# The Impact of Structural Variation on Alzheimer’s Disease in the Alzheimer’s Disease Sequencing Project

## TL;DR

This study explores how structural variations in DNA contribute to Alzheimer's disease using a new method to analyze genetic data from diverse populations.

## Contribution

The paper introduces a novel graph-based SV calling pipeline and identifies new structural variants associated with Alzheimer's disease in diverse ancestry groups.

## Key findings

- High-quality structural variants were identified in 16,841 individuals, with 65% being novel.
- Two significant low-frequency deletions associated with Alzheimer's disease were found in European ancestry individuals.
- Rare pathogenic structural variants were identified in known Alzheimer's disease genes like PSEN1 and ABCA7.

## 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 analyzed 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 identified 456,644 high-quality SVs, 65% of which were novel. Of these, 272,728 SVs directly impact genes, including 86 AD-related genes. Association analyses were performed within three ancestry groups, including 3,371 African (AFR), 6,327 European (EUR), and 2,126 Latin (LAT). Multiple deletions and insertions were observed in moderate to high linkage disequilibrium with known AD loci, including TPCN1 and TMEM106B. In EUR, genome-wide association analysis identified 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 identified 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 identified high-quality SVs across a large and ancestrally diverse cohort. Our analyses revealed 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.