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  • Review Article
  • Published:

Genetics