Identification of genetic risk factors in the Chinese population implicates a role of immune system in Alzheimer's disease pathogenesis

Abstract

Alzheimer's disease (AD) is a leading cause of mortality among the elderly. We performed a whole-genome sequencing study of AD in the Chinese population. In addition to the variants identified in or around the APOE locus (sentinel variant rs73052335, P = 1.44 × 10^-14), two common variants, GCH1 (rs72713460, P = 4.36 × 10^-5) and KCNJ15 (rs928771, P = 3.60 × 10^-6), were identified and further verified for their possible risk effects for AD in three small non-Asian AD cohorts. Genotype-phenotype analysis showed that KCNJ15 variant rs928771 affects the onset age of AD, with earlier disease onset in minor allele carriers. In addition, altered expression level of the KCNJ15 transcript can be observed in the blood of AD subjects. Moreover, the risk variants of GCH1 and KCNJ15 are associated with changes in their transcript levels in specific tissues, as well as changes of plasma biomarkers levels in AD subjects. Importantly, network analysis of hippocampus and blood transcriptome datasets suggests that the risk variants in the APOE, GCH1, and KCNJ15 loci might exert their functions through their regulatory effects on immune-related pathways. Taking these data together, we identified common variants of GCH1 and KCNJ15 in the Chinese population that contribute to AD risk. These variants may exert their functional effects through the immune system.

Keywords: Alzheimer’s disease; GWAS; immune; risk variant; whole-genome sequencing.

Fig. 1.

Study design schematic for the discovery of AD susceptibility loci. HWE, Hardy–Weinberg equilibrium; PCA, principal-component analysis; QC, quality control.

Fig. 2.

GWAS results in the Chinese AD WGS study. (A) Manhattan plots showing AD susceptibility loci discovered in the Chinese AD WGS dataset. Horizontal lines represent the suggestive threshold (P = 1E−5, blue) and genome-wide threshold (P = 5E−8, red). The 59 sites that survived the genome-wide threshold in the validation stage are shown as enlarged red dots, with gene symbols marked in the plot. (B–D) Regional association plots of the APOC1 (B), GCH1 (C), and KCNJ15 (D) loci. Horizontal lines separate the association results from the stage 1 and stage 1+2 analyses. Purple diamonds specify the sentinel variant in the corresponding locus. Colors illustrate the LD measured as R² between the sentinel variant and its neighboring variants. cMMb, centimorgans per megabase.

Fig. 3.

Replication study in non-Asian AD cohorts. (A and B) Forest plots representing the meta-analysis results of rs72713460 (GCH1; A) and rs928771 (KCNJ15; B) from non-Asian AD cohorts. Values of effect size (log OR) obtained from independent datasets or metaresults are denoted by rectangles and diamonds, respectively. For the independent dataset, lines indicate the range of 95% confidence intervals, and the sizes of rectangles are proportional to the weights used in the meta-analysis. For the meta-analysis results, the widths of the diamonds cover the range of 95% confidence intervals. ADNI (AD: 515, NC: 339), ADC (AD: 3,946, NC: 1,746), and LOAD (AD: 464, NC: 2,231). GCH1 rs72713460: RE P = 1.55E−02, effect size = 0.1039; KCNJ15 rs928771: RE P = 1.19E−01, effect size = 0.0554. (C and D) Association of KCNJ15 with age at onset of AD. (C) Survival plot of cumulative dementia-free probabilities in AD subjects from the LOAD cohort stratified by rs928771 genotypes (P = 0.0057, Cox proportional hazards model with adjustment for gender and top-5 PCs; HR = 1.1974; n = 126, 204, and 125 for rs928771 genotypes TT, TG, and GG, respectively). (D) Dot plot of individual age at AD onset stratified by KCNJ15 rs928771 genotype. Data are presented as mean ± SEM, *P < 0.05, ANCOVA with Bonferroni correction [n = 126, 204, and 125; average (SD) onset age of dementia = 73.4 (7.6), 73.0 (6.6), and 71.2 (6.8) for rs928771 genotypes TT, TG, and GG, respectively; F = 6.35 for rs928771 genotype TT vs. GG in AD].

