Genome-wide association analyses

There were 9479 JPSC-AD participants, 4020 men and 5459 women. Supplementary Table 1 summarizes the characteristics of the participants of JPSC-AD and the UK Biobank. The mean age of the JPSC-AD participants was 72.9 years (range 59–101 yrs), which was higher than that of the UK Biobank. We note that 499 JPSC-AD participants reported a history of stroke. The genomic inflation factor of the GWAS in JPSC-AD was 1.052 (Supplementary Fig. 1), and the linkage disequilibrium (LD) score regression intercept (standard error [SE]) was 1.024 (0.007), indicating that bias due to population stratification and cryptic relatedness was negligible. The estimated per-SNP heritability h2 (SE) was 0.28 (0.06), which was appeared to be higher than that in the European population (h2 = 0.18, SE = 0.02)8. The GWAS of JPSC-AD participants identified three loci that satisfied the genome-wide significance level (P < 5.0 × 10−8): 10q24.33 [SH3PXD2A], 17q21.31 [GFAP] and 17q25.1 [TRIM47]); Table 1 and Fig. 1a. All of these significantly associated loci have been reported previously. To search for associated variants independent of the lead variants, conditional analyses adjusted for the lead variants for these three loci were performed within ±500 kb of the lead variants. Although no variant satisfied the Bonferroni significance level, a rare stop codon of GFAP (R414*; rs180974014) showed the strongest association at GFAP (P = 3.8 × 10−4, beta = 0.288, SE = 0.081). Next, we combined the results of JPSC-AD GWAS with the published GWAS summary statistics of UK Biobank. The meta-GWAS revealed 20 significant loci (Fig. 1b and Table 2). Of these 20 variants, nine showed nominal associations (P < 0.05) in the JPSC-AD group, and SLC2A12 has not been previously reported (P = 6.61 × 10−9, beta = 0.053, SE = 0.017; Fig. 2). To assess the impact of the MRI field strength on the effect sizes of the associated variants, we performed a sensitivity analysis by subdividing the participants into two groups based on their MRI scans (1.5 T and 3.0 T; Supplementary Table 2 and Supplementary Fig. 2). We found no significant difference in the effect sizes between the two groups (P for heterogeneity > 0.05).

Table 1 Lead variants of the genome-wide significant loci for white matter hyperintensity lesion volume, the JPSC-AD StudyFig. 1: Manhattan plots of genome-wide association studies.

Manhattan plots of white matter lesion volume in (a) the JPSC-AD Study and (b) the Meta-analysis (fixed effect model). The red horizontal line indicates P = 5.0 × 10−8. The blue horizontal line indicates a suggestive level (P = 1.0 × 10−5). The novel locus (SLC2A12) is highlighted in green.

Table 2 Lead variants of genome-wide significant loci for white matter hyperintense lesion volume in the JPSC-AD Study and UK Biobank meta-analysisFig. 2: Regional association plots around SLC2A12.

Regional association plots for a JPSC-AD, b UK Biobank, and c Meta-GWAS generated using LocusZoom. The colors of the plots indicate LDs, from the Japanese reference panels in a and c and from Europeans of the 1000 Genomes Project in b.

We looked up the associations of identified variants (shown in Tables 1 and 2) in the publicly available GWAS summary statistics of the CHARGE Consortium. Of these variants, we identified the associations of the two variants listed in Table 1 and the nine variants in Table 2 in the summary statistics. We confirmed that the lead variants identified in the present study were also strongly associated in the CHARGE Consortium (Supplementary Table 3).

Comparison of effect sizes and signals at associated loci

To assess differences of genetic components associated with WML between JPSC-AD and UK Biobank, we compared the effect size of 20 significantly associated variants in the meta-GWAS (Fig. 3). As a result, the effect sizes of these variants were significantly correlated (Pearson’s r = 0.65, P = 0.002). Among the 20 variants evaluated, effect sizes of 19 variants (95%) were in the same direction (P for sign test < 0.001), although three variants showed significant heterogeneity (P for heterogeneity < 2.5 × 10−3 [ = 0.05/20]). Visual inspections of the regional association plots suggested different association signals at three loci (6q25.1 [PLEKHG1], 10q24.33 [SH3PXD2A], and 17q21.31 [NMT1]) between the JPSC-AD and UK Biobank groups (Fig. 4). Among these, the lead variants of NMT1 (near GFAP; rs1126642) and SH2PXD2A (rs11191817) in the JPSC-AD group met the genome-wide significance level (Table 1). Supplementary Table 4 shows the relationship between the JPSC-AD lead variants and UK Biobank lead variants for these three regions. Lead variants of these loci were more than 100 kb apart from each other, and were not in LD in either Japanese or European populations.

