Biology subjects

van Diemen, C. C.

Publications and source records attributed to van Diemen, C. C..

3 recordsLinked to original sources

bioRxiv2023

Identification and copy number variant analysis of enhancer regions of genes causing spinocerebellar ataxia

Currently, routine diagnostics for spinocerebellar ataxia (SCA) consist of analyses that look for polyQ repeat expansions and conventional variations affecting the proteins encoded by known SCA genes. Despite all the effort, [~]40% of the patients still remain without a genetic diagnosis after routine tests. Increasing evidence suggests that variations in the enhancer regions of genes involved in neurodegenerative disorders can also be disease-causing. Since the enhancers of SCA genes are not yet known, it remains to be determined whether variations in these regions are a cause of SCA. In this pilot project, we aimed to identify the enhancers of the SCA genes ATXN1, ATXN3, TBP and ITPR1 in human cerebellum using 4C-seq experiments, publicly available datasets, reciprocal 4C-seq and luciferase assays. We then screened these enhancers for copy number variants (CNVs) in a cohort of genetically undiagnosed SCA patients. We identified two active enhancers for each of the four SCA genes in human cerebellar tissue. In addition, for TBP and ITPR1, we observed interactions between the putative enhancers of each gene. CNV analysis did not reveal any CNVs in the active enhancers of the four SCA genes in any of the genetically undiagnosed SCA patients. However, in one patient, we noted a CNV deletion with unknown clinical significance near one of the active enhancers of ITPR1. These results not only reveal elements involved in regulation of SCA genes, they can also lead to the discovery of novel SCA-causing genetic variants. As enhancer variations are increasingly recognized to cause brain disorders, screening the enhancers of ATXN1, ATXN3, TBP and ITPR1 for variations other than CNVs and identifying and screening enhancers of other SCA genes might elucidate the genetic cause in genetically undiagnosed patients.

genetics↗

bioRxiv2023

Cas9-directed long-read sequencing to resolve optical genome mapping findings in leukemia diagnostics.

Leukemias are genetically heterogeneous, and knowing the type of genetic aberration is clinically important for progression prognosis and choice of treatment. The standard-of-care (SOC) methods in leukemia diagnostics are karyotyping, SNP-array and FISH. Optical genome mapping (OGM) has emerged as their replacement as it can detect different types of structural aberrations simultaneously. However, although OGM can additionally detect much smaller aberrations (500 bp vs. 5-10 Mb with karyotyping), its resolution is too low to define the location of aberrations between two labels and the breakpoints are disputable when labels are not distinct enough. Here, we test whether Cas9-directed long-read sequencing (LRS) can fill this gap. From an internal Bionano implementation study we selected ten OGM calls either located in low resolution areas or with disputable breakpoints that could not be validated with SOC methods. Per variant we designed crRNAs for Cas9 enrichment, prepared libraries and sequenced them on a MinION / GridION device. We could confirm all the OGM aberrations with Cas9-directed LRS and the actual breakpoints of the OGM calls were located between 0.2-5.5 kb of the OGM-estimated breakpoints, confirming the high reliability of OGM. Furthermore, we show examples of redefinition of aberrations between labels that enable judgment of clinical relevance. We show that Cas9-directed LRS can fill the gap of low resolution OGM areas thereby improving the prediction of clinical significance. We envisage that this technique can be an relevant secondary technique in diagnostic workflows including OGM.

genetics↗

bioRxiv2018

Improving the diagnostic yield of exome-sequencing, by predicting gene-phenotype associations using large-scale gene expression analysis

Clinical interpretation of exome and genome sequencing data remains challenging and time consuming, with many variants with unknown effects found in genes with unknown functions. Automated prioritization of these variants can improve the speed of current diagnostics and identify previously unknown disease genes. Here, we used 31,499 RNA-seq samples to predict the phenotypic consequences of variants in genes. We developed GeneNetwork Assisted Diagnostic Optimization (GADO), a tool that uses these predictions in combination with a patients phenotype, denoted using HPO terms, to prioritize identified variants and ease interpretation. GADO is unique because it does not rely on existing knowledge of a gene and can therefore prioritize variants missed by tools that rely on existing annotations or pathway membership. In a validation trial on patients with a known genetic diagnosis, GADO prioritized the causative gene within the top 3 for 41% of the cases. Applying GADO to a cohort of 38 patients without genetic diagnosis, yielded new candidate genes for seven cases. Our results highlight the added value of GADO (www.genenetwork.nl) for increasing diagnostic yield and for implicating previously unknown disease-causing genes.

genetics↗