Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer’s Disease
1
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
2
School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
3
School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Domenico Lio
Genes 2022, 13(2), 176; https://doi.org/10.3390/genes13020176
Received: 4 December 2021
/
Revised: 11 January 2022
/
Accepted: 16 January 2022
/
Published: 19 January 2022
(This article belongs to the Section Human Genomics and Genetic Diseases)
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.
View Full-Text
Keywords:
vGWAS; FPM; Eclat; association rules; FI; Alzheimer’s disease
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
- Supplementary File 1:
ZIP-Document (ZIP, 98 KiB)
MDPI and ACS Style
Liang, H.; Cao, L.; Gao, Y.; Luo, H.; Meng, X.; Wang, Y.; Li, J.; Liu, W. Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer’s Disease. Genes 2022, 13, 176. https://doi.org/10.3390/genes13020176
AMA Style
Liang H, Cao L, Gao Y, Luo H, Meng X, Wang Y, Li J, Liu W. Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer’s Disease. Genes. 2022; 13(2):176. https://doi.org/10.3390/genes13020176
Chicago/Turabian StyleLiang, Hong, Luolong Cao, Yue Gao, Haoran Luo, Xianglian Meng, Ying Wang, Jin Li, and Wenjie Liu. 2022. "Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer’s Disease" Genes 13, no. 2: 176. https://doi.org/10.3390/genes13020176
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
