“…• TIGRAMITE (Python package) (Runge et al, 2019) developed a more flexible Bayesian MR method that can handle one, two and overlapping samples. Bayesian MR has an advantage in its flexibility of coping with complex data structures, such as overlapping samples, horizontal pleiotropy, study heterogeneity and multiple exposure and outcomes, all in a single model (Berzuini et al, 2020;Zou et al, 2020Zou et al, , 2021. Advanced MR methods have been developed more recently, such as MR-ConMix (contamination mixture method for robust and efficient estimation) (Burgess et al, 2020) and GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) (Wang et al, 2021), that utilises both strongly and weakly associated SNPs to identify multiple pleiotropic pathways.…”