A long-standing question in evolutionary biology is whether phenotypic plasticity influences adaptive evolutionary change (e.g. Waddington 1961; Price et al. 2003; Ghalambor et al. 2007; Lande 2009; Wund 2012). The same genotype can produce different phenotypes in response to different environments, but whether such plasticity constrains or facilitates evolutionary change remains an unresolved and controversial problem (Ghalambor et al. 2007). Theoretical models to date have made diverse predictions on the role of plasticity in evolutionary divergence (e.g. Ancel 2000; Paenke et al. 2007; Thibert-Plante and Hendry 2011), and empirical studies have largely been limited to retrospective approaches that infer the role of plasticity long-after populations and species have diverged (e.g. Losos et al. 2000; Wund et al. 2007; Scoville and Pfrender 2010). Field and lab studies that combine transcriptomic methods with recently diverged population comparisons provide a potentially powerful framework for quantifying patterns of plasticity for large numbers of molecular phenotypes within a generation, and how these phenotypes evolve across generations in response to either natural or artificial selection (e.g. Yampolsky et al. 2012; Ghalambor et al. 2015; Huang and Agrawal 2016). However, the high dimensionality of transcriptomic data imposes some computational challenges when attempting to infer the role of various evolutionary processes.

Untangling the role of selection and drift in population divergence via transcriptional network simulations: Extended analysis of Ghalambor et al. (2015)