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Inferring Demographic History Using Genomic Data

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Book cover Population Genomics

Part of the book series: Population Genomics ((POGE))

Abstract

Characterizing population histories has been a major focus in evolutionary and conservation biology for decades. Driven by a desire to understand population histories, researchers have been modeling simple demographic scenarios with genetic data since the 1970s. In the last decade, the availability of genomic data and the number of demographic inference methods have dramatically increased and constitute a continuously evolving sub-discipline within population genetics. Genome sequences—both reduced representation and whole-genome sequencing and re-sequencing—contain a trove of information related to population histories and permit reconstructing complex demographic scenarios. In combination with new powerful and flexible analytical methods, population demographic inference from genomic data has revealed surprising, dynamic, and conservation-relevant histories. This chapter discusses recent advancements in demographic inference made possible by genome sequence and new analytical tools. As the theory and models of demographic inference have matured, and data sets have grown, likewise has the recognition of limitations and confounding effects. We caution that the increasing sophistication of methods should not override the critical evaluation of the researcher. Demographic inferences with genomic data offer powerful windows into the past but we encourage users to recognize inherent limitations of model assumptions, use simulations to identify potential biases, and include complementary and supporting analyses.

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Authors and Affiliations

  1. Laboratoire Evolution and Diversité Biologique, UMR 5174, CNRS/Université Toulouse III Paul Sabatier, 118, route de Narbonne, Toulouse, Cedex 9, 31062, France

    Jordi Salmona

  2. Université de Toulouse, UMR 5174 EDB, Toulouse, France

    Jordi Salmona

  3. Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, Copenhagen N, 2200, Denmark

    Rasmus Heller

  4. Department of Ecology and Genetics, Uppsala University, Uppsala, 75 236, Sweden

    Martin Lascoux

  5. Forensic Science and Environmental and Life Sciences, Trent University, Peterborough, ON, Canada, K9J 7B8

    Aaron Shafer

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  1. Jordi Salmona
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  2. Rasmus Heller
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  3. Martin Lascoux
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  4. Aaron Shafer
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Correspondence to Aaron Shafer .

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Editors and Affiliations

  1. Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB, Canada

    Om P. Rajora

Glossary

Approximate Bayesian computation (ABC)

compares summary statistics from observed and simulated data to make demographic and statistical inferences. ABC does not rely on computing a likelihood-function.

Bottleneck

a massive and temporary reduction in (effective) population size that results in an associated reduction of genetic diversity.

Genetic drift

changes in the frequency of alleles due to random mating (and allele segregation in diploids). Changes are more pronounced in small populations.

Coalescent theory

mathematical model governing the expected distribution of coalescence times back to a common ancestor in a population sample.

Diffusion approximation

approximation of the Wright-Fisher (WF) model that leads to a continuous time stochastic process that is easier to study mathematically. It is used to derive useful formulas such as the expected time to fixation of a mutation.

Divergence time (T)

estimated divergence time between two populations measured as the number of generations, typically divided by 2N e.

Effective population size (N e)

the size of an idealized (Wright-Fisher) population with the same amount of genetic drift as the given real population. In most organisms, effective size is less than census size because of factors such as overlapping generations, reproductive inequality, and sex bias.

Genealogy

the ancestral relationship, for a particular segment of the genome, among sampled chromosomes. This takes the form of a branching tree for non-recombining data, but becomes a tangled graph (the “ancestral recombination graph”) with recombination.

Generation time

is the average interval between identical life history stages across successive generations. Generation time is often expressed in years.

Migration (M)

is the average number of migrants entering each population per generation defined as 4N e m where m is the proportion of individuals per generation in each population that are immigrants.

Recombination

the process of exchanging genetic material between homologous chromosomes during meiosis resulting in new combinations of alleles in the resulting gametes.

Rho (ρ)

is the population-scaled recombination rate defined as 4N e r in diploid organisms.

Panmictic population

a population in which all pairs of individuals are equally likely to mate.

Site frequency spectrum (SFS)

also called the allele frequency spectrum, is the distribution of the allele frequencies of a given set of loci in a sample, and is often visualized as a histogram.

Tajima’s D

a summary statistic that compares two estimators of the population-scaled mutation rate Θ to detect departures from the standard coalescent model. Departures can reflect demography or selection.

Theta (Θ)

is the population-scaled mutation rate equal to 4N e μ in diploid organisms. It is the product of the N e and mutation rate μ and measures the capacity of a population to maintain genetic variability. Among organisms of similar μ, it functions as a measure of relative effective population size.

Wright-Fisher model

is a discrete-time model of stochastic reproduction (see also genetic drift) that assumes a population of size N, random mating, and non-overlapping generations.

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Salmona, J., Heller, R., Lascoux, M., Shafer, A. (2017). Inferring Demographic History Using Genomic Data. In: Rajora, O. (eds) Population Genomics. Population Genomics. Springer, Cham. https://doi.org/10.1007/13836_2017_1

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