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  1. Evaluation Summary:

    Lu et al. provide a new method that looks at whether disorders tend to share excess de novo mutations in genes across the genome. The authors apply the method to nine disorders including a developmental disorder, autism spectrum disorder, congenital heart disease, schizophrenia, and intellectual disability, finding statistically significant overlap between 12 pairs of disorders in de novo mutations that cause a loss of gene function. This method will be of interest to researchers working on disorders caused by de novo mutations, but further clarification of its strengths and weaknesses compared to alternative approaches (mTADA in particular) would strengthen the paper.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

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  2. Reviewer #1 (Public Review):

    In this article, the authors introduce EncoreDNM (Enrichment correlation estimator for De Novo Mutations), a novel statistical framework that leverages exome-wide DNM counts, including genes that do not reach exome-wide statistical significance in single-disorder analysis, to estimate concordant DNM associations between disorders. EncoreDNM uses a generalized linear mixed-effects model to quantify the occurrence of DNMs while accounting for de novo mutability of each gene and technical inconsistencies between studies. They demonstrate the performance of EncoreDNM through extensive simulations and analyses of DNM data of nine disorders. Compared with existing methods, EncoreDNM is statistically more powerful while better controls the type-I error rates.

    The authors provide a useful tool to leverage exome-wide DNM counts, including genes that do not reach exome-wide statistical significance in single-disorder analysis, to estimate concordant DNM associations between disorders. EncoreDNM uses a generalized linear mixed-effects model to quantify the occurrence of DNMs while accounting for de novo mutability of each gene and technical inconsistencies between studies.

    The major strength of EncoreDNM is that as a rigorous statistical model, its type-I error rate is well controlled compared to mTADA. Therefore, anyone using this tool could confidently claim the findings with false-positive well controlled.

    The results of this study largely support the authors' conclusion.

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  3. Reviewer #2 (Public Review):

    The authors present the development and application of EncoreDNM, a computational tool to identify shared genetic architecture between various disorders based on de novo mutations (DNMs). The authors present compelling evidence that their tool is able to find shared genetic architecture between various disorders that have previously been shown to have a shared common genetic component, which lends credence to their results.

    While I appreciate that EncoreDNM may improve researcher's ability to find such an enrichment and putatively identify genes correlated between various disorders, I found the comparison to previous approaches (particularly mTADA) lacking. Additionally, the description of the method itself is sparse and the reasoning behind the inclusion of various parameters in their model needs to be expanded upon for readers.

    As such, I cannot determine if EncoreDNM represents a significant advance for the field. Clarification on these points would be needed to evaluate this article's relative contribution to the field.

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