scholarly journals The IICR and the non-stationary structured coalescent: demographic inference with arbitrary changes in population structure

2018 ◽  
Author(s):  
Willy Rodríguez ◽  
Olivier Mazet ◽  
Simona Grusea ◽  
Simon Boitard ◽  
Lounès Chikhi
Keyword(s):  
Population Structure ◽  
Population Size ◽  
Environmental Changes ◽  
Genomic Data ◽  
Size Change ◽  
Structured Model ◽  
Coalescence Rate ◽  
Wide Range ◽  
Structured Coalescent ◽  
Demographic Inference

AbstractIn the last years, a wide range of methods allowing to reconstruct past population size changes from genome-wide data have been developed. At the same time, there has been an increasing recognition that population structure can generate genetic data similar to those produced under models of population size change. Recently, Mazet et al. (2016) showed that, for any model of population structure, it is always possible to find a panmictic model with a particular function of population size changes, having exactly the same distribution of T2 (the coalescence time for a sample of size two) to that of the structured model. They called this function IICR (Inverse Instantaneous Coalescence Rate) and showed that it does not necessarily correspond to population size changes under non panmictic models. Besides, most of the methods used to analyse data under models of population structure tend to arbitrarily fix that structure and to minimise or neglect population size changes. Here we extend the seminal work of Herbots (1994) on the structured coalescent and propose a new framework, the Non-Stationary Structured Coalescent (NSSC) that incorporates demographic events (changes in gene flow and/or deme sizes) to models of nearly any complexity. We show how to compute the IICR under a wide family of stationary and non-stationary models. As an example we address the question of human and Neanderthal evolution and discuss how the NSSC framework allows to interpret genomic data under this new perspective.Author summaryGenomic data are becoming available for a rapidly increasing number of species, and contain information about their recent evolutionary history. If we wish to understand how they expanded, contracted or admixed as a consequence of recent and ancient environmental changes, we need to develop general inferential methods. Currently, demographic inference is either done assuming that a species is a single panmictic population or using arbitrary structured models. We use the concept of IICR (Inverse of the Instantaneous Coalescence Rate) together with Markov chains theory to develop a general inferential framework which we call the Non-Stationary Structured Coalescent and apply it to explain human and Neanderthal genomic data in a single structured model.

scholarly journals On the importance of being structured: instantaneous coalescence rates and a re-evaluation of human evolution

2015 ◽  
Author(s):  
Olivier Mazet ◽  
Willy Rodríguez ◽  
Simona Grusea ◽  
Simon Boitard ◽  
Lounès Chikhi
Keyword(s):  
Population Structure ◽  
Gene Flow ◽  
Population Size ◽  
Genomic Data ◽  
Middle Pleistocene ◽  
Demographic Model ◽  
Size Change ◽  
Population Genetic Inference ◽  
Demographic Inference ◽  
Inference Methods

Most species are structured and influenced by processes that either increased or reduced gene flow between populations. However, most population genetic inference methods ignore population structure and reconstruct a history characterized by population size changes under the assumption that species behave as panmictic units. This is potentially problematic since population structure can generate spurious signals of population size change. Moreover, when the model assumed for demographic inference is misspecified, genomic data will likely increase the precision of misleading if not meaningless parameters. In a context of model uncertainty (panmixia \textit{versus} structure) genomic data may thus not necessarily lead to improved statistical inference. We consider two haploid genomes and develop a theory which explains why any demographic model (with or without population size changes) will necessarily be interpreted as a series of changes in population size by inference methods ignoring structure. We introduce a new parameter, the IICR (inverse instantaneous coalescence rate), and show that it is equivalent to a population size only in panmictic models, and mostly misleading for structured models. We argue that this general issue affects all population genetics methods ignoring population structure. We take the PSMC method as an example and show that it infers population size changes that never took place. We apply our approach to human genomic data and find a reduction in gene flow at the start of the Pleistocene, a major increase throughout the Middle-Pleistocene, and an abrupt disconnection preceding the emergence of modern humans.

scholarly journals Correction to: The IICR and the non-stationary structured coalescent: towards demographic inference with arbitrary changes in population structure

