Biology subjects

Arredondo, A.

Publications and source records attributed to Arredondo, A..

6 recordsLinked to original sources

bioRxiv2024

Extending the IICR to complex non-stationary structured models

Population genetic studies use genetic data to understand aspects of the evolutionary history of species, particularly to infer their demography. Here, we extend the non-stationary structured coalescent framework to complex models where the parameters of the model, and thus the state space, change between stationary periods. To do this, we introduce glue matrices that allow us to map one state space to another, hence enabling the computation of the Inverse Instantaneous Coalescence Rate (IICR) using Q-matrices of different dimensions. This approach allows us to study models where the number of demes changes due to extinction or foundation. Our analysis confirms that interpreting IICR curves as indicators of population size changes can be misleading. However, we also show that there are cases where new deme foundations can generate an increase in the IICR (forward in time). We explore the impact of changes in migration rates, timing of deme creation or deletion, and sampling strategies on the IICR, identifying several counter-intuitive results. For instance, we find that the IICR starts to decrease several generations before any change in the number of demes, as if the population "knew" something was about to happen. It is particularly unexpected when a deme is founded and the population is only increasing. Our results underscore the need for careful interpretation of PSMC and similar curves, which are often used and interpreted as population size changes. By presenting examples of IICRs for various transitions and sampling scenarios, we emphasize the importance of nuanced approaches in population genetic studies.

evolutionary biology↗

bioRxiv2024

Extending the IICR to multiple genomes and identification of limitations of some demographic inferential methods

Reconstructing the demographic history of populations and species is one of the greatest challenges facing population geneticists. [50] introduced, for a sample of size k = 2 haploid genomes, a time- and sample-dependent parameter which they called the IICR (inverse instantaneous coalescence rate). Here we extend their work to larger sample sizes and focus on Tk, the time to the first coalescence event in a haploid sample of size k where k [≥] 2. We define the IICRk as the Inverse Instantaneous Coalescence Rate among k lineages. We show that (i) under a panmictic population [Formula] is equivalent to Ne, (ii) the IICRk can be obtained by either simulating Tk values or by using the Q-matrix approach of [61] and we provide the corresponding Python and R scripts. We then study the properties of the [Formula] under a limited set of n-island and stepping-stone models. We show that (iii) in structured models the [Formula] is dependent on the sample size and on the sampling scheme, even when the genomes are sampled in the same deme. For instance, we find that [Formula] plots for individuals sampled in the same deme will be shifted towards recent times with a lower plateau as k increases. We thus show that (iv) the [Formula] cannot be used to represent "the demographic history" in a general sense, (v) the [Formula] can be estimated from real or simulated genomic data using the PSMC/MSMC methods [44, 65] (vi) the MSMC2 method produces smoother curves that infer something that is not the [Formula], but are close to the [Formula] in the recent past when all samples are obtained from the same deme. Altogether we argue that the PSMC, MSMC and MSMC2 plots are not expected to be identical even when the genomes are sampled from the same deme, that none can be said to represent the "demographic history of populations" and that they should be interpreted with care. We suggest that the PSMC, MSMC and MSMC2 could be used together with the [Formula] to identify the signature of population structure, and to develop new strategies for model choice.

evolutionary biology↗

bioRxiv2024

On the demographic history of chimpanzees and some consequences of integrating population structure in chimpanzees and other great apes

