GWAS summary data collection
To assess blood pressure as comprehensively as possible and diminish false results, we regarded blood pressure as a continuous variable instead of a binary variable considering the presence of hypertension or not. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were included as exposures separately, in order to reduce the impact of stratification. We obtained their summary-level data from the latest and largest GWASs involving more than 757600 European individuals, which performed a meta-analysis on the data from the UK Biobank and ICBP after adjusting for age, sex. Blood pressure was measured by automated measurement or manual measurement. The mean SBP was 141.1 mmHg (standard deviation (SD) = 20.7), and the mean DBP was 84.3 mmHg (SD = 11.3).
We further included the targeted proteins of antihypertension drugs to mimic the effect of corresponding drugs on ALS. Proxies for the common classes of drugs that lower SBP (including angiotensin converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers (BBs), and calcium channel blocker (CCB)) based on DrugBank (https://www.drugbank.ca/) and GeneCards (https://www.genecards.org/).
ALS GWAS summary data available online were retrieved from a study by Nicolas, A. et al with 80610 European individuals, in whom the proportion of cases was 0.258. All the patients had disease onset after 18 years of age and were diagnosed at probable or definite levels according to the El Escorial criteria.
Genetic variants selection
We estimated causal relationship between exposures and outcome by genetic instrumental variables (IVs) adequately related to exposure. SNPs independently (r2 < 0.001) associated with blood pressure at the genome-wide significance level (P < 5E-8) were strictly selected. IVs for antihypertension drugs were all significantly associated with SBP (P < 5E-8) and in a relatively modest linkage disequilibrium (LD) correlation (r2 < 0.4), which increased the variance explained proportion and statistical power, as described in detail elsewhere. The genetic contribution of each allele changes in 1 SD SBP and DBP were 0.016 and 0.025, respectively.
IVs absent in the ALS dataset were replaced with proxies in strong LD (r2 > 0.9) by searching in the SNiPA (http://snipa.helmholtz-muenchen.de/snipa3/). Those without reported proxies were removed from downstream MR analysis. Because of the calculation requirements, an exposure would be excluded when its available IVs were less than two. Altogether, 400 SNPs were identified as IVs for SBP, 397 SNPs were identified as IVs for DBP, 47 SNPs were identified as IVs for CCB, and 5 SNPs were identified as IVs for ALS in reverse MR estimates. An additional table shows this in more detail [see Additional file 1]. ACEI, ARB and BB were deleted due to insufficient IVs. In multivariable MR (MVMR) analysis (see below), 62 SNPs of SBP and 30 SNPs of DBP were excluded because they were palindromic with intermediate allele frequencies.
The theoretical basis of MR research relies on three assumptions: assumption 1 exclusion restriction, the selected genetic variations are not associated with other confounders; assumption 2 relevance, the selected genetic variations are significantly associated with exposure; and assumption 3 independence pathway, the selected genetic variations are significantly associated with the risk of outcome only through the pathway from exposure. The strict selection of IVs satisfied assumption 1. Assumptions 2 and 3 were met through MR approaches.
We implemented the multiplicative random effects inverse variance weighted (IVW) method as the main approach to examine the overall causal relationship between exposure and ALS based on the effect of SNPs on blood pressure and the effect of SNPs on ALS. To validate the results from the IVW method, we applied the weighted median method, simple median method , MR Egger method  and MR-PRESSO method as sensitivity analyses. To test potential pleiotropy, the MR Egger method, which reminds the presence of pleiotropy when the intercept significantly deviates from the origin, and MR-PRESSO analysis, which was used to detect the influence of outliers , were employed. The heterogeneity of SNPs used in IVW estimates was tested by Cochran's Q test, which suggests the presence of heterogeneity when it is lower than the significant P value. Leave-one-out analysis and single SNP analysis were employed to evaluate the robustness of the significant results and the possibility of results being driven by a single SNP. We also calculated F statistics for IVs to demonstrate their strength. Given the close correlation between SBP and DBP, we employed multivariable MR (MVMR) to diminish the influence of the other result of blood pressure measurement and assess the causal association between SBP and ALS with regarding DBP as a covariate and the association between DBP and ALS with regarding SBP as a covariate. Additionally, we investigated reverse causality between blood pressure and ALS by bidirectional MR, which made ALS as an exposure and SBP and DBP as the outcomes. The process was shown in Fig. 1.
P values less than 0.05/3 were considered significant with Bonferroni correction. A P value between 0.017 and 0.05 was regarded as a suggestive significance level. All analyses were performed by “TwoSampleMR” package (version 0.5.6) in R software (version 3.6.3).