reported in the female relatives of affected males (Kolvenbach et al., 2019). This sexual dimorphism is most likely the result of anatomical differences in the development and length of the urethra between males and females, with the association between rs10774740 at the TBX5 locus and female genital prolapse and urinary incontinence suggesting that females may also manifest lower urinary tract phenotypes. While X-linked inheritance has been reported in two patients with syndromic PUV (Wade et al., 2021), we detected no significant common or rare variant associations on chromosome X in this cohort to suggest that genetics underpin the male specific nature of this condition.

Rare CNVs, detected using a microarray-based approach, have recently been shown to be enriched in patients with kidney and urinary tract anomalies, but were not found to be enriched in 141 PUV patients (Verbitsky et al., 2019). Similarly, we did not identify an increased burden of rare CNVs in individuals with PUV, but we did observe a higher number of rare, exonic CNVs than Verbitsky et al. (2019) (82.6% vs. 32.6%), most likely reflecting the increased sensitivity and resolution of WGS for SV detection as well as the difference in size threshold for inclusion (>10 kb compared with >100 kb). Importantly, none of the CNVs recurrently affected a particular gene in our cohort which, in combination with the lack of gene-based enrichment seen in our rare SNV/indel analysis, suggests that monogenic causes of PUV are rare, although our sample size would be underpowered to detect significant genetic heterogeneity.

Intriguingly, we demonstrated an enrichment of rare duplications and inversions affecting cCREs. Genomic duplications have previously been linked with PUV (Schierbaum et al., 2021; Verbitsky et al., 2019) but this is the first study to examine balanced inversions in a PUV cohort. Current understanding of the functional relevance of inversions is limited as the balanced nature and location of breakpoints within complex repeat regions make detection challenging (Puig et al., 2015). Inversions can directly disrupt coding sequences or regulatory elements, as well as predispose to other SVs, and have been associated with hemophilia A (Lakich et al., 1993), Hunter syndrome (Bondeson et al., 2016), neurodegenerative (Webb et al., 2008) and autoimmune disease (Namjou et al., 2021; Salm et al., 2012). The strongest signal we observed was for inversions affecting CTCF-only regions, potentially implicating disrupted chromatin looping in the underlying pathogenesis of PUV and raising the interesting possibility that non-specific perturbation of long-range regulatory networks or TADs could manifest as PUV, perhaps due to sensitivity of integration of the mesonephric duct into the posterior urethra to even minor abnormalities of gene expression. While the SV analyses described here generate some interesting hypotheses, these findings should be considered exploratory until they can be replicated in an independent cohort.

In summary, we have shown that both rare and common variation can contribute to an individual’s risk of PUV, and that structural variation affecting regulatory networks may also play a role. Given that PUV does not usually exhibit a Mendelian pattern of inheritance, our data provides support for a complex genetic architecture for this rare disorder, where different types of genetic variation across the allele frequency spectrum may interact to influence susceptibility. Better powered studies will be needed to identify additional genetic risk factors with smaller effect sizes and to explore how these variants might be influenced by maternal or in utero factors (Nicolaou et al., 2015).

This study has several strengths. WGS enables ancestry-independent variant detection, uniform genome-wide coverage, improved SV resolution and detection, as well as direct sequencing of underlying causal variants. Using case-control data from over 20,000 individuals sequenced on the same platform also minimizes confounding by technical artefacts. Inclusion of diverse ancestries increased our power to detect both novel associations and the underlying causal variant, with the lack of genomic inflation and subsequent replication indicating these associations are robust. Furthermore, we integrated seqGWAS, epigenomic and chromatin interaction data to ascertain the functional relevance of loci and identify biologically plausible genes, validated by detection of the relevant proteins at the appropriate site and developmental stage.

A limitation of this study is its relatively low statistical power to detect association due to the limited size of the cohort; sufficient power exists to detect low-frequency variants with large effects (e.g., MAF 1% with OR >7.5), but rare variants, or those with smaller effect sizes, may be missed with this sample size (Figure 2—figure supplement 2). Future meta-analyses will be necessary to identify additional loci, as well as expanded efforts to recruit non-Europeans given the low absolute numbers of non-European cases in this study. Furthermore, while WGS offers improved SV resolution over microarrays, false positives may occur and are dependent on the SV calling algorithm used. Ideally, long-read sequencing and independent validation would provide more comprehensive SV detection, especially of larger variants in complex, repetitive and GC-rich regions. Finally, although we have assessed the relevance of the associated loci using bioinformatic approaches and shown that publicly available and our own experimental data support the association, the biological mechanisms linking these genes with PUV have yet to be elucidated.

To our knowledge, this is the first study to utilize diverse ancestry WGS for association testing in a rare disease. Combining WGS data across ancestries increased power to detect the first two robust genetic associations for PUV and identified the likely causal variants through enhanced fine mapping. Finally, integration of functional genomic and experimental data implicated TBX5 and PTK7 in the pathogenesis of PUV, providing new insights into the biological pathways underlying this important but poorly understood disorder.

All genetic and phenotypic data from the 100,000 Genomes Project and can be accessed by application to Genomics England Ltd (https://www.genomicsengland.co.uk/about-gecip/joining-research-community/). Access is free for academic research institutions and universities as well as public and private healthcare organisations that undertake significant research activity. This dataset includes de-identified, linked information for each participant including genome sequence data, variant call files, phenotype/clinical data and Hospital Episode Statistics (HES) with access gained through a secure Research Environment. No sequencing or identifiable personal data is available for download. The full GWAS summary statistics have been uploaded to the NHGRI-EBI GWAS Catalog (http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90134001-GCST90135000/GCST90134254). Source data files have been provided for Figures 2, 6, 9 and 10 containing the numerical data used to generate figures. Code for SAIGE and SAIGE-GENE can be found at https://github.com/weizhouUMICH/SAIGE, (copy archived at swh:1:rev:a41727267cb5f843a4446e4d4809cafc72687a5d). Code for PAINTOR is available at https://github.com/gkichaev/PAINTOR_V3.0, (copy archived at swh:1:rev:ffce82a7114eeb98220db39ba5d6b69e5a419a9e) Functional annotation and MAGMA gene and gene-set analysis were performed using the web-based platform FUMA (https://fuma.ctglab.nl). Custom R Code for the case-control ancestry-matching and structural variant burden analysis have been uploaded as Source code 1 (ancestry matching) and Source code 2 (SV burden testing).

