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Decoding Genetics, Ancestry, and Geospatial Context for Precision Health
Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, env...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635180/ https://www.ncbi.nlm.nih.gov/pubmed/37961173 http://dx.doi.org/10.1101/2023.10.24.23297096 |
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author | Koyama, Satoshi Wang, Ying Paruchuri, Kaavya Uddin, Md Mesbah Cho, So Mi J. Urbut, Sarah M. Haidermota, Sara Hornsby, Whitney E. Green, Robert C. Daly, Mark J. Neale, Benjamin M. Ellinor, Patrick T. Smoller, Jordan W. Lebo, Matthew S. Karlson, Elizabeth W. Martin, Alicia R. Natarajan, Pradeep |
author_facet | Koyama, Satoshi Wang, Ying Paruchuri, Kaavya Uddin, Md Mesbah Cho, So Mi J. Urbut, Sarah M. Haidermota, Sara Hornsby, Whitney E. Green, Robert C. Daly, Mark J. Neale, Benjamin M. Ellinor, Patrick T. Smoller, Jordan W. Lebo, Matthew S. Karlson, Elizabeth W. Martin, Alicia R. Natarajan, Pradeep |
author_sort | Koyama, Satoshi |
collection | PubMed |
description | Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization. |
format | Online Article Text |
id | pubmed-10635180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-106351802023-11-13 Decoding Genetics, Ancestry, and Geospatial Context for Precision Health Koyama, Satoshi Wang, Ying Paruchuri, Kaavya Uddin, Md Mesbah Cho, So Mi J. Urbut, Sarah M. Haidermota, Sara Hornsby, Whitney E. Green, Robert C. Daly, Mark J. Neale, Benjamin M. Ellinor, Patrick T. Smoller, Jordan W. Lebo, Matthew S. Karlson, Elizabeth W. Martin, Alicia R. Natarajan, Pradeep medRxiv Article Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization. Cold Spring Harbor Laboratory 2023-10-25 /pmc/articles/PMC10635180/ /pubmed/37961173 http://dx.doi.org/10.1101/2023.10.24.23297096 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Koyama, Satoshi Wang, Ying Paruchuri, Kaavya Uddin, Md Mesbah Cho, So Mi J. Urbut, Sarah M. Haidermota, Sara Hornsby, Whitney E. Green, Robert C. Daly, Mark J. Neale, Benjamin M. Ellinor, Patrick T. Smoller, Jordan W. Lebo, Matthew S. Karlson, Elizabeth W. Martin, Alicia R. Natarajan, Pradeep Decoding Genetics, Ancestry, and Geospatial Context for Precision Health |
title | Decoding Genetics, Ancestry, and Geospatial Context for Precision Health |
title_full | Decoding Genetics, Ancestry, and Geospatial Context for Precision Health |
title_fullStr | Decoding Genetics, Ancestry, and Geospatial Context for Precision Health |
title_full_unstemmed | Decoding Genetics, Ancestry, and Geospatial Context for Precision Health |
title_short | Decoding Genetics, Ancestry, and Geospatial Context for Precision Health |
title_sort | decoding genetics, ancestry, and geospatial context for precision health |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635180/ https://www.ncbi.nlm.nih.gov/pubmed/37961173 http://dx.doi.org/10.1101/2023.10.24.23297096 |
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