Cargando…
Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics
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 da...
| Autores principales: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794298/ https://www.ncbi.nlm.nih.gov/pubmed/33420026 http://dx.doi.org/10.1038/s41467-020-20211-2 |
| _version_ | 1783634175470862336 |
|---|---|
| author | Li, Ruowang Duan, Rui Zhang, Xinyuan Lumley, Thomas Pendergrass, Sarah Bauer, Christopher Hakonarson, Hakon Carrell, David S. Smoller, Jordan W. Wei, Wei-Qi Carroll, Robert Velez Edwards, Digna R. Wiesner, Georgia Sleiman, Patrick Denny, Josh C. Mosley, Jonathan D. Ritchie, Marylyn D. Chen, Yong Moore, Jason H. |
| author_facet | Li, Ruowang Duan, Rui Zhang, Xinyuan Lumley, Thomas Pendergrass, Sarah Bauer, Christopher Hakonarson, Hakon Carrell, David S. Smoller, Jordan W. Wei, Wei-Qi Carroll, Robert Velez Edwards, Digna R. Wiesner, Georgia Sleiman, Patrick Denny, Josh C. Mosley, Jonathan D. Ritchie, Marylyn D. Chen, Yong Moore, Jason H. |
| author_sort | Li, Ruowang |
| collection | PubMed |
| description | 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. |
| format | Online Article Text |
| id | pubmed-7794298 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | Nature Publishing Group UK |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-77942982021-01-15 Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics Li, Ruowang Duan, Rui Zhang, Xinyuan Lumley, Thomas Pendergrass, Sarah Bauer, Christopher Hakonarson, Hakon Carrell, David S. Smoller, Jordan W. Wei, Wei-Qi Carroll, Robert Velez Edwards, Digna R. Wiesner, Georgia Sleiman, Patrick Denny, Josh C. Mosley, Jonathan D. Ritchie, Marylyn D. Chen, Yong Moore, Jason H. Nat Commun Article 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. Nature Publishing Group UK 2021-01-08 /pmc/articles/PMC7794298/ /pubmed/33420026 http://dx.doi.org/10.1038/s41467-020-20211-2 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| spellingShingle | Article Li, Ruowang Duan, Rui Zhang, Xinyuan Lumley, Thomas Pendergrass, Sarah Bauer, Christopher Hakonarson, Hakon Carrell, David S. Smoller, Jordan W. Wei, Wei-Qi Carroll, Robert Velez Edwards, Digna R. Wiesner, Georgia Sleiman, Patrick Denny, Josh C. Mosley, Jonathan D. Ritchie, Marylyn D. Chen, Yong Moore, Jason H. Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics |
| title | Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics |
| title_full | Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics |
| title_fullStr | Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics |
| title_full_unstemmed | Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics |
| title_short | Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics |
| title_sort | lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics |
| topic | Article |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794298/ https://www.ncbi.nlm.nih.gov/pubmed/33420026 http://dx.doi.org/10.1038/s41467-020-20211-2 |
| work_keys_str_mv | AT liruowang losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT duanrui losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT zhangxinyuan losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT lumleythomas losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT pendergrasssarah losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT bauerchristopher losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT hakonarsonhakon losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT carrelldavids losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT smollerjordanw losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT weiweiqi losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT carrollrobert losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT velezedwardsdignar losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT wiesnergeorgia losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT sleimanpatrick losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT dennyjoshc losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT mosleyjonathand losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT ritchiemarylynd losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT chenyong losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics AT moorejasonh losslessintegrationofmultipleelectronichealthrecordsforidentifyingpleiotropyusingsummarystatistics |