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Author details

1. Melanie MY Chan

   Department of Renal Medicine, University College London, London, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1968-1734

2. Omid Sadeghi-Alavijeh

   Department of Renal Medicine, University College London, London, United Kingdom

   Contribution

   Data curation, Formal analysis, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

3. Filipa M Lopes

   Division of Cell Matrix Biology & Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom

   Contribution

   Validation, Investigation, Visualization

   Competing interests

   No competing interests declared

4. Alina C Hilger

   1. Children's Hospital, University of Bonn, Bonn, Germany
   2. Institute of Human Genetics, University of Bonn, Bonn, Germany

   Contribution

   Validation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Horia C Stanescu

   Department of Renal Medicine, University College London, London, United Kingdom

   Contribution

   Supervision

   Competing interests

   No competing interests declared

6. Catalin D Voinescu

   Department of Renal Medicine, University College London, London, United Kingdom

   Contribution

   Software

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3636-8689

7. Glenda M Beaman

   1. Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom
   2. Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, United Kingdom

   Contribution

   Validation

   Competing interests

   No competing interests declared

8. William G Newman

   1. Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, United Kingdom
   2. Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, United Kingdom

   Contribution

   Validation

   Competing interests

   No competing interests declared

9. Marcin Zaniew

   Department of Pediatrics, University of Zielona Góra, Zielona Gora, Poland

   Contribution

   Validation

   Competing interests

   No competing interests declared

10. Stefanie Weber

    Department of Pediatric Nephrology, University of Marburg, Marburg, Germany

    Contribution

    Validation

    Competing interests

    No competing interests declared

11. Yee Mang Ho

    Division of Cell Matrix Biology & Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. John O Connolly

    1. Department of Renal Medicine, University College London, London, United Kingdom
    2. Department of Adolescent Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom

    Contribution

    Resources, Project administration

    Competing interests

    No competing interests declared

13. Dan Wood

    Department of Adolescent Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom

    Contribution

    Resources, Project administration

    Competing interests

    No competing interests declared

14. Carlo Maj

    1. Center for Human Genetics, University of Marburg, Marburg, Germany
    2. Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany

    Contribution

    Data curation, Formal analysis

    Competing interests

    No competing interests declared

15. Alexander Stuckey

    Genomics England, Queen Mary University of London, London, United Kingdom

    Contribution

    Software, Methodology

    Competing interests

    No competing interests declared

16. Athanasios Kousathanas

    Genomics England, Queen Mary University of London, London, United Kingdom

    Contribution

    Software, Methodology

    Competing interests

    No competing interests declared

17. Genomics England Research Consortium

    Contribution

    Data curation, Resources

    Competing interests

    No competing interests declared

    1. JC Ambrose
    2. P Arumugam
    3. R Bevers
    4. M Bleda
    5. F Boardman-Pretty
    6. CR Boustred
    7. H Brittain
    8. MA Brown
    9. MJ Caulfield
    10. GC Chan
    11. A Giess
    12. JN Griffin
    13. A Hamblin
    14. S Henderson
    15. TJP Hubbard
    16. R Jackson
    17. LJ Jones
    18. D Kasperaviciute
    19. M Kayikci
    20. A Kousathanas
    21. L Lahnstein
    22. A Lakey
    23. SEA Leigh
    24. IUS Leong
    25. FJ Lopez
    26. F Maleady-Crowe
    27. M McEntagart
    28. F Minneci
    29. J Mitchell
    30. L Moutsianas
    31. M Mueller
    32. N Murugaesu
    33. AC Need
    34. P O'Donovan
    35. CA Odhams
    36. C Patch
    37. D Perez-Gil
    38. MB Pereira
    39. J Pullinger
    40. T Rahim
    41. A Rendon
    42. T Rogers
    43. K Savage
    44. K Sawant
    45. RH Scott
    46. A Siddiq
    47. A Sieghart
    48. SC Smith
    49. A Sosinsky
    50. A Stuckey
    51. M Tanguy
    52. AL Taylor Tavares
    53. ERA Thomas
    54. SR Thompson
    55. A Tucci
    56. MJ Welland
    57. E Williams
    58. K Witkowska
    59. SM Wood
    60. M Zarowiecki

18. Robert Kleta

    1. Department of Renal Medicine, University College London, London, United Kingdom
    2. Nephrology Department, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom

    Contribution

    Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

19. Adrian S Woolf

    1. Division of Cell Matrix Biology & Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    2. Royal Manchester Children’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom

    Contribution

    Supervision, Validation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5541-1358

20. Detlef Bockenhauer

    1. Department of Renal Medicine, University College London, London, United Kingdom
    2. Nephrology Department, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom

    Contribution

    Supervision, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

21. Adam P Levine

    1. Department of Renal Medicine, University College London, London, United Kingdom
    2. Research Department of Pathology, University College London, London, United Kingdom

    Contribution

    Conceptualization, Software, Formal analysis, Supervision, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

22. Daniel P Gale

    Department of Renal Medicine, University College London, London, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Methodology, Writing – original draft, Project administration

    For correspondence

    d.gale@ucl.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9170-1579

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