The impact of structural variation on Alzheimer’s disease in the Alzheimer’s disease sequencing project

Abstract

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. 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. 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.