Predicting the direction of phenotypic difference

Predicting precise phenotypes from genomic data is a key goal in genetics, but it is often hampered by incomplete genotype-to-phenotype data. Here, we describe a more attainable approach than quantitative predictions, aimed at qualitatively predicting phenotypic differences, i.e., which individual has the higher phenotypic value. This approach could be useful in a wide variety of scenarios, e.g., estimating if an individual has an increased disease risk, or if genetically modifying a crop would increase yield. To investigate whether limited genotype-to-phenotype information can still be used to predict which individual has the higher phenotypic value, we developed an estimator of the ratio between known and unknown effects on the phenotype. We formalize a model to delineate the scenarios in which accurate predictions can be achieved and evaluate performance in real-world data from tens of thousands of individuals from either the same family, same population, different populations, or separate species. We find that even in phenotypes where only a small fraction of the genetic effects are known, our estimator can allow for the identification of the individual with the higher phenotypic value, often with over 90% accuracy. We also find that our approach circumvents some of the limitations in transferring association data across populations. Overall, our study introduces an approach for accurately predicting a key feature of phenotypes—their direction—and suggests that more phenotypic information can be extracted from genomes than previously appreciated.