Heredity ◽  
2021 ◽  
Vol 126 (4) ◽  
pp. 706-706
Author(s):  
Willy Rodríguez ◽  
Olivier Mazet ◽  
Simona Grusea ◽  
Armando Arredondo ◽  
Josué M. Corujo ◽  
...  
Keyword(s):  
Population Structure ◽  
Structured Coalescent ◽  
Demographic Inference

scholarly journals Structured coalescent with nonconservative migration

2006 ◽  
Vol 43 (2) ◽  
pp. 351-362 ◽  
Author(s):  
Koffi Y. Sampson
Keyword(s):  
Population Structure ◽  
Population Size ◽  
Time Scale ◽  
Total Population ◽  
Total Population Size ◽  
Coalescence Time ◽  
Subdivided Population ◽  
Structured Coalescent ◽  
And Migration ◽  
Coalescent Time

We study the ancestral process of a sample from a subdivided population with stochastically varying subpopulation sizes. The sizes of the subpopulations change very rapidly (almost every generation) with respect to the coalescent time scale. For haploid populations of size N, one coalescence time unit corresponds to N generations. Coalescence and migration events occur on the same time scale. We show that, when the total population size tends to infinity, the structured coalescent is obtained, thus confirming the robustness of the coalescent. Many population structure models have been shown to converge to the structured coalescent (see Herbots (1997), Hudson (1998), Nordborg (2001), Nordborg and Krone (2002), and Notohara (1990)).

scholarly journals Demographic inference using genetic data from a single individual: separating population size variation from population structure

2014 ◽  
Author(s):  
Olivier Mazet ◽  
Willy Rodríguez ◽  
Lounès Chikhi
Keyword(s):  
Population Size ◽  
Genomic Data ◽  
Genetic Data ◽  
Structured Populations ◽  
Parameter Estimates ◽  
Single Individual ◽  
Model Choice ◽  
Size Change ◽  
History Of ◽  
Population Size Change

The rapid development of sequencing technologies represents new opportunities for population genetics research. It is expected that genomic data will increase our ability to reconstruct the history of populations. While this increase in genetic information will likely help biologists and anthropologists to reconstruct the demographic history of populations, it also represents new challenges. Recent work has shown that structured populations generate signals of population size change. As a consequence it is often difficult to determine whether demographic events such as expansions or contractions (bottlenecks) inferred from genetic data are real or due to the fact that populations are structured in nature. Given that few inferential methods allow us to account for that structure, and that genomic data will necessarily increase the precision of parameter estimates, it is important to develop new approaches. In the present study we analyse two demographic models. The first is a model of instantaneous population size change whereas the second is the classical symmetric island model. We (i) re-derive the distribution of coalescence times under the two models for a sample of size two, (ii) use a maximum likelihood approach to estimate the parameters of these models (iii) validate this estimation procedure under a wide array of parameter combinations, (iv) implement and validate a model choice procedure by using a Kolmogorov-Smirnov test. Altogether we show that it is possible to estimate parameters under several models and perform efficient model choice using genetic data from a single diploid individual.

scholarly journals Structured coalescent with nonconservative migration

2006 ◽  
Vol 43 (02) ◽  
pp. 351-362 ◽  
Author(s):  
Koffi Y. Sampson
Keyword(s):  
Population Structure ◽  
Population Size ◽  
Time Scale ◽  
Total Population ◽  
Total Population Size ◽  
Coalescence Time ◽  
Subdivided Population ◽  
Structured Coalescent ◽  
And Migration ◽  
Coalescent Time

We study the ancestral process of a sample from a subdivided population with stochastically varying subpopulation sizes. The sizes of the subpopulations change very rapidly (almost every generation) with respect to the coalescent time scale. For haploid populations of sizeN, one coalescence time unit corresponds toNgenerations. Coalescence and migration events occur on the same time scale. We show that, when the total population size tends to infinity, the structured coalescent is obtained, thus confirming the robustness of the coalescent. Many population structure models have been shown to converge to the structured coalescent (see Herbots (1997), Hudson (1998), Nordborg (2001), Nordborg and Krone (2002), and Notohara (1990)).

scholarly journals The IICR and the non-stationary structured coalescent: towards demographic inference with arbitrary changes in population structure

Heredity ◽  
2018 ◽  
Vol 121 (6) ◽  
pp. 663-678 ◽  
Author(s):  
Willy Rodríguez ◽  
Olivier Mazet ◽  
Simona Grusea ◽  
Armando Arredondo ◽  
Josué M. Corujo ◽  
...  
Keyword(s):  
Population Structure ◽  
Structured Coalescent ◽  
Demographic Inference

scholarly journals Genomic data detect corresponding signatures of population size change on an ecological time scale in two salamander species

