CALCULATING POLYGENIC RISK FOR INDIVIDUALS WITH SPORADIC EARLY ONSET ALZHEIMER’S DISEASE

Abstract

Sporadic early onset Alzheimer’s disease (sEOAD) exhibits the symptoms of late onset Alzheimer’s disease (LOAD) but lacks the familial aspect of the early onset familial form. The genetics of Alzheimer’s disease (AD) identifies the APOE e4 allele to be the greatest risk factor; however, it is a complex disease involving both environmental risk factors and multiple genetic loci. Polygenic risk scores (PRS) accumulate the total risk of a phenotype in an individual based on variants present in their genome. We determined whether sEOAD cases had a higher PRS compared to controls. A cohort of sEOAD cases were genotyped on the NeuroX array and PRS were generated using PRSice. The target dataset consisted of 408 sEOAD cases and 436 controls. The base dataset was collated by the IGAP consortium, with association data from 17,008 LOAD cases and 37,154 controls, which can be used for identifying sEOAD cases due to having shared phenotype. PRS were generated using all common SNPs between the base and target dataset, PRS were also generated using only SNPs within a 500kb region surrounding the APOE gene. Sex and number of APOE e2 or e4 alleles were used as variables for logistic regression and combined with PRS. The results show that PRS is higher on average in sEOAD cases than controls, although there is still overlap amongst the whole cohort. Predictive ability of identifying cases and controls using PRSice was calculated with 72.9% accuracy, greater than the APOE locus alone (65.2%). Predictive ability was further improved with logistic regression, identifying cases and controls with 75.5% accuracy.