Ethics
The study was approved by the Ethics Committee of the Beijing Institute of Brain Disorders in Capital Medical University. Our analyses were based on summary statistics from published GWAS or the data were publicly accessible. No original data were used for this manuscript, and thus, no ethical committee approval was required.
Mr And Assumptions
Our study is a two-sample MR study by using genetic instruments (SNPs) predicting glioma and AD from the latest GWAS. MR relies on three assumptions[17, 20, 21]which were described in Fig. 1. Assumption 1 was that the genetic predictors of glioma are strongly associated with the glioma of interest. Assumption 2 was that the genetic predictors of glioma (exposure) are independent from confounders of glioma- AD (exposure–outcome) relation; Assumption 3 was that the genetic predictors are only linked to the AD through affecting the glioma of interest but not through other pathways.
Glioma Genetic Instrumental Variants (Ivs)
The largest GWAS (12,496 cases and 18,190 controls) for glioma was reported by Beatrice S Melin et al.[22]. Its primary aim is to use GWAS of glioma subtypes to identify specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors. This glioma GWAS has 6811 European participants (Supplementary table 1). The summary statistics for genetic associations of glioma is available at https://gwas.mrcieu.ac.uk/datasets/ieu-a-1013.Three eligible instrumental SNPs were selected by a series of quality control steps[23, 24]. First, SNPs associated with glioma with genome-wide significance (P < 5 × 10− 8) were extracted; Second, no linkage disequilibrium (LD) (R2 < 0.001) among the included instrumental variables were conducted; Finally, no effects on other potential risk factors including body mass index, smoking, and blood pressure were related. The information about these IVs is shown in Table 1.
Table 1
Glioma genetic instrumental variables
SNP
|
EA
|
NEA
|
EAF
|
Beta
|
SE
|
p val
|
rs2736100
|
A
|
C
|
0.494
|
0.259
|
0.044
|
4.28E-09
|
rs2151280
|
A
|
G
|
0.537
|
0.281
|
0.045
|
2.56E-10
|
rs6010620
|
G
|
A
|
0.229
|
0.355
|
0.055
|
1.13E-10
|
Abbreviations: SNP, single-nucleotide polymorphism; EA, effect allele; NEA, non‐effect allele; EAF, effect allele frequency; Beta, the regression coefficient based on the glioma effect allele; SE, standard error. Three SNPs with p < 5 × 10− 8 were selected as independent genetic instrumental variants. |
Ad Gwas Dataset
The largest AD GWAS was provided by Psychiatric Genomics Consortium (PGC) in 2019[4]. The AD GWAS includes 24,087 clinically diagnosed late-onset AD cases, paired with 55,058 healthy controls from European ancestry. The demographic profiles about AD GWAS were summarized in Table 2. The largest European AD GWAS summary statistics from the PGC is available in https://pgc.unc.edu/for-researchers/download-results/alz2019/30617256.
Table 2
Alzheimer's disease genome-wide association study.
Year
|
Trait
|
ncase
|
ncontrol
|
Consortium
|
Population
|
PMID
|
2019
|
Alzheimer disease
|
24,087
|
55,058
|
PGC
|
European
|
30617256
|
Abbreviations: ncase: the number of Alzheimer's disease cases; ncontrol: the number of the controls; PGC: Psychiatric Genomics Consortium; PMID: pubMed unique identifier. |
Association Of Glioma Genetic Ivs With Ad Gwas Dataset
We successfully extracted the summary statistics corresponding to three glioma genetic IVs from Alzheimer's disease GWAS. The summary about the association of the glioma genetic IVs in AD GWAS dataset is shown in Table 3.
Table 3
Association of glioma genetic instrumental variants with Alzheimer's disease genome-wide association study
SNP
|
Exposure (glioma)
|
Outcome (Alzheimer's disease)
|
|
Beta
|
SE
|
p val
|
Beta
|
SE
|
p val
|
rs2151280
|
0.281
|
0.045
|
2.56E-10
|
0.005
|
0.002
|
0.038
|
rs2736100
|
0.259
|
0.044
|
4.28E-09
|
0.004
|
0.002
|
0.055
|
rs6010620
|
-0.355
|
0.055
|
1.13E-10
|
-0.006
|
0.003
|
0.016
|
Abbreviations: SNP, single-nucleotide polymorphism; GWAS, genome-wide association study; Beta, the regression coefficient based on the glioma effect allele; SE, standard error. |
Pleiotropy Test
MR-egger_intercept and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods[23, 25]were used to test the pleiotropy of three independent glioma genetic IVs in the AD GWAS dataset. However, MR-PRESSO test needs to use more than four SNPs. Thus, we did not perform the MR-PRESSO test. MR_Egger is based on the same regression model with inverse variance weighted (IVW), but allows and accounts for the potential pleiotropy using the MR-Egger intercept test[24, 25]. If Eggers intercept is not significantly, the MR_Egger intercept term should tend to zero as the sample size increases[26]. The summary of the pleiotropy test is shown in Table 4. P ≥ 0.05 represents no significant pleiotropy of three independent glioma genetic IVs in the AD GWAS.
