Genetic redundancy is a phenomenon where more than one gene encodes products that perform the same function. This frequently manifests experimentally as a single gene knockout mutant which does not demonstrate a phenotypic change compared to the wild type due to the presence of a paralogous gene performing the same function; a phenotype is only observed when one or more paralogs are knocked out in combination. This presents a challenge in a fundamental goal of genetics, linking genotypes to phenotypes, especially because it is difficult to determine a priori which gene pairs are redundant. Furthermore, while some factors that are associated with redundant genes have been identified, little is known about factors contributing to long-term maintenance of genetic redundancy. Here, we applied a machine learning approach to predict redundancy among benchmark redundant and nonredundant gene pairs in the model plant Arabidopsis thaliana. Predictions were validated using well-characterized redundant and nonredundant gene pairs. Additionally, we leveraged the availability of fitness and multi-omics data in the budding yeast Saccharomyces cerevisiae to build machine learning models for predicting genetic redundancy and related phenotypic outcomes (single and double mutant fitness) among paralogs, and to identify features important in generating these predictions. Collectively, our models of genetic redundancy provide quantitative assessments of how well existing data allow predictions of fitness and genetic redundancy, shed light on characteristics that may contribute to long-term maintenance of paralogs that are seemingly functionally redundant, and will ultimately allow for more targeted generation of phenotypically informative mutants, advancing functional genomic studies.
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