A decade of gene discovery by the Alzheimer's Disease Sequencing Project. (20th December 2022) - Schellenberg, Gerard D.; Blue, Elizabeth E
Abstract: Background: Alzheimer's disease (AD) is caused in part by variation in genes and genetic regulatory elements. Early work identified autosomal dominant mutations in APP, PSEN1 and PSEN2 that cause AD, and large effect size variants in APOE . However, most of AD genetic risk is caused by variants that result in a modest change in risk. These variants are spread across many genetic elements. In 2009, genome‐wide association studies (GWAS) began to identify these AD loci (> 60 sites). While these studies continue to be productive, GWAS lacks the resolution to identify the critical AD genes and causative variants responsible for risk/protective signals. In 2012, the Alzheimer's Disease Sequencing Project (ADSP) was initiated to use Next Generation Sequencing methods for AD gene discovery. Sequence data has single nucleotide resolution with the potential to identify causative common and rare variants and to detect structural variants (SVs; insertions, deletions, copy number variation, etc .). Method: The ADSP is a collaborative network of investigators that assembles cohorts for sequencing, processes the data, and performs genetic analysis. In the past 10 years, the ADSP generated whole exome (WES) and whole‐genome‐sequence (WGS) data for 20, 504 case and 43, 004 control subjects. Subjects sequenced are or clinically diagnosed or neuropathologically confirmed AD cases and cognitively normal elderly subjects. Subjects include non‐Hispanic whites, Hispanics, African Americans, and Asians. In progress are WGS data for an additional ∼20, 000 subjects. Sequence data are harmonized by the Genome Center for Alzheimer Disease and shared with the research community via the National Institute on Aging Genetics Data Storage site. Result: Analysis of WES and WGS data nominates 46 AD genes and regions of interest (PMID: 35005195). These genes implicate known and novel AD pathways providing new insight into AD pathogenesis and novel therapeutic targets. SVs were detected in ABCA7 and NCK2 . The ADSP includes functional genomics studies to connect risk variants to specific genes and to understand the function of these variants. Analysis methods also includes artificial intelligence/machine learning approaches. Conclusion: The genes and pathways identified by the ADSP are a rich source of validated targets for future therapeutic development.