Selecting Question-Specific Genes to Reduce Incongruence in Phylogenomics: A Case Study of Jawed Vertebrate Backbone Phylogeny

Abstract

Incongruence between different phylogenomic analyses is the main challenge faced by phylogeneticists in the genomic era. To reduce incongruence, phylogenomic studies normally adopt some data filtering approaches, such as reducing missing data or using slowly evolving genes, to improve the signal quality of data. Here, we assembled a phylogenomic data set of 58 jawed vertebrate taxa and 4682 genes to investigate the backbone phylogeny of jawed vertebrates under both concatenation and coalescent-based frameworks. To evaluate the efficiency of extracting phylogenetic signals among different data filtering methods, we chose six highly intractable internodes within the backbone phylogeny of jawed vertebrates as our test questions. We found that our phylogenomic data set exhibits substantial conflicting signal among genes for these questions. Our analyses showed that non-specific data sets that are generated without bias toward specific questions are not sufficient to produce consistent results when there are several difficult nodes within a phylogeny. Moreover, phylogenetic accuracy based on non-specific data is considerably influenced by the size of data and the choice of tree inference methods. To address such incongruences, we selected genes that resolve a given internode but not the entire phylogeny. Notably, not only can this strategy yield correct relationships for the question, but it also reduces inconsistency associated with data sizes and inference methods. Our study highlights the importance of gene selection in phylogenomic analyses, suggesting that simply using a large amount of data cannot guarantee correct results. Constructing question-specific data sets may be more powerful for resolving problematic nodes.

Phylogenomic subsampling: a brief review

“…Now, in the next-generation era, matrices are so large that we can envision situations where smaller (perhaps more complete or less biased) matrices may actually perform better than large ones that include conspicuous gaps or a greater diversity of conflicting signals. Phylogenomic subsampling is a useful addition to other increasing practices, such as data filtering, whether based on parameters of the loci or on some a priori phylogenetic hypothesis Chen et al 2015). At the very least, the writing of this brief review, and especially my fortunate attendance at the Zoologica Scripta meeting in Oslo, revealed a case of parallel evolution between the phylogenomic practices of vertebrate and invertebrate systematistseven those working under the same roof in Cambridge!…”

“…; Chen et al . ). At the very least, the writing of this brief review, and especially my fortunate attendance at the Zoologica Scripta meeting in Oslo, revealed a case of parallel evolution between the phylogenomic practices of vertebrate and invertebrate systematists – even those working under the same roof in Cambridge!…”

Phylogenomics — principles, opportunities and pitfalls of big‐data phylogenetics

“…Current models of evolution may be overly simplistic in many cases, and model misspecification, regarded as insignificant in the Sanger era of phylogenetic reconstruction, may adversely affect phylogenomic-scale analyses (Philippe et al, 2011). Several approaches to alleviate the effects of systematic bias in phylogenomics have been proposed, including tests of compositional heterogeneity (Duchêne et al, 2017), posterior predictive approaches to assess model fit (Bollback, 2002;Doyle et al, 2015), and approaches to evaluate the sensitivity of results to removal of sites or loci likely to introduce bias (Chen et al, 2015;Goremykin et al, 2015) and to explore genealogical concordance among different regions of the genome (Minh et al, 2018).…”

A Genome-Scale Investigation of How Sequence, Function, and Tree-Based Gene Properties Influence Phylogenetic Inference

“…Several studies have examined the effect of sequence- and tree-based properties, such as the number of variable sites (Rokas et al 2003; Aguileta et al 2008), gene alignment length (Rokas et al 2003; Aguileta et al 2008; Betancur et al 2014), amount of missing data (Wiens and Morrill 2011; Roure et al 2013), base composition (Collins et al 2005; Betancur et al 2013; Romiguier et al 2013), evolutionary rate (Betancur et al 2014; Xi et al 2014; Doyle et al 2015), phylogenetic information content (Dell’Ampio et al 2014; Doyle et al 2015), and the recovery of known bipartitions or topologies (Regier et al 2010; Salichos and Rokas 2013; Capella-Gutierrez et al 2014; Chen et al 2015) on phylogenetic inference. However, these studies usually examine certain properties in isolation from other properties (Collins et al 2005; Wiens and Morrill 2011; Romiguier et al 2013; Capella-Gutierrez et al 2014), they often involve small numbers of genes (Regier et al 2010; Betancur et al 2013; Roure et al 2013), and do not include function-based properties of genes.…”