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.
Methods
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.
Results
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.
Conclusion
The identified genotypes rs1876152, rs1501228, and rs1946867, have significant correlation with FDG,
[18F]AV45, [11C]PIB PET measurements, respectively.
ApoE genotype is a coarser genetic risk factor for AD. To monitor the AD progress more accurately,
our study identified the genes that have remarkable correlations with quantitative traits of
3 PET tracers than the ApoE genotype.
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.
