Advancements in high-throughput sequencing technologies have accelerated the discovery of human genetic variation. However, leveraging genomic information for precision medicine is currently limited by the relatively small number of variants for which there is enough supporting evidence to interpret them clinically. An example of clinically actionable diseases for which large-scale functional data can have an enormous impact on patient health is for serine biosynthesis defects; a group of rare inherited metabolic disorders caused by pathogenic variants in PHGDH, PSAT1, and PSPH. However, because L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is highly actionable. Here, we use a yeast-based complementation assay to measure the functional impact of 1,914 amino acid substitutions in human PSAT, ~88% of all unique SNVaccessible missense variants. Our assay scores agree well with known biological features of the enzyme and existing clinical annotations, supporting its use as functional evidence for variant interpretation. We then extend this approach to assay a subset of pairwise PSAT1 allele combinations in yeast diploids. Results from our diploid assay successfully distinguish patient genotypes from those of healthy carriers and agree well with disease severity. Additionally, we developed a linear model that uses individual allele measurements (in haploid yeast cells) to accurately predict the biallelic function (in diploid yeast cells) of ~1.8 million allele combinations corresponding to potential human genotypes. Finally, we present a method that could be used to experimentally measure large numbers of variant combinations in yeast diploids. Taken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied in the study of rare disease and to inform future diagnostic efforts.