ATLAS: An automated association test using probabilistically linked health records with application to genetic studies (original) (raw)

, Boris P. Hejblum, Griffin M. Weber, Nathan P. Palmer, Susanne E. Churchill, Peter Szolovits, Shawn N. Murphy, Katherine P. Liao, Isaac S. Kohane, Tianxi Cai

doi: https://doi.org/10.1101/2021.05.02.21256490

Abstract

Objective Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases with no gold standard mappings between records may provide a more complete picture of patient health and enable novel research studies. To do so, researchers may probabilistically link databases and conduct inference using the linked data. However, previous inference methods for linked data are constrained to specific linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data.

Materials and Methods Missing variables are imputed at various thresholds using a weighted average method that propagates uncertainty from the linkage process. Next, an estimated effect size is obtained using a generalized linear model. ATLAS then conducts the threshold combination test by optimally combining p-values obtained from data imputed at varying thresholds using Fisher’s method and perturbation resampling.

Results In simulations, ATLAS controls for type I error and exhibits high power compared to previous methods. In a real-world application study, incorporation of linked data-enabled analyses using ATLAS yielded two additional signifigant associations between rheumatoid arthritis genetic risk score and biomarkers.

Discussion The ATLAS weighted average imputation weathers false matches and increases contribution of true matches to mitigate linkage error induced bias. ATLAS’ threshold combination test avoids arbitrarily choosing a threshold to rule a match, thus automating linked data-enabled analyses and preserving power.

Conclusion ATLAS promises to enable novel and powerful research studies using linked data to capitalize on all available data sources.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported in part by the U.S. National Institutes of Health Grant U54-HG007963.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Mass General Brigham IRB Committee

All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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Data Availability

Datasets used in the simulation study are provided in the "ludic" package on CRAN.

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