We aim to study the adaptation abilities of a genotype in terms of both the ability to be consistent across different environments (i.e. robustness) and the ability to cope with perturbations of the environment (i.e. resilience). This means making trade-offs between life functions, especially when resources are limited. Usually, to study those trade-offs between functions, geneticists calculate genetic correlations, which indicate the common genetic share of two traits, and thus common requirements for biological processes and resources. The main drawback of genetic correlations is that they are population parameters. However, we know that individuals in a population will make different trade-offs. To study the inter-individual variability of trade-offs, we have developed an individualized indicator based on resource acquisition and allocation concepts: the Life-Functions Ratio (LFR). We focused on the trade-off between two traits first, to establish the link between LFR and the genetic correlation between both traits. LFR is defined as a product of the transformed traits. It is mathematically related to the conversion efficiencies of both traits and the resource allocation coefficient (alpha). To describe this new indicator trait, LFR between backfat and egg number was calculated on a pedigreed purebred layers’ population from the breeding company Novogen, late in the laying period (80 wk of age), when a trade-off occurs between body reserves and egg production. Traits displayed moderate heritabilities: 0.23 for egg number, 0.52 for backfat, 0.45 for LFR, and 0.31 for alpha. LFR was favorably genetically correlated with backfat (+0.79) and with egg number (+0.76). LFR was genetically very different from alpha with a genetic correlation of 0.02. We are currently exploring the genetic architecture of LFR, expecting to find cryptic quantitative trait loci for backfat and egg number. We are also exploring the effect of selecting for LFR on the initial genetic correlation across generations through simulations. Future works will focus on integrating more traits into the equation.