The rate of secondary genomic findings in the Saudi population.

Secondary findings (SF) are defined as genetic conditions discovered unintentionally during an evaluation of raw data for another disease. We aimed to identify the rate of secondary genetic findings in the Saudi population in the 59 genes of the American College of Medical Genetics and Genomics (ACMG) list. In our study, the raw data of 1254 individuals, generated from exome sequencing for clinical purposes, were studied. Variants detected in the 59 genes on the ACMG list of secondary findings were investigated. Pathogenicity classifications were assigned to those variants based on the ACMG scoring system. We identified 2409 variants in the 59 gene list, 45 variants were classified as pathogenic/likely pathogenic variants according to the ACMG classification. The LDLR gene had the greatest number of pathogenic/likely pathogenic variants 12%. Cardiovascular genetic diseases had the highest frequency of disorders detected as secondary findings. In this study, the overall rate of positive cases identified with secondary findings in the Saudi population was 8%. The different in our current study and the previous studies in Saudi Arabia can be explained by the differences between the sequencing method, the criteria used for variant classification, the availability of newer evidence at the time of the publication, and the fact that we identified Saudi novel variants never reported in other populations.

Genomic testing and counseling: The contribution of next-generation sequencing to epilepsy genetics.

INTRODUCTION: Currently, next-generation sequencing (NGS) technology is more accessible and available to detect the genetic causation of diseases. Though NGS technology benefited some clinical phenotypes, for some clinical diagnoses such as seizures and epileptic disorders, adaptation occurred slowly. The genetic diagnosis was mainly based on epilepsy gene panels and not on whole exome and/or genome sequencing. METHOD: We retrospectively analyzed 420 index cases, referred for NGS over a period of 18 months, to investigate the challenges in diagnosing epilepsy. RESULT: Of the 420 cases, 65 (15%) were referred due to epilepsy with one third having a positive family history. The result of the NGS was 14 positive cases (21.5%), 16 inconclusive cases (24%), and 35 (53%) negative cases. No gene has been detected twice in the inconclusive and positive groups. Comparative genomic hybridization has been performed for all 30 NGS negative cases and four cases with pathogenic variants (deletion in 15q11.213.1, deletion of 2p16.3, deletion in Xq22.1, and deletion in 17p13.3) were identified. CONCLUSION: These findings have implications for our understanding of the approach to genetic testing and counseling of patients affected with seizures and epilepsy disorders. The overall diagnostic yield of exome/genome sequencing in our cohort was 23%. The main characteristic is genetic heterogeneity, supporting NGS technology as a suitable testing approach for seizures and epilepsy disorders. Genetic counseling for newly identified disease-causing variants depends on the pedigree interpretation, within the context of disease penetrance and variable expressivity.

The variant artificial intelligence easy scoring (VARIES) system.

PURPOSE: Medical artificial intelligence (MAI) is artificial intelligence (AI) applied to the healthcare field. AI can be applied to many different aspects of genetics, such as variant classification. With little or no prior experience in AI coding, we share our experience with variant classification using the Variant Artificial Intelligence Easy Scoring (VARIES), an open-access platform, and the Automatic Machine Learning (AutoML) of the Google Cloud Platform. METHODS: We investigated exome sequencing data from a sample of 1410 individuals. The majority (80%) were used for training and 20% for testing. The user-friendly Google Cloud Platform was used to create the VARIES model, and the TRIPOD checklist to develop and validate the prediction model for the development of the VARIES system. RESULTS: The learning rate of the training dataset reached optimal results at an early stage of iteration, with a loss value near zero in approximately 4 min. For the testing dataset, the results for F1 (micro average) was 0.64, F1 (macro average) 0.34, micro-average area under the curve AUC (one-over-rest) 0.81 and the macro-average AUC (one-over-rest) 0.73. The overall performance characteristics of the VARIES model suggest the classifier has a high predictive ability. CONCLUSION: We present a systematic guideline to create a genomic AI prediction tool with high predictive power, using a graphical user interface provided by Google Cloud Platform, with no prior experience in creating the software programs required.