bioRxivEvolutionary BiologyPreprint
Genetic drift drives faster-Z evolution in the salmon louse Lepeophtheirus salmonis
Abstract
Sex chromosome evolution is a particularly complex sub-field of population genetics and there are still unresolved questions about how quickly and adaptively these chromosomes should evolve compared to autosomes. One key limitation to existing knowledge is an intense focus on only a handful of taxa in existing literature, resulting in uncertainty about whether observed patterns reflect general processes or are idiosyncratic to the more widely studied clades. In particular, the Z chromosomes of female heterogametic (ZW) systems tend to be quickly but not adaptively evolving in birds, while in butterflies and moths Z chromosomes tend to be evolving adaptively, but not always faster than autosomes. To understand how these two observations fit into broader evolutionary patterns, we explore, for the first time, patterns of Z chromosome evolution outside of these two well-studied clades. We utilize a publicly available high quality genome, gene expression, population, and outgroup data for the salmon louse Lepeophtheirus salmonis, an important aquacultural pest copepod. We find that the Z chromosome is faster evolving than the autosomes, but that this increased effect is driven by drift rather than adaptive evolution. This faster-Z effect seems to be a result of a very low effective population size of the Z chromosome, as well as high rates of female reproductive failure contributing to decreased efficiency of hemizygous selection acting on the Z. These results highlight the usefulness of organismal life history in calibrating population genetic expectations and demonstrate the usefulness of the ever-expanding wealth of modern publicly available genomic data to help resolve outstanding evolutionary questions.
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: genome assembly
- research focus: variant calling
- research focus: gene regulatory network inference
- approach: statistical model
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