Abstract
As an efficient method, genome-wide association study (GWAS) is used to identify the association between genetic variation and pathological phenotypes, and many significant genetic variations founded by GWAS are closely associated with human diseases. However, it is not enough to mine only a single marker effect variation on complex biological phenotypes. Mining highly correlated single nucleotide polymorphisms (SNP) is more meaningful for the study of Alzheimer's disease (AD). In this paper, we used two frequent pattern mining (FPM) framework, the FP-Growth and Eclat algorithms, to analyze the GWAS results of functional magnetic resonance imaging (fMRI) phenotypes. Moreover, we applied the definition of confidence to FP-Growth and Eclat to enhance the FPM framework. By calculating the conditional probability of identified SNPs, we obtained the corresponding association rules to provide support confidence between these important SNPs. The resulting SNPs showed close correlation with hippocampus, memory, and AD. The experimental results also demonstrate that our framework is effective in identifying SNPs and provide candidate SNPs for further research.
Highlights
The brain imaging genetics, as an emerging research field, provides a new approach to study the effect of genetic variations on the brain
Most of these variants are located in nongenetic regions, and further research is needed to determine whether these variants directly cause the disease through affecting the regulatory factors, or whether they are in a state of linkage disequilibrium with the pathogenic variants
It showed that as the support rate thresholds increased, the number of frequent itemsets (FIs) decreased with the increase of support rate threshold s, and the 1-item numbers mined from different algorithms are the same when the support rate threshold is 0.25
Summary
The brain imaging genetics, as an emerging research field, provides a new approach to study the effect of genetic variations on the brain. Genome-wide association study (GWAS), proposed by Christopher et al, is a method to find the associations between genetic variations and pathological phenotypes [3] It combines genetic variations at the single nucleotide polymorphism (SNP) level with imaging phenotype and analyzed the associations between a region of interest (ROI) and SNPs without any prior knowledge of pathology. Stein et al [4] proposed a voxel based GWAS (vGWAS) method to identify mutations in the entire human genome, reducing the probability of missing important genes and diseased brain regions. The vGWAS was the first voxel based GWAS to find genetic variations associated with brain structure in higher level of refinement These methods were merely useful to find single SNP associated with biological phenotypes [5]. Most of these variants are located in nongenetic regions, and further research is needed to determine whether these variants directly cause the disease through affecting the regulatory factors, or whether they are in a state of linkage disequilibrium with the pathogenic variants
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