A Bayesian multidimensional approach to decipher the genetic basis of dynamic phenotypes in multiple species

Deciphering the genetic architecture of complex quantitative phenotypes remains challenging in quantitative genetics. These traits not only depend of multiple genetic factors but are also established over time and environments. Although quantitative genetics has investigated the genetic determinism of phenotypic plasticity in contrasted environmental conditions, the time related phenotypic plasticity has received less attention. Here we proposed a multivariate Bayesian framework, the Bayesian Varying Coefficient Model, designed for analysing the genetic architecture of the time related phenotypic plasticity by a multilocus approach. We applied the BVCM to time series phenotypes measured at various time scales (daily, monthly, yearly) across a diverse set of biological species. We included in this study: yeast (Saccharomyces cerevisiae), fungi (Fusarium graminearum), eucalyptus (Eucalyptus urophylla x E. grandis), and sweet cherry tree (Prunus avium). The BVCM results were compared with those obtained with a known genome-wide association method carried out time by time. For all species and traits, the BVCM was able to detect the major QTL identified by marker-trait association methods and revealed additional genetic regions of weak effect. It also increased the phenotypic variance explained for most of the phenotypes considered. It revealed dynamic QTLs with transitory, increasing or decreasing effects over time. By considering both the temporal and genetic multivariate structures in a single statistical model, we increased our understanding of the genetic architecture of complex traits notably by reducing the issue of missing heritability. More broadly, this work raises the foundation for extended applications in functional genomics, evolutionary ecology, and crop breeding programs, in which time-related phenotypic plasticity remains crucial for predicting and selecting key quantitative complex traits.