Phenotypic sexual dimorphism is associated with genomic signatures of resolved sexual conflict

Intra‐locus sexual conflict, where an allele benefits one sex at the expense of the other, has an important role in shaping genetic diversity of populations through balancing selection. However, the potential for mating systems to exert balancing selection through sexual conflict on the genome remains unclear. Furthermore, the nature and potential for resolution of sexual conflict across the genome has been hotly debated. To address this, we analysed de novo transcriptomes from six avian species, chosen to reflect the full range of sexual dimorphism and mating systems. Our analyses combine expression and population genomic statistics across reproductive and somatic tissue, with measures of sperm competition and promiscuity. Our results reveal that balancing selection is weakest in the gonad, consistent with the resolution of sexual conflict and evolutionary theory that phenotypic sex differences are associated with lower levels of ongoing conflict. We also demonstrate a clear link between variation in sexual conflict and levels of genetic variation across phylogenetic space in a comparative framework. Our observations suggest that this conflict is short‐lived, and is resolved via the decoupling of male and female gene expression patterns, with important implications for the role of sexual selection in adaptive potential and role of dimorphism in facilitating sex‐specific fitness optima

Detecting signatures of selection on gene expression

A substantial amount of phenotypic diversity results from changes in gene expression levels and patterns. Understanding how the transcriptome evolves is therefore a key priority in identifying mechanisms of adaptive change. However, in contrast to powerful models of sequence evolution, we lack a consensus model of gene expression evolution. Furthermore, recent work has shown that many of the comparative approaches used to study gene expression are subject to biases that can lead to false signatures of selection. Here, we first outline the main approaches for describing expression evolution and their inherent biases. Next, we bridge the gap between the fields of phylogenetic comparative methods and transcriptomics to reinforce the main pitfalls of inferring selection on expression patterns and use simulation studies to show that shifts in tissue composition can heavily bias inferences of selection. We close by highlighting the multi-dimensional nature of transcriptional variation and identifying major, unanswered questions in disentangling how selection acts on the transcriptome