A0747
Title: Detection of genetic variation related to Alzheimer's disease using functional data analysis
Authors: Yoon Seok Lee - Chungnam National University (Korea, South) [presenting]
Eunjee Lee - Chungnam National University (Korea, South)
Abstract: Alzheimer's disease (AD) is a neurodegenerative disease that affects the elderly and significantly impacts society. It is the most common form of dementia, accounting for 60~80% of patients, and is a genetic disease influenced by environmental and genetic factors as the nervous system ages. Single nucleotide polymorphisms (SNPs), which are genetic mutations that occur frequently in the human genome, have been identified as a risk factor for the onset of AD. In particular, Apolipoprotein-E is a genetic risk factor for AD, directly related to cognitive decline, and various SNPs such as CR-1 and BIN-1 have been studied as potential risk factors for AD in Genome-Wide Association Studies (GWAS). SNPs are expressed in a densely distributed chromosomal region consisting of as few as 300,000 and as many as 1 million, and adjacent SNPs tend to have similar values. So it's possible to assume that these observed SNPs are discretized observations of a continuous function whose domain is a continuous chromosomal region. Based on this assumption, the SNP data can be considered functional data, a continuum of stochastic sequence data in a continuous space rather than discrete observations. Therefore, this study is proposed to incorporate the millions of SNP data as functional covariates in multiple linear regression model and detecting significant SNP related to onset of AD.
