The Importance of Automation in Genetic Diagnosis: Lessons from Analyzing an Inherited Retinal Degeneration Cohort with the Mendelian Analysis Toolkit (MATK)

ABSTRACT Purpose In Mendelian disease diagnosis, variant analysis is a repetitive, error-prone, and time-consuming process. To address this, we have developed the Mendelian Analysis Toolkit (MATK), a configurable automated variant ranking program. Methods MATK aggregates variant information from multiple annotation sources and uses expert-designed rules with parameterized weights to produce a ranked list of potentially causal solutions. MATK performance was measured by a comparison of MATK-aided versus human domain-expert analyses of 1060 inherited retinal degeneration (IRD) families investigated with an IRD-specific gene panel (589 individuals) and exome sequencing (471 families). Results When comparing MATK-assisted analysis to expert curation in both IRD-specific and exome sequencing (1060 subjects), 97.3% of potential solutions found by experts were also identified by the MATK-assisted analysis (541 solutions identified with MATK of 556 solutions found by conventional analysis). Furthermore, MATK-assisted analysis identified 114 additional potential solutions from the 504 cases unsolved by the conventional analysis. Conclusion MATK expedites the process of identifying likely solving variants in Mendelian traits and reduces variability coming from human error and researcher bias. MATK facilitates data re-analysis to keep up with the constantly improving annotation sources and NGS processing pipelines. The software is open source, available at https://gitlab.com/matthew_maher/mendelanalysis