In this study, we compare next-generation sequencing (NGS) approaches (targeted panel (tNGS), whole exome sequencing (WES), and whole genome sequencing (WGS)) for application in newborn screening (NBS). DNA was extracted from dried blood spots (DBS) from 50 patients with genetically confirmed inherited metabolic disorders (IMDs) and 50 control samples. One hundred IMD-related genes were analyzed. Two data-filtering strategies were applied: one to detect only (likely) pathogenic ((L)P) variants, and one to detect (L)P variants in combination with variants of unknown significance (VUS). The variants were filtered and interpreted, defining true/false positives (TP/FP) and true/false negatives (TN/FN). The variant filtering strategies were assessed in a background cohort (BC) of 4833 individuals. Reliable results were obtained within 5 days. TP results (47 patient samples) for tNGS, WES, and WGS results were 33, 31, and 30, respectively, using the (L)P filtering, and 40, 40, and 38, respectively, when including VUS. FN results were 11, 13, and 14, respectively, excluding VUS, and 4, 4, and 6, when including VUS. The remaining FN were mainly samples with a homozygous VUS. All controls were TN. Three BC individuals showed a homozygous (L)P variant, all related to a variable, mild phenotype. The use of NGS-based workflows in NBS seems promising, although more knowledge of data handling, automated variant interpretation, and costs is needed before implementation.

Inherited neurological disorders, such as spinocerebellar ataxia (SCA) and fragile X (FraX), are frequently caused by short tandem repeat (STR) expansions. The detection and assessment of STRs is important for diagnostics and prognosis. We tested the abilities of nanopore long-read sequencing (LRS) using a custom panel including the nine most common SCA-related genes and FraX and created raw data to report workflow. Using known STR lengths for 23 loci in 12 patients, a pipeline was validated to detect and report STR lengths. In addition, we assessed the capability to detect SNVs, indels, and the methylation status in the same test. For the 23 loci, 22 were concordant with known STR lengths, while for the last, one of three replicates differed, indicating an artefact. All positive control STRs were detected as likely pathogenic, with no additional findings after a visual assessment of repeat motifs. Out of 226 SNV and Indel variants, two were false positive and one false negative (accuracy 98.7%). In all FMR1 controls, a methylation status could be determined. In conclusion, LRS is suitable as a diagnostic workflow for STR analysis in neurological disorders and can be generalized to other diseases. The addition of SNV/Indel and methylation detection promises to allow for a one-test-fits-all workflow.

Diagnostic yield in NGS genome diagnostics is constraint by the high fraction of variants of uncertain significance (VUS), in large part due to insufficient interpretability of missense variation. Existing pathogenicity predictors offer strong performance, but often produce an unexplainable score lacking mechanistic insight. Here, we present the Digital Approximation of Variant Effects (MOLGENIS DAVE), an explainable missense variant predictor built on 12 biophysically grounded features spanning stability, hydrophobicity, electrostatics, and molecular interactions. Trained on curated Dutch diagnostic data, DAVE reliably classifies and breaks down predictions into interpretable feature contributions. With a focus on explainability, this framework aims to alleviate the VUS burden, advances clinically actionable variant interpretation and enables mechanistic follow-up. All source code used to process and integrate the data, along with the data required to reproduce all annotations and analyses, is available at https://github.com/molgenis/dave.

Pharmacogenomic (PGx) information is essential for precision medicine, enabling drug prescriptions to be personalized according to an individual’s genetic background. Almost all individuals will carry a genetic marker that affects their drug response, so the ideal drug prescription for these individuals will differ from the population-level guidelines. Currently, PGx information is often not available at first prescription, reducing its effectiveness. Pharmacogenetic information is most often obtained using special assays, making it expensive and time-consuming to generate. We therefore hypothesized that we could also use genome-wide oligonucleotide genotyping arrays to generate comprehensive PGx information (PGx passports), thereby decreasing the cost and time required for PGx testing, and lowering the barrier to generating PGx information prior to first prescription. Taking advantage of existing genetic data generated in two biobanks, we developed and validated Asterix, a low-cost clinical-grade PGx passport pipeline for 12 PGx genes. In these biobanks we performed and clinically validated genetic variant calling and statistical phasing and imputation. In addition, we developed and validated a novel CYP2D6 copy number variant calling tool, foregoing the need to use separate PCR-based copy number detection. Ultimately, we returned 1227 PGx passports to biobank participants via a layperson-friendly app, improving knowledge of PGx among citizens. Our study demonstrates the feasibility of a low-cost clinical-grade PGx passport pipeline that could be readily implemented in clinical settings to enhance personalized healthcare, ensuring that patients receive the most effective and safe drug therapy based on their unique genetic makeup.