Etiological involvement of KCND1 variants in an X-linked neurodevelopmental disorder with variable expressivity.

Utilizing trio whole-exome sequencing and a gene matching approach, we identified a cohort of 18 male individuals from 17 families with hemizygous variants in KCND1, including two de novo missense variants, three maternally inherited protein-truncating variants, and 12 maternally inherited missense variants. Affected subjects present with a neurodevelopmental disorder characterized by diverse neurological abnormalities, mostly delays in different developmental domains, but also distinct neuropsychiatric signs and epilepsy. Heterozygous carrier mothers are clinically unaffected. KCND1 encodes the α-subunit of Kv4.1 voltage-gated potassium channels. All variant-associated amino acid substitutions affect either the cytoplasmic N- or C-terminus of the channel protein except for two occurring in transmembrane segments 1 and 4. Kv4.1 channels were functionally characterized in the absence and presence of auxiliary ß subunits. Variant-specific alterations of biophysical channel properties were diverse and varied in magnitude. Genetic data analysis in combination with our functional assessment shows that Kv4.1 channel dysfunction is involved in the pathogenesis of an X-linked neurodevelopmental disorder frequently associated with a variable neuropsychiatric clinical phenotype.

Comprehensive SMN1 and SMN2 profiling for spinal muscular atrophy analysis using long-read PacBio HiFi sequencing.

Spinal muscular atrophy, a leading cause of early infant death, is caused by bi-allelic mutations of SMN1. Sequence analysis of SMN1 is challenging due to high sequence similarity with its paralog SMN2. Both genes have variable copy numbers across populations. Furthermore, without pedigree information, it is currently not possible to identify silent carriers (2+0) with two copies of SMN1 on one chromosome and zero copies on the other. We developed Paraphase, an informatics method that identifies full-length SMN1 and SMN2 haplotypes, determines the gene copy numbers, and calls phased variants using long-read PacBio HiFi data. The SMN1 and SMN2 copy-number calls by Paraphase are highly concordant with orthogonal methods (99.2% for SMN1 and 100% for SMN2). We applied Paraphase to 438 samples across 5 ethnic populations to conduct a population-wide haplotype analysis of these highly homologous genes. We identified major SMN1 and SMN2 haplogroups and characterized their co-segregation through pedigree-based analyses. We identified two SMN1 haplotypes that form a common two-copy SMN1 allele in African populations. Testing positive for these two haplotypes in an individual with two copies of SMN1 gives a silent carrier risk of 88.5%, which is significantly higher than the currently used marker (1.7%-3.0%). Extending beyond simple copy-number testing, Paraphase can detect pathogenic variants and enable potential haplotype-based screening of silent carriers through statistical phasing of haplotypes into alleles. Future analysis of larger population data will allow identification of more diverse haplotypes and genetic markers for silent carriers.

De novo mutation hotspots in homologous protein domains identify function-altering mutations in neurodevelopmental disorders.

Variant interpretation remains a major challenge in medical genetics. We developed Meta-Domain HotSpot (MDHS) to identify mutational hotspots across homologous protein domains. We applied MDHS to a dataset of 45,221 de novo mutations (DNMs) from 31,058 individuals with neurodevelopmental disorders (NDDs) and identified three significantly enriched missense DNM hotspots in the ion transport protein domain family (PF00520). The 37 unique missense DNMs that drive enrichment affect 25 genes, 19 of which were previously associated with NDDs. 3D protein structure modeling supports the hypothesis of function-altering effects of these mutations. Hotspot genes have a unique expression pattern in tissue, and we used this pattern alongside in silico predictors and population constraint information to identify candidate NDD-associated genes. We also propose a lenient version of our method, which identifies 32 hotspot positions across 16 different protein domains. These positions are enriched for likely pathogenic variation in clinical databases and DNMs in other genetic disorders.

Evidence for 28 genetic disorders discovered by combining healthcare and research data.

