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Identifying and ranking potential driver genes of Alzheimer’s Disease using multi-view evidence aggregation

View ORCID ProfileSumit Mukherjee, Thanneer Perumal, Kenneth Daily, Solveig Sieberts, Larsson Omberg, Christoph Preuss, Gregory Carter, Lara Mangravite, Benjamin Logsdon
doi: https://doi.org/10.1101/534305
Sumit Mukherjee
1Sage Bionetworks, Seattle, WA, USA.
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Thanneer Perumal
1Sage Bionetworks, Seattle, WA, USA.
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Kenneth Daily
1Sage Bionetworks, Seattle, WA, USA.
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Solveig Sieberts
1Sage Bionetworks, Seattle, WA, USA.
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Larsson Omberg
1Sage Bionetworks, Seattle, WA, USA.
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Christoph Preuss
2The Jackson Laboratory, Bar Harbor, ME, USA.
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Gregory Carter
2The Jackson Laboratory, Bar Harbor, ME, USA.
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Lara Mangravite
1Sage Bionetworks, Seattle, WA, USA.
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Benjamin Logsdon
1Sage Bionetworks, Seattle, WA, USA.
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ABSTRACT

Motivation Late onset Alzheimers disease (LOAD) is currently a disease with no known effective treatment options. To address this, there have been a recent surge in the generation of multi-modality data (Hodes and Buckholtz, 2016; Mueller et al., 2005) to understand the biology of the disease and potential drivers that causally regulate it. However, most analytic studies using these data-sets focus on uni-modal analysis of the data. Here we propose a data-driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our paper are: i) A general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature-sets and identifying other potential driver genes which have similar feature representations, and ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study (GWAS) summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types.

Results We demonstrate the utility of our machine learning algorithm on two benchmark multi-view datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimers. We show that our ranked genes show a significant enrichment for SNPs associated with Alzheimers, and are enriched in pathways that have been previously associated with the disease.

Availability Source code and link to all feature sets is availabile at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.

Contact ben.logsdon{at}sagebionetworks.org

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 January 29, 2019.
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Identifying and ranking potential driver genes of Alzheimer’s Disease using multi-view evidence aggregation
Sumit Mukherjee, Thanneer Perumal, Kenneth Daily, Solveig Sieberts, Larsson Omberg, Christoph Preuss, Gregory Carter, Lara Mangravite, Benjamin Logsdon
bioRxiv 534305; doi: https://doi.org/10.1101/534305
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Identifying and ranking potential driver genes of Alzheimer’s Disease using multi-view evidence aggregation
Sumit Mukherjee, Thanneer Perumal, Kenneth Daily, Solveig Sieberts, Larsson Omberg, Christoph Preuss, Gregory Carter, Lara Mangravite, Benjamin Logsdon
bioRxiv 534305; doi: https://doi.org/10.1101/534305

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