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Interpretable network-guided epistasis detection

Diane Duroux, Héctor Climente-González, Chloé-Agathe Azencott, Kristel Van Steen
doi: https://doi.org/10.1101/2020.09.24.310136
Diane Duroux
1BIO3 - GIGA-R Medical Genomics, University of Liege, Liege, Belgium
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  • For correspondence: diane.duroux@uliege.be
Héctor Climente-González
2Institut Curie, PSL Research University, F-75005 Paris, France
3INSERM, U900, F-75005 Paris, France
4MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
5RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
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Chloé-Agathe Azencott
4MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France
2Institut Curie, PSL Research University, F-75005 Paris, France
3INSERM, U900, F-75005 Paris, France
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Kristel Van Steen
1BIO3 - GIGA-R Medical Genomics, University of Liege, Liege, Belgium
6BIO3 - Department of Human Genetics, KU Leuven, Herestraat 49, B-3000 Leuven, Belgium
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Abstract

Detecting epistatic interactions at the gene level is essential to understanding the biological mechanisms of complex diseases. Unfortunately, genome-wide interaction association studies (GWAIS) involve many statistical challenges that make such detection hard. We propose a multi-step protocol for epistasis detection along the edges of a gene-gene co-function network. Such an approach reduces the number of tests performed and provides interpretable interactions, while keeping type I error controlled. Yet, mapping gene-interactions into testable SNP-interaction hypotheses, as well as computing gene pair association scores from SNP pair ones, is not trivial. Here we compare three SNP-gene mappings (positional overlap, eQTL and proximity in 3D structure) and use the adaptive truncated product method to compute gene pair scores. This method is non-parametric, does not require a known null distribution, and is fast to compute. We apply multiple variants of this protocol to a GWAS inflammatory bowel disease (IBD) dataset. Different configurations produced different results, highlighting that various mechanisms are implicated in IBD, while at the same time, results overlapped with known disease biology. Importantly, the proposed pipeline also differs from a conventional approach were no network is used, showing the potential for additional discoveries when prior biological knowledge is incorporated into epistasis detection.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Detecting epistatic interactions at the gene level is essential to understanding the biological mechanisms of complex diseases. Unfortunately, genome-wide interaction association studies (GWAIS) involve many statistical challenges that make such detection hard. We propose a multi-step protocol for epistasis detection along the edges of a gene-gene co-function network. Such an approach reduces the number of tests performed and provides interpretable interactions, while keeping type I error controlled. Yet, mapping gene-interactions into testable SNP-interaction hypotheses, as well as computing gene pair association scores from SNP pair ones, is not trivial. Here we compare three SNP-gene mappings (positional overlap, eQTL and proximity in 3D structure) and use the adaptive truncated product method to compute gene pair scores. This method is non-parametric, does not require a known null distribution, and is fast to compute. We apply multiple variants of this protocol to a GWAS inflammatory bowel disease (IBD) dataset. Different configurations produced different results, highlighting that various mechanisms are implicated in IBD, while at the same time, results overlapped with known disease biology. Importantly, the proposed pipeline also differs from a conventional approach were no network is used, showing the potential for additional discoveries when prior biological knowledge is incorporated into epistasis detection.

  • https://github.com/hclimente/gwas-tools

  • List of abbreviations

    ATPM
    adaptive truncated product method
    eQTL
    expression quantitative trait loci
    FWER
    family-wise error rate
    GWAS
    genome-wide association study
    GWAIS
    genome-wide association interaction study
    IBD
    inflammatory bowel disease
    LD
    linkage disequilibrium
    SNP
    single-nucleotide polymorphism
    TPM
    truncated product method
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    Posted October 22, 2021.
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    Interpretable network-guided epistasis detection
    Diane Duroux, Héctor Climente-González, Chloé-Agathe Azencott, Kristel Van Steen
    bioRxiv 2020.09.24.310136; doi: https://doi.org/10.1101/2020.09.24.310136
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    Interpretable network-guided epistasis detection
    Diane Duroux, Héctor Climente-González, Chloé-Agathe Azencott, Kristel Van Steen
    bioRxiv 2020.09.24.310136; doi: https://doi.org/10.1101/2020.09.24.310136

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