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. 2016 Jul;21(7):916-24.
doi: 10.1038/mp.2015.177. Epub 2015 Dec 1.

APOE*E2 allele delays age of onset in PSEN1 E280A Alzheimer's disease

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APOE*E2 allele delays age of onset in PSEN1 E280A Alzheimer's disease

J I Vélez et al. Mol Psychiatry. 2016 Jul.

Abstract

Alzheimer's disease (AD) age of onset (ADAOO) varies greatly between individuals, with unique causal mutations suggesting the role of modifying genetic and environmental interactions. We analyzed ~50 000 common and rare functional genomic variants from 71 individuals of the 'Paisa' pedigree, the world's largest pedigree segregating a severe form of early-onset AD, who were affected carriers of the fully penetrant E280A mutation in the presenilin-1 (PSEN1) gene. Affected carriers with ages at the extremes of the ADAOO distribution (30s-70s age range), and linear mixed-effects models were used to build single-locus regression models outlining the ADAOO. We identified the rs7412 (APOE*E2 allele) as a whole exome-wide ADAOO modifier that delays ADAOO by ~12 years (β=11.74, 95% confidence interval (CI): 8.07-15.41, P=6.31 × 10(-8), PFDR=2.48 × 10(-3)). Subsequently, to evaluate comprehensively the APOE (apolipoprotein E) haplotype variants (E1/E2/E3/E4), the markers rs7412 and rs429358 were genotyped in 93 AD affected carriers of the E280A mutation. We found that the APOE*E2 allele, and not APOE*E4, modifies ADAOO in carriers of the E280A mutation (β=8.24, 95% CI: 4.45-12.01, P=3.84 × 10(-5)). Exploratory linear mixed-effects multilocus analysis suggested that other functional variants harbored in genes involved in cell proliferation, protein degradation, apoptotic and immune dysregulation processes (i.e., GPR20, TRIM22, FCRL5, AOAH, PINLYP, IFI16, RC3H1 and DFNA5) might interact with the APOE*E2 allele. Interestingly, suggestive evidence as an ADAOO modifier was found for one of these variants (GPR20) in a set of patients with sporadic AD from the Paisa genetic isolate. This is the first study demonstrating that the APOE*E2 allele modifies the natural history of AD typified by the age of onset in E280A mutation carriers. To the best of our knowledge, this is the largest analyzed sample of patients with a unique mutation sharing uniform environment. Formal replication of our results in other populations and in other forms of AD will be crucial for prediction, follow-up and presumably developing new therapeutic strategies for patients either at risk or affected by AD.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(a) Filtering process applied to exonic variants. Filter 1 includes common and uncommon variants between the Illumina's HumanExome-12V1_A BeadChip and the whole-exome capture (see Patients and methods). Filter 2 excludes variants with a genotype call rate <90%, in Hardy–Weinberg disequilibrium and with one or more than two alleles. Filter 3 excludes variants with minor allele frequency (MAF) <1% and Filter 4 excludes nonfunctional variants. A total of 49 191 variants (39 753 common and 9438 rare) with potential functional effects remained for genetic analyses. (b) Average Alzheimer's disease (AD) age of onset (ADAOO) by APOE allele combination. (c) Average ADAOO (blue dot) as a function of the APOE alleles. The red and gray lines represent the ADAOO of 48 years and ±1.5 s.d., respectively, the latter calculated using nonparametric bootstrap with B=10 000 replicates. Analysis of variance (ANOVA) showed that the average ADAOO differs among allele groups (F4,88=4.74, P=0.00163). (d) Effect of the presence/absence of the APOE*E2 allele on ADAOO. A two-sample t-test indicates that the presence of this allele increases ADAOO by ~8.2 years (95% confidence interval (CI): 4.45–12.01, P=3.84 × 10−5) in presenilin-1 (PSEN1) E280A mutation carriers.
Figure 2
Figure 2
(a) Classification tree for predicting late- (LO) and early-onset (EO) Alzheimer's disease in E280A mutation carriers. Numbers in gray represent the split number, and N the sample size within each node. (b) Variable importance (left) and receiver operating characteristic (ROC) curve (right) for the Classification and Regression Tree (CART), Random Forest and TreeNet strategies. (c) Performance measures for the learning (blue) and test (pink) data sets for each model (b, right panel). AUC, area under the curve; CI, confidence interval; CR, classification rate.
Figure 3
Figure 3
Resulting network involving genes harboring Alzheimer's disease age of onset (ADAOO) modifier mutations (red dot) in presenilin-1 (PSEN1) E280A Alzheimer's disease. Here, the 'Shortest Path' algorithm was used. B, binding; C, cleavage; gray, unspecified; green, positive/activation; red, negative/inhibition; TR, transcription/regulation.

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