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Learning epistatic polygenic phenotypes with Boolean interactions

Merle Behr, Karl Kumbier, Aldo Cordova-Palomera, Matthew Aguirre, Euan Ashley, Atul J. Butte, Rima Arnaout, Ben Brown, James Priest, Bin Yu
doi: https://doi.org/10.1101/2020.11.24.396846
Merle Behr
1Department of Statistics, University of California at Berkeley, Berkeley, CA, USA
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Karl Kumbier
2Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
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Aldo Cordova-Palomera
3Department of Pediatrics, Stanford Medicine, Stanford, CA, USA
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Matthew Aguirre
3Department of Pediatrics, Stanford Medicine, Stanford, CA, USA
4Department of Biomedical Data Science, Stanford Medicine, Stanford, CA, USA
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Euan Ashley
5Division of Cardiovascular Medicine, Stanford Medicine, Stanford, CA, USA
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Atul J. Butte
6Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
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Rima Arnaout
6Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
7Division of Cardiology, Department of Medicine, University of California, San Francisco, CA, USA
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Ben Brown
1Department of Statistics, University of California at Berkeley, Berkeley, CA, USA
8Biosciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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James Priest
3Department of Pediatrics, Stanford Medicine, Stanford, CA, USA
9BioMarin Pharmaceuticals, San Rafael, CA, USA
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  • For correspondence: jpriest@stanford.edu binyu@berkeley.edu
Bin Yu
1Department of Statistics, University of California at Berkeley, Berkeley, CA, USA
10Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California at Berkeley, Berkeley, CA, USA
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  • For correspondence: jpriest@stanford.edu binyu@berkeley.edu
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Abstract

Detecting epistatic drivers of human phenotypes remains a challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving single pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests from interactions by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that evaluate the stability of improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline using the phenotype of red-hair from the UK Biobank, where several genes are known to demonstrate epistatic interactions. epiTree recovers both previously reported and novel interactions, which represent forms of non-linearities not captured by logistic regression models. Additionally, epiTree suggests interactions between genes such as PKHD1 and XPOTP1, which are unlinked to MC1R, as novel candidate interactions associated with the red hair phenotype. Last but not least, we find that individual Boolean or tree-based epistasis models generally provide higher prediction accuracy than classical logistic regression.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 25, 2020.
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Learning epistatic polygenic phenotypes with Boolean interactions
Merle Behr, Karl Kumbier, Aldo Cordova-Palomera, Matthew Aguirre, Euan Ashley, Atul J. Butte, Rima Arnaout, Ben Brown, James Priest, Bin Yu
bioRxiv 2020.11.24.396846; doi: https://doi.org/10.1101/2020.11.24.396846
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Learning epistatic polygenic phenotypes with Boolean interactions
Merle Behr, Karl Kumbier, Aldo Cordova-Palomera, Matthew Aguirre, Euan Ashley, Atul J. Butte, Rima Arnaout, Ben Brown, James Priest, Bin Yu
bioRxiv 2020.11.24.396846; doi: https://doi.org/10.1101/2020.11.24.396846

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