An alternative method of SNP inclusion to develop a generalized polygenic risk score analysis across Alzheimer's disease cohorts - Brookes, Keeley J.; Guetta-Baranes, Tamar; Thomas, Alan; Morgan, Kevin (Frontiers in Dementia, 2023)
Polygenic risk scores (PRS) have huge clinical potential for late-onset diseases such as Alzheimer’s disease (AD), allowing the identification of those most at risk years before symptoms present. Although many studies use various and complicated machine learning algorithms to determine the best discriminatory values for PRS, few studies look at the commonality of the SNPs utilised in these models. This investigation focuses on identifying SNPs that tag blocks of linkage disequilibrium across the genome, allowing for a generalised PRS model across cohorts and genotyping panels. PRS modelling was conducted on five AD development cohorts with the best discriminatory models explored for commonality of linkage disequilibrium clumps. Clumps contributing to the discrimination of cases from controls that occurred in multiple cohorts were used to create a generalised model of PRS, which was then tested in the five development cohorts and three further AD cohorts. The model developed provided a discriminability accuracy average of over 70% in multiple AD cohorts and includes variants in several well-known AD risk genes.