Nicolas Bierne<p><a href="https://ecoevo.social/tags/PopGen" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PopGen</span></a> New preprint of the team on the discovery of a very high rate of polymorphic duplication in a marine bivalve.</p><p>Imagine looking at the distribution of the allelic coverage fraction at heterozygous calls in an indivdual and getting this distribution 👇 😱😱😱</p>
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Population genetics: an introduction for physicists https://arxiv.org/abs/2408.02650 #genetics #physics #popgen
arXiv.orgPopulation genetics: an introduction for physicistsPopulation genetics lies at the heart of evolutionary theory. This topic forms part of many biological science curricula but is rarely taught to physics students. Since physicists are becoming increasingly interested in biological evolution, we aim to provide a brief introduction to population genetics, written for physicists. We start with two background chapters: chapter 1 provides a brief historical introduction to the topic, while chapter 2 provides some essential biological background. We begin our main content with chapter 3 which discusses the key concepts behind Darwinian natural selection and Mendelian inheritance. Chapter 4 covers the basics of how variation is maintained in populations, while chapter 5 discusses mutation and selection. In chapter 6 we discuss stochastic effects in population genetics using the Wright-Fisher model as our example, and finally we offer concluding thoughts and references to excellent textbooks in chapter 7.
Another tool for detecting sex-linked genome sequences! SLRfinder: A method to detect candidate sex-linked regions with linkage disequilibrium clustering: https://doi.org/10.1111/1755-0998.13985
Hi everybody,
I am pleased to share with you my latest work in collaboration with Augustin Cléssin and Nicolas Lartillot:
https://www.biorxiv.org/content/10.1101/2024.06.21.600052v1
GC-biased gene conversion (gBGC) is a process that distorts the segregation of heterozygous AT:GC polymorphisms during meiotic #recombination. This genome-wide non-Mendelian segregation has been shown to be a major source of #genetic load, earning the nickname of “Achille’s heel” of #genomes. (1/2)
bioRxiv · The evolution of GC-biased gene conversion by means of natural selectionGC-biased gene conversion (gBGC) is a recombination-associated evolutionary process that biases the segregation ratio of AT:GC polymorphisms in the gametes of heterozygotes, in favour of GC alleles. This process is the major determinant of variation in base composition across the human genome and can be the cause of a substantial burden of GC deleterious alleles. While the importance of GC-biased gene conversion in molecular evolution is increasingly recognised, the reasons for its existence and its variation between species remain largely unknown. Using simulations and semi-analytical approximations, we investigated the evolution of gBGC as a quantitative trait evolving by mutation, drift and natural selection. We show that in a finite population where most mutations are deleterious, gBGC is under weak stabilising selection around a positive value that mainly depends on the intensity of the mutation bias and on the intensity of selective constraints exerted on the genome. Importantly, the levels of gBGC that evolve by natural selection do not minimize the load in the population, and even increase it substantially in regions of high recombination rate. Therefore, despite reducing the population's fitness, levels of gBGC that are currently observed in humans could in fact have been (weakly) positively selected.
### Competing Interest Statement
The authors have declared no competing interest.
Nice new work from Doc @edge on recovering polygenic scores from ancestral recombination graphs.
Scalability vs accuracy.
bioRxiv · Evaluating ARG-estimation methods in the context of estimating population-mean polygenic score historiesScalable methods for estimating marginal coalescent trees across the genome present new opportunities for studying evolution and have generated considerable excitement, with new methods extending scalability to thousands of samples. Benchmarking of the available methods has revealed general tradeoffs between accuracy and scalability, but performance in downstream applications has not always been easily predictable from general performance measures, suggesting that specific features of the ARG may be important for specific downstream applications of estimated ARGs. To exemplify this point, we benchmark ARG estimation methods with respect to a specific set of methods for estimating the historical time course of a population-mean polygenic score (PGS) using the marginal coalescent trees encoded by the ancestral recombination graph (ARG). Here we examine the performance in simulation of six ARG estimation methods: ARGweaver, RENT+, Relate, tsinfer+tsdate, ARG-Needle/ASMC-clust, and SINGER, using their estimated coalescent trees and examining bias, mean squared error (MSE), confidence interval coverage, and Type I and II error rates of the downstream methods. Although it does not scale to the sample sizes attainable by other new methods, SINGER produced the most accurate estimated PGS histories in many instances, even when Relate, tsinfer+tsdate, and ARG-Needle/ASMC-clust used samples ten times as large as those used by SINGER. In general, the best choice of method depends on the number of samples available and the historical time period of interest. In particular, the unprecedented sample sizes allowed by Relate, tsinfer+tsdate, and ARG-Needle/ASMC-clust are of greatest importance when the recent past is of interest—further back in time, most of the tree has coalesced, and differences in contemporary sample size are less salient.
### Competing Interest Statement
The authors have declared no competing interest.
Still open PhD position
#Job #phd #Evolution #Genomics #Cancer #TransCan #MarBiol #Polyploidy #PopGen #France
Here is an offer for a PhD (ERC HYPERCAN project) on the evolution of a transmissible cancer, with a focus on hyperploidy variations:
euraxess.ec.europa.eu/jobs/230401
Please boost
The Australian Institute of Marine Science (Townsville, Australia) is advertising for an evolutionary modeller who can use models to forecast possible evolutionary trajectories for coral reef communities under various scenarios including restoration interventions. #Govjobs #academicjobs #PopGen
Deadline for applications: June 10, 2024
For full details and instructions: https://www.aims.gov.au/about/careers-aims