Clinical exome sequencing routinely identifies missense variants in disease-related genes， but functional characterization is rarely undertaken， leading to diagnostic uncertainty. For example， mutations in PPARG cause Mendelian lipodystrophy and increase risk of type 2 diabetes (T2D). Although approximately 1 in 500 people harbor missense variants in PPARG， most are of unknown consequence. To prospectively characterize PPARγ variants， we used highly parallel oligonucleotide synthesis to construct a library encoding all 9，595 possible single-amino acid substitutions. We developed a pooled functional assay in human macrophages， experimentally evaluated all protein variants， and used the experimental data to train a variant classifier by supervised machine learning. When applied to 55 new missense variants identified in population-based and clinical sequencing， the classifier annotated 6 variants as pathogenic; these were subsequently validated by single-variant assays. Saturation mutagenesis and prospective experimental characterization can support immediate diagnostic interpretation of newly discovered missense variants in disease-related genes.