De novo mutations in protein-coding genes are a well-established cause of developmental disorders1. However, genes known to be associated with developmental disorders account for only a minority of the observed excess of such de novo mutations1,2. Here, to identify previously undescribed genes associated with developmental disorders, we integrate healthcare and research exome-sequence data from 31,058 parent-offspring trios of individuals with developmental disorders, and develop a simulation-based statistical test to identify gene-specific enrichment of de novo mutations. We identified 285 genes that were significantly associated with developmental disorders, including 28 that had not previously been robustly associated with developmental disorders. Although we detected more genes associated with developmental disorders, much of the excess of de novo mutations in protein-coding genes remains unaccounted for. Modelling suggests that more than 1,000 genes associated with developmental disorders have not yet been described, many of which are likely to be less penetrant than the currently known genes. Research access to clinical diagnostic datasets will be critical for completing the map of genes associated with developmental disorders.

Exome sequencing identified rare recurrent copy number variants and hereditary breast cancer susceptibility.

Copy number variants (CNVs) are a major source of genetic variation and can disrupt genes or affect gene dosage. They are known to be causal or underlie predisposition to various diseases. However, the role of CNVs in inherited breast cancer susceptibility has not been thoroughly investigated. To address this, we performed whole-exome sequencing based analysis of rare CNVs in 98 high-risk Northern Finnish breast cancer cases. After filtering, selected candidate alleles were validated and characterized with a combination of orthogonal methods, including PCR-based approaches, optical genome mapping and long-read sequencing. This revealed three recurrent alterations: a 31 kb deletion co-occurring with a retrotransposon insertion (delins) in RAD52, a 13.4 kb deletion in HSD17B14 and a 64 kb partial duplication of RAD51C. Notably, all these genes encode proteins involved in pathways previously identified as essential for breast cancer development. Variants were genotyped in geographically matched cases and controls (altogether 278 hereditary and 1983 unselected breast cancer cases, and 1229 controls). The RAD52 delins and HSD17B14 deletion both showed significant enrichment among cases with indications of hereditary disease susceptibility. RAD52 delins was identified in 7/278 cases (2.5%, P = 0.034, OR = 2.86, 95% CI = 1.10-7.45) and HSD17B14 deletion in 8/278 cases (2.9%, P = 0.014, OR = 3.28, 95% CI = 1.31-8.23), the frequency of both variants in the controls being 11/1229 (0.9%). This suggests a role for RAD52 and HSD17B14 in hereditary breast cancer susceptibility. The RAD51C duplication was very rare, identified only in 2/278 of hereditary cases and 2/1229 controls (P = 0.157, OR = 4.45, 95% CI = 0.62-31.70). The identification of recurrent CNVs in these genes, and especially the relatively high frequency of RAD52 and HSD17B14 alterations in the Finnish population, highlights the importance of studying CNVs alongside single nucleotide variants when searching for genetic factors underlying hereditary disease predisposition.

ANK2 loss-of-function variants are associated with epilepsy, and lead to impaired axon initial segment plasticity and hyperactive network activity in hiPSC-derived neuronal networks.

PURPOSE: To characterize a novel neurodevelopmental syndrome due to loss-of-function (LoF) variants in Ankyrin 2 (ANK2), and to explore the effects on neuronal network dynamics and homeostatic plasticity in human-induced pluripotent stem cell-derived neurons. METHODS: We collected clinical and molecular data of 12 individuals with heterozygous de novo LoF variants in ANK2. We generated a heterozygous LoF allele of ANK2 using CRISPR/Cas9 in human-induced pluripotent stem cells (hiPSCs). HiPSCs were differentiated into excitatory neurons, and we measured their spontaneous electrophysiological responses using micro-electrode arrays (MEAs). We also characterized their somatodendritic morphology and axon initial segment (AIS) structure and plasticity. RESULTS: We found a broad neurodevelopmental disorder (NDD), comprising intellectual disability, autism spectrum disorders and early onset epilepsy. Using MEAs, we found that hiPSC-derived neurons with heterozygous LoF of ANK2 show a hyperactive and desynchronized neuronal network. ANK2-deficient neurons also showed increased somatodendritic structures and altered AIS structure of which its plasticity is impaired upon activity-dependent modulation. CONCLUSIONS: Phenotypic characterization of patients with de novo ANK2 LoF variants defines a novel NDD with early onset epilepsy. Our functional in vitro data of ANK2-deficient human neurons show a specific neuronal phenotype in which reduced ANKB expression leads to hyperactive and desynchronized neuronal network activity, increased somatodendritic complexity and AIS structure and impaired activity-dependent plasticity of the AIS.

