Gene-level pathogenicity scores for alzheimer’s disease using genomic variants from rna-seq data
Year: 2022
Type: conference-paper
Abstract: Alzheimer’s disease (AD) is a complex neurodegenerative disorder affecting millions of people worldwide. Next-generation sequencing technologies such as whole-exome/genome sequencing have been widely used for detecting the variants in the genome to understand the disease etiology and unravel underlying molecular mechanisms. Alternatively, RNA-Seq data can also be used to detect variants. Since AD is a complex disease, several variants are involved in the disease pathogenesis. By using scoring algorithms, it is possible to estimate gene pathogenicity combining the pathogenicity scores of all variants in that gene into a single score. In this project, three large RNA-Seq datasets comprising 704 AD patients and 313 controls in total were used to determine gene pathogenicity: The Religious Orders Study/Memory and Aging Project (ROSMAP) [1], Mayo Clinic [2], and Gene Expression Omnibus (GSE125583) [3]. To identify variants, the raw data was analyzed, and variant call format (VCF) files were generated. The VCF files annotated by ANNOVAR [4] were used for calculating gene pathogenicity scores (per-gene, per-individual) by GenePy algorithm [5]. These scores reflect the combined pathogenic burden of non-synonymous, frameshift, and stop-loss/gain variants. One- tailed Mann-Whitney u-test was performed to detect genes with significantly greater GenePy scores in AD patients compared to controls. Gene Ontology (GO) enrichment analysis of genes with high pathogenicity identified GO terms known to be associated with AD such as neuron differentiation, neurogenesis, neuron projection morphogenesis, nervous system development, and several terms related to synapse, axon, and dendrite. Among the predicted pathogenic genes, several genes were detected to be reported as AD-related by previous studies, and as AD risk loci by genome-wide association studies (GWAS). This work provides an example for using RNA-Seq data to detect variants in individuals with AD and using gene pathogenicity scoring algorithms to predict pathogenic genes.
Authors: Bozkurt, Fatma Betül, İlgün, Atılay, Uzuner, Dilara, Çakır, Tunahan
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7110463493