Biology subjects

Duchene, D.

Publications and source records attributed to Duchene, D..

4 recordsLinked to original sources

bioRxiv2026

Evolutionary history and genomic vulnerability of the extinct giant deer Megaloceros giganteus

The extinct giant deer (Megaloceros giganteus) was one of the most striking megafaunal species of the Late Quaternary, distinguished by its enormous palmated antlers reaching up to 3.5 m across, the largest known among both living and extinct cervids. Despite its iconic status, little is known about its genomic history prior to extinction [~]8 thousand years ago (kya). We generated the first nuclear palaeogenomes for Megaloceros, represented by nine individuals from Germany ([~]40 kya) and Ireland ([~]11 kya), mapped to a new chromosome-level reference genome of the fallow deer (Dama dama). Phylogenomic analyses placed Megaloceros as sister to Dama (divergence [~]3.5 Ma) and revealed evidence of gene flow with ancestral Cervus lineages. Population analyses identified clear differentiation between German and Irish lineages, with higher genetic diversity in the German individuals. Two genes under strong positive selection, BNIPL and SLC10A7, are associated with apoptosis regulation and skeletal development/bone mineralisation, respectively, and may relate to the species large body and antler size. Demographic reconstructions indicate a long-term decline in effective population size, extremely low heterozygosity, little evidence of extensive runs of homozygosity, and an elevated burden of predicted deleterious alleles. Together, these results suggest that Megaloceros entered the terminal Pleistocene in a genomically fragile state, offering new insight into the biology and evolutionary legacy of one of the largest and most distinctive cervids that ever lived.

evolutionary biology↗

bioRxiv2022

Genomic insights into the evolutionary relationships and demographic history of kiwi

Kiwi are a unique and emblematic group of birds endemic to New Zealand. Deep-time evolutionary relationships among the five extant kiwi species have been difficult to resolve, in part due to the absence of pre-Quaternary fossils to inform speciation events among them. Here, we utilise single representative nuclear genomes of all extant kiwi species (great spotted kiwi, little spotted kiwi, Okarito brown kiwi, North Island brown kiwi, and southern brown kiwi) and investigate their evolutionary histories with phylogenomic, genetic diversity, and deep-time (past million years) demographic analyses. We uncover low levels of gene-tree phylogenetic discordance across the genomes, suggesting clear distinction between species. However, we also find indications of post-divergence gene flow, concordant with recent reports of interspecific hybrids. The four species with available reference assemblies show relatively low levels of genetic diversity, which we suggest reflects a combination of older environmental as well as more recent anthropogenic influence. In addition, we uncover similarities and differences in the demographic histories of the five kiwi species over the past million years, and suggest hypotheses regarding the impact of known past environmental events, such as volcanic eruptions and glacial periods, on the evolutionary history of the group.

evolutionary biology↗

bioRxiv2018

A Simulation-Based Evaluation of Total-Evidence Dating Under the Fossilized Birth-Death Process

Bayesian molecular dating is widely used to study evolutionary timescales. This procedure usually involves phylogenetic analysis of nucleotide sequence data, with fossil-based calibrations applied as age constraints on internal nodes of the tree. An alternative approach is Bayesian total-evidence dating, which involves the joint analysis of molecular data from present-day taxa and morphological data from both extant and fossil taxa. Part of its appeal stems from the fossilized birth-death process, which provides a model of lineage diversification for the prior on the tree topology and node times. However, total-evidence dating faces a number of considerable challenges, especially those associated with fossil sampling and evolutionary models for morphological characters. We conducted a simulation study to evaluate the performance of total-evidence dating with the fossilized birth-death model. We simulated fossil occurrences and the evolution of nucleotide sequences and morphological characters under a wide range of conditions. Our analyses show that fossil occurrences have a greater influence than the degree of among-lineage rate variation or the number of morphological characters on estimates of node times and the tree topology. Total-evidence dating generally performs well in recovering the relationships among extant taxa, but has difficulties in correctly placing fossil taxa in the tree and identifying the number of sampled ancestors. The method yields accurate estimates of the origin time of the fossilized birth-death process and the ages of the root and crown group, although the precision of these estimates varies with the probability of fossil occurrence. The exclusion of morphological characters results in a slight overestimation of node times, whereas the exclusion of nucleotide sequences has a negative impact on inference of the tree topology. Overall, our results provide a detailed view of the performance of total-evidence dating, which will inform further development of the method and its application to key questions in evolutionary biology.

evolutionary biology↗

bioRxiv2017

Inferring demographic parameters in bacterial genomic data using Bayesian and hybrid phylogenetic methods

BackgroundRecent developments in sequencing technologies make it possible to obtain genome sequences from a large number of isolates in a very short time. Bayesian phylogenetic approaches can take advantage of these data by simultaneously inferring the phylogenetic tree, evolutionary timescale, and demographic parameters (such as population growth rates), while naturally integrating uncertainty in all parameters. Despite their desirable properties, Bayesian approaches can be computationally intensive, hindering their use for outbreak investigations involving genome data for a large numbers of pathogen isolates. An alternative to using full Bayesian inference is to use a hybrid approach, where the phylogenetic tree and evolutionary timescale are estimated first using maximum likelihood. Under this hybrid approach, demographic parameters are inferred from estimated trees instead of the sequence data, using maximum likelihood, Bayesian inference, or approximate Bayesian computation. This can vastly reduce the computational burden, but has the disadvantage of ignoring the uncertainty in the phylogenetic tree and evolutionary timescale.\n\nResultsWe compared the performance of a fully Bayesian and a hybrid method by analysing six whole-genome SNP data sets from a range of bacteria and simulations. The estimates from the two methods were very similar, suggesting that the hybrid method is a valid alternative for very large datasets. However, we also found that congruence between these methods is contingent on the presence of strong temporal structure in the data (i.e. clocklike behaviour), which is typically verified using a date-randomisation test in a Bayesian framework. To reduce the computational burden of this Bayesian test we implemented a date-randomisation test using a rapid maximum likelihood method, which has similar performance to its Bayesian counterpart.\n\nConclusionsHybrid approaches can produce reliable inferences of evolutionary timescales and phylodynamic parameters in a fraction of the time required for fully Bayesian analyses. As such, they are a valuable alternative in outbreak studies involving a large number of isolates.

bioinformatics↗