The impact of structural variation on Alzheimer's disease in the Alzheimer's disease sequencing project.
15 affiliations
- 1Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA. [email protected].
- 2Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA. [email protected].
- 3Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- 4Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
- 5Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA.
- 6Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.
- 7Framingham Heart Study, Framingham, MA, USA.
- 8Department of Neurology, Boston University, Avedisian School of Medicine, Chobanian &, Boston, MA, USA.
- 9Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
- 10Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
- 11Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA.
- 12Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA. [email protected].
- 13Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA. [email protected].
- 14Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
- 15Department of Computer Science, Rice University, Houston, TX, USA.
Abstract
Introduction
Structural variants (SV), genomic alterations spanning more than 50 base pairs, can significantly impact gene expression and protein function. However, their contribution to Alzheimer's disease (AD) remains poorly understood. Leveraging a novel SV calling pipeline, we identified SVs with high accuracy in a diverse sample of the Alzheimer's Disease Sequencing Project (ADSP) and investigated the role of SVs in AD.Results
We analyze SVs in 16,841 individuals from ADSP whole genome sequencing data using BioGraph, a semi-assembly-based method that employs graph-based representation for accurate SV detection. We identify 456,644 high-quality SVs, 65% of which were novel. Of these, 272,728 SVs directly impact genes, including 86 AD-related genes. We perform association analyses within three ancestry groups, including 3,371 African (AFR), 6,327 European (EUR), and 2,126 Latin (LAT). We observe multiple deletions and insertions in moderate to high linkage disequilibrium with known AD loci, including TPCN1 and TMEM106B. In EUR, genome-wide association analysis identifies two significant low-frequency deletions associated with AD, located in introns of CCDC12 and CCDC88B, both encoding coiled-coil domain-containing proteins. Gene-based analyses further identify rare pathogenic SVs in several known AD genes, including PSEN1 in LAT and ABCA7 in AFR.Conclusions
Using a novel graph-based SV calling pipeline, we identify high-quality SVs across a large and ancestrally diverse cohort. Our analyses reveal both common and rare SVs associated with AD. These findings provide valuable insights into the genetic architecture of AD, emphasizing the value of including diverse populations in AD genomic studies.
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BibTeX
@article{lee2026impact,
title = {{The impact of structural variation on Alzheimer's disease in the Alzheimer's disease sequencing project.}},
author = {Songmi Lee and Adam C English and Gina M Peloso and Joshua C Bis and Eric Boerwinkle and Seung Hoan Choi and Nancy L Heard-Costa and Honghuang Lin and Rui Xia and Sudha Seshadri and Anita L Destefano and Myriam Fornage and Fritz J Sedlazeck},
journal = {Genome biology},
year = {2026},
month = aug,
doi = {10.1186/s13059-026-04206-8},
url = {https://europepmc.org/article/MED/42958586},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - The impact of structural variation on Alzheimer's disease in the Alzheimer's disease sequencing project.
AU - Songmi Lee
AU - Adam C English
AU - Gina M Peloso
AU - Joshua C Bis
AU - Eric Boerwinkle
AU - Seung Hoan Choi
AU - Nancy L Heard-Costa
AU - Honghuang Lin
AU - Rui Xia
AU - Sudha Seshadri
AU - Anita L Destefano
AU - Myriam Fornage
AU - Fritz J Sedlazeck
PY - 2026
DA - 2026/08/07
JO - Genome biology
DO - 10.1186/s13059-026-04206-8
UR - https://europepmc.org/article/MED/42958586
AB - Introduction Structural variants (SV), genomic alterations spanning more than 50 base pairs, can significantly impact gene expression and protein function. However, their contribution to Alzheimer's disease (AD) remains poorly understood. Leveraging a novel SV calling pipeline, we identified SVs with high accuracy in a diverse sample of the Alzheimer's Disease Sequencing Project (ADSP) and investigated the role of SVs in AD.Results We analyze SVs in 16,841 individuals from ADSP whole genome sequencing data using BioGraph, a semi-assembly-based method that employs graph-based representation for accurate SV detection. We identify 456,644 high-quality SVs, 65% of which were novel. Of these, 272,728 SVs directly impact genes, including 86 AD-related genes. We perform association analyses within three ancestry groups, including 3,371 African (AFR), 6,327 European (EUR), and 2,126 Latin (LAT). We observe multiple deletions and insertions in moderate to high linkage disequilibrium with known AD loci, including TPCN1 and TMEM106B. In EUR, genome-wide association analysis identifies two significant low-frequency deletions associated with AD, located in introns of CCDC12 and CCDC88B, both encoding coiled-coil domain-containing proteins. Gene-based analyses further identify rare pathogenic SVs in several known AD genes, including PSEN1 in LAT and ABCA7 in AFR.Conclusions Using a novel graph-based SV calling pipeline, we identify high-quality SVs across a large and ancestrally diverse cohort. Our analyses reveal both common and rare SVs associated with AD. These findings provide valuable insights into the genetic architecture of AD, emphasizing the value of including diverse populations in AD genomic studies.
SN - 1474-7596
SN - 1474-760X
ER -