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Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects
Structural variations (SVs) are important contributors to the genetics of human diseases. However, their role in Alzheimer’s disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. We analyzed whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602095/ https://www.ncbi.nlm.nih.gov/pubmed/37886469 http://dx.doi.org/10.21203/rs.3.rs-3353179/v1 |
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author | Lee, Wan-Ping Wang, Hui Dombroski, Beth Cheng, Po-Liang Tucci, Albert Si, Ya-qin Farrell, John Tzeng, Jung-Ying Leung, Yuk Yee Malamon, John Wang, Li-San Vardarajan, Badri Farrer, Lindsay Schellenberg, Gerard |
author_facet | Lee, Wan-Ping Wang, Hui Dombroski, Beth Cheng, Po-Liang Tucci, Albert Si, Ya-qin Farrell, John Tzeng, Jung-Ying Leung, Yuk Yee Malamon, John Wang, Li-San Vardarajan, Badri Farrer, Lindsay Schellenberg, Gerard |
author_sort | Lee, Wan-Ping |
collection | PubMed |
description | Structural variations (SVs) are important contributors to the genetics of human diseases. However, their role in Alzheimer’s disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. We analyzed whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (N = 16,905) and identified 400,234 (168,223 high-quality) SVs. Laboratory validation yielded a sensitivity of 82% (85% for high-quality). We found a significant burden of deletions and duplications in AD cases, particularly for singletons and homozygous events. On AD genes, we observed the ultra-rare SVs associated with the disease, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1. Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, exemplified by a 5k deletion in complete LD with rs143080277 in NCK2. We also identified 16 SVs associated with AD and 13 SVs linked to AD-related pathological/cognitive endophenotypes. This study highlights the pivotal role of SVs in shaping our understanding of AD genetics. |
format | Online Article Text |
id | pubmed-10602095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-106020952023-10-27 Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects Lee, Wan-Ping Wang, Hui Dombroski, Beth Cheng, Po-Liang Tucci, Albert Si, Ya-qin Farrell, John Tzeng, Jung-Ying Leung, Yuk Yee Malamon, John Wang, Li-San Vardarajan, Badri Farrer, Lindsay Schellenberg, Gerard Res Sq Article Structural variations (SVs) are important contributors to the genetics of human diseases. However, their role in Alzheimer’s disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. We analyzed whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (N = 16,905) and identified 400,234 (168,223 high-quality) SVs. Laboratory validation yielded a sensitivity of 82% (85% for high-quality). We found a significant burden of deletions and duplications in AD cases, particularly for singletons and homozygous events. On AD genes, we observed the ultra-rare SVs associated with the disease, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1. Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, exemplified by a 5k deletion in complete LD with rs143080277 in NCK2. We also identified 16 SVs associated with AD and 13 SVs linked to AD-related pathological/cognitive endophenotypes. This study highlights the pivotal role of SVs in shaping our understanding of AD genetics. American Journal Experts 2023-10-05 /pmc/articles/PMC10602095/ /pubmed/37886469 http://dx.doi.org/10.21203/rs.3.rs-3353179/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Lee, Wan-Ping Wang, Hui Dombroski, Beth Cheng, Po-Liang Tucci, Albert Si, Ya-qin Farrell, John Tzeng, Jung-Ying Leung, Yuk Yee Malamon, John Wang, Li-San Vardarajan, Badri Farrer, Lindsay Schellenberg, Gerard Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects |
title | Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects |
title_full | Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects |
title_fullStr | Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects |
title_full_unstemmed | Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects |
title_short | Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects |
title_sort | structural variation detection and association analysis of whole-genome-sequence data from 16,905 alzheimer’s diseases sequencing project subjects |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602095/ https://www.ncbi.nlm.nih.gov/pubmed/37886469 http://dx.doi.org/10.21203/rs.3.rs-3353179/v1 |
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