Copy number variants from 4800 exomes contribute to ~7% of genetic diagnoses in movement disorders, muscle disorders and neuropathies

Comprehensive reanalysis for CNVs in ES data from unsolved rare disease cases results in new diagnoses

We report the results of a comprehensive copy number variant (CNV) reanalysis of 9171 exome sequencing datasets from 5757 families affected by a rare disease (RD). The data reanalysed was extremely heterogeneous, having been generated using 28 different enrichment kits by 42 different research groups across Europe partnering in the Solve-RD project. Each research group had previously undertaken their own analysis of the data but failed to identify disease-causing variants. We applied three CNV calling algorithms to maximise sensitivity, and rare CNVs overlapping genes of interest, provided by four partner European Reference Networks, were taken forward for interpretation by clinical experts. This reanalysis has resulted in a molecular diagnosis being provided to 51 families in this sample, with ClinCNV performing the best of the three algorithms. We also identified partially explanatory pathogenic CNVs in a further 34 individuals. This work illustrates the value of reanalysing ES cold cases for CNVs.

Identifying pathogenic variants in rare pediatric neurological diseases using exome sequencing

Variant annotations are crucial for efficient identification of pathogenic variants. In this study, we retrospectively analyzed the utility of four annotation tools (allele frequency, ClinVar, SpliceAI, and Phenomatcher) in identifying 271 pathogenic single nucleotide and small insertion/deletion variants (SNVs/small indels). Although variant filtering based on allele frequency is essential for narrowing down on candidate variants, we found that 13 de novo pathogenic variants in autosomal dominant or X-linked dominant genes are registered in gnomADv4.0 or 54KJPN, with an allele frequency of less than 0.001%, suggesting that very rare variants in large cohort data can be pathogenic de novo variants. Notably, 38.4% candidate SNVs/small indels are registered in the ClinVar database as pathogenic or likely pathogenic, which highlights the significance of this database. SpliceAI can detect candidate variants affecting RNA splicing, leading to the identification of four variants located 11 to 50 bp away from the exon-intron boundary. Prioritization of candidate genes by proband phenotype using the PhenoMatcher module revealed that approximately 95% of the candidate genes had a maximum PhenoMatch score ≥ 0.6, suggesting the utility of phenotype-based variant prioritization. Our results suggest that a combination of multiple annotation tools and appropriate evaluation can improve the diagnosis of rare diseases.

Uncovering recessive alleles in rare Mendelian disorders by genome sequencing of 174 individuals with monoallelic pathogenic variants

Clinical exome sequencing (ES) has facilitated genetic diagnosis in individuals with a rare genetic disorder by analysis of all protein-coding sequences in a single experiment. However, in 40-60% of patients, a conclusive diagnosis remains elusive. In 2-5% of these individuals, ES does identify a disease-associated monoallelic variant in a recessive disorder. We hypothesized that short-read genome sequencing (GS) might uncover a pathogenic variant on the second allele, thereby increasing diagnostic yield. We performed GS for 174 individuals in whom ES identified a monoallelic pathogenic variant in a gene associated with recessive disease related to their phenotype. GS interpretation was limited to the (non-)coding parts of the gene in which this first pathogenic variant was identified, focusing on splice-disrupting variants. Firstly, we uncovered a second pathogenic variant affecting coding sequence in five individuals, including two SNV/indel variants, two copy number variants, and one insertion. Secondly, for 24 individuals, we identified a total of 31 rare non-coding intronic SNV/indel variants, all predicted to disrupt splicing. Using functional follow-up assays, we confirmed an effect on splicing for three of these variants (in ABCA4, POLR3A and COL4A4) in three individuals. In summary, we identified a (likely) pathogenic second variant in 4.6% (8/174), and a possible diagnosis for 12.1% (21/174) of our cohort. Hence, when performing GS as first-tier diagnostic test, including the interpretation of SVs and rare intronic variants in known recessive disease genes, the overall diagnostic yield of rare disease will increase. The added diagnostic value of GS for recessive disease In our cohort of 174 individuals (84 males and 90 females) with a monoallelic pathogenic variant in genes associated with a wide and diverse range of recessive diseases (pie chart), using genome sequencing (GS) and a systematic approach (methods), we identified eight new diagnoses (4.6%). We identified a second likely pathogenic variant in eight individuals (results); In two a second coding variant was found, in three others, a rare non-coding SNV anticipated to disrupt splicing was uncovered, and in three individuals a structural rearrangement was identified (two copy number variants (CNV), and one structural variant (SV)).

