Quantifying concordant genetic effects of de novo mutations on multiple disorders

Author:

Guo Hanmin12ORCID,Hou Lin123,Shi Yu4,Jin Sheng Chih5,Zeng Xue67,Li Boyang8,Lifton Richard P67,Brueckner Martina69,Zhao Hongyu6810,Lu Qiongshi11ORCID

Affiliation:

1. Center for Statistical Science, Tsinghua University

2. Department of Industrial Engineering, Tsinghua University

3. MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University

4. Yale School of Management, Yale University

5. Department of Genetics, Washington University in St. Louis

6. Department of Genetics, Yale University

7. Laboratory of Human Genetics and Genomics, Rockefeller University

8. Department of Biostatistics, Yale School of Public Health

9. Department of Pediatrics, Yale University

10. Program of Computational Biology and Bioinformatics, Yale University

11. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison

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.

Funder

National Science Foundation of China

Shanghai Municipal Science and Technology Major Project

Wisconsin Alumni Research Foundation

Waisman Center pilot grant program at University of Wisconsin-Madison

National Institutes of Health

National Science Foundation

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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