Measuring natural selection on the transcriptome

The level and pattern of gene expression is increasingly recognized as a principal determinant of plant phenotypes and thus of fitness. The estimation of natural selection on the transcriptome is an emerging research discipline. We here review recent progress and consider the challenges posed by the high dimensionality of the transcriptome for the multiple regression methods routinely used to characterize selection in field experiments. We consider several different methods, including classical multivariate statistical approaches, regularized regression, latent factor models, and machine learning, that address the fact that the number of traits potentially affecting fitness (each expressed gene) can greatly exceed the number of plants that researchers can reasonably monitor in a field study. While such studies are currently few in number, extant data is sufficient to illustrate several of these approaches. With additional methodological development coupled with applications to a broader range of species, we believe prospects are favorable for directly characterizing selection on gene expression within natural plant populations.