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Quantifying concordant genetic effects of de novo mutations on multiple disorders

View ORCID ProfileHanmin Guo, View ORCID ProfileLin Hou, Yu Shi, Sheng Chih Jin, Xue Zeng, Boyang Li, Richard P. Lifton, Martina Brueckner, Hongyu Zhao, View ORCID ProfileQiongshi Lu
doi: https://doi.org/10.1101/2021.06.13.448234
Hanmin Guo
1Center for Statistical Science, Tsinghua University, Beijing, China
2Department of Industrial Engineering, Tsinghua University, Beijing, China
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  • ORCID record for Hanmin Guo
Lin Hou
1Center for Statistical Science, Tsinghua University, Beijing, China
2Department of Industrial Engineering, Tsinghua University, Beijing, China
3MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
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Yu Shi
4Yale School of Management, Yale University, New Haven, CT, USA
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Sheng Chih Jin
5Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
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Xue Zeng
6Department of Genetics, Yale University, New Haven, CT, USA
7Laboratory of Human Genetics and Genomics, Rockefeller University, New York, USA
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Boyang Li
8Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Richard P. Lifton
6Department of Genetics, Yale University, New Haven, CT, USA
7Laboratory of Human Genetics and Genomics, Rockefeller University, New York, USA
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Martina Brueckner
6Department of Genetics, Yale University, New Haven, CT, USA
9Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA
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Hongyu Zhao
6Department of Genetics, Yale University, New Haven, CT, USA
8Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
10Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
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Qiongshi Lu
11Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
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  • ORCID record for Qiongshi Lu
  • For correspondence: qlu@biostat.wisc.edu
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵# These authors should be considered shared last author

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 14, 2021.
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Quantifying concordant genetic effects of de novo mutations on multiple disorders
Hanmin Guo, Lin Hou, Yu Shi, Sheng Chih Jin, Xue Zeng, Boyang Li, Richard P. Lifton, Martina Brueckner, Hongyu Zhao, Qiongshi Lu
bioRxiv 2021.06.13.448234; doi: https://doi.org/10.1101/2021.06.13.448234
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Quantifying concordant genetic effects of de novo mutations on multiple disorders
Hanmin Guo, Lin Hou, Yu Shi, Sheng Chih Jin, Xue Zeng, Boyang Li, Richard P. Lifton, Martina Brueckner, Hongyu Zhao, Qiongshi Lu
bioRxiv 2021.06.13.448234; doi: https://doi.org/10.1101/2021.06.13.448234

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