Prediction algorithms: pitfalls in interpreting genetic variants of autosomal dominant monogenic diabetes

The increasing availability of DNA sequence data and access to sophisticated bioinformatic algorithms mean that an unbiased bioinformatics-based assessment of the predicted impact of a genomic variant is rapidly available.The key point of this Viewpoint article is that such bioinformatic assessments are not equivalent to an expert diagnostic interpretation and may be misleading in both research and clinical care. Prediction algorithms in genomic medicineRecently published examples involving monogenic diabetes demonstrate how pathogenicity prediction algorithms can be very inaccurate for predicting which genetic variants are likely causal of dominant monogenic disease (1-4).Here, we highlight the potential pitfalls of variant classification and how they can be avoided.A recent study used a bioinformatic algorithm to identify 88 "likely pathogenic" monogenic diabetes variants in 80 individuals (8.6%) from a cohort of 1019 individuals with type 1 diabetes for 50 or more years (4).Application of the widely used American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) standards and guidelines (5) classifies only nine of these 88 variants as likely pathogenic or pathogenic variants that would be reported by our clinical diagnostic laboratory as likely causative of the patients' diabetes.This is not an isolated occurrence; other published research studies with an overreliance on in silico prediction tools have reported high levels (~90%) of false positive "likely pathogenic" monogenic diabetes variants (1-3).We have seen clinical diagnostic reports from laboratories in eight coun- Conflict of interest:The authors have declared that no conflict of interest exists.