Fig. 4.

Functional evidence of KCNJ15 in AD. (A) Among different tissue samples, the KCNJ15 transcript was most abundant in blood as suggested by the lowest P value of enrichment (P = 6.33E−7, fold-enrichment = 10.6; nine blood samples in 735 total samples). The figure was adopted from the FANTOM CAT database. (B) Genotype- and phenotype-dependent regulation of KCNJ15 transcript level in blood transcriptome data. (Left) Increased KCNJ15 transcript level in the whole blood of AD subjects (NC: 244, MCI: 369, AD: 106). Data are presented as mean ± SEM **P < 0.01, *P < 0.05, ANCOVA with Bonferroni correction (F = 10.38 and 5.93, for AD vs. NC, AD vs. MCI, respectively). (Right) The change of KCNJ15 transcript level in whole blood is associated with KCNJ15 rs928771 genotypes in NC and AD subjects; data are presented as mean ± SEM, *P < 0.05 for NC subjects (n = 77, 114, and 53 for rs928771 genotypes TT, TG, and GG, respectively; F = 8.22, for rs928771 TT vs. GG in NC). ^##P < 0.01, ^###P < 0.001 for AD subjects, ANCOVA with Bonferroni correction (n = 28, 49, and 29 for rs928771 genotypes TT, TG, and GG, respectively; F = 13.55, 10.21 for rs928771 genotypes TT vs. GG, TG vs. GG, respectively). (C) Associations between KCNJ15 rs928771 genotype and biomarker levels in AD cases and NCs. Data are presented as mean ± SEM. Test for genotypes, **P < 0.01, ***P < 0.001; test for phenotypes, ^###P < 0.001 (AD: n = 31, 46, and 26; NC: n = 23, 23, and 11 for rs928771 genotypes TT, TG, and GG, respectively), ANCOVA with Bonferroni correction. cpm, counts per million mapped reads.

Fig. 5.

Functional evidence of GCH1 in AD. (A) GCH1 transcript was most abundant in the hematopoietic cells among all samples as suggested by lowest P value of enrichment (P = 6.67E−59, fold-enrichment = 16.9; 163 hematopoietic cell samples in 581 total samples). The figure was adopted from the FANTOM CAT database. (B) Associations between GCH1 transcript levels and GCH1 rs72713460 genotypes in the brain caudate region (n = 57, 34, and 3 for rs72713460 genotypes GG, GT, and TT, respectively). Data are presented as mean ± SEM, *P < 0.05 (F = 6.58); ANCOVA with Bonferroni correction. (C) GCH1 rs72713460 genotype is associated with plasma MMP-2 levels. Levels of MMP-2 in AD cases and NCs [AD: n = 64, 32, and 7; NC: n = 37, 19, and 1 (removed from analysis) for rs72713460 genotypes GG, GT, and TT, respectively]. Data are presented as mean ± SEM **P < 0.01 (F = 8.59); ANCOVA with Bonferroni correction.

Fig. 6.

Enrichment of immune-associated context in the regulatory network of AD risk loci. Association of candidate target genes with AD risk loci by global genotype-expression analysis. (A) Interaction network of the target genes regulated by the AD susceptibility loci in the hippocampus (n = 87, Left) and blood (n = 365, Right). Each circle represents a target gene, with different colors specifying the corresponding AD risk loci. Lines between circles indicate gene–gene interactions. The strength of the pairwise interaction between the target genes is reflected by the color intensity of the lines. (B) GO analysis of the target genes. Representative enriched GO terms for target genes from hippocampal data (Left) and blood data (Right) are shown; x axis indicates the corresponding FDR (in log₁₀ scale), with corresponding ontology categories marked on the y axis.