An Alzheimer’s disease gene prediction method based on ensemble of genome-wide association study summary statistics
- Resource Type
- Conference
- Authors
- Song, Jia-Hao; Lin, Cui-Xiang; Li, Hong-Dong
- Source
- 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2022 IEEE International Conference on. :555-560 Dec, 2022
- Subject
- Bioengineering
Computing and Processing
Signal Processing and Analysis
Pathology
Genomics
Prediction methods
Bioinformatics
Alzheimer's disease
Diseases
Ensemble
Transcriptome-wide association studies
Alzheimer’s disease
Gene prediction
- Language
The hitherto unknown specific etiology of Alzheimer’s disease (AD) poses a challenge for its prevention, diagnosis and treatment. Although genome-wide association studies (GWAS) are currently making rapid progress in identifying genetic variants associated with AD, the pathogenic mechanisms of the genetic loci identified are largely unknown. Transcriptome-wide association studies (TWAS) are an important class of methods for predicting disease genes. TWAS can explore the association of genes with the disease in relevant tissues by integrating genome-wide genetic regulatory data from specific tissues and disease-associated GWAS summary statistics. We found that TWAS analysis using different GWAS summary statistics may produce inconsistent results. To address this issue, we used ensemble summary statistics for AD-associated gene prediction considering the complementary nature of different datasets and the comparative nature between the results generated from different datasets. The prediction results were compared and analyzed to identify AD associated genes. The predicted genes were validated. In case study of an individual genes, we identified a potential association between AZGP1 and AD disease by this method.