Table 4
Pleiotropy and heterogeneity test of glioma genetic instrumental variants in Alzheimer's disease genome-wide association study
Pleiotropy test
|
Heterogeneity test
|
MR_Egger
|
MR Egger
|
IVW
|
Intercept
|
SE
|
p val
|
Q
|
Q_df
|
Q_pval
|
Q
|
Q_df
|
Q_pval
|
-0.002
|
0.010
|
0.890
|
0.003
|
1
|
0.954
|
0.034
|
2
|
0.983
|
SE, standard error; p val ≥ 0.05 represents no significant pleiotropy; Q_pval ≥ 0.05 represents no significant heterogeneity. |
Heterogeneity Test
MR_egger and inverse variance weighted (IVW) in Cochran’s Q statistic[27, 28]have been broadly used to quantify the heterogeneity among the selected SNPs. The summary results of heterogeneity test are shown in Table 4. P ≥ 0.05 represents no significant heterogeneity of three independent glioma genetic IVs in the AD GWAS.
Mr Analysis
Two-sample MR was conducted using the TwoSampleMR R package. The MR analysis was performed by the function “mr” In the study, we combined the summary statistics
to estimate the causal associations between glioma and AD using different methods. Since it is unlikely that all genetic variants would be valid instrumental variables, several robust methods have been proposed. The methods included weighted median and IVW[29]. P < 0.05 represents the causal link between the glioma and AD. The results of MR analysis were shown in Table 5.
Table 5
The causal association of glioma with Alzheimer's disease
Method
|
nsnp
|
Beta
|
SE
|
p val
|
OR
|
OR_lci95
|
OR_uci95
|
IVW
|
3
|
0.0166
|
0.0045
|
0.0002
|
1.0167
|
1.0079
|
1.0257
|
Weighted median
|
3
|
0.0161
|
0.0053
|
0.0023
|
1.0162
|
1.0058
|
1.0267
|
Note: p < 0.05 represents the causal association of the increased glioma with Alzheimer's disease. |
Abbreviations: IVW, inverse variance weighted; nsnp, the number of single-nucleotide polymorphisms; Beta, the regression coefficient based on the glioma effect allele; SE, standard error; OR, odds ratio; OR_lci95, lower limit of 95% confidence interval for OR; OR_uci95, upper limit of 95% confidence interval for OR. |
Single SNP effect analysisIn the TwoSampleMR R package, two functions, “mr” and “mr_scatter_plot,” were used to test the individual causal effect of glioma on AD (Fig. 2). To determine the single SNP effect size for glioma on AD, two functions, “mr_singlesnp” and “mr_forest_plot,” were used in the TwoSampleMR R package (Fig. 3). To determine the single SNP bias of three independent glioma genetic IVs in AD, two functions “mr_singlesnp” and “mr_leaveoneout_plot” in the TwoSampleMR R package were used to analyze the effect of leave-one-out (Fig. 4).
Reverse Mr Analysis
Nine independent genetic IVs were chosen from the largest AD GWAS includes 24,087 clinically diagnosed late-onset AD cases and 55,058 healthy controls from European ancestry provided by the PGC[4].
The glioma GWAS consists of 6811 individuals of European ancestry. This GWAS summary statistics is available in https://gwas.mrcieu.ac.uk/datasets/ ieu-a-1013 /.
Both MR-egger_intercept and PRESSO methods were used to test the pleiotropy of AD-associated genetic IVs in glioma GWAS. Both MR Egger and IVW in Cochran’s Q statistic were used to determine the heterogeneity of AD-associated genetic IVs in glioma GWAS. Reverse MR analysis was performed using MR Egger, weighted median, IVW, simple mode, and weighted mode.