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Updated: May 7, 2025

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
Published on: January 9, 2020
Complex genetic interactions affect susceptibility to Alzheimer's disease risk in the BIN1 and MS4A6A loci
Alireza Nazarian1, Marissa Morado2, Alexander M Kulminski3
1Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Erwin Mill Building, 2024 W. Main St, Durham, NC, 27705, USA. alireza.nazarian@duke.edu.
Abstract:
Genetics is the second strongest risk factor for Alzheimer's disease (AD) after age. More than 70 loci have been implicated in AD susceptibility so far, and the genetic architecture of AD entails both additive and nonadditive contributions from these loci. To better understand nonadditive impact of single-nucleotide polymorphisms (SNPs) on AD risk, we examined individual, joint, and interacting (SNPxSNP) effects of 139 and 66 SNPs mapped to the BIN1 and MS4A6A AD-associated loci, respectively. The analyses were conducted by fitting three respective dominant allelic-effect models using data from four independent studies. Joint effects were analyzed by considering pairwise combinations of genotypes of the selected SNPs, i.e., compound genotypes (CompG). The individual SNP analyses showed associations of 18 BIN1 SNPs and 4 MS4A6A SNPs with AD. We identified 589 BIN1 and 217 MS4A6A SNP pairs associated with AD in the CompG analysis, although their individual SNPs were not linked to AD independently. Notably, 34 BIN1 and 10 MS4A6A SNP pairs exhibited both significant SNPxSNP interaction effects and significant CompG effects. The vast majority of nonadditive effects were captured through the CompG analysis. These results expand the current understanding of the contributions of the BIN1 and MS4A6A loci to AD susceptibility. The identified nonadditive effects suggest a significant genetic modulation mechanism underlying the genetic heterogeneity of AD in these loci. Our findings highlight the importance of considering nonadditive genetic impacts on AD risk beyond the traditional SNPxSNP approximation, as they may uncover critical mechanisms not apparent when examining SNPs individually.
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