Quantifying concordant genetic effects of de novo mutations on multiple disorders
- Tsinghua University, China
- Yale University, United States
- Washington University in St. Louis, United States
- Rockefeller University, United States
- University of Wisconsin-Madison, United States
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.
Data availability
The current manuscript is a computational study, so no data have been generated for this manuscript.
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-
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-
Exome sequencing in schizophrenia-affected parent-offspring trios reveals risk conferred by protein-coding de novo mutationshttps://doi.org/10.1038/s41593-019-0564-3.
-
Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probandshttps://doi.org/10.1038/ng.3970.
-
Meta-analysis of 2,104 trios provides support for 10 new genes for intellectual disabilityhttps://doi.org/10.1038/nn.4352.
-
De Novo Coding Variants Are Strongly Associated with Tourette Disorderhttps://doi.org/10.1016/j.neuron.2017.04.024.
-
De novo mutations in epileptic encephalopathieshttps://doi.org/10.1038/nature12439.
-
Mutations disrupting neuritogenesis genes confer risk for cerebral palsyhttps://doi.org/10.1038/s41588-020-0695-1.
-
Exome sequencing implicates genetic disruption of prenatal neuro-gliogenesis in sporadic congenital hydrocephalushttps://doi.org/10.1038/s41591-020-1090-2.
-
Excess of rare, inherited truncating mutations in autismhttps://doi.org/10.1038/ng.3303.
Article and author information
Author details
Funding
National Science Foundation of China (No. 12071243)
- Lin Hou
Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01)
- Lin Hou
Wisconsin Alumni Research Foundation
- Qiongshi Lu
Waisman Center pilot grant program at University of Wisconsin-Madison
- Qiongshi Lu
National Institutes of Health (No. R03HD100883 and R01GM134005)
- Hongyu Zhao
National Science Foundation (DMS 1902903)
- Hongyu Zhao
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Guo et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Quantifying concordant genetic effects of de novo mutations on multiple disorders
eLife 11:e75551.
https://doi.org/10.7554/eLife.75551
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