A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression

被引：4

作者：

Bae, Jinhyeong E. ^{[1
]}

Logan, Paige J. ^{[1
]}

Acri, Dominic ^{[2
]}

Bharthur, Apoorva ^{[1
]}

Nho, Kwangsik J. ^{[3
]}

Saykin, Andrew L. ^{[3
]}

Risacher, Shannon ^{[3
]}

Nudelman, Kelly J. ^{[2
]}

Polsinelli, Angelina ^{[1
]}

Pentchev, Valentin ^{[4
]}

Kim, Jungsu B. ^{[2
]}

Hammers, Dustin G. ^{[1
]}

Apostolova, Liana ^{[1
,2
,3
]}

机构：

[1] Indiana Univ Sch Med, Sch Med, Dept Neurol, Indianapolis, IN 46202 USA

[2] Indiana Univ Sch Med, Sch Med, Dept Med & Mol Genet, Indianapolis, IN 46202 USA

[3] Indiana Univ Sch Med, Dept Radiol & Imaging Sci, Indianapolis, IN 46202 USA

[4] Indiana Univ, Network Sci Inst, Dept Informat Technol, Bloomington, IN USA

来源：

ALZHEIMERS & DEMENTIA | 2023年 / 19卷 / 12期

基金：

美国国家卫生研究院;

关键词：

Alzheimer's disease; deep learning; genetics; GENOME-WIDE ASSOCIATION; COMPOSITE SCORE; REPAIR; MEMORY; AGE; VARIANTS; DEMENTIA; LOCUS;

D O I：

10.1002/alz.13319

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

BACKGROUNDIdentifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies. METHODSWe implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed. RESULTSRs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression. DISCUSSIONThe model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.

引用

页码：5690 / 5699

页数：10

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