Physician
Mathilde Faurholdt Lauridsen
Dept. of Clinical Genetics, Vejle Hospital
| Project management | ||
| Project status | Open | |
| Data collection dates | ||
| Start | - | |
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Exploring the Genetic Landscape of Neurological Diseases in Adult Patients: Analysis of National Genomic Data
Short summary
Neurogenetic diseases often overlap with other neurological disorders, making genetic evaluations challenging. In Denmark, WES or WGS is used for adults suspected of having neurogenetic diseases. Since 2022, the National Genome Center has sequenced about 2,000 patients. The studies evaluate diagnostic yield, re-analyze unsolved cases to find missed genetic variants, and examine patients with one likely pathogenic variant in recessive gene to identify a second variant.
Rationale
Neurogenetic diseases are complex and exhibit a wide range of clinical manifestations that overlap with other neurologic disorders, complicating genetic evaluations. This leads to lower diagnostic yields compared to other patient groups. In Denmark, genetic testing for adults with suspected neurogenetic diseases is performed using predefined in silico gene panels through whole exome sequencing (WES) or whole genome sequencing (WGS). Additional tests like Multiplex Ligation-dependent Probe Amplification (MLPA) and repeat expansion analysis are used when needed. Since early 2022, Denmark's National Genome Center (NGC) has offered WGS for neurogenetic diseases such as early-onset dementia, hereditary neuropathy, ataxia, and epilepsy. Specific gene panels for each category have been developed, and a national consensus on their content has been established. By 2024, approximately 2,000 patients had undergone genome sequencing, but a national assessment of diagnostic yields is still awaited. Study 1: Diagnostic Yield of Clinical Genomic Data Interpretation This study aims to evaluate the overall diagnostic yield of genome sequencing for patients suspected of neurogenetic diseases. Previous studies, such as a German study, have shown a low diagnostic yield of 10% in patients with unsolved neurogenetic disorders. The yield varies by disease category and test strategy. This study will analyze data from NGC neurogenetic patients in Denmark, focusing on their diagnoses, genetic tests, and outcomes. The study will examine how patient factors such as age, gender, and disease category influence the likelihood of a positive genetic finding. Logistic regression analysis will be used to identify predictors of successful genetic diagnosis. Study 2: Re-analysis of National Genomic Data The goal of this study is to re-analyze genomic data from unsolved cases to identify causal genetic variants. New bioinformatics tools will be used to explore variants that may have been missed in earlier analyses, such as copy number variants, structural variants, and non-coding region variants. Previous studies have shown that re-analysis using updated tools can improve diagnostic yields. For instance, a study involving 122 patients found causal genetic variants in 39%, including novel genes and structural variants, suggesting that re-analysis can uncover new disease-causing variants. Study 3: Re-analysis of Data from Patients with One Likely Pathogenic Variant This study will focus on patients who have one likely pathogenic variant in a gene related to autosomal recessive diseases. The aim is to identify a second variant that could explain the disease phenotype. Previous research has demonstrated that re-analysis can uncover second variants, such as intronic variants or hypomorphic alleles, which are difficult to detect initially. WGS data from these patients will be re-analyzed to search for potential second variants. The analysis will be compared with control cohorts, including a group of 1,000 oncogenetic patients, for validation. Methods for Studies 2 and 3 The re-analysis will include data from different neurogenetic disease categories and the entire unsolved cohort. Patients under 18, those with identified genetic causes, or those registered in the tissue utilization register will be excluded. Data comparisons with external cohorts, such as gnomAD and NGC's control cohort, will be essential for validating the findings.
Description of the cohort
Patients with suspicion of neurogenetic conditions
Data and biological material
NGC data from the neurogenetic patient category