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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
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.
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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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