Identifying genetic modifiers of APOE in Alzheimer's disease using evolutionary information and regression analyses: Genetics/genetic factors of Alzheimer's disease. (7th December 2020) - Kim, Young Won; Al‐Ramahi, Ismael; Koire, Amanda; Botas, Juan; Lichtarge, Olivier
Abstract: Background: To date, the APOE ɛ4 allele is the best known genetic risk factor of late‐onset Alzheimer's Disease (AD), while the APOE ɛ2 allele is known to protect against AD. However, some APOE ɛ4 allele carriers remain AD‐free, and some APOE ɛ2 carriers develop dementia. Method: We focused on the whole‐exome sequences of patients with paradoxical genotype‐phenotype relationships ( APOE ɛ2 AD carriers and APOEɛ4 healthy individuals) and assessed the predicted impact of coding variants using Evolutionary Action equation. Then, for each gene, we measured the mutational EA burden given its background mutation rate and quantified the deviance in mutational EA burden of a gene against those with similar background mutation rate. We repeated this in the two populations of interest to identify outliers with apparent potential for protective or pathogenic qualities. The predicted modifiers of APOE were tested computationally through statistical analyses as well as experimentally by assessing neuronal dysfunction in a Drosophila AD model. Result: We found ∼200 genes with differential mutational signatures in the paradoxical patient groups. These genes were differentially expressed in AD brains and shown to include or interact with known AD genes. Nearly half of the fly homologs tested modified motor performance deficits induced by tau or β42, both of which are responsible for pathological hallmarks of AD. Conclusion: We conclude that this method successfully identifies protective or pathogenic nature of genes related to AD and allow for a tractable analysis of specific variants often missed by conventional methods, by quantitatively distinguishing genes under differential selection pressures. Identification of such dependencies and how they interact together would allow better understanding of epistatic relationships in Alzheimer's disease and provide additional insight into novel drug targets for therapeutic exploration.