Abstract

Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients’ data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.

Thus far, pleiotropy analysis using individual-level Electronic Health Records data has been limited to data from one site. Here, the authors introduce Sum-Share, a method designed to efficiently and losslessly integrate EHR and genetic data from multiple sites to perform pleiotropy analysis.

Details

Title
Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics
Author
Li Ruowang 1 ; Duan Rui 2   VIAFID ORCID Logo  ; Zhang, Xinyuan 1 ; Lumley, Thomas 3 ; Pendergrass, Sarah 4 ; Bauer, Christopher 4   VIAFID ORCID Logo  ; Hakonarson Hakon 5   VIAFID ORCID Logo  ; Carrell, David S 6   VIAFID ORCID Logo  ; Smoller, Jordan W 7   VIAFID ORCID Logo  ; Wei-Qi, Wei 8 ; Carroll, Robert 8 ; Velez Edwards Digna R 9 ; Wiesner, Georgia 9 ; Sleiman, Patrick 5 ; Denny, Josh C 8 ; Mosley, Jonathan D 8   VIAFID ORCID Logo  ; Ritchie, Marylyn D 10   VIAFID ORCID Logo  ; Chen, Yong 1 ; Moore, Jason H 1 

1  University of Pennsylvania, Department of Biostatistics, Epidemiology & Informatics, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) 
2  Harvard T.H. Chan School of Public Health, Department of Biostatistics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X) 
3  University of Auckland, Department of Statistics, Auckland, New Zealand (GRID:grid.9654.e) (ISNI:0000 0004 0372 3343) 
4  Biomedical and Translational Informatics Institute, Danville, USA (GRID:grid.9654.e) 
5  Children’s Hospital of Philadelphia, Center for Applied Genomics, Philadelphia, USA (GRID:grid.239552.a) (ISNI:0000 0001 0680 8770) 
6  Kaiser Permanente Washington Health Research Institute, Seattle, USA (GRID:grid.488833.c) (ISNI:0000 0004 0615 7519) 
7  Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Boston, USA (GRID:grid.32224.35) (ISNI:0000 0004 0386 9924) 
8  Vanderbilt University Medical Centre, Department of Biomedical Informatics, Nashville, USA (GRID:grid.412807.8) (ISNI:0000 0004 1936 9916) 
9  Vanderbilt University, Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Nashville, USA (GRID:grid.152326.1) (ISNI:0000 0001 2264 7217) 
10  Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41467-020-20211-2
ProQuest document ID
2476251221
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.