Whole-genome sequencing reveals novel ethnicity-specific rare variants associated with Alzheimer's disease

Abstract

Alzheimer's disease (AD) is the most common multifactorial neurodegenerative disease among elderly people. Genome-wide association studies (GWAS) have been highly successful in identifying genetic risk factors. However, GWAS investigate common variants, which tend to have small effect sizes, and rare variants with potentially larger phenotypic effects have not been sufficiently investigated. Whole-genome sequencing (WGS) enables us to detect those rare variants. Here, we performed rare-variant association studies by using WGS data from 140 individuals with probable AD and 798 cognitively normal elder controls (CN), as well as single-nucleotide polymorphism genotyping data from an independent large Japanese AD cohort of 1604 AD and 1235 CN subjects. We identified two rare variants as candidates for AD association: a missense variant in OR51G1 (rs146006146, c.815 G > A, p.R272H) and a stop-gain variant in MLKL (rs763812068, c.142 C > T, p.Q48X). Subsequent in vitro functional analysis revealed that the MLKL stop-gain variant can contribute to increases not only in abnormal cells that should die by programmed cell death but do not, but also in the ratio of Aβ42 to Aβ40. We further detected AD candidate genes through gene-based association tests of rare variants; a network-based meta-analysis using these candidates identified four functionally important hub genes (NCOR2, PLEC, DMD, and NEDD4). Our findings will contribute to the understanding of AD and provide novel insights into its pathogenic mechanisms that can be used in future studies.

Fig. 1. Functional annotations of coding variants.

Coding variants were assigned the indicated functional annotations by using ANNOVAR. Of the annotations, frameshift deletion/insertion, splicing variants, stop-gain/stop-loss, and nonsynonymous SNVs were defined as potentially deleterious mutations. On the basis of the minor allele frequency (MAF), available from ToMMo 8.3KJPN, the variants were classified into three groups: rare (MAF < 0.01), infrequent (0.01 ≤ MAF ≤ 0.05), and common (0.05 < MAF).

Fig. 2. MLKL variants identified in a gene-based association study.

Two variants (rs763812068 and rs778326056) were genotyped by using a replication data set. a For rs763812068, a subsequent meta-analysis combining results from the discovery and replication datasets showed a significant association, with the same direction of effect in both datasets (P_meta = 0.046). Abbreviations: A1 allele 1, A2 allele 2. b MLKL stop-gain variants (rs763812068) detected with WGS were validated by using Sanger sequencing. c All four of the stop-gain variants were observed in females with AD, most of whom were APOE ε4-positive. d The stop-gain variant appears especially in Asian populations. The source databases are noted: KRGDB, Korean Reference Genome Database; ToMMo, Tohoku Medical Megabank Organization; gnomAD, Genome Aggregation Database.

Fig. 3. Functional analysis of MLKL variants.

a Localization of Myc-MLKL was visualized by immunocytochemistry in human HEK293 cells. Myc: Myc vector. b Proportions of cell death using SYTOX Green nuclear stain in human HEK293 cells transfected with the MLKL variant proteins. c The ratio of Aβ42 to Aβ40 was calculated from the ELISA results. Statistical analysis of the ratio of Aβ42 to Aβ40 between two groups was performed by using Welch’s t-test. Statistical significance was set at P < 0.05. *: Welch’s t-test P < 0.05; n.s: not significant.

Fig. 4. Network-based meta-analysis using candidate pathogenic genes.

Through genome-wide gene-based burden testing on rare coding variants, candidate pathogenic genes with P_BH < 0.1 were detected. A PPI network analysis was performed on the candidates by using NetworkAnalyst with the STRING Interactome database. Eight genes with degree of centrality (DC) ≥15 and betweenness of centrality (BC) ≥3,000 were identified as hub genes. Hub genes size corresponds to the DC. Hub genes are labeled with the gene names.

Fig. 5. Validation of potential blood biomarkers by using qRT-PCR.

The expression of all four hub genes in brain tissues was validated in the Human Protein Atlas database. Differential gene expression between AD and CN subjects (n = 20; 10 AD and 10 CN) was investigated. No differences were significant.