Genomic Balancing Act: deciphering DNA rearrangements in the complex chromosomal aberration involving 5p15.2, 2q31.1, and 18q21.32

Despite extensive research into the genetic underpinnings of neurodevelopmental disorders (NDD), many clinical cases remain unresolved. We studied a female proband with a NDD, mildly dysmorphic facial features, and brain stem hypoplasia on neuroimaging. Comprehensive genomic analyses revealed a terminal 5p loss and a terminal 18q gain in the proband while a diploid copy number for chromosomes 5 and 18 in both parents. Genomic investigations in the proband identified an unbalanced translocation t(5;18) with additional genetic material from chromosome 2 (2q31.3) inserted at the breakpoint, pointing to a complex chromosomal rearrangement (CCR) involving 5p15.2, 2q31.3, and 18q21.32. Breakpoint junction analyses enabled by long-read genome sequencing unveiled the presence of four distinct junctions in the father, who is a carrier of a balanced CCR. The proband inherited from the father both the abnormal chromosome 5 resulting in segmental aneusomies of chr5 (loss) and chr18 (gain) and a der(2) homologue. Evidences suggest a chromoplexy mechanism for this CCR derivation, involving double-strand breaks (DSBs) repaired by non-homologous end joining (NHEJ) or alternative end joining (alt-EJ). The complexity of the CCR and the segregation of homologues elucidate the genetic model for this family. This study demonstrates the importance of combining multiple genomic technologies to uncover genetic causes of complex neurodevelopmental syndromes and to better understand genetic disease mechanisms.

Whole genome sequencing increases the diagnostic rate in Charcot-Marie-Tooth disease

Charcot-Marie-Tooth disease (CMT) is one of the most common and genetically heterogeneous inherited neurological diseases, with more than 130 disease-causing genes. Whole genome sequencing (WGS) has improved diagnosis across genetic diseases, but the diagnostic impact in CMT is yet to be fully reported. We present the diagnostic results from a single specialist inherited neuropathy centre, including the impact of WGS diagnostic testing. Patients were assessed at our specialist inherited neuropathy centre from 2009 to 2023. Genetic testing was performed using single gene testing, next-generation sequencing targeted panels, research whole exome sequencing and WGS and, latterly, WGS through the UK National Health Service. Variants were assessed using the American College of Medical Genetics and Genomics and Association for Clinical Genomic Science criteria. Excluding patients with hereditary ATTR amyloidosis, 1515 patients with a clinical diagnosis of CMT and related disorders were recruited. In summary, 621 patients had CMT1 (41.0%), 294 CMT2 (19.4%), 205 intermediate CMT (CMTi, 13.5%), 139 hereditary motor neuropathy (HMN, 9.2%), 93 hereditary sensory neuropathy (HSN, 6.1%), 38 sensory ataxic neuropathy (2.5%), 72 hereditary neuropathy with liability to pressure palsies (HNPP, 4.8%) and 53 'complex' neuropathy (3.5%). Overall, a genetic diagnosis was reached in 76.9% (1165/1515). A diagnosis was most likely in CMT1 (96.8%, 601/621), followed by CMTi (81.0%, 166/205) and then HSN (69.9%, 65/93). Diagnostic rates remained less than 50% in CMT2, HMN and complex neuropathies. The most common genetic diagnosis was PMP22 duplication (CMT1A; 505/1165, 43.3%), then GJB1 (CMTX1; 151/1165, 13.0%), PMP22 deletion (HNPP; 72/1165, 6.2%) and MFN2 (CMT2A; 46/1165, 3.9%). We recruited 233 cases to the UK 100 000 Genomes Project (100KGP), of which 74 (31.8%) achieved a diagnosis; 28 had been otherwise diagnosed since recruitment, leaving a true diagnostic rate of WGS through the 100KGP of 19.7% (46/233). However, almost half of the solved cases (35/74) received a negative report from the study, and the diagnosis was made through our research access to the WGS data. The overall diagnostic uplift of WGS for the entire cohort was 3.5%. Our diagnostic rate is the highest reported from a single centre and has benefitted from the use of WGS, particularly access to the raw data. However, almost one-quarter of all cases remain unsolved, and a new reference genome and novel technologies will be important to narrow the 'diagnostic gap'.

The Importance of Copy Number Variant Analysis in Patients with Monogenic Kidney Disease

Introduction

Genetic testing can reveal monogenic causes of kidney diseases, offering diagnostic, therapeutic, and prognostic benefits. Although single nucleotide variants (SNVs) and copy number variants (CNVs) can result in kidney disease, CNV analysis is not always included in genetic testing.

Methods

We investigated the diagnostic value of CNV analysis in 2432 patients with kidney disease genetically tested at the University Medical Centre Utrecht between 2014 and May 2022. We combined previous diagnostic testing results, encompassing SNVs and CNVs, with newly acquired results based on retrospective CNV analysis. The reported yield considers both the American College of Medical Genetics and Genomics (ACMG) classification and whether the genotype actually results in disease.

Results

We report a diagnostic yield of at least 23% for our complete diagnostic cohort. The total diagnostic yield based solely on CNVs was 2.4%. The overall contribution of CNV analysis, defined as the proportion of positive genetic tests requiring CNV analysis, was 10.5% and varied among different disease subcategories, with the highest impact seen in congenital anomalies of the kidney and urinary tract (CAKUT) and chronic kidney disease at a young age. We highlight the efficiency of exome-based CNV calling, which reduces the need for additional diagnostic tests. Furthermore, a complex structural variant, likely a COL4A4 founder variant, was identified. Additional findings unrelated to kidney diseases were reported in a small percentage of cases.

Conclusion

In summary, this study demonstrates the substantial diagnostic value of CNV analysis, providing insights into its contribution to the diagnostic yield and advocating for its routine inclusion in genetic testing of patients with kidney disease.

Whole genome sequencing for copy number variant detection to improve diagnosis and management of rare diseases

First-line genetic investigations for rare neurological and developmental conditions have limitations in their ability to detect and characterize copy number variants (CNVs). Whole genome sequencing (WGS) offers potential advantages over other methods of CNV analysis. We aimed to demonstrate the utility of CNV detection using WGS through description of three clinical cases. WGS analysis was undertaken in three patients presenting to a national rare disease service, in whom a genetic aetiology remained uncertain after gene panel testing or microarray based comparative genomic hybridization (array CGH). In all three cases, WGS identified CNVs and confirmed zygosity and pathogenicity, resulting in genetic diagnoses of PRKN-related Parkinson disease, TAOK1-related neurodevelopmental disorder, and AP1G1-related Usmani-Riazuddin syndrome. This case series demonstrates the value of WGS analysis in identifying or better characterizing CNVs that were missed or deemed of uncertain significance using conventional methods of testing. Importantly, our approach facilitated accurate genetic diagnosis and counselling for the families involved.

Whole exome sequencing of 491 individuals with inherited retinal diseases reveals a large spectrum of variants and identification of novel candidate genes

BACKGROUND

Inherited retinal diseases (IRDs) include a range of vision loss conditions caused by variants in different genes. The clinical and genetic heterogeneity make identification of the genetic cause challenging. Here, a cohort of 491 unsolved cases from our cohort of Israeli and Palestinian families with IRDs underwent whole exome sequencing (WES), including detection of CNVs as well as single nucleotide variants (SNVs).

METHODS

All participants underwent clinical examinations. Following WES on DNA samples by 3 billion, initial SNV analysis was performed by 3 billion and SNV and CNV analysis by Franklin Genoox. The CNVs indicated by the programme were confirmed by PCR followed by gel electrophoresis.

RESULTS

WES of 491 IRD cases revealed the genetic cause of disease in 51% of cases, of which 11% were due wholly or in part to CNVs. In two cases, we clarified previously incorrect or unclear clinical diagnoses. This analysis also identified ESRRB and DNM1 as potential novel genes.

CONCLUSION

This analysis is the most extensive one to include CNVs to examine IRD causing genes in the Israeli and Palestinian populations. It has allowed us to identify the causative variant of many patients with IRDs including ones with unclear diagnoses and potential novel genes.

Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases

Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3-91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.

Genetic landscape of Parkinson's disease and related diseases in Luxembourg

Objectives

To explore the genetic architecture of PD in the Luxembourg Parkinson's Study including cohorts of healthy people and patients with Parkinson's disease (PD) and atypical parkinsonism (AP).

Methods

809 healthy controls, 680 PD and 103 AP were genotyped using the Neurochip array. We screened and validated rare single nucleotide variants (SNVs) and copy number variants (CNVs) within seven PD-causing genes (LRRK2, SNCA, VPS35, PRKN, PARK7, PINK1 and ATP13A2). Polygenic risk scores (PRSs) were generated using the latest genome-wide association study for PD. We then estimated the role of common variants in PD risk by applying gene-set-specific PRSs.

Results

We identified 60 rare SNVs in seven PD-causing genes, nine of which were pathogenic in LRRK2, PINK1 and PRKN. Eleven rare CNVs were detected in PRKN including seven duplications and four deletions. The majority of PRKN SNVs and CNVs carriers were heterozygous and not differentially distributed between cases and controls. The PRSs were significantly associated with PD and identified specific molecular pathways related to protein metabolism and signal transduction as drivers of PD risk.

Conclusion

We performed a comprehensive genetic characterization of the deep-phenotyped individuals of the Luxembourgish Parkinson's Study. Heterozygous SNVs and CNVs in PRKN were not associated with higher PD risk. In particular, we reported novel digenic variants in PD related genes and rare LRRK2 SNVs in AP patients. Our findings will help future studies to unravel the genetic complexity of PD.