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 between MATK-aided and human-domain expert analyses of 1060 families with inherited retinal degeneration (IRD), analyzed using an IRD-specific gene panel (589 individuals) and exome sequencing (471 families). Results: When comparing MATK-assisted analysis with expert curation in both the IRDspecific gene panel 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 conventional analysis. Conclusion: MATK expedites the process of identification of likely solving variants in Mendelian traits, and reduces variability stemming from human error and researcher bias. MATK facilitates data reanalysis to keep up with the constantly improving annotation sources and nextgeneration sequencing processing pipelines. The software is open source and available at https:// gitlab.com/matthew_maher/mendelanalysis.

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 families) and exome sequencing (471 families). Results: When comparing MATK-assisted analysis to expert curation, we found that 97.3% (541/556) of potential solutions found by experts were also identified by the MATK-assisted analysis. Furthermore, MATK-assisted analysis identified 114 additional potential solutions. The software also showed utility in data reanalysis after remapping to the GRCh38 genome build. Conclusion: MATK expedites the process of identifying likely solving variants in Mendelian traits and helps to remove 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 and available at https://gitlab.partners.org/meei-ogi-bioinformatics/MendelAnalysis

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