Integrated gene analyses of de novo variants from 46,612 trios with autism and developmental disorders.

Most genetic studies consider autism spectrum disorder (ASD) and developmental disorder (DD) separately despite overwhelming comorbidity and shared genetic etiology. Here, we analyzed de novo variants (DNVs) from 15,560 ASD (6,557 from SPARK) and 31,052 DD trios independently and also combined as broader neurodevelopmental disorders (NDDs) using three models. We identify 615 NDD candidate genes (false discovery rate [FDR] < 0.05) supported by ≥1 models, including 138 reaching Bonferroni exome-wide significance (P < 3.64e-7) in all models. The genes group into five functional networks associating with different brain developmental lineages based on single-cell nuclei transcriptomic data. We find no evidence for ASD-specific genes in contrast to 18 genes significantly enriched for DD. There are 53 genes that show mutational bias, including enrichments for missense (n = 41) or truncating (n = 12) DNVs. We also find 10 genes with evidence of male- or female-bias enrichment, including 4 X chromosome genes with significant female burden (DDX3X, MECP2, WDR45, and HDAC8). This large-scale integrative analysis identifies candidates and functional subsets of NDD genes.

Automatized detection of uniparental disomies in a large cohort.

Uniparental disomy (UPD) is the inheritance of both homologues of a chromosome from only one parent. The detection of UPDs in sequencing data is not well established and a common gap in genetic diagnostics. We applied our in-house UPD detection pipeline to evaluate a cohort of 9212 samples, including multigene panels as well as exome sequencing data in a single, duo or trio constellation. We used the results to inform the design of our publicly available web app altAFplotter. UPDs categorized as heterodisomy, whole chromosome or segmental isodisomy were identified and validated with microsatellites, multiplex ligation-dependent probe amplification as well as Sanger sequencing. We detected 14 previously undiagnosed UPDs including nine isodisomies, four segmental isodisomies as well as one heterodisomy on chromosome 22. We characterized eight findings as potentially causative through homozygous pathogenic variants or imprinting disorders. Overall, our study demonstrates the utility of our UPD detection pipeline with our web app, altAFplotter, to reliably identify UPDs. This not only increases the diagnostic yield of cases with growth and metabolic disturbances, as well as developmental delay, but also enhances the understanding of UPDs that may be relevant for recurrence risks and genetic counseling.

The landscape of autosomal-recessive pathogenic variants in European populations reveals phenotype-specific effects.

The number and distribution of recessive alleles in the population for various diseases are not known at genome-wide-scale. Based on 6,447 exome sequences of healthy, genetically unrelated Europeans of two distinct ancestries, we estimate that every individual is a carrier of at least 2 pathogenic variants in currently known autosomal-recessive (AR) genes and that 0.8%-1% of European couples are at risk of having a child affected with a severe AR genetic disorder. This risk is 16.5-fold higher for first cousins but is significantly more increased for skeletal disorders and intellectual disabilities due to their distinct genetic architecture.

Mutation-specific pathophysiological mechanisms define different neurodevelopmental disorders associated with SATB1 dysfunction.

