Adiposity has been associated with a number of markers of well-being in observational studies. However, conventional observational studies are subject to confounding and reverse causation. Mendelian Randomisation (MR) has been proposed as an analytic method that is free of these issues as genetic markers, which can be used as ‘instrumental variables’, are randomly distributed and thus should be free of confounding and are present at birth and therefore not subject to reverse causation. However, this method is not free of challenges. These include ‘pleiotropy’ where genetic markers are potentially independently associated to outcomes of interest and thus violate the assumptions in instrumental variable analysis. In this paper we use MR to investigate causal effects of BMI on well-being (e.g. life satisfaction, positive affect, neuroticism and depressive symptoms). We use genetic variants as instrumental variables (IVs). Specifically, 71 BMI associated Single-nucleotide polymorphisms (SNPs) were selected as multiple instruments. Two stage least squares (2SLS) estimates from one sample MR using UK Longitudinal Household study (Understanding Society) data as well as inverse-variance weighted (IVW) estimates from two sample MR using genome wide association study (GWAS) results of BMI from large scale consortia (e.g. GIANT) and GWAS results of Well-being from Social Science Genetic Association Consortium (SSGAC) are presented. MR-Egger and Median methods are used to assess the robustness of these results to the potential effects of pleiotropy.