Ancient DNA and the Population Genetics of Cave Bears (Ursus spelaeus) Through Space and Time

Abstract The geographic distribution of genetic variation is an important theoretical and experimental component of population genetics. Previous characterizations of genetic structure of populations have used measures of spatial variance and spatial correlations. Yet a full understanding of the causes and consequences of spatial structure requires complete characterization of the underlying space-time system. This paper examines important interactions between processes and spatial structure in systems of subpopulations with migration and drift, by analyzing correlations of gene frequencies over space and time. We develop methods for studying important features of the complete set of space-time correlations of gene frequencies for the first time in population genetics. These methods also provide a new alternative for studying the purely spatial correlations and the variance, for models with general spatial dimensionalities and migration patterns. These results are obtained by employing theorems, previously unused in population genetics, for space-time autoregressive (STAR) stochastic spatial time series. We include results on systems with subpopulation interactions that have time delay lags (temporal orders) greater than one. We use the space-time correlation structure to develop novel estimators for migration rates that are based on space-time data (samples collected over space and time) rather than on purely spatial data, for real systems. We examine the space-time and spatial correlations for some specific stepping stone migration models. One focus is on the effects of anisotropic migration rates. Partial space-time correlation coefficients can be used for identifying migration patterns. Using STAR models, the spatial, space-time, and partial space-time correlations together provide a framework with an unprecedented level of detail for characterizing, predicting and contrasting space-time theoretical distributions of gene frequencies, and for identifying features such as the pattern of migration and estimating migration rates in experimental studies of genetic variation over space and time.

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Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues.

10.1101/022251 ◽

2015 ◽

Author(s):

Patrick Smadbeck ◽

Michael P.H. Stumpf

Keyword(s):

Developmental Biology ◽

Population Genetics ◽

Large Scale ◽

Cell Tracking ◽

Temporal Dynamics ◽

Growth Models ◽

Tissue Growth ◽

Lineage Tracing ◽

Space And Time ◽

Spatio Temporal

Development is a process that needs to tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny in both space and time. A generic feature of developing (as well as homeostatic) tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end fairly quickly. This has spurned the interest also of computational and theoretical biologists/physicists who have developed a range of modelling -- perhaps most notably are the agent-based modelling (ABM) --- approaches. These can become computationally prohibitively expensive but seem to capture some of the features observed in experiments. Here we develop a complementary perspective that allows us to understand the dynamics leading to the formation of a tissue (or colony of cells). Borrowing from the rich population genetics literature we develop genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We apply this approach to tissues that grow under confined conditions --- as would, for example, be appropriate for the neural crest --- and unbounded growth --- illustrative of the behaviour of 2D tumours or bacterial colonies. The classical coalescent model from population genetics is readily adapted to capture tissue genealogies for different models of tissue growth and development. We show that simple but universal scaling relationships allow us to establish relationships between the coalescent and different fractal growth models that have been extensively studied in many different contexts, including developmental biology. Using our genealogical perspective we are able to study the statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.

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A conditional likelihood is required to estimate the selection coefficient in ancient DNA

10.1101/063396 ◽

2016 ◽

Author(s):

Angelo Valleriani

Keyword(s):

Time Series ◽

Population Genetics ◽

Natural Selection ◽

Genetic Drift ◽

Ancient Dna ◽

Fitness Landscape ◽

Likelihood Function ◽

Allele Frequencies ◽

Conditional Likelihood ◽

Selection Coefficient

AbstractTime-series of allele frequencies are a useful and unique set of data to determine the strength of natural selection on the background of genetic drift. Technically, the selection coefficient is estimated by means of a likelihood function built under the hypothesis that the available trajectory spans a sufficiently large portion of the fitness landscape. Especially for ancient DNA, however, often only one single such trajectories is available and the coverage of the fitness landscape is very limited. In fact, one single trajectory is more representative of a process conditioned both in the initial and in the final condition than of a process free to visit the available fitness landscape. Based on two models of population genetics, here we show how to build a likelihood function for the selection coefficient that takes the statistical peculiarity of single trajectories into account. We show that this conditional likelihood delivers a precise estimate of the selection coefficient also when allele frequencies are close to fixation whereas the unconditioned likelihood fails. Finally, we discuss the fact that the traditional, unconditioned likelihood always delivers an answer, which is often unfalsifiable and appears reasonable also when it is not correct.

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Population Genetics of the Aquatic Fungus Tetracladium marchalianum over Space and Time

PLoS ONE ◽

10.1371/journal.pone.0015908 ◽

2011 ◽

Vol 6 (1) ◽

pp. e15908 ◽

Cited By ~ 14

Author(s):

Jennifer L. Anderson ◽

Carol A. Shearer

Keyword(s):

Population Genetics ◽

Aquatic Fungus ◽

Space And Time ◽

Tetracladium Marchalianum

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Spatial Population Genetics: It's About Time

Annual Review of Ecology Evolution and Systematics ◽

10.1146/annurev-ecolsys-110316-022659 ◽

2019 ◽

Vol 50 (1) ◽

pp. 427-449 ◽

Cited By ~ 20

Author(s):

Gideon S. Bradburd ◽

Peter L. Ralph

Keyword(s):

Population Genetics ◽

Genetic Variation ◽

Evolutionary Process ◽

Genetic Data ◽

Space And Time ◽

Spatial Population ◽

Alive Today

Many important questions about the history and dynamics of organisms have a geographical component: How many are there, and where do they live? How do they move and interbreed across the landscape? How were they moving a thousand years ago, and where were the ancestors of a particular individual alive today? Answers to these questions can have profound consequences for our understanding of history, ecology, and the evolutionary process. In this review, we discuss how geographic aspects of the distribution, movement, and reproduction of organisms are reflected in their pedigree across space and time. Because the structure of the pedigree is what determines patterns of relatedness in modern genetic variation, our aim is to thus provide intuition for how these processes leave an imprint in genetic data. We also highlight some current methods and gaps in the statistical toolbox of spatial population genetics.

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Entanglements of Time, Temperature, Technology, and Place in Ancient DNA Research: The Case of the Denisovan Hominin

Science Technology & Human Values ◽

10.1177/0162243919879991 ◽

2019 ◽

Vol 45 (6) ◽

pp. 1119-1141

Author(s):

Venla Oikkonen

Keyword(s):

Population Genetics ◽

Ancient Dna ◽

Science And Technology Studies ◽

Evolutionary History ◽

Science And Society ◽

Hominin Evolution ◽

The Status ◽

History Of ◽

Specific Configuration

The study of ancient DNA (aDNA) has gained increasing attention in science and society as a tool for tracing hominin evolution. While aDNA research overlaps with the history of population genetics, it embodies a specific configuration of technology, temporality, temperature, and place that, this article suggests, cannot be fully unpacked with existing science and technology studies approaches to population genetics. This article explores this configuration through the 2010 discovery of the Denisovan hominin based on aDNA retrieved from a finger bone and tooth in Siberia. The analysis explores how the Denisovan was enacted as a technoscientific object through the cool and even temperatures of Denisova Cave, assumptions about the connection between individual and population, the status of populations as evolutionary entities, and underlying colonialist and imperialist imaginaries of Siberia and Melanesia. The analysis sheds light on how aDNA research is changing the parameters within which evolutionary history is imagined and conceptualized. Through the case study, it also outlines some ways in which the specific technoscientific and cultural entanglements of aDNA can be critically explored.

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