Measurement and meaning in gene expression evolution

The genetic control of biological traits is not predetermined. Rather, phenotypes emerge through stochastic interactions among genes, their products, and environments. A century since Fisher’s synthesis of Mendelian inheritance with observations from biometric studies, the fundamental challenge toward a population genetics of phenotypic evolution has been to dissect the genetic and nongenetic causes of variation in quantitative traits. Some of the earliest accessible phenotypic variation is expressed in the production and action of messenger RNA (mRNA). Recently, progress in transcriptome profiling has made this biological information technically more accessible. Measurement of its quantity, timing, and sequence has become feasible at genomic scales. However, proper instrumental and theoretical insight is required to make meaning of “gene expression” from count data. Gene expression encompasses cascades of dynamic molecular adjustments that are not directly represented in static measurements. Nonetheless, bulk ribonucleic acid sequencing (RNA-seq) can be performed to generate time-courses sampling relative mRNA abundance across conditions. In turn, time-courses can be analyzed to infer the connectivity of transcriptional networks and to inform experimental design for testing genotype–phenotype hypotheses. Moreover, comparative analyses of predicted gene interactions across environmental, demographic, and developmental conditions can advance functional genomics in nonmodel species by providing a means to assign gene function without the need for well-annotated reference genomes, as well as studies into the effects of epistasis and the conditional strength of selection for individual gene expression. Integrative strategies for linking genetic factors to gene expression variation can therefore empower the study of phenotypic evolution to move beyond phenotypic variance and on to genotype frequencies.