Variant predictions in congenital adrenal hyperplasia caused by mutations in CYP21A2

Development and validation of animal variant classification guidelines to objectively evaluate genetic variant pathogenicity in domestic animals

Assessing the pathogenicity of a disease-associated genetic variant in animals accurately is vital, both on a population and individual scale. At the population level, breeding decisions based on invalid DNA tests can lead to the incorrect inclusion or exclusion of animals and compromise the long-term health of a population, and at the level of the individual animal, lead to incorrect treatment and even life-ending decisions. Criteria to determine pathogenicity are not standardized, i.e., no guidelines for animal variants are available. Here, we aimed to develop and validate guidelines to be used by the community for Mendelian disorders in domestic animals to classify variants in categories based on standardized criteria. These so-called animal variant classification guidelines (AVCG) were based on those developed for humans by The American College of Medical Genetics and Genomics (ACMG). In a direct comparison, 83% of the pathogenic variants were correctly classified with ACMG, while this increased to 92% with AVCG. We described methods to develop datasets for benchmarking the criteria and identified the most optimal in silico variant effect predictor tools. As the reproducibility was high, we classified 72 known disease-associated variants in cats and 40 other disease-associated variants in eight additional species.

Comprehensive genome analysis and variant detection at scale using DRAGEN

Research and medical genomics require comprehensive, scalable methods for the discovery of novel disease targets, evolutionary drivers and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size or location. Here we present DRAGEN, which uses multigenome mapping with pangenome references, hardware acceleration and machine learning-based variant detection to provide insights into individual genomes, with ~30 min of computation time from raw reads to variant detection. DRAGEN outperforms current state-of-the-art methods in speed and accuracy across all variant types (single-nucleotide variations, insertions or deletions, short tandem repeats, structural variations and copy number variations) and incorporates specialized methods for analysis of medically relevant genes. We demonstrate the performance of DRAGEN across 3,202 whole-genome sequencing datasets by generating fully genotyped multisample variant call format files and demonstrate its scalability, accuracy and innovation to further advance the integration of comprehensive genomics. Overall, DRAGEN marks a major milestone in sequencing data analysis and will provide insights across various diseases, including Mendelian and rare diseases, with a highly comprehensive and scalable platform.

Comprehensive and accurate genome analysis at scale using DRAGEN accelerated algorithms

Research and medical genomics require comprehensive and scalable solutions to drive the discovery of novel disease targets, evolutionary drivers, and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size (e.g., SNV/SV) or location (e.g., repeats). Here we present DRAGEN that utilizes novel methods based on multigenomes, hardware acceleration, and machine learning based variant detection to provide novel insights into individual genomes with ~30min computation time (from raw reads to variant detection). DRAGEN outperforms all other state-of-the-art methods in speed and accuracy across all variant types (SNV, indel, STR, SV, CNV) and further incorporates specialized methods to obtain key insights in medically relevant genes (e.g., HLA, SMN, GBA). We showcase DRAGEN across 3,202 genomes and demonstrate its scalability, accuracy, and innovations to further advance the integration of comprehensive genomics for research and medical applications.

Exploring Novel Variants of the Cytochrome P450 Reductase Gene (POR) from the Genome Aggregation Database by Integrating Bioinformatic Tools and Functional Assays

Cytochrome P450 oxidoreductase (POR) is an essential redox partner for steroid and drug-metabolizing cytochromes P450 located in the endoplasmic reticulum. Mutations in POR lead to metabolic disorders, including congenital adrenal hyperplasia, and affect the metabolism of steroids, drugs, and xenobiotics. In this study, we examined approximately 450 missense variants of the POR gene listed in the Genome Aggregation Database (gnomAD) using eleven different in silico prediction tools. We found that 64 novel variants were consistently predicted to be disease-causing by most tools. To validate our findings, we conducted a population analysis and selected two variations in POR for further investigation. The human POR wild type and the R268W and L577P variants were expressed in bacteria and subjected to enzyme kinetic assays using a model substrate. We also examined the activities of several cytochrome P450 proteins in the presence of POR (WT or variants) by combining P450 and reductase proteins in liposomes. We observed a decrease in enzymatic activities (ranging from 35% to 85%) of key drug-metabolizing enzymes, supported by POR variants R288W and L577P compared to WT-POR. These results validate our approach of curating a vast amount of data from genome projects and provide an updated and reliable reference for diagnosing POR deficiency.