Gene-level pathogenicity scores for alzheimer’s disease using genomic variants from rna-seq data

Download

HIBIT22_paper_72.pdf

Date

2022-10

Author

Bozkurt, Fatma Betül
İlgün, Atılay
Uzuner, Dilara
Çakır, Tunahan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

225
views

39
downloads

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.

URI

https://hibit2022.ims.metu.edu.tr
https://hdl.handle.net/11511/102010

Conference Name

The International Symposium on Health Informatics and Bioinformatics

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Investigating conversion from mild cognitive impairment to alzheimer's disease using latent space manipulation Ayvaz, Deniz Sezin; Baytaş, İnci M. (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10) Alzheimer’s disease, a progressive neurologic disorder, is the most common cause of dementia, affecting millions worldwide. Mild Cognitive Impairment (MCI) is considered an intermediate stage before Alzheimer's. Early prediction of the conversion from MCI to Alzheimer's is crucial to take necessary precautions for decelerating the disease progression and developing suitable treatments. This study proposes a deep learning framework to identify patients whose diagnoses might change from MCI to Alzheimer’s in ...
Fluorescent labeling of SPOCK1 and localization studies in live neural cell lines Karaboğa, Zeynep Eda; Son, Çağdaş Devrim; Aydın Son, Yeşim; Department of Biology (2022-11-30) Alzheimer's disease is the most common form of dementia and is incurable. The late onset Alzheimer’s disease (LOAD) is associated with complex genetic transition, and the molecular background has not been enlightened yet. Our previous research on the meta-analysis of genome-wide association studies (GWAS) showed a statistical correlation between SPOCK1 variants and LOAD. Also, it is co-regulated with apolipoprotein (APP), one of the proteins clinically linked with LOAD. Furthermore, the SPOCK1 protein ...
COMPARATIVE ANALYSIS OF BRAIN CELL CULTURES AND TISSUES IN ALZHEIMER’S DISEASE BASED ON DIFFERENTIAL EXPRESSION AND GENE SET ENRICHMENT Burduroğlu, Hüseyin Cahit; Aydın Son, Yeşim; Department of Bioinformatics (2023-1-25) Alzheimer’s disease is currently the most common cause of dementia in the world. It is a neurodegenerative disease that is diagnosed neuropathologically by observing B-amyloid plaques and neurofibrillary tangles in the brain. Transcriptional differentiations, protein regulations, and the interactions in between have been investigated by recent studies to understand from which brain cell type the disease stems, such as microglia, astrocytes, and neurons. These studies are mostly performed on brain tiss...
Personalized analysis of metabolic changes in Alzheimer's disease with a systems biology approach Uzuner, Dilara; Düz, Elif Emanetçi; ilgün, Atılay; Bozkurt, Fatma Betül; Çakır, Tunahan (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10) Alzheimer's disease (AD) is a multifactorial neurodegenerative disease without a cure. Metabolic changes are known to take place in the brains of patients. Metabolic reactions are catalysed by enzymes coded by genes. Therefore, genetic mutations can indirectly affect metabolic changes. Genome scale metabolic models (GEMs) are common tools used in systems biology to understand changes in metabolism. GEMs include all metabolic reactions and reaction-enzyme-gene relationships in the organism. Integrative Metab...
Computer-aided diagnosis of alzheimer’s disease and mild cognitive impairment with MARS/CMARS classification using structural MR images Çevik, Alper; Eyüboğlu, Behçet Murat; Weber, Gerhard Wilhelm; Department of Biomedical Engineering (2017) Early detection of Alzheimer’s disease (AD) and its prodromal stage, amnestic mild cognitive impairment (MCI), has drawn remarkable attention in recent years. Despite the impressive developments in fields of image analysis, pattern classification, and machine learning, no computer-aided diagnosis system has yet been a part of the clinical routine to diagnose the AD. This thesis study aims to propose a thorough procedure which involves detecting the early signs of disease-originated deformations by fully-aut...

Citation Formats

F. B. Bozkurt, A. İlgün, D. Uzuner, and T. Çakır, “Gene-level pathogenicity scores for alzheimer’s disease using genomic variants from rna-seq data,” Erdemli, Mersin, TÜRKİYE, 2022, p. 3072, Accessed: 00, 2023. [Online]. Available: https://hibit2022.ims.metu.edu.tr.