Cardiovascular diseases are leading global causes of death and disability, often presenting as interrelated phenotypes of atherosclerotic vascular disease, heart failure and arrhythmias. Cardiovascular diseases arise from interactions between environmental factors and predisposing genotypes and include common Mendelian lipid disorders, cardiomyopathies and arrhythmia syndromes. The identification of a pathogenic variant through genetic testing can inform disease diagnosis, risk prediction, treatment and family screening. However, a major roadblock in genomic medicine is that for many variants, especially missense variants, we lack sufficient evidence to enable a definitive classification, and therefore these variants are deemed as 'variants of uncertain significance'. In this Review, we describe how multiplexed assays of variant effects can enable the functional assessment of nearly all coding variants in a target sequence, potentially offering a proactive approach to identifying the functional significance of gene variants that are observed later in a patient. We discuss validation, including the role of in silico variant effect predictors, and how multiplexed experimental methods are informing cardiovascular disease biology and ultimately resolving the problem of variants of uncertain significance at scale.