2017 ◽  
Vol 26 (4) ◽  
pp. 1060-1074 ◽  
Author(s):  
Schyler O. Nunziata ◽  
Stacey L. Lance ◽  
David E. Scott ◽  
Emily Moriarty Lemmon ◽  
David W. Weisrock
Keyword(s):  
Population Size ◽  
Time Scale ◽  
Genomic Data ◽  
Size Change ◽  
Salamander Species ◽  
Population Size Change

scholarly journals Identification of Putative Virulence Genes by DNA Methylation Studies in the Cereal Pathogen Fusarium graminearum

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1192
Author(s):  
Francesco Tini ◽  
Giovanni Beccari ◽  
Gianpiero Marconi ◽  
Andrea Porceddu ◽  
Micheal Sulyok ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Fusarium Graminearum ◽  
Environmental Changes ◽  
Virulent Strain ◽  
Fungal Virulence ◽  
Wheat Seedlings ◽  
Wide Range ◽  
Conidia Production ◽  
Natural Hosts

DNA methylation mediates organisms’ adaptations to environmental changes in a wide range of species. We investigated if a such a strategy is also adopted by Fusarium graminearum in regulating virulence toward its natural hosts. A virulent strain of this fungus was consecutively sub-cultured for 50 times (once a week) on potato dextrose agar. To assess the effect of subculturing on virulence, wheat seedlings and heads (cv. A416) were inoculated with subcultures (SC) 1, 23, and 50. SC50 was also used to re-infect (three times) wheat heads (SC50×3) to restore virulence. In vitro conidia production, colonies growth and secondary metabolites production were also determined for SC1, SC23, SC50, and SC50×3. Seedling stem base and head assays revealed a virulence decline of all subcultures, whereas virulence was restored in SC50×3. The same trend was observed in conidia production. The DNA isolated from SC50 and SC50×3 was subject to a methylation content-sensitive enzyme and double-digest, restriction-site-associated DNA technique (ddRAD-MCSeEd). DNA methylation analysis indicated 1024 genes, whose methylation levels changed in response to the inoculation on a healthy host after subculturing. Several of these genes are already known to be involved in virulence by functional analysis. These results demonstrate that the physiological shifts following sub-culturing have an impact on genomic DNA methylation levels and suggest that the ddRAD-MCSeEd approach can be an important tool for detecting genes potentially related to fungal virulence.

scholarly journals How Do the Population Structure Changes of China Affect Carbon Emissions? An Empirical Study Based on Ridge Regression Analysis

2021 ◽  
Vol 13 (6) ◽  
pp. 3319
Author(s):  
Chulin Pan ◽  
Huayi Wang ◽  
Hongpeng Guo ◽  
Hong Pan
Keyword(s):  
Population Structure ◽  
Regression Analysis ◽  
Population Size ◽  
Carbon Emissions ◽  
Ridge Regression ◽  
Employment Structure ◽  
Consumption Structure ◽  
Structure Changes ◽  
Size Population ◽  
Ridge Regression Analysis

This study focuses on the impact of population structure changes on carbon emissions in China from 1995 to 2018. This paper constructs the multiple regression model and uses the ridge regression to analyze the relationship between population structure changes and carbon emissions from four aspects: population size, population age structure, population consumption structure, and population employment structure. The results showed that these four variables all had a significant impact on carbon emissions in China. The ridge regression analysis confirmed that the population size, population age structure, and population employment structure promoted the increase in carbon emissions, and their contribution ratios were 3.316%, 2.468%, 1.280%, respectively. However, the influence of population consumption structure (−0.667%) on carbon emissions was negative. The results showed that the population size had the greatest impact on carbon emissions, which was the main driving factor of carbon emissions in China. Chinese population will bring huge pressure on the environment and resources in the future. Therefore, based on the comprehensive analysis, implementing the one-child policy will help slow down China’s population growth, control the number of populations, optimize the population structure, so as to reduce carbon emissions. In terms of employment structure and consumption structure, we should strengthen policy guidance and market incentives, raising people’s low-carbon awareness, optimizing energy-consumption structure, improving energy efficiency, so as to effectively control China’s carbon emissions.

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