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networkGWAS: A network-based approach for genome-wide association studies in structured populations

View ORCID ProfileGiulia Muzio, View ORCID ProfileLeslie O’Bray, View ORCID ProfileLaetitia Meng-Papaxanthos, View ORCID ProfileJuliane Klatt, View ORCID ProfileKarsten Borgwardt
doi: https://doi.org/10.1101/2021.11.11.468206
Giulia Muzio
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
2Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
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  • ORCID record for Giulia Muzio
Leslie O’Bray
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
2Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
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Laetitia Meng-Papaxanthos
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
2Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
3Google Research, Brain Team
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Juliane Klatt
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
2Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
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Karsten Borgwardt
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
2Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
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  • For correspondence: karsten.borgwardt@bsse.ethz.ch
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Abstract

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose networkGWAS, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. networkGWAS successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

Availability https://github.com/BorgwardtLab/networkGWAS.git

Competing Interest Statement

The authors have declared no competing interest.

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 4.0 International license.
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Posted November 13, 2021.
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networkGWAS: A network-based approach for genome-wide association studies in structured populations
Giulia Muzio, Leslie O’Bray, Laetitia Meng-Papaxanthos, Juliane Klatt, Karsten Borgwardt
bioRxiv 2021.11.11.468206; doi: https://doi.org/10.1101/2021.11.11.468206
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networkGWAS: A network-based approach for genome-wide association studies in structured populations
Giulia Muzio, Leslie O’Bray, Laetitia Meng-Papaxanthos, Juliane Klatt, Karsten Borgwardt
bioRxiv 2021.11.11.468206; doi: https://doi.org/10.1101/2021.11.11.468206

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