Image_1_Phylogenomics Yields New Insight Into Relationships Within Vernonieae (Asteraceae).pdf (1.16 MB)

Image_1_Phylogenomics Yields New Insight Into Relationships Within Vernonieae (Asteraceae).pdf

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posted on 17.10.2019, 14:31 by Carolina M. Siniscalchi, Benoit Loeuille, Vicki A. Funk, Jennifer R. Mandel, José R. Pirani

Asteraceae, or the sunflower family, is the largest family of flowering plants and is usually considered difficult to work with, not only due to its size, but also because of the abundant cases of polyploidy and ancient whole-genome duplications. Traditional molecular systematics studies were often impaired by the low levels of variation found in chloroplast markers and the high paralogy of traditional nuclear markers like ITS. Next-generation sequencing and novel phylogenomics methods, such as target capture and Hyb-Seq, have provided new ways of studying the phylogeny of the family with great success. While the resolution of the backbone of the family is in progress with some results already published, smaller studies focusing on internal clades of the phylogeny are important to increase sampling and allow morphological, biogeography, and diversification analyses, as well as serving as basis to test the current infrafamilial classification. Vernonieae is one of the largest tribes in the family, accounting for approximately 1,500 species. From the 1970s to the 1990s, the tribe went through several reappraisals, mainly due to the splitting of the mega genus Vernonia into several smaller segregates. Only three phylogenetic studies focusing on the Vernonieae have been published to date, both using a few molecular markers, overall presenting low resolution and support in deepest nodes, and presenting conflicting topologies when compared. In this study, we present the first attempt at studying the phylogeny of Vernonieae using phylogenomics. Even though our sampling includes only around 4% of the diversity of the tribe, we achieved complete resolution of the phylogeny with high support recovering approximately 700 nuclear markers obtained through target capture. We also analyzed the effect of missing data using two different matrices with different number of markers and the difference between concatenated and gene tree analysis.