Large-scale GWAS have the power to detect very small effects. Steiger filtering is a method for selecting valid genetic IVs from very large GWAS with statistical power to identify genetic variants that are related to the trait of interest at a genome-wide significance threshold because it is downstream of many other traits that the genetic variant influences. For example, a sufficiently large GWAS of a biomarker (A) may identify numerous genetic variants that influence another biomarker (B) because A is influenced by B.
If those variants are included in an MR study of the effect of A on B without knowledge of their biology (as is typical from GWAS results), the result could (erroneously) suggest that A causes B (when the opposite is true). These reverse causal IVs will bias the MR estimate. In these situations, Steiger filtering uses a statistical test to identify the stronger of bidirectional effects (e.g., A on B or B on A) to select valid IVs from very large GWAS that can be used in MR. Steiger filtering assumes that a valid IV should explain more variance in the exposure than the outcome and removes those genetic variants that do not satisfy this criterion. If A has much more measurement error than B, then the variance explained by the SNP on A might be smaller than on B even though it primarily influences A. Some patterns of confounding can also lead to this problem.
- Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLOS Genetics 2017;13:e1007081.
- Hemani G, Bowden J, Haycock PC et al. Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome. bioRxiv 2017:173682.