Abstract Aims Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder. Methods and results We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors. Conclusion We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-β signalling molecules and spectrin β. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention. Structured Graphical Abstract Structured Graphical Abstract This study meta-analysed 4884 mitral valve prolapse (MVP) cases versus 434 649 controls, and discovered 16 genetic loci associated to MVP. Downstream analyses implicated candidate genes involved in TGF-beta signalling, cardiomyopathy and the cytoskeleton. The results from the meta-analysis were used to calculate a polygenic risk score (PRS) to aid prediction of MVP. Adding the PRS to a model with age, sex and clinical risk factors improved MVP risk prediction. Abbreviations, MVP, mitral valve prolapse, p, p-value, PRS, polygenic risk score, RF, risk factors.