Whereas large-scale statistical analyses can robustly identify disease-gene relationships, they do not accurately capture genotype-phenotype correlations or disease mechanisms. We use multiple lines of independent evidence to show that different variant types in a single gene, SATB1, cause clinically overlapping but distinct neurodevelopmental disorders. Clinical evaluation of 42 individuals carrying SATB1 variants identified overt genotype-phenotype relationships, associated with different pathophysiological mechanisms, established by functional assays. Missense variants in the CUT1 and CUT2 DNA-binding domains result in stronger chromatin binding, increased transcriptional repression, and a severe phenotype. In contrast, variants predicted to result in haploinsufficiency are associated with a milder clinical presentation. A similarly mild phenotype is observed for individuals with premature protein truncating variants that escape nonsense-mediated decay, which are transcriptionally active but mislocalized in the cell. Our results suggest that in-depth mutation-specific genotype-phenotype studies are essential to capture full disease complexity and to explain phenotypic variability.

Differences in the number of de novo mutations between individuals are due to small family-specific effects and stochasticity.

The number of de novo mutations (DNMs) in the human germline is correlated with parental age at conception, but this explains only part of the observed variation. We investigated whether there is a family-specific contribution to the number of DNMs in offspring. The analysis of DNMs in 111 dizygotic twin pairs did not identify a substantial family-specific contribution. This result was corroborated by comparing DNMs of 1669 siblings to those of age-matched unrelated offspring following correction for parental age. In addition, by modeling DNM data from 1714 multi-offspring families, we estimated that the family-specific contribution explains ∼5.2% of the variation in DNM number. Furthermore, we found no substantial difference between the observed number of DNMs and those predicted by a stochastic Poisson process. We conclude that there is a small family-specific contribution to DNM number and that stochasticity explains a large proportion of variation in DNM counts.

Detection of DNA Contamination in Prenatal Samples from Whole Exome Sequencing Data.

BACKGROUND: Maternal cell contamination (MCC) in prenatal samples poses a risk for misdiagnosis, and therefore, testing for contamination is necessary during genetic analysis of prenatal specimens. MCC testing is currently performed as a method separate from the diagnostic method. With the increasing application of whole exome sequencing (WES) in prenatal diagnosis, we sought to develop a method to estimate the level of contamination from WES data, aiming to eliminate the need for a separate MCC test. METHODS: To investigate the impact of MCC on the distribution of the variant allele fraction in WES data, contamination was both simulated in silico and artificially induced. Subsequently, a bioinformatic WES contamination method was developed and validated by comparing its performance to that of the gold standard (short tandem repeat [STR]) MCC test, validated for detecting ≥5% contamination. Finally, post-implementation performance was monitored for a 15-month period. RESULTS: During validation, 270 prenatal samples underwent analysis with both WES and the gold standard test. In 259 samples, the results were concordant (248 not contaminated, 11 contaminated with both tests). In 11 samples, contamination was only detected in WES data (2 of which contained ≥5% contamination with WES, which is above the detection limit of the gold standard test). The data of the post-implementation evaluation on 361 samples, of which 68 were contaminated, were in line with the validation data. CONCLUSIONS: Contamination can reliably be detected in WES data, rendering a separate contamination test unnecessary for the majority of samples.

Next-generation sequencing to genetically diagnose a diverse range of inherited eye disorders in 15 consanguineous families from Pakistan.

Inherited retinal dystrophies (IRDs) are characterized by photoreceptor dysfunction or degeneration. Clinical and phenotypic overlap between IRDs makes the genetic diagnosis very challenging and comprehensive genomic approaches for accurate diagnosis are frequently required. While there are previous studies on IRDs in Pakistan, causative genes and variants are still unknown for a significant portion of patients. Therefore, there is a need to expand the knowledge of the genetic spectrum of IRDs in Pakistan. Here, we recruited 52 affected and 53 normal individuals from 15 consanguineous Pakistani families presenting non-syndromic and syndromic forms of IRDs. We employed single molecule Molecular Inversion Probes (smMIPs) based panel sequencing and whole genome sequencing to identify the probable disease-causing variants in these families. Using this approach, we obtained a 93% genetic solve rate and identified 16 (likely) causative variants in 14 families, of which seven novel variants were identified in ATOH7, COL18A1, MERTK, NDP, PROM1, PRPF8 and USH2A while nine recurrent variants were identified in CNGA3, CNGB1, HGSNAT, NMNAT1, SIX6 and TULP1. The novel MERTK variant and one recurrent TULP1 variant explained the intra-familial locus heterogeneity in one of the screened families while two recurrent CNGA3 variants explained compound heterozygosity in another family. The identification of variants in known disease-associated genes emphasizes the utilization of time and cost-effective screening approaches for rapid diagnosis. The timely genetic diagnosis will not only identify any associated systemic issues in case of syndromic IRDs, but will also aid in the acceleration of personalized medicine for patients affected with IRDs.

