WGS Rare Variants

Objective: To nominate putatively functional rare noncoding variants in AD

Study Design:
We have developed a novel pipeline for noncoding variant prioritization that combines principles from statistical genetics, gene regulation, and machine learning. We have previously used this type of pipeline to prioritize noncoding variants within known genetic risk loci for AD (PMID: 33106633). In brief, this pipeline:
1. Identifies all known LD-expanded variants that have been significantly associated with AD
2. Filters these variants for those overlapping gene regulatory elements in specific cell types of the brain
3. Uses machine learning to predict which variants will have strong effects on transcription factor binding
4. Uses functional genomics technologies including massively parallel reporter assays and CRISPR-based genome editing to pinpoint which of the nominated variants have validated functional effects

So far, this pipeline has only been applied to common variants identified by GWAS but we aim to apply this same methodology to nominate functional noncoding rare variants. With this in mind, we will:

1. Download all ADSP WGS datasets to identify all variants discovered in AD cases and controls
2. Annotate each variant with its frequency both within the ADSP cohorts and within the general population using resources such as gnomAD and TOPMED.
3. Input rare variants implicated in AD (i.e. either only observed in AD or observed more frequently in AD than in the general population) in the above described pipeline to functionally validate a subset of rare variants as putative noncoding drivers of disease.
4. Link any putative functional variants to their cell type-specific target genes

The result of this work would be a list of variants with putative functional roles in AD and their putative target genes.
In this study, we do not plan to associate any phenotypic characteristics other than AD vs Non-AD. No collaboration is anticipated.

Alzheimer's disease (AD) is driven by both genetic and non-genetic factors. Previous studies have estimated that ~60% of the susceptibility to AD can be attributed to genetic factors. This heritability can be roughly evenly divided between (i) common genetic variation and (ii) rare or structural variation. Of the rare genetic variation driving AD, we understand vanishingly little. This is because >90% of rare genetic variants lie within the noncoding regions of the genome. These noncoding regions harbor gene regulatory elements but do not code for proteins. As we lack a fundamental understanding of how genetic variation impacts the noncoding genome, it has remained challenging to predict which of these noncoding rare variants might have functional effects in AD. We have developed a pipeline to prioritize these rare noncoding variants using a combination of epigenetics and machine learning approaches. We will use this pipeline to nominate putatively functional rare noncoding variants and then use functional genomics assays to validate these predicted effects.