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Research Project Bioinformatics Data Mining Python Matlab

Publication and Source code


This is a research for Alzheimer's disease (AD). AD is a disease that causes brain cell gradually die. The ApoE genotype in the form of \( \epsilon 4 \) is a well-know mutation considered as a genetic risk factor for AD. Nowadays, the development of PET/MRI can help in identify AD by detecting the changing in brain. In this study, we examined single nucleotide polymorphisms (SNPs) in the whole genome sequencing (WGS) data. We found that some SNPs have strong association with quantitative traits (QTs) of PET imaging. The experiment verified that the discovered SNPs can better map QTs of PET measurements than ApoE.


We use linear model to find the genetic risk factors for Alzheimer's disease by mapping it to the longitudinal quantitative FDG and amyloid PET measurements. In the study, 75 subjects with 7 years measurement histories are chosen from ADNI GWAS data set. We used python to process data, do some statistics, and extract useful information. Then the PLINK toolkit was used for further data processing and analysis. Matlab was used for visualization of the results. In all, we analyzed 539,803 genotypes. We use a linear model that is defined as \( \text{SUVR}(Tracer) = \beta_0 + \beta_1 \cdot Age + \sum^n_{i=1} \alpha_i \cdot Freq(A_i) \), where \( \text{SUVR}(Tracer) \) denotes the intensity of a tracer at one region.


We identified 3 SNPs that have the highest p-values for the 3 tracers, respectively. The results on Manhattan plots show that the discovered SNPs have stronger correlations with the tracers' measurements than ApoE. The following figures show 3 subjects with 3 distinct variations on the SNP rs1876152. The subject with GG at the SNP shows the smallest changing of SUVR(FDG) measurement. The subject with AA shows remarkable changing after 72 months. For all these subjects, the ApoE genotype for the subjects are same. Therefore, the different decreasing speed is not related to the variation of ApoE in these cases.