Twist exome capture allows for lower average sequence coverage in clinical exome sequencing.

BACKGROUND: Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. RESULTS: We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. CONCLUSION: We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques.

DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes the alignment of sequence reads for a trio as 160$ \times$164 resolution images. DeNovoCNN was trained on DNMs of 5616 whole exome sequencing (WES) trios achieving total 96.74% recall and 96.55% precision on the test dataset. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing DNM calling approaches (GATK, DeNovoGear, DeepTrio, Samtools) based on the Genome in a Bottle reference dataset and independent WES and WGS trios. Validations of DNMs based on Sanger and PacBio HiFi sequencing confirm that DeNovoCNN outperforms existing methods. Most importantly, our results suggest that DeNovoCNN is likely robust against different exome sequencing and analyses approaches, thereby allowing the application on other datasets. DeNovoCNN is freely available as a Docker container and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.

Structural Variants Create New Topological-Associated Domains and Ectopic Retinal Enhancer-Gene Contact in Dominant Retinitis Pigmentosa.

The cause of autosomal-dominant retinitis pigmentosa (adRP), which leads to loss of vision and blindness, was investigated in families lacking a molecular diagnosis. A refined locus for adRP on Chr17q22 (RP17) was delineated through genotyping and genome sequencing, leading to the identification of structural variants (SVs) that segregate with disease. Eight different complex SVs were characterized in 22 adRP-affected families with >300 affected individuals. All RP17 SVs had breakpoints within a genomic region spanning YPEL2 to LINC01476. To investigate the mechanism of disease, we reprogrammed fibroblasts from affected individuals and controls into induced pluripotent stem cells (iPSCs) and differentiated them into photoreceptor precursor cells (PPCs) or retinal organoids (ROs). Hi-C was performed on ROs, and differential expression of regional genes and a retinal enhancer RNA at this locus was assessed by qPCR. The epigenetic landscape of the region, and Hi-C RO data, showed that YPEL2 sits within its own topologically associating domain (TAD), rich in enhancers with binding sites for retinal transcription factors. The Hi-C map of RP17 ROs revealed creation of a neo-TAD with ectopic contacts between GDPD1 and retinal enhancers, and modeling of all RP17 SVs was consistent with neo-TADs leading to ectopic retinal-specific enhancer-GDPD1 accessibility. qPCR confirmed increased expression of GDPD1 and increased expression of the retinal enhancer that enters the neo-TAD. Altered TAD structure resulting in increased retinal expression of GDPD1 is the likely convergent mechanism of disease, consistent with a dominant gain of function. Our study highlights the importance of SVs as a genomic mechanism in unsolved Mendelian diseases.

De Novo Variants in SPOP Cause Two Clinically Distinct Neurodevelopmental Disorders.

Recurrent somatic variants in SPOP are cancer specific; endometrial and prostate cancers result from gain-of-function and dominant-negative effects toward BET proteins, respectively. By using clinical exome sequencing, we identified six de novo pathogenic missense variants in SPOP in seven individuals with developmental delay and/or intellectual disability, facial dysmorphisms, and congenital anomalies. Two individuals shared craniofacial dysmorphisms, including congenital microcephaly, that were strikingly different from those of the other five individuals, who had (relative) macrocephaly and hypertelorism. We measured the effect of SPOP variants on BET protein amounts in human Ishikawa endometrial cancer cells and patient-derived cell lines because we hypothesized that variants would lead to functional divergent effects on BET proteins. The de novo variants c.362G>A (p.Arg121Gln) and c. 430G>A (p.Asp144Asn), identified in the first two individuals, resulted in a gain of function, and conversely, the c.73A>G (p.Thr25Ala), c.248A>G (p.Tyr83Cys), c.395G>T (p.Gly132Val), and c.412C>T (p.Arg138Cys) variants resulted in a dominant-negative effect. Our findings suggest that these opposite functional effects caused by the variants in SPOP result in two distinct and clinically recognizable syndromic forms of intellectual disability with contrasting craniofacial dysmorphisms.

