In the context of the material genome, genes are functional groups of elements acting as descriptors for a particular property. Studying ionic conductivity in glasses from the Na2O-P2O5-AlF3-SO3 family, we presented them as six fictive chemical entities with a characteristic stoichiometry derived from strong linear component analysis. SLCA maximizes the quality of linear regression between a property (here: ionic conductivity) and champion compositions from all possible combinations of elements. Family trees and matrix rotation allow for the identification of essential genes, which are filtered from the set of all possible combinations of elements present in the considered dataset.