Dataset Information
Key variants via Alzheimer's Disease Sequencing Project whole genome sequence data.
Ontology highlight
ABSTRACT: Introduction
Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci.Methods
We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants.Results
Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses.Discussion
This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.
SUBMITTER: Wang Y
PROVIDER: S-EPMC10491364 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
Publications
Key variants via Alzheimer's Disease Sequencing Project whole genome sequence data.
medRxiv : the preprint server for health sciences 20230829
<h4>Introduction</h4>Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci.<h4>Methods</h4>We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,1 ...[more]