Optical genome mapping and revisiting short-read genome sequencing data reveal previously overlooked structural variants disrupting retinal disease-associated genes.

PURPOSE: Structural variants (SVs) play an important role in inherited retinal diseases (IRD). Although the identification of SVs significantly improved upon the availability of genome sequencing, it is expected that involvement of SVs in IRDs is higher than anticipated. We revisited short-read genome sequencing data to enhance the identification of gene-disruptive SVs. METHODS: Optical genome mapping was performed to improve SV detection in short-read genome sequencing-negative cases. In addition, reanalysis of short-read genome sequencing data was performed to improve the interpretation of SVs and to re-establish SV prioritization criteria. RESULTS: In a monoallelic USH2A case, optical genome mapping identified a pericentric inversion (173 megabase), with 1 breakpoint disrupting USH2A. Retrospectively, the variant could be observed in genome sequencing data but was previously deemed false positive. Reanalysis of short-read genome sequencing data (427 IRD cases) was performed which yielded 30 pathogenic SVs affecting, among other genes, USH2A (n = 15), PRPF31 (n = 3), and EYS (n = 2). Eight of these (>25%) were overlooked during previous analyses. CONCLUSION: Critical evaluation of our findings allowed us to re-establish and improve our SV prioritization and interpretation guidelines, which will prevent missing pathogenic events in future analyses. Our data suggest that more attention should be paid to SV interpretation and the current contribution of SVs in IRDs is still underestimated.

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing.

PURPOSE: Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the "ClinVar low-hanging fruit" reanalysis, reasons for the failure of previous analyses, and lessons learned. METHODS: Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. RESULTS: We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). CONCLUSION: The "ClinVar low-hanging fruit" analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock.

BBS1 branchpoint variant is associated with non-syndromic retinitis pigmentosa.

BACKGROUND: Inherited retinal diseases (IRDs) can be caused by variants in >270 genes. The Bardet-Biedl syndrome 1 (BBS1) gene is one of these genes and may be associated with syndromic and non-syndromic autosomal recessive retinitis pigmentosa (RP). Here, we identified a branchpoint variant in BBS1 and assessed its pathogenicity by in vitro functional analysis. METHODS: Whole genome sequencing was performed for three unrelated monoallelic BBS1 cases with non-syndromic RP. A fourth case received MGCM 105 gene panel analysis. Functional analysis using a midigene splice assay was performed for the putative pathogenic branchpoint variant in BBS1. After confirmation of its pathogenicity, patients were clinically re-evaluated, including assessment of non-ocular features of Bardet-Biedl syndrome. RESULTS: Clinical assessments of probands showed that all individuals displayed non-syndromic RP with macular involvement. Through detailed variant analysis and prioritisation, two pathogenic variants in BBS1, the most common missense variant, c.1169T>G (p.(Met390Arg)), and a branchpoint variant, c.592-21A>T, were identified. Segregation analysis confirmed that in all families, probands were compound heterozygous for c.1169T>G and c.592-21A>T. Functional analysis of the branchpoint variant revealed a complex splicing defect including exon 8 and exon 7/8 skipping, and partial in-frame deletion of exon 8. CONCLUSION: A putative severe branchpoint variant in BBS1, together with a mild missense variant, underlies non-syndromic RP in four unrelated individuals. To our knowledge, this is the first report of a pathogenic branchpoint variant in IRDs that results in a complex splice defect. In addition, this research highlights the importance of the analysis of non-coding regions in order to provide a conclusive molecular diagnosis.