Fig. 3: Comparison of effect sizes for white matter lesions between JPSC-AD and UK Biobank.

The plot shows the effect sizes of the lead variants of the 20 signals that met the genome-wide significance level (5.0 × 10−8) in the GWAS meta-analysis. The horizontal axis indicates the beta values in the UK Biobank and the vertical axis denotes the effect sizes in the JPSC-AD GWAS. Nineteen of the 20 variants have concordant positive and negative beta values, with a sign test of P < 0.001. The plots are colored according to their associations in the JPSC-AD GWAS.

Fig. 4: Regional association plots of loci showing different association signals between JPSC-AD and UK Biobank.

Regional association plot around (a) PLEKHG1, (b) SH3PXD2A, and (c) NMT1. Regional association plots for (1) JPSC-AD, (2) UK Biobank and (3) Meta-GWAS, generated using LocusZoom. The colors of the plots indicate LDs, from the Japanese reference panels in (1) and (3) and from Europeans of the 1000 Genomes Project in (2).

To assess whether causal variants of these loci were shared between the JPSC-AD and UK Biobank groups, we performed conditional analyses in the JPSC-AD group by adjusting for the variants that were most strongly associated in the UK Biobank group. We observed that rs1126642 in the GFAP region continued to exhibit genome-wide significance (P = 1.3 × 10−10), and an association was also suggested for rs11191817 in SH3PXD2A (P = 6.5 × 10−8). In contrast, the association of rs4600514 in TRIM47 was attenuated (P = 8.1 × 10−6; Supplementary Figs. 3–5).

Annotation and biological interpretation of identified regions

We annotated the variants in LD (r2 > 0.7) with the lead variants of significantly associated loci in both JPSC-AD GWAS and meta-GWAS. Four nonsynonymous variants were found in GFAP, TFPI, B9D1, and TRIM47 (Supplementary Table 5). These variants have not been previously reported, with the exception of TRIM47, p.R187W.

We note that rs1126642 (GFAP, p.D295N) is common in the Japanese population (18% in JPSC-AD); however, the allele frequencies of this variant were rare or low-frequency (<5%) in all non-Asian populations according to the gnomAD browser (https://gnomad.broadinstitute.org/). Next, we considered the functional impact of GFAP, p.D295N using in silico prediction tools (Supplementary Table 5). The majority of the results suggested a deleterious effect for this variant. When we explored the pleiotropic effects of rs1126642 through PheWeb in three large biobanks (Biobank Japan, UK Biobank, and FinnGen; Supplementary Fig. 6), a significant association was only reported in UK Biobank (association with Underlying [primary] cause of death: ICD10: K56.6 Other and unspecified intestinal obstruction; P = 2.1 × 10−6). Two other nonsynonymous variants, i.e., TFPI, p.N221S and B9D1, p.Y256C, were suggested to have little functional impact (Supplementary Table 5). Since a recent study reported that rs1126642 is associated with expanded perivascular space (EPVS)12, we evaluated the association in the JPSC-AD group, and we successfully confirmed that rs1126642 was associated with EPVS (Beta [SE] = −0.108 [0.020], P = 1.0 × 10−7). EPVS, like WML, is one of the small vessel diseases. Although its pathophysiology is not well understood, it may have a similar background to WML.

We also considered the functional role of SLC2A12. The allele frequency of the lead variant rs1002559 was similar between JPSC-AD (22%) and UK Biobank (19%). There were no significantly associated phenotypes among the three large biobanks. We found that rs1002559 was reported as eQTL for SLC2A12 in three tissues (Nerve–Tibial, Artery–Aorta, and Artery–Tibial) and one cell-type (Cells–Cultured fibroblasts) in the GTEx project. We assessed the colocalization of associated variants in WML-GWAS in UK Biobank and eQTL analysis (Supplementary Fig. 7), and considered that associations of WML-GWAS were likely to be overlapped with those in the eQTL analysis of Nerve–Tibial (Supplementary Fig. 8).

Genetic correlations between WML and other traits

We examined the genetic correlations between the WML-GWAS and the published GWAS summary statistics of the Biobank Japan project for the five traits with significant correlations described in the previous report8. We observed a significant positive genetic correlation with ischemic stroke (genetic correlation [SE] = 0.511 [0.121], P = 2.48 × 10−5). No significant genetic correlation with other traits was identified (Supplementary Table 6).