The ADNI has collected longitudinal MRI scans and genotypes from ~ 100 individuals with AD, ~200 with MCI, and ~150 healthy eldery controls, making it quite the boon for Alzheimer’s research. In a paper that caught my eye, Silver et al took this data set and did some cool things with it.
First, they did some genotype filtering, such as removing SNPs with a frequency of less than 10% for the less common allele. This means that both alleles for each SNP they include will be relatively common, which ensured that their subsequent regression would have adequate sample sizes for each group. Next, they map the SNPs to genes, and then they map the genes to genetic pathways.
To integrate the brain data, the researchers calculated the change from baseline in each voxel in scans separated by 6 and 18 months. They then did a voxel-wise ANOVA on the rate of change to determine which voxels show differences between AD and healthy controls.
What I find amazing is that even using a conservative correction for multiple comparisons, they still found that 148,023 out of 2,153,231 (7%) of the voxels showed a difference between AD cases and healthy controls. That shows you the extent of AD damage; see below for the specific regions most affected.
Finally, they estimate which SNPs and pathways have the strongest associations with changes in AD-affected brain regions. The top three pathways they blame are the chemokine pathway, the jak-stat pathway, and the tight junction pathway.
The first two of these are related to cytokine signaling and thus continue to emphasize the role of inflammation in AD progression. Tight junction proteins have also been associated with AD to explain the loss in BBB integrity. So, although the AD picture remains messy, studies and data sets like this should help.
Silver M, et al. 2012 Identification of gene pathways implicated in Alzheimer’s disease using longitudinal imaging phenotypes with sparse regression. arXiv:1204.1937v1