Reconstructing the evolutionary history of great apes is of particular importance for our understanding of the demographic history of humans. The reason for this is that modern humans and their hominin ancestors evolved in Africa and thus shared the continent with the ancestors of chimpanzees and gorillas. Common chimpanzees (Pan troglodytes) are our closest relatives with bonobos (Pan paniscus) and most of what we know about their evolutionary history comes from genetic and genomic studies. Most evolutionary studies of common chimpanzees have assumed that the four currently recognised subspecies can be modelled using simple tree models where each subspecies is panmictic and represented by one branch of the evolutionary tree. However, several studies have identified the existence of significant population structure, both within and between subspecies, with evidence of isolation-by-distance (IBD) patterns. This suggests that demographic models integrating population structure may be necessary to improve our understanding of their evolutionary history. Here we propose to use n-island models within each subspecies to infer a demographic history integrating population structure and changes in connectivity (i.e. gene flow). For each subspecies, we use SNIF (structured non-stationary inference framework), a method developed to infer a piecewise stationary n-island model using PSMC (pairwise sequentially Markovian coalescent) curves as summary statistics. We then propose a general model integrating the four subspecies metapopulations within a phylogenetic tree. We find that this model correctly predicts estimates of within subspecies genetic diversity and differentiation, but overestimates genetic differentiation between subspecies as a consequence of the tree structure. We argue that spatial models integrating gene flow between subspecies should improve the prediction of between subspecies differentiation and IBD patterns. We also use a simple spatially structured model for bonobos and chimpanzees (without admixture) and find that it explains signals of admixture between the two species that have been reported and could thus be spurious. This may have implications for our understanding of the evolutionary history of the Homo genus.

evolutionary biology↗

bioRxiv2023

Exact calculation of the expected SFS in structured populations

The Site Frequency Spectrum (SFS), summary statistic of the distribution of derived allele frequencies in a sample of DNA sequences, provides information about genetic variation and can be used to make population inferences. The exact calculation of the expected SFS in panmictic population has been derived in the Markovian coalescent theory for decades, but its generalization to the structured coalescent is hampered by the almost exponential growth of the states space. We propose here a complete algorithmic procedure, from how to build a suitable state space and sort it, to how to take advantage of the sparsity of the rate matrix and to solve numerically the linear system by an iterative method. The simplest case of the symmetrical n-island is then processed to arrive at a ready-to-use demographic parameters inference framework.

genetics↗

bioRxiv2021

Heterogeneity in effective size across the genome: effects on the Inverse Instantaneous Coalescence Rate (IICR) and implications for demographic inference under linked selection.

The relative contribution of selection and neutrality in shaping species genetic diversity is one of the most central and controversial questions in evolutionary theory. Genomic data provide growing evidence that linked selection, i.e. the modification of genetic diversity at neutral sites through linkage with selected sites, might be pervasive over the genome. Several studies proposed that linked selection could be modelled as first approximation by a local reduction (e.g. purifying selection, selective sweeps) or increase (e.g. balancing selection) of effective population size (Ne). At the genome-wide scale, this leads to variations of Ne from one region to another, reflecting the heterogeneity of selective constraints and recombination rates between regions. We investigate here the consequences of such genomic variations of Ne on the genome-wide distribution of coalescence times. The underlying motivation concerns the impact of linked selection on demographic inference, because the distribution of coalescence times is at the heart of several important demographic inference approaches. Using the concept of Inverse Instantaneous Coalescence Rate, we demonstrate that in a panmictic population, linked selection always results in a spurious apparent decrease of Ne along time. Balancing selection has a particularly large effect, even when it concerns a very small part of the genome. We also study more general models including genuine population size changes, population structure or transient selection and find that the effect of linked selection can be significantly reduced by that of population structure. The models and conclusions presented here are also relevant to the study of other biological processes generating apparent variations of Ne along the genome.

genetics↗

bioRxiv2019

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: M319C, T431I, and K337D

The use of computational tools has become an increasingly popular tool for engineering protein function. While there are numerous examples of computational tools enabling the design of novel protein functions, there remains room for improvement in both prediction accuracy and success. To improve algorithms for functional and stability predictions, we have initiated the development of a data set designed to be used for training new computational algorithms for enzyme design. To date our dataset is composed of over 129 mutants with associated expression levels, kinetic data, and thermal stability for the enzyme {beta}-glucosidase B (BglB) from Paenibacillus polymyxa. In this study, we introduced three new variants (M319C, T431I, and K337D) to our existing dataset with the goal of cultivating a larger dataset to train new design algorithms and more broadly explore structure-function relationships in BglB.

biochemistry↗