ABSTRACT: OBJECTIVE:Several genome-wide association studies (GWAS) have associated variants in late-onset Alzheimer disease (LOAD) susceptibility genes; however, these single nucleotide polymorphisms (SNPs) have very modest effects, suggesting that single SNP approaches may be inadequate to identify genetic risks. An alternative approach is the use of multilocus genotype patterns (MLGPs) that combine SNPs at different susceptibility genes. METHODS:Using data from 1,365 subjects in the National Institute on Aging Late-Onset Alzheimer's Disease Family Study, we conducted a family-based association study in which we tabulated MLGPs for SNPs at CR1, BIN1, CLU, PICALM, and APOE. We used generalized estimating equations to model episodic memory as the dependent endophenotype of LOAD and the MLGPs as predictors while adjusting for sex, age, and education. RESULTS:Several genotype patterns influenced episodic memory performance. A pattern that included PICALM and CLU was the strongest genotypic profile for lower memory performance (? = -0.32, SE = 0.19, p = 0.021). The effect was stronger after addition of APOE (p = 0.016). Two additional patterns involving PICALM, CR1, and APOE and another pattern involving PICALM, BIN1, and APOE were also associated with significantly poorer memory performance (? = -0.44, SE = 0.09, p = 0.009 and ? = -0.29, SE = 0.07, p = 0.012) even after exclusion of patients with LOAD. We also identified genotype pattern involving variants in PICALM, CLU, and APOE as a predictor of better memory performance (? = 0.26, SE = 0.10, p = 0.010). CONCLUSIONS:MLGPs provide an alternative analytical approach to predict an individual's genetic risk for episodic memory performance, a surrogate indicator of LOAD. Identifying genotypic patterns contributing to the decline of an individual's cognitive performance may be a critical step along the road to preclinical detection of Alzheimer disease.
