Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification

  1. Zhanying Feng
  2. Zhana Duren
  3. Jingxue Xin
  4. Qiuyue Yuan
  5. Yaoxi He
  6. Bing Su
  7. Wing Hung Wong  Is a corresponding author
  8. Yong Wang  Is a corresponding author
  1. Chinese Academy of Sciences, China
  2. Clemson University, United States
  3. Stanford University, United States

Abstract

Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with GWAS summary statistics, identify relevant tissues, and depict common genetic factors acting in the shared regulatory networks between traits by relevance correlation. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess heritability enrichments and by explicitly prioritizing gene regulations underlying relevant tissues. Ablation studies, independent data validation, and comparison experiments with existing methods on GWAS of six phenotypes show that SpecVar can improve heritability enrichment, accurately detect relevant tissues, and reveal causal regulations. Furthermore, SpecVar correlates the relevance patterns for pairs of phenotypes and better reveals shared SNP associated regulations of phenotypes than existing methods. Studying GWAS of 206 phenotypes in UK-Biobank demonstrates that SpecVar leverages the context-specific regulatory network atlas to prioritize phenotypes' relevant tissues and shared heritability for biological and therapeutic insights. SpecVar provides a powerful way to interpret SNPs via context-specific regulatory networks and is available at https://github.com/AMSSwanglab/SpecVar.

Data availability

Codes and regulatory network resources are available at https://github.com/AMSSwanglab/SpecVar. Expression and chromatin accessibility data were summarized in Table S1. GWAS data used: GWAS summary statistics of LDL and TC were downloaded at http://csg.sph.umich.edu/willer/public/lipids2013/; GWAS summary statistics of EA (GCST006442), CP (GCST006572), BrainShape (GCST90012880-GCST90013164), and Face (GCST009464) were downloaded at GWAS catalog https://www.ebi.ac.uk/gwas/summary-statistics; GWAS summary statistics of UK-Biobank were downloaded at http://www.nealelab.is/uk-biobank. The LDSC genetic correlation and phenotypic correlation computed from individual phenotypic data were downloaded at https://ukbb-rg.hail.is/.

Article and author information

Author details

  1. Zhanying Feng

    Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Zhana Duren

    Department of Genetics and Biochemistry, Clemson University, Greenwood, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jingxue Xin

    Department of Statistics, Stanford University, Palo Alto, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Qiuyue Yuan

    Department of Genetics and Biochemistry, Clemson University, Greenwood, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Yaoxi He

    Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
    Competing interests
    The authors declare that no competing interests exist.
  6. Bing Su

    Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Wing Hung Wong

    Department of Statistics, Stanford University, Palo Alto, United States
    For correspondence
    whwong@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
  8. Yong Wang

    Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
    For correspondence
    zyfeng@amss.ac.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0695-5273

Funding

National Key Research and Development Program of China (2022YFA1004800,2020YFA0712402)

  • Yong Wang

Strategic Priority Research Program of the Chinese Academy of Science (XDPB17)

  • Yong Wang

CAS Young Scientists in Basic esearch (YSBR-077)

  • Yong Wang

National Natural Science Foundation of China (12025107,11871463,11688101)

  • Yong Wang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Charles Farber, University of Virginia, United States

Version history

  1. Received: August 8, 2022
  2. Preprint posted: September 7, 2022 (view preprint)
  3. Accepted: December 13, 2022
  4. Accepted Manuscript published: December 16, 2022 (version 1)
  5. Version of Record published: January 3, 2023 (version 2)

Copyright

© 2022, Feng 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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  1. Zhanying Feng
  2. Zhana Duren
  3. Jingxue Xin
  4. Qiuyue Yuan
  5. Yaoxi He
  6. Bing Su
  7. Wing Hung Wong
  8. Yong Wang
(2022)
Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification
eLife 11:e82535.
https://doi.org/10.7554/eLife.82535

Share this article

https://doi.org/10.7554/eLife.82535

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