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Loss of function mutations in the checkpoint kinase gene CHEK2 are associated with increased risk of breast and other cancers. Most of the 3,188 unique amino acid changes that can result from non-synonymous single nucleotide variants (SNVs) of CHEK2, however, have not been tested for their impact on the function of the CHEK2-enocded protein (CHK2). One successful approach to testing the function of variants has been to test for their ability to complement mutations in the yeast ortholog of CHEK2, RAD53. This approach has been used to provide functional information on over 100 CHEK2 SNVs and the results align with functional assays in human cells and known pathogenicity. Here we tested all but two of the 4,887 possible SNVs in the CHEK2 open reading frame for their ability to complement RAD53 mutants using a high throughput technique of deep mutational scanning (DMS). Among the non-synonymous changes, 770 were damaging to protein function while 2,417 were tolerated. The results correlate well with previous structure and function data and provide a first or additional functional assay for all the variants of uncertain significance identified in clinical databases. Combined, this approach can be used to help predict the pathogenicity of CHEK2 variants of uncertain significance that are found in susceptibility screening and could be applied to other cancer risk genes. Author summary: Variations in gene sequences account for many of the phenotypic differences between individuals, including different susceptibilities to diseases like cancer. Individuals who inherit specific variants in the CHEK2 gene, for example, have increased risk of developing breast cancer and other cancers. These cancer-associated variants all abolish the normal activity of the CHEK2-encoded protein, a protein kinase called Chk2. Most of the CHEK2 variants found in individuals, however, have an unknown effect on the function of the Chk2 protein, and therefore an unknown potential role in cancer. This is because it is cumbersome to individually make and test each newly identified variant to see whether it affects protein function. Here we use an efficient version of a technique called deep mutational scanning to test nearly all possible CHEK2 single nucleotide variants for their impact on Chk2 protein function. Analyses of the resulting data indicate that it will be useful for developing models to predict the cancer risk in individuals who inherit any CHEK2 variants. This study also demonstrates a viable efficient approach to study variants of other genes, including other cancer related genes. [ABSTRACT FROM AUTHOR]