Abstract
Mendelian randomization (MR) is becoming more and more popular for inferring causal relationship between an exposure and a trait. Typically, instrument SNPs are selected from an exposure GWAS based on their summary statistics and the same summary statistics on the selected SNPs are used for subsequent analyses. However, this practice suffers from selection bias and can invalidate MR methods, as showcased via two popular methods: the summary data-based MR (SMR) method and the two-sample MR Steiger method. The SMR method is conservative while the MR Steiger method can be either conservative or liberal. A simple and yet more powerful alternative to SMR is proposed.
Highlights
Causal relationship between exposure x and y exists if the following test statistic is significant[2]: TSMR
We compared the performance of conditional test we proposed and the summary data-based MR (SMR) test on an empirical study of schizophrenia
Summary statistics Mendelian randomization (MR) is subject to selection bias, resulting in excessive false positives or missed discoveries
Summary
The summary exposure GWAS statistics are those used in Step 1 for SNP selection. When conducting causal inference in Step 2 with respect to the SNPs selected in Step 1, the summary statistics from the exposure GWAS can not be regarded as random samples for the true population association strength[4,5,6]. For the summary data-based MR method, the most significant SNP (instead of several SNPs) from a gene is selected as the instrument from the exposure GWAS.
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