Identification of genetic factors for Alzheimer´s disease using large multi-omics analyses

Understanding the neurobiological mechanisms of Alzheimer's disease is crucial for developing potential treatments. However, most research currently focuses on European populations. It is anticipated that by conducting analyses on diverse populations, such as Asians and Africans, new signals not previously identified in existing research may be uncovered.

In this study, we aim to discover new signals through large-scale Alzheimer's disease Whole Genome Sequencing (WGS) analysis across different populations. First, we plan to perform single variant and gene burden association analyses specific to each ethinic group to observe how signals may vary among them. We will identify associated risk factors for AD from independent genomics datasets including the Alzheimer's Disease Sequencing Project (ADSP) and other trans-ethnic genomics datasets. We will employ the REGENIE program, widely used in large-scale genomics datasets. Our phenotypes of interest include Alzheimer's disease status and related variables.

Secondly, we will conduct causal variant discovery through fine mapping analysis. We will use various brain tissue expression Quantitative Trait Locus (eQTL) datasets like MetaBrain to identify genes influenced by genetic variants. Additionally, we plan to investigate enhancer or candidate Cis-Regulatory Element (CRE) regions using single-cell level ATAC sequencing data or HiC data to determine whether AD associated variants exist within these regions.

Through this research, we aim to uncover differences in GWAS signals across different ethnic groups and identify causal variants by leveraging a variety of data sources.

Alzheimer's disease is known to have a significant genetic influence, and this genetic influence varies across different ethnic groups. In this study, we aim to identify which genetic variations contribute to Alzheimer's disease on an ethnic-specific basis. Identifying ethnic-specific genetic variations could assist in developing tailored treatments for each ethnic group. Additionally, we intend to explore various datasets to understand how certain genetic variations influence particular genes, ultimately contributing to the development of Alzheimer's disease. Through these analyses, we aim to enhance our understanding of the mechanisms underlying Alzheimer's disease.