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Unpaired Data Empowers Association Tests

Mingming Gong, Peng Liu, Frank C. Sciurba, Petar Stojanov, Dacheng Tao, View ORCID ProfileGeorge C. Tseng, Kun Zhang, Kayhan Batmanghelich
doi: https://doi.org/10.1101/839159
Mingming Gong
1University of Pittsburgh
2Carnegie Mellon University
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Peng Liu
1University of Pittsburgh
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Frank C. Sciurba
1University of Pittsburgh
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Petar Stojanov
2Carnegie Mellon University
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Dacheng Tao
3University of Sydney
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George C. Tseng
1University of Pittsburgh
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  • ORCID record for George C. Tseng
Kun Zhang
2Carnegie Mellon University
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Kayhan Batmanghelich
1University of Pittsburgh
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  • For correspondence: kayhan@pitt.edu
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Abstract

To achieve a holistic view of the underlying mechanisms of human diseases, the biomedical research community is moving toward harvesting retrospective data available in Electronic Healthcare Records (EHRs). The first step for causal understanding is to perform association tests between types of potentially high-dimensional biomedical data, such as genetic, blood biomarkers, and imaging data. To obtain a reasonable power, current methods require a substantial sample size of individuals with both data modalities. This prevents researchers from using much larger EHR samples that include individuals with at least one data type, limits the power of the association test, and may result in higher false discovery rate. We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and, under some conditions, improve the association test power. We study the properties of SAT theoretically and empirically, through simulations and application to real studies in the context of Chronic Obstructive Pulmonary Disease. Our method identifies an association between the high-dimensional characterization of Computed Tomography (CT) chest images and blood biomarkers as well as the expression of dozens of genes involved in the immune system.

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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. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 12, 2019.
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Unpaired Data Empowers Association Tests
Mingming Gong, Peng Liu, Frank C. Sciurba, Petar Stojanov, Dacheng Tao, George C. Tseng, Kun Zhang, Kayhan Batmanghelich
bioRxiv 839159; doi: https://doi.org/10.1101/839159
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Unpaired Data Empowers Association Tests
Mingming Gong, Peng Liu, Frank C. Sciurba, Petar Stojanov, Dacheng Tao, George C. Tseng, Kun Zhang, Kayhan Batmanghelich
bioRxiv 839159; doi: https://doi.org/10.1101/839159

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