Genetic diagnosis of facioscapulohumeral muscular dystrophy (FSHD) remains a challenge in clinical practice as it cannot be detected by standard sequencing methods despite being the third most common muscular dystrophy. The conventional diagnostic strategy addresses the known genetic parameters of FSHD: the required presence of a permissive haplotype, a size reduction of the D4Z4 repeat of chromosome 4q35 (defining FSHD1), or a pathogenic variant in an epigenetic suppressor gene (consistent with FSHD2). Incomplete penetrance and epistatic effects of the underlying genetic parameters as well as epigenetic parameters (D4Z4 methylation) pose challenges to diagnostic accuracy and hinder prediction of clinical severity. In order to circumvent the known limitations of conventional diagnostics and to complement genetic parameters with epigenetic ones, we developed and validated a multistage diagnostic workflow that consists of a haplotype analysis and a high throughput methylation profile analysis (FSHD-MPA). FSHD-MPA determines the average global methylation level of the D4Z4 repeat array as well as the regional methylation of the most distal repeat unit by combining bisulfite conversion with next-generation sequencing (NGS) and a bioinformatics pipeline and uses these as diagnostic parameters. We applied the diagnostic workflow to a cohort of 148 patients and compared the epigenetic parameters based on FSHD-MPA to genetic parameters of conventional genetic testing. In addition, we studied the correlation of repeat length and methylation level within the most distal repeat unit with age-corrected clinical severity and age at disease onset in FSHD patients. The results of our study show that FSHD-MPA is a powerful tool to accurately determine the epigenetic parameters of FSHD, allowing discrimination between FSHD patients and healthy individuals, while simultaneously distinguishing FSHD1 and FSHD2. The strong correlation between methylation level and clinical severity indicates that the methylation level determined by FSHD-MPA accounts for differences in disease severity among individuals with similar genetic parameters. Thus, our findings further confirm that epigenetic parameters rather than genetic parameters represent FSHD disease status and may serve as a valuable biomarker for disease status.
Background: The importance of early diagnosis of 5q-Spinal muscular atrophy (5q-SMA) has heightened as early intervention can significantly improve clinical outcomes. In 96% of cases, 5q-SMA is caused by a homozygous deletion of SMN1. Around 4 % of patients carry a SMN1 deletion and a single-nucleotide variant (SNV) on the other allele. Traditionally, diagnosis is based on multiplex ligation probe amplification (MLPA) to detect homozygous or heterozygous exon 7 deletions in SMN1. Due to high homologies within the SMN1/SMN2 locus, sequence analysis to identify SNVs of the SMN1 gene is unreliable by standard Sanger or short-read next-generation sequencing (srNGS) methods. Objective: The objective was to overcome the limitations in high-throughput srNGS with the aim of providing SMA patients with a fast and reliable diagnosis to enable their timely therapy. Methods: A bioinformatics workflow to detect homozygous SMN1 deletions and SMN1 SNVs on srNGS analysis was applied to diagnostic whole exome and panel testing for suggested neuromuscular disorders (1684 patients) and to fetal samples in prenatal diagnostics (260 patients). SNVs were detected by aligning sequencing reads from SMN1 and SMN2 to an SMN1 reference sequence. Homozygous SMN1 deletions were identified by filtering sequence reads for the ,, gene-determining variant“ (GDV). Results: 10 patients were diagnosed with 5q-SMA based on (i) SMN1 deletion and hemizygous SNV (2 patients), (ii) homozygous SMN1 deletion (6 patients), and (iii) compound heterozygous SNVs in SMN1 (2 patients). Conclusions: Applying our workflow in srNGS-based panel and whole exome sequencing (WES) is crucial in a clinical laboratory, as otherwise patients with an atypical clinical presentation initially not suspected to suffer from SMA remain undiagnosed.
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