Redefining Possible: Combining Phylogenomic and Supersparse Data in Frogs

Daniel M. Portik, Jeffrey W. Streicher, David C. Blackburn, Daniel S. Moen, Carl R. Hutter, John J. Wiens

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The data available for reconstructing molecular phylogenies have become wildly disparate. Phylogenomic studies can generate data for thousands of genetic markers for dozens of species, but for hundreds of other taxa, data may be available from only a few genes. Can these two types of data be integrated to combine the advantages of both, addressing the relationships of hundreds of species with thousands of genes? Here, we show that this is possible, using data from frogs. We generated a phylogenomic data set for 138 ingroup species and 3,784 nuclear markers (ultraconserved elements [UCEs]), including new UCE data from 70 species. We also assembled a supermatrix data set, including data from 97% of frog genera (441 total), with 1–307 genes per taxon. We then produced a combined phylogenomic–supermatrix data set (a “gigamatrix”) containing 441 ingroup taxa and 4,091 markers but with 86% missing data overall. Likelihood analysis of the gigamatrix yielded a generally well-supported tree among families, largely consistent with trees from the phylogenomic data alone. All terminal taxa were placed in the expected families, even though 42.5% of these taxa each had >99.5% missing data and 70.2% had >90% missing data. Our results show that missing data need not be an impediment to successfully combining very large phylogenomic and supermatrix data sets, and they open the door to new studies that simultaneously maximize sampling of genes and taxa.

Original languageEnglish (US)
Article numbermsad109
JournalMolecular biology and evolution
Volume40
Issue number5
DOIs
StatePublished - May 1 2023

Keywords

  • amphibians
  • missing data
  • phylogenomics
  • phylogeny
  • supermatrix

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics

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