WGDTree: a phylogenetic software tool to examine conditional probabilities of retention following whole genome duplication events

Species-tree topology impacts the inference of ancient whole-genome duplications across the angiosperm phylogeny

PREMISE

The history of angiosperms is marked by repeated rounds of ancient whole-genome duplications (WGDs). Here we used state-of-the-art methods to provide an up-to-date view of the distribution of WGDs in the history of angiosperms that considers both uncertainty introduced by different WGD inference methods and different underlying species-tree hypotheses.

METHODS

We used the distribution synonymous divergences (Ks) of paralogs and orthologs from transcriptomic and genomic data to infer and place WGDs across two hypothesized angiosperm phylogenies. We further tested these WGD hypotheses with syntenic inferences and Bayesian models of duplicate gene gain and loss.

RESULTS

The predicted number of WGDs in the history of angiosperms (~170) based on the current taxon sampling is largely similar across different inference methods, but varies in the precise placement of WGDs on the phylogeny. Ks-based methods often yield alternative hypothesized WGD placements due to variation in substitution rates among lineages. Phylogenetic models of duplicate gene gain and loss are more robust to topological variation. However, errors in species-tree inference can still produce spurious WGD hypotheses, regardless of method used.

CONCLUSIONS

Here we showed that different WGD inference methods largely agree on an average of 3.5 WGD in the history of individual angiosperm species. However, the precise placement of WGDs on the phylogeny is subject to the WGD inference method and tree topology. As researchers continue to test hypotheses regarding the impacts ancient WGDs have on angiosperm evolution, it is important to consider the uncertainty of the phylogeny as well as WGD inference methods.

Models for the retention of duplicate genes and their biological underpinnings

Gene content in genomes changes through several different processes, with gene duplication being an important contributor to such changes. Gene duplication occurs over a range of scales from individual genes to whole genomes, and the dynamics of this process can be context dependent. Still, there are rules by which genes are retained or lost from genomes after duplication, and probabilistic modeling has enabled characterization of these rules, including their context-dependence. Here, we describe the biology and corresponding mathematical models that are used to understand duplicate gene retention and its contribution to the set of biochemical functions encoded in a genome.

Expectations of duplicate gene retention under the gene duplicability hypothesis

BACKGROUND

Gene duplication is an important process in evolution. What causes some genes to be retained after duplication and others to be lost is a process not well understood. The most prevalent theory is the gene duplicability hypothesis, that something about the function and number of interacting partners (number of subunits of protein complex, etc.), determines whether copies have more opportunity to be retained for long evolutionary periods. Some genes are also more susceptible to dosage balance effects following WGD events, making them more likely to be retained for longer periods of time. One would expect these processes that affect the retention of duplicate copies to affect the conditional probability ratio after consecutive whole genome duplication events. The probability that a gene will be retained after a second whole genome duplication event (WGD2), given that it was retained after the first whole genome duplication event (WGD1) versus the probability a gene will be retained after WGD2, given it was lost after WGD1 defines the probability ratio that is calculated.

RESULTS

Since duplicate gene retention is a time heterogeneous process, the time between the events (t1) and the time since the most recent event (t2) are relevant factors in calculating the expectation for observation in any genome. Here, we use a survival analysis framework to predict the probability ratio for genomes with different values of t1 and t2 under the gene duplicability hypothesis, that some genes are more susceptible to selectable functional shifts, some more susceptible to dosage compensation, and others only drifting. We also predict the probability ratio with different values of t1 and t2 under the mutational opportunity hypothesis, that probability of retention for certain genes changes in subsequent events depending upon how they were previously retained. These models are nested such that the mutational opportunity model encompasses the gene duplicability model with shifting duplicability over time. Here we present a formalization of the gene duplicability and mutational opportunity hypotheses to characterize evolutionary dynamics and explanatory power in a recently developed statistical framework.

CONCLUSIONS

This work presents expectations of the gene duplicability and mutational opportunity hypotheses over time under different sets of assumptions. This expectation will enable formal testing of processes leading to duplicate gene retention.