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Prediction of Alzheimer's disease-associated genes by integration of GWAS summary data and expression data

2018/11/12 by Sicheng Hao, Rui Wang, Hao, Sicheng +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #Alzheimer's disease research and treatments #Applications (stat.AP) #Bioinformatics and Genomic Networks #FOS: Biological sciences #FOS: Computer and information sciences #Genomics (q-bio.GN) #Metabolomics and Mass Spectrometry Studies #q-bio.GN #stat.AP

paper · pdf · doi:10.48550/arxiv.1811.04987

11 pages, 3 figures

arxiv created 2018/11/12 · openalex publication_date 2018/11/12 · arxiv updated 2018/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Alzheimer's disease is the most common cause of dementia. It is the fifth-leading cause of death among elderly people. With high genetic heritability (79%), finding disease causal genes is a crucial step in find treatment for AD. Following the International Genomics of Alzheimer's Project (IGAP), many disease-associated genes have been identified; however, we don't have enough knowledge about how those disease-associated genes affect gene expression and disease-related pathways. We integrated GWAS summary data from IGAP and five different expression level data by using TWAS method and identified 15 disease causal genes under strict multiple testing (alpha<0.05), 4 genes are newly identified; identified additional 29 potential disease causal genes under false discovery rate(alpha < 0.05), 21 of them are newly identified. Many genes we identified are also associated with some autoimmune disorder.

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