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Sikkema-Raddatz, B.

Publications and source records attributed to Sikkema-Raddatz, B..

2 recordsLinked to original sources

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↗

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↗