The Apolipoprotein E (APOE) genotype has been shown to be the strongest genetic risk factor for Alzheimer's disease (AD). Moreover, both the lipolysis-stimulated lipoprotein receptor (LSR) and the vascular endothelial growth factor A (VEGF-A) are involved in the development of AD. The aim of the study was to develop a prediction model for AD including single nucleotide polymorphisms (SNP) of APOE, LSR and VEGF-A-related variants. The population consisted of 323 individuals (143 AD cases and 180 controls). Genotyping was performed for: the APOE common polymorphism (rs429358 and rs7412), two LSR variants (rs34259399 and rs916147) and 10 VEGF-A-related SNPs (rs6921438, rs7043199, rs6993770, rs2375981, rs34528081, rs4782371, rs2639990, rs10761741, rs114694170, rs1740073), previously identified as genetic determinants of VEGF-A levels in GWAS studies. The prediction model included direct and epistatic interaction effects, age and sex and was developed using the elastic net machine learning methodology. An optimal model including the direct effect of the APOE e4 allele, age and eight epistatic interactions between APOE and LSR, APOE and VEGF-A-related variants was developed with an accuracy of 72%. Two epistatic interactions (rs7043199*rs6993770 and rs2375981*rs34528081) were the strongest protective factors against AD together with the absence of ε4 APOE allele. Based on pathway analysis, the involved variants and related genes are implicated in neurological diseases. In conclusion, this study demonstrated links between APOE, LSR and VEGF-A-related variants and the development of AD and proposed a model of nine genetic variants which appears to strongly influence the risk for AD.
Keywords: Alzheimer’s disease; Apolipoprotein E; LSR; VEGF-A; elastic net; epistasis; machine learning; prediction.