Biology subjects

Rivas-Gonzalez, I.

Publications and source records attributed to Rivas-Gonzalez, I..

4 recordsLinked to original sources

bioRxiv2026

Accelerated Evolution of Context-Dependent Gene Expression in Primate Immune Cells

Regulatory variation is widely recognized as a driver of evolution within and between species, including in the primate lineage. Evidence for potentially adaptive regulatory evolution in primates comes from both sequence-based tests for selection and cross-species comparisons of gene expression. However, comparative studies of gene expression have tended to focus on baseline cellular states, despite growing evidence that context-dependent gene regulation is important. Here, we investigate the evolution of gene expression in lymphoblastoid cell lines (LCLs) from 6 primate lineages (humans, chimpanzees, bonobos, western gorillas, Sumatran orangutans, and rhesus macaques) using data from both control and 5 perturbed cellular conditions (n=22-25 individuals per condition). Analyzing baseline expression data, we find that genes involved in immunity often exhibit lineage-specific transcriptional shifts, and that these genes occur near regions with lineage-specific sequence acceleration and lineage-specific epigenomic states. However, the subset of genes that respond to in vitro perturbations have evolved at significantly higher rates than environmentally-insensitive genes, a finding we replicate in an independent four-species dataset of primary white blood cells (n=26). Together, our findings support the argument that focusing on baseline states alone provides a biased view of gene regulatory evolution, and suggest that studying gene regulation in dynamic contexts can enrich our understanding of primate evolution.

evolutionary biology↗

bioRxiv2025

Demographic, behavioral, and ecological data from a long-term field study of wild baboons in Amboseli, Kenya

Long-term data sets on individually recognized animals and their environments are critical to understanding animal behavior, evolution, and ecology. However, they are resource- and time-intensive and seldom made publicly available. The Amboseli Baboon Research Project (ABRP) is one of the longest-running studies of a wild mammal population in the world and has collected extensive data on the baboon population of the Amboseli ecosystem in Kenya since 1971. Here, we describe four ABRP data sets newly available to the evolutionary biology, behavioral ecology, and primatology communities: (1) the sizes and demographic compositions of 21 social groups from 1971-2023; (2) the activity budgets of adult females and immatures from 1984-2023; (3) behavioral data on diet for adult females and immatures from 1984-2023; and (4) weather data, including precipitation from 1976-2023 and temperature from 1976-2022. Data are aggregated annually and monthly to enable cross-data set analyses. These data offer a rare longitudinal perspective on behavioral and ecological change in a wild mammal population.

animal behavior and cognition↗

bioRxiv2023

TRAILS: tree reconstruction of ancestry using incomplete lineage sorting

Genome-wide genealogies of multiple species carry detailed information about demographic and selection processes on individual branches of the phylogeny. Here, we introduce TRAILS, a hidden Markov model that accurately infers time-resolved population genetics parameters, such as ancestral effective population sizes and speciation times, for ancestral branches using a multi-species alignment of three species and an outgroup. TRAILS leverages the information contained in incomplete lineage sorting fragments by modelling genealogies along the genome as rooted three-leaved trees, each with a topology and two coalescent events happening in discretized time intervals within the phylogeny. Posterior decoding of the hidden Markov model can be used to infer the ancestral recombination graph for the alignment and details on demographic changes within a branch. Since TRAILS performs posterior decoding at the base-pair level, genome-wide scans based on the posterior probabilities can be devised to detect deviations from neutrality. Using TRAILS on a human-chimp-gorilla-orangutan alignment, we recover speciation parameters and extract information about the topology and coalescent times at high resolution. Author summaryDNA sequences can be compared to reconstruct the evolutionary history of different species. While the ancestral history is usually represented by a single phylogenetic tree, speciation is a more complex process, and, due to the effect of recombination, different parts of the genome might follow different genealogies. For example, even though humans are more closely related to chimps than to gorillas, around 15% of our genome is more similar to the gorilla genome than to the chimp one. Even for those parts of the genome that do follow the same human-chimp topology, we might encounter a last common ancestor at different time points in the past for different genomic fragments. Here, we present TRAILS, a new framework that utilizes the information contained in all these genealogies to reconstruct the speciation process. TRAILS infers unbiased estimates of the speciation times and the ancestral effective population sizes, improving the accuracy when compared to previous methods. TRAILS also reconstructs the genealogy at the highest resolution, inferring, for example, when common ancestry was found for different parts of the genome. This information can also be used to detect deviations from neutrality, effectively inferring natural selection that happened millions of years ago. We validate the method using extensive simulations, and we apply TRAILS to a human-chimp-gorilla multiple genome alignment, from where we recover speciation parameters that are in good agreement with previous estimates.

evolutionary biology↗

bioRxiv2022

PhaseTypeR: phase-type distributions in R with reward transformations and a view towards population genetics

Phase-type distributions are a general class of models that are traditionally used in actuarial sciences and queuing theory, and more recently in population genetics. A phase-type distributed random variable is the time to absorption in a discrete or continuous time Markov chain on a finite state space with an absorbing state. The R package PhaseTypeR contains all the key functions--mean, (co)variance, probability density function, cumulative distribution function, quantile function, random sampling and reward transformations--for both continuous (PH) and discrete (DPH) phase-type distributions. Additionally, we have also implemented the multivariate continuous case (MPH) and the multivariate discrete case (MDPH). We illustrate the usage of PhaseTypeR in simple examples from population genetics (e.g. the time until the most recent common ancestor or the total number of mutations in an alignment of homologous DNA sequences), and we demonstrate the power of PhaseTypeR in more involved applications from population genetics, such as the coalescent with recombination and the structured coalescent. The multivariate distributions and ability to reward-transform are particularly important in population genetics, and a unique feature of PhaseTypeR.

bioinformatics↗