[BUG]:Failed to connect to api.opengwas.io port 443: Connection refused

[Suuzh0329]

Error in api_query("user", opengwas_jwt = opengwas_jwt, override_429 = TRUE) :
The following error was encountered in trying to query the MR-Base server:
Error in curl::curl_fetch_memory(url, handle = handle): Couldn't connect to server [api.opengwas.io]: Failed to connect to api.opengwas.io port 443: Connection refused
When I use the package TwoSample MR, it couldn't query the MR-base, could you please grant me the access? Thank you very much!

[TheodoreLevitt]

facing the same issue. Could you please help if you have solve the issues

[TheodoreLevitt]

@mrciu.. could you please at least let us know if this package is working at all.. even the tutorial is not working ..
Do u think this package is repairable.

Please dont delete comments.. rather answer something so that we can understand....
Everything was working fine and now when i have to submit my thesis...its annoying .. at least if you all comment about the problem ...we user know ...being silent when other users are suffering .. that annoying

[TheodoreLevitt]

@mrciu .. any suggestion or brief tutorial ?? or is it possible we can access the dataset at FTP ??? we would be thankful if we get any suggestions..

[TheodoreLevitt]

If others are looking for alternative approaches... This might be one solution but manually you have to download dataset

import numpy as np
import scipy.stats as stats

def two_sample_mr_test(exposure_snps, exposure_betas, exposure_ses,
outcome_snps, outcome_betas, outcome_ses):
# Ensure SNPs are aligned
common_snps = list(set(exposure_snps) & set(outcome_snps))

if len(common_snps) == 0:
    raise ValueError("No common SNPs between exposure and outcome")

# Extract aligned data
aligned_exposure_betas = [exposure_betas[exposure_snps.index(snp)] for snp in common_snps]
aligned_exposure_ses = [exposure_ses[exposure_snps.index(snp)] for snp in common_snps]
aligned_outcome_betas = [outcome_betas[outcome_snps.index(snp)] for snp in common_snps]
aligned_outcome_ses = [outcome_ses[outcome_snps.index(snp)] for snp in common_snps]

# Inverse variance weighted method
weights = 1 / np.array(aligned_outcome_ses)**2
causal_effect = np.sum(weights * np.array(aligned_exposure_betas) * np.array(aligned_outcome_betas)) / np.sum(weights)

# Standard error of causal effect
se_causal_effect = np.sqrt(1 / np.sum(weights))

# Calculate t-statistic and p-value
t_statistic = causal_effect / se_causal_effect
p_value = 2 * (1 - stats.t.cdf(abs(t_statistic), df=len(common_snps)-1))

return causal_effect, se_causal_effect, p_value

Example usage

np.random.seed(42)

Simulated exposure SNPs

exposure_snps = ['rs1', 'rs2', 'rs3', 'rs4', 'rs5']
exposure_betas = [0.1, 0.2, 0.15, 0.18, 0.12]
exposure_ses = [0.05, 0.04, 0.06, 0.05, 0.07]

Simulated outcome SNPs

outcome_snps = ['rs1', 'rs2', 'rs3', 'rs4', 'rs5']
outcome_betas = [0.2, 0.25, 0.18, 0.22, 0.15]
outcome_ses = [0.06, 0.05, 0.07, 0.06, 0.08]

Perform two-sample MR test

causal_effect, se, p_value = two_sample_mr_test(
exposure_snps, exposure_betas, exposure_ses,
outcome_snps, outcome_betas, outcome_ses
)

print("Two-Sample MR Test Results:")
print(f"Causal Effect Estimate: {causal_effect:.4f}")
print(f"Standard Error: {se:.4f}")
print(f"P-value: {p_value:.4f}")
print(f"Significant at 5% level: {p_value < 0.05}")