bioRxivEvolutionary Biology
Evaluating the role of reference-genome phylogenetic distance on evolutionary inference
Abstract
When a high-quality genome assembly of a target species is unavailable, an option to avoid the costly de novo assembly process is a mapping-based assembly. However, mapping shotgun data to a distant relative may lead to biased or erroneous evolutionary inference. Here, we used short-read data from a mammal and a bird species (beluga and rowi kiwi) to evaluate whether reference genome phylogenetic distance can impact downstream demographic (PSMC) and genetic diversity (heterozygosity, runs of homozygosity) analyses. We mapped to assemblies of species of varying phylogenetic distance (conspecific to genome-wide divergence of >7%), and de novo assemblies created using cross-species scaffolding. We show that while reference genome phylogenetic distance has an impact on demographic analyses, it is not pronounced until using a reference genome with >3% divergence from the target species. When mapping to cross-species scaffolded assemblies, we are unable to replicate the original beluga demographic analyses, but can with the rowi kiwi, presumably reflecting the more fragmented nature of the beluga assemblies. As for genetic diversity estimates, we find that increased phylogenetic distance has a pronounced impact; heterozygosity estimates deviate incrementally as phylogenetic distance increases. Moreover, runs of homozygosity are removed when mapping to any non-conspecific assembly. However, these biases can be reduced when mapping to a cross-species scaffolded assembly. Taken together, our results show that caution should be exercised when selecting the reference genome for mapping assemblies. Cross-species scaffolding may offer a way to avoid a costly, traditional de novo assembly, while still producing robust, evolutionary inference.
- organism: mouse
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: phylogenetics / evolution
- approach: statistical model
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