Domestication obscures genomic estimates of population history

Abstract

Domesticated species are valuable models to examine phenotypic evolution, and knowledge on domestication history is critical for understanding the trajectories of evolutionary changes. Sequentially Markov Coalescent models are often used to infer domestication history. However, domestication practices may obscure the signal left by population history, affecting demographic inference. Here we assembled the genomes of a recently domesticated species-the society finch-and its parent speciesthe white-rumped munia-to examine its domestication history. We applied genomic analyses to two society finch breeds and white-rumped munias to test whether domestication of the former resulted from inbreeding or hybridization. The society finch showed longer and more runs of homozygosity and lower genomic heterozygosity than the white-rumped munia, supporting an inbreeding origin in the former. Blocks of white-rumped munia and other ancestry in society finch genomes showed similar genetic distance between the two taxa, inconsistent with the hybridization origin hypothesis. We then applied two Sequentially Markov Coalescent modelspsmc and smc++-to infer the demographic histories of both. Surprisingly, the two models did not reveal a recent population bottleneck, but instead the psmc model showed a specious, dramatic population increase in the society finch. Subsequently, we used simulated genomes based on an array of demographic scenarios to demonstrate that recent inbreeding, not hybridization, caused the distorted psmc population trajectory.Such analyses could have misled our understanding of the domestication process. Our findings stress caution when interpreting the histories of recently domesticated species inferred by psmc, arguing that these histories require multiple analyses to validate.

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“…The differences in orders of magnitude estimated by SMC++ and Stairway Plot should also be noted. The SMC++ results tended to mask the uncalled and genomic repeat regions due to uncertainty in the quality of the variant calls (Lu et al., 2022; Patton et al., 2019). Thus, these results might lead to lower estimates of effective population size (Patil et al., 2021).…”

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