Genetic Variation Underlying Traumatic Brain Injury (TBI) and Late-Onset Alzheimer's Disease (LOAD)

There is a significant deficit in the literature investigating the possible association between early traumatic brain injury (TBI) and increased susceptibility to develop Late-Onset Alzheimer's Disease (LOAD) later in life. Some studies have shown that patients with LOAD had a significantly greater incidence of history of head trauma while others report were unable to confirm this association. It is highly likely that TBI influences the aging process, producing adverse clinical consequences such as permanent neurological deficits among others.

Genetic variation has been investigated as a potential modulator of the development of LOAD after TBI. We hypothesize that the effect of some genetic variants that exacerbate or protect against the risk of LOAD are amplified in the presence of TBI. One potential gene that might be influenced by early TBI in life is the APOE locus, which is the biggest genetic risk factor for LOAD. Several studies have examined the possible role of APOE locus in the association between TBI and LOAD. Epidemiological studies have shown that individuals with TBI that also carry APOE epsilon4 allele were 10 times more likely to develop subsequent LOAD compared to the non-carriers of APOE epsilon4. However, there is no conclusive evidence linking APOE with the development of LOAD following TBI. The possibility that TBI may increase the risk of developing LOAD in later life has significant social and medical implications.

In this proposal, we will explore the interaction of genetic risk factors with TBI in predicting the risk of LOAD. We hypothesize that TBI interferes with aging process by accelerating individual's memory decline and possibly accelerating LOAD like neurodegeneration. First, using a longitudinal cohort of 4,878 samples, we will characterize the memory trajectories in samples from the four groups: with LOAD and TBI, with only LOAD or TBI, without LOAD and TBI. We believe that the samples that have LOAD and were exposed to TBI will show rapidly declining memory measures while healthy controls without LOAD or TBI will have flat plateau-like memory performance in the same period of time. This analysis will determine a subset of rapid decliners and non-decliners (of memory performance) in the dataset. We will then test genome-wide the interaction of all genes with TBI in predicting risk of LOAD. We will use genome-wide single nucleotide polymorphism data, which catalogues common variation across the genome. Genes that show nominal interaction with TBI in predicting risk of LOAD will be further tested in the subsample of rapid decliners and non-decliners. The effect of these genes should be amplified in this subsample. We will also use other publicly available datasets to validate our findings. For genes that are also significant in publicly available datasets, we will repeat the analyses using whole-exome sequencing data, which catalogue coding variation in the genome. The variants are usually highly penetrant coding changes that might induce changes in the resulting proteins. Coding variants identified to interact with TBI in predicting LOAD risk can aid in identifying therapeutic targets and diagnostic tests to predict risk of Alzheimer's disease following TBI.