Polygenic analysis for Alzheimer's disease
Approved Research ID: 61467
Approval date: July 28th 2020
Late-onset Alzheimer's disease (LOAD) is a neurodegenerative disease characterized by cognitive impairment and has a high heritability of 60%-80%. However, the APOE alleles, which is the strongest genetic factor, only explain approximately 6% of LOAD phenotypic variance. The polygenic model explains complex traits, including diseases based on multiple single nucleotide polymorphisms (SNPs) with weak effects as well as several dozen SNPs identified by genome-wide association studies (GWASs). In this study, we will perform the following within three years. 1) We calculate polygenic scores to examine predictive performance between patients with Alzheimer's disease and healthy controls. 2) We compare the predictive performance of polygenic scores in UKBB with that in our Japanese dataset to examine the difference between different ethnicity/races. 3) We develop a novel polygenic score using genomic data and the other phenotype data from UKBB to raise the predictive performance of polygenic scores. Our study may provide the utility of the polygenic score through different ethnicity/races. Furthermore, we may provide a novel polygenic risk score combined with information associated with each individual.