Quantifying concordant genetic effects of de novo mutations on multiple disorders
Curation statements for this article:-
Curated by eLife
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.)
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
- Genetics and Genomics (eLife)
Abstract
Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies.
Article activity feed
-
-
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 …
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.)
-
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 …
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.
-
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 …
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.
-
-