As the field of genomics continues to grow, the identification of substantial genes as it relates to rare diseases remains a significant challenge. Traditionally, researchers work backwards starting from the disease to determine associated proteins. Then use expensive and time-consuming methods, such as mass spectroscopy or immunofluorescence staining, to determine genetic codes. While this method may work with single gene disorders, it is lacking when dealing with complex, polygenic diseases. To address this, we aimed to develop a novel method of discovering genes that displayed similar phenotypes when knocked out in mice using data from the International Mouse Phenotyping Consortium (IMPC), streamlining gene identification for complex genetic conditions. We used the National Organization for Rare Diseases (NORD) to select the Map2K2 gene, associated with cardiofaciocutaneous syndrome, as our starting gene. We then used the IMPC database to identify phenotypes associated with Map2k2 knockout mice. These phenotypes were organized into an Excel spreadsheet. Next, we searched the IMPC database for genes that exhibited the same phenotypes when knocked out in mice. This information was compiled into separate Excel tabs, one for each phenotype. We then applied variations of a conditional format to visualize the frequencies of each gene, assigning specific colors based on the number of hits. The IMPC database revealed five phenotypes associated with Map2k2 knockout mice: increased mean corpuscular volume, decreased total body fat, decreased body weight, abnormal gait, and decreased body length. Each phenotype had varying amounts of contributing genes, the most being decreased body weight with 403 associated genes, and the least being abnormal gait with 241 associated genes. Of the 947 unique genes found, 129 genes contributed to two phenotypes, 24 contributed to three phenotypes, and four contributed to four phenotypes. Notably, the gene Slc38a10 was the only gene associated with all five Map2k2 knockout phenotypes. While the Map2k2 gene’s involvement in the MAPK signaling pathway has been extensively studied, literature regarding interacting genes has been sparse. Our analysis identified Slc38a10 as a potential interacting gene, suggesting interplay between the pathways regulated by Map2k2 and Slc38a10. The Slc38a10 gene encodes an amino acid transporter crucial for metabolism and neurotransmission. Given the role of Map2k2 in the MAPK signaling pathway, it is plausible that these two genes have downstream interactions. Our gene detection method proved to be inexpensive and quick, offering a valuable tool for future genetic research. We hope that novel approaches like ours will contribute to enhancing the understanding of complex genetic disorders and advancing personalized medicine.