Prediction of Alzheimer's disease using multi-variants from a Chinese genome-wide association study

Abstract

Previous genome-wide association studies have identified dozens of susceptibility loci for sporadic Alzheimer's disease, but few of these loci have been validated in longitudinal cohorts. Establishing predictive models of Alzheimer's disease based on these novel variants is clinically important for verifying whether they have pathological functions and provide a useful tool for screening of disease risk. In the current study, we performed a two-stage genome-wide association study of 3913 patients with Alzheimer's disease and 7593 controls and identified four novel variants (rs3777215, rs6859823, rs234434, and rs2255835; Pcombined = 3.07 × 10-19, 2.49 × 10-23, 1.35 × 10-67, and 4.81 × 10-9, respectively) as well as nine variants in the apolipoprotein E region with genome-wide significance (P < 5.0 × 10-8). Literature mining suggested that these novel single nucleotide polymorphisms are related to amyloid precursor protein transport and metabolism, antioxidation, and neurogenesis. Based on their possible roles in the development of Alzheimer's disease, we used different combinations of these variants and the apolipoprotein E status and successively built 11 predictive models. The predictive models include relatively few single nucleotide polymorphisms useful for clinical practice, in which the maximum number was 13 and the minimum was only four. These predictive models were all significant and their peak of area under the curve reached 0.73 both in the first and second stages. Finally, these models were validated using a separate longitudinal cohort of 5474 individuals. The results showed that individuals carrying risk variants included in the models had a shorter latency and higher incidence of Alzheimer's disease, suggesting that our models can predict Alzheimer's disease onset in a population with genetic susceptibility. The effectiveness of the models for predicting Alzheimer's disease onset confirmed the contributions of these identified variants to disease pathogenesis. In conclusion, this is the first study to validate genome-wide association study-based predictive models for evaluating the risk of Alzheimer's disease onset in a large Chinese population. The clinical application of these models will be beneficial for individuals harbouring these risk variants, and particularly for young individuals seeking genetic consultation.

Keywords: Alzheimer’s disease; Chinese; genome-wide association study; longitudinal cohort; predictive model.

© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.

Figures

Figure 1

Study flow chart. AD = Alzheimer’s disease.

Figure 2

Regional association plots. (A–D) Association results are shown for the analysed SNPs with recombination rates in the four loci associated with genome-wide significance at chromosome 5 (A and B), 14 (C), and 21 (D). The −log10 (P-values) (y-axis) of SNPs within the ±500 kb region centred on each marker SNP are presented according to the chromosomal positions of the SNPs (x-axis; NCBI Build 37). Purple diamonds represent the most significantly associated SNP (marker SNP) in the combined analysis. SNPs are coloured according to their linkage disequilibrium with the marker SNP. Linkage disequilibrium values were based on the 1000 Genome Project Asian data. Blue lines represent the estimated recombination rates based on the 1000 Genome Project samples. Arrows depict genes in the regions of interest annotated from the UCSC Genome Browser.

Figure 3

Differential expression of the annotated genes in Gene Expression Omnibus datasets. (A–C) shows the differential expression of RHOBTB3 (A), GLRX (B), CHODL (C) in frontal cortex, hippocampus, and temporal cortex. The bold red line indicates the median of each group, and the black dotted lines show the quartiles. AD = Alzheimer’s disease; CN = cognitively normal; FC = fold change; GSE = Gene Expression Omnibus Series; ns = no significance; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Figure 4

ROC curves for 11 predictive models with different predictors in the three cohorts and survival curves in a longitudinal cohort. The factors included in the 11 models are as follows. (A) A1: APOE ε4 status, rs3777215, rs6859823, rs234434 and rs2255835; (B) A2: APOE ε4 status, rs3777215, rs6859823, rs234434, rs2255835, rs11668861, rs71352238 and rs4420638; (C) A3: APOE ε4 status, rs3777215, rs6859823, rs234434, rs2255835, rs11668861, rs6859, rs3852860, rs71352238, rs157580, rs2075650, rs157582, rs439401 and rs4420638; (D) A4: rs3777215, rs6859823, rs234434, rs2255835, rs11668861, rs6859, rs3852860, rs71352238, rs157580, rs2075650, rs157582, rs439401 and rs4420638; (E) B1: rs3777215, rs6859823, rs234434 and rs2255835; (F) B2: rs3777215, rs6859823, rs234434, rs2255835, rs11668861, rs71352238 and rs4420638; (G) B3: rs3777215, rs6859823, rs234434, rs71352238 and rs4420638; (H) B4: rs3777215, rs234434, rs71352238 and rs4420638; (I) B5: rs6859823, rs234434, rs71352238 and rs4420638; (J) B6: rs3777215, rs6859823, rs234434 and rs71352238; (K) B7: rs3777215, rs6859823, rs234434 and rs4420638. (L) Survival curves of the longitudinal cohort, *P < 0.001. AUC1 indicates AUC of the first stage; AUC2 indicates AUC of the second stage; AUC3 indicates AUC of the longitudinal cohort; ROC = receiver operating characteristic curve.