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Observational studies can mostly estimate correlations between potential risk factors and diseases, which has limited utility for public health interventions. Mendelian randomisation methods leverage information from genetic correlates of risk factors and complex diseases in order to identify causal relationships and its extent. I will present several extensions of this principle. These extensions led to identification of different obesity subtypes with distinct cardio-metabolic consequences. Furthermore, another new method (distinguishing genetic confounding and causation) revealed that being diagnosed with Type 2 diabetes causally reduces body weight, possible reflecting medical doctors' lifestyle recommendations. Finally, such methods enable the detection of sex-specific driving forces in mate choice.