Genome-wide Interaction Analysis Reveals Replicated Epistatic Effects on Brain Structure
Overview
Neurology
Physiology
Authors
Affiliations
The discovery of several genes that affect the risk for Alzheimer's disease ignited a worldwide search for single-nucleotide polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted because of the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole brain, voxelwise effects of the interaction in the Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.
Pourtoy-Brasselet S, Sciauvaud A, Boza-Moran M, Cailleret M, Jarrige M, Polveche H Am J Hum Genet. 2021; 108(11):2171-2185.
PMID: 34699745 PMC: 8595949. DOI: 10.1016/j.ajhg.2021.10.001.
Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage.
Li J, Liu W, Li H, Chen F, Luo H, Bao P BMC Bioinformatics. 2021; 22(1):223.
PMID: 33931008 PMC: 8086096. DOI: 10.1186/s12859-021-04145-0.
The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease.
Mishra R, Li B Aging Dis. 2020; 11(6):1567-1584.
PMID: 33269107 PMC: 7673858. DOI: 10.14336/AD.2020.0312.
Brain Imaging Genomics: Integrated Analysis and Machine Learning.
Shen L, Thompson P Proc IEEE Inst Electr Electron Eng. 2020; 108(1):125-162.
PMID: 31902950 PMC: 6941751. DOI: 10.1109/JPROC.2019.2947272.
Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease.
Yuan S, Li H, Xie J, Sun X Int J Mol Sci. 2019; 20(23).
PMID: 31775305 PMC: 6928939. DOI: 10.3390/ijms20235912.