Quantifying concordant genetic effects of de novo mutations on multiple disorders

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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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Article and author information

Author details

Hanmin Guo

Center for Statistical Science, Tsinghua University, Beijing, China

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The authors declare that no competing interests exist.
Lin Hou

Center for Statistical Science, Tsinghua University, Beijing, China

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The authors declare that no competing interests exist.
Yu Shi

Yale University, New Haven, United States

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The authors declare that no competing interests exist.
Sheng Chih Jin

Department of Genetics, Washington University in St. Louis, St. Louis, United States

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Xue Zeng

Yale University, New Haven, United States

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Boyang Li

Yale University, New Haven, United States

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The authors declare that no competing interests exist.
Richard Lifton

Laboratory of Human Genetics and Genomics, Rockefeller University, New York, United States

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The authors declare that no competing interests exist.
Martina Brueckner

Yale University, New Haven, United States

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Hongyu Zhao

Yale University, New Haven, United States

For correspondence
Hongyu.Zhao@yale.edu

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The authors declare that no competing interests exist.
Qiongshi Lu

Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, United States

For correspondence
qlu@biostat.wisc.edu

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The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-4514-0969

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.

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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.