Heterogeneous Models Place the Root of the Placental Mammal Phylogeny

Abstract

Heterogeneity among life traits in mammals has resulted in considerable phylogenetic conflict, particularly concerning the position of the placental root. Layered upon this are gene- and lineage-specific variation in amino acid substitution rates and compositional biases. Life trait variations that may impact upon mutational rates are longevity, metabolic rate, body size, and germ line generation time. Over the past 12 years, three main conflicting hypotheses have emerged for the placement of the placental root. These hypotheses place the Atlantogenata (common ancestor of Xenarthra plus Afrotheria), the Afrotheria, or the Xenarthra as the sister group to all other placental mammals. Model adequacy is critical for accurate tree reconstruction and by failing to account for these compositional and character exchange heterogeneities across the tree and data set, previous studies have not provided a strongly supported hypothesis for the placental root. For the first time, models that accommodate both tree and data set heterogeneity have been applied to mammal data. Here, we show the impact of accurate model assignment and the importance of data sets in accommodating model parameters while maintaining the power to reject competing hypotheses. Through these sophisticated methods, we demonstrate the importance of model adequacy, data set power and provide strong support for the Atlantogenata over other competing hypotheses for the position of the placental root.

Integrative modeling of gene and genome evolution roots the archaeal tree of life

“…S14), where the bacterial stem was predicted to have experienced 4.79 substitutions per site, compared with a mean of 0.192 (range, 0.0157-0.546) for within-domain branches. The use of long outgroup branches is a general problem that has contributed to disagreements about the archaeal root as well as about the roots of other major radiations (58)(59)(60)(61), motivating a search for alternative rooting methods.…”

Mammal madness: is the mammal tree of life not yet resolved?

“…At present, the field advances on two fronts. On one front, the supermatrix approaches to solve these problems have been advanced through the application of better fitting heterogeneous models of sequence evolution, compared with analyses employing widely and in some cases inappropriately used homogeneous models [6,53]. On the other front, as outlined above, the limitations, powers and pitfalls of novel gene tree estimation methods must be elucidated to ascertain the best methods to analyse these new whole genome data; however, the refinement of statistical binning [82] shows promise to address some of the criticisms of this approach.…”

A Guide to Phylogenetic Reconstruction Using Heterogeneous Models—A Case Study from the Root of the Placental Mammal Tree

“…This is also the case within more closely related groups where variation in body size, metabolic rate, longevity and germ-line generation time contribute to variation in rates of change and also to differences in composition biases among species. This heterogeneity in evolutionary rates and compositions is particularly evident in mammals [5,6] and it follows that the application of models that do not account for this heterogeneity between and within species (i.e., homogeneous models) may be inadequate for such data [7].…”

“…This effectively removes a layer of composition heterogeneity in the data, which can be useful when models are not adequate for the data. Using Dayhoff categories reduces the number of character states allowing for parameters to be estimated directly from the AA data [7,17]. For nucleotide substitution models, it is possible to estimate all parameters from the data, as there are only four character states (A, G, T, C) and recoding the data as purines (R) and pyrimidines (Y), or RY-coding for short, can further reduce the character states to just two [18].…”

“…These differ in their construction/assumptions and in their interpretation of biology. We define homogeneous models as those that do not account for compositional variation and exchange rate variation across the phylogeny (between species in the alignment) or across the data (e.g., between sites in the alignment) [7]. The most advanced homogeneous models can model rate heterogeneity but are unable to model compositional heterogeneity.…”