Quantification of Noncoding Variant Burden in DNA De-Identified Samples and Data from Patients with Alzheimer's Disease Versus Controls.

Our proposed research has two main objectives: 1) identify genetic variants that can influence appropriate splicing of Alzheimer’s disease (AD) related genes and 2) estimate the length of tandem repeats present throughout the genome to identify novel genetic risk factors for AD. We have evaluated RNA sequencing data from brain samples from patients with AD and age-matched controls. Our analysis has revealed several alternative splice products and intronic tandem repeat sequences that are enriched in patients with AD in genes implicated in disease (PSEN1 and PSEN2 and others). Our first objective is to integrate our RNA sequencing results with whole genome sequence data to identify noncoding variants that in combination with alternative splicing products and gene expression may predispose to Alzheimer’s disease (AD). We have identified microhomology to bacterial genomes at some of these alternative splice sites and will test whether the corresponding bacteria can be detected in DNA samples. We will then build on evaluated RNA sequenced data in combination with whole genome sequence data from the NIAGADS database to find novel genomic areas such as tandem repeat expansions that predispose humans to develop AD. We will study AD cases and controls to identify rare variant burden analysis across defined genomic regions. Our analysis plan is as follows: we will extract genomic regions corresponding from CRAM files. We will quantify the presence of small insertions, deletions and variants in cases and controls. We will determine the burden of nucleotide changes in cases, controls and large sequencing databases (e.g. gnomAD) and perform ttests and Chi-square tests to quantify whether an enrichment of variants are present in patients with AD.

The genetic information that can be ascertained from large scale sequencing projects can enable novel discoveries for genes that can contribute to disease. The primary objective of many of these projects is to detect nucleotide changes that alter the protein encoded by the host gene. However, the same sequencing information can be used to identify non-coding elements of the genome that can contribute to disease, including variants that can influence splicing – the appropriate assembly of exons that are spliced together to form a gene. As we age, certain elements that preserve tight regulation of exon choice lose effectiveness, particularly when faced with injury or various stressors. We wish to detect variants that influence alternative splicing products in the context of relaxed regulation of exon choice and integrate findings with RNA sequencing databases to identify methods to preserve appropriate splicing.