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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 numerous human diseases. However, their role in Alzheimer’s disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. Here, we analyzed whole-genome sequencing data from the Alzheimer’s Disease Sequ...
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/PMC10516060/ https://www.ncbi.nlm.nih.gov/pubmed/37745545 http://dx.doi.org/10.1101/2023.09.13.23295505 |
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author | Wang, Hui Dombroski, Beth A Cheng, Po-Liang Tucci, Albert Si, Ya-Qin Farrell, John J Tzeng, Jung-Ying Leung, Yuk Yee Malamon, John S Wang, Li-San Vardarajan, Badri N Farrer, Lindsay A Schellenberg, Gerard D Lee, Wan-Ping |
author_facet | Wang, Hui Dombroski, Beth A Cheng, Po-Liang Tucci, Albert Si, Ya-Qin Farrell, John J Tzeng, Jung-Ying Leung, Yuk Yee Malamon, John S Wang, Li-San Vardarajan, Badri N Farrer, Lindsay A Schellenberg, Gerard D Lee, Wan-Ping |
author_sort | Wang, Hui |
collection | PubMed |
description | Structural variations (SVs) are important contributors to the genetics of numerous human diseases. However, their role in Alzheimer’s disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. Here, we analyzed whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP, N=16,905 subjects) and identified 400,234 (168,223 high-quality) SVs. We found a significant burden of deletions and duplications in AD cases (OR=1.05, P=0.03), particularly for singletons (OR=1.12, P=0.0002) and homozygous events (OR=1.10, P<0.0004). On AD genes, the ultra-rare SVs, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1, were associated with AD (SKAT-O P=0.004). Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, e.g., a deletion (chr2:105731359-105736864) in complete LD (R(2)=0.99) with rs143080277 (chr2:105749599) in NCK2. We also identified 16 SVs associated with AD and 13 SVs associated with AD-related pathological/cognitive endophenotypes. Our findings demonstrate the broad impact of SVs on AD genetics. |
format | Online Article Text |
id | pubmed-10516060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-105160602023-09-23 Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer’s Diseases Sequencing Project Subjects Wang, Hui Dombroski, Beth A Cheng, Po-Liang Tucci, Albert Si, Ya-Qin Farrell, John J Tzeng, Jung-Ying Leung, Yuk Yee Malamon, John S Wang, Li-San Vardarajan, Badri N Farrer, Lindsay A Schellenberg, Gerard D Lee, Wan-Ping medRxiv Article Structural variations (SVs) are important contributors to the genetics of numerous human diseases. However, their role in Alzheimer’s disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. Here, we analyzed whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP, N=16,905 subjects) and identified 400,234 (168,223 high-quality) SVs. We found a significant burden of deletions and duplications in AD cases (OR=1.05, P=0.03), particularly for singletons (OR=1.12, P=0.0002) and homozygous events (OR=1.10, P<0.0004). On AD genes, the ultra-rare SVs, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1, were associated with AD (SKAT-O P=0.004). Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, e.g., a deletion (chr2:105731359-105736864) in complete LD (R(2)=0.99) with rs143080277 (chr2:105749599) in NCK2. We also identified 16 SVs associated with AD and 13 SVs associated with AD-related pathological/cognitive endophenotypes. Our findings demonstrate the broad impact of SVs on AD genetics. Cold Spring Harbor Laboratory 2023-09-13 /pmc/articles/PMC10516060/ /pubmed/37745545 http://dx.doi.org/10.1101/2023.09.13.23295505 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 Wang, Hui Dombroski, Beth A Cheng, Po-Liang Tucci, Albert Si, Ya-Qin Farrell, John J Tzeng, Jung-Ying Leung, Yuk Yee Malamon, John S Wang, Li-San Vardarajan, Badri N Farrer, Lindsay A Schellenberg, Gerard D Lee, Wan-Ping 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/PMC10516060/ https://www.ncbi.nlm.nih.gov/pubmed/37745545 http://dx.doi.org/10.1101/2023.09.13.23295505 |
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