Quantifying concordant genetic effects of de novo mutations on multiple disorders

  1. Hanmin Guo
  2. Lin Hou
  3. Yu Shi
  4. Sheng Chih Jin
  5. Xue Zeng
  6. Boyang Li
  7. Richard Lifton
  8. Martina Brueckner
  9. Hongyu Zhao  Is a corresponding author
  10. Qiongshi Lu  Is a corresponding author
  1. Tsinghua University, China
  2. Yale University, United States
  3. Washington University in St. Louis, United States
  4. Rockefeller University, United States
  5. 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.

The following previously published data sets were used

Article and author information

Author details

  1. Hanmin Guo

    Center for Statistical Science, Tsinghua University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Lin Hou

    Center for Statistical Science, Tsinghua University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Yu Shi

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Sheng Chih Jin

    Department of Genetics, Washington University in St. Louis, St. Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Xue Zeng

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Boyang Li

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Richard Lifton

    Laboratory of Human Genetics and Genomics, Rockefeller University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Martina Brueckner

    Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Hongyu Zhao

    Yale University, New Haven, United States
    For correspondence
    Hongyu.Zhao@yale.edu
    Competing interests
    The authors declare that no competing interests exist.
  10. Qiongshi Lu

    Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, United States
    For correspondence
    qlu@biostat.wisc.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "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.

Reviewing Editor

  1. Alexander Young, University of California, Los Angeles, United States

Version history

  1. Preprint posted: June 14, 2021 (view preprint)
  2. Received: November 14, 2021
  3. Accepted: June 1, 2022
  4. Accepted Manuscript published: June 6, 2022 (version 1)
  5. Version of Record published: June 22, 2022 (version 2)

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.

Metrics

  • 911
    views
  • 191
    downloads
  • 4
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Hanmin Guo
  2. Lin Hou
  3. Yu Shi
  4. Sheng Chih Jin
  5. Xue Zeng
  6. Boyang Li
  7. Richard Lifton
  8. Martina Brueckner
  9. Hongyu Zhao
  10. Qiongshi Lu
(2022)
Quantifying concordant genetic effects of de novo mutations on multiple disorders
eLife 11:e75551.
https://doi.org/10.7554/eLife.75551

Share this article

https://doi.org/10.7554/eLife.75551

Further reading

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics
    Lisa Baumgartner, Jonathan J Ipsaro ... Julius Brennecke
    Research Advance

    Members of the diverse heterochromatin protein 1 (HP1) family play crucial roles in heterochromatin formation and maintenance. Despite the similar affinities of their chromodomains for di- and tri-methylated histone H3 lysine 9 (H3K9me2/3), different HP1 proteins exhibit distinct chromatin-binding patterns, likely due to interactions with various specificity factors. Previously, we showed that the chromatin-binding pattern of the HP1 protein Rhino, a crucial factor of the Drosophila PIWI-interacting RNA (piRNA) pathway, is largely defined by a DNA sequence-specific C2H2 zinc finger protein named Kipferl (Baumgartner et al., 2022). Here, we elucidate the molecular basis of the interaction between Rhino and its guidance factor Kipferl. Through phylogenetic analyses, structure prediction, and in vivo genetics, we identify a single amino acid change within Rhino’s chromodomain, G31D, that does not affect H3K9me2/3 binding but disrupts the interaction between Rhino and Kipferl. Flies carrying the rhinoG31D mutation phenocopy kipferl mutant flies, with Rhino redistributing from piRNA clusters to satellite repeats, causing pronounced changes in the ovarian piRNA profile of rhinoG31D flies. Thus, Rhino’s chromodomain functions as a dual-specificity module, facilitating interactions with both a histone mark and a DNA-binding protein.

    1. Genetics and Genomics
    2. Neuroscience
    Yifei Weng, Shiyi Zhou ... Coleen T Murphy
    Research Article

    Cognitive decline is a significant health concern in our aging society. Here, we used the model organism C. elegans to investigate the impact of the IIS/FOXO pathway on age-related cognitive decline. The daf-2 Insulin/IGF-1 receptor mutant exhibits a significant extension of learning and memory span with age compared to wild-type worms, an effect that is dependent on the DAF-16 transcription factor. To identify possible mechanisms by which aging daf-2 mutants maintain learning and memory with age while wild-type worms lose neuronal function, we carried out neuron-specific transcriptomic analysis in aged animals. We observed downregulation of neuronal genes and upregulation of transcriptional regulation genes in aging wild-type neurons. By contrast, IIS/FOXO pathway mutants exhibit distinct neuronal transcriptomic alterations in response to cognitive aging, including upregulation of stress response genes and downregulation of specific insulin signaling genes. We tested the roles of significantly transcriptionally-changed genes in regulating cognitive functions, identifying novel regulators of learning and memory. In addition to other mechanistic insights, a comparison of the aged vs young daf-2 neuronal transcriptome revealed that a new set of potentially neuroprotective genes is upregulated; instead of simply mimicking a young state, daf-2 may enhance neuronal resilience to accumulation of harm and take a more active approach to combat aging. These findings suggest a potential mechanism for regulating cognitive function with age and offer insights into novel therapeutic targets for age-related cognitive decline.