Phylogenomic insights into mouse evolution using a pseudoreference approach

Brice A.J. Sarver, Sara Keeble, Ted Cosart, Priscilla K. Tucker, Matthew D. Dean, Jeffrey M. Good

Research output: Contribution to journalArticlepeer-review

Abstract

Comparative genomic studies are now possible across a broad range of evolutionary timescales, but the generation and analysis of genomic data across many different species still present a number of challenges. The most sophisticated genotyping and downstream analytical frameworks are still predominantly based on comparisons to high-quality reference genomes. However, established genomic resources are often limited within a given group of species, necessitating comparisons to divergent reference genomes that could restrict or bias comparisons across a phylogenetic sample. Here, we develop a scalable pseudoreference approach to iteratively incorporate sample-specific variation into a genome reference and reduce the effects of systematic mapping bias in downstream analyses. To characterize this framework, we used targeted capture to sequence whole exomes (~54 Mbp) in 12 lineages (ten species) of mice spanning the Mus radiation. We generated whole exome pseudoreferences for all species and show that this iterative reference-based approach improved basic genomic analyses that depend on mapping accuracy while preserving the associated annotations of the mouse reference genome. We then use these pseudoreferences to resolve evolutionary relationships among these lineages while accounting for phylogenetic discordance across the genome, contributing an important resource for comparative studies in the mouse system. We also describe patterns of genomic introgression among lineages and compare our results to previous studies. Our general approach can be applied to whole or partitioned genomic data and is easily portable to any system with sufficient genomic resources, providing a useful framework for phylogenomic studies in mice and other taxa.

Original languageEnglish
Pages (from-to)726-739
Number of pages14
JournalGenome Biology and Evolution
Volume9
Issue number3
DOIs
StatePublished - Mar 2017

Keywords

  • Bioinformatics
  • Comparative genomics
  • Introgression
  • Mapping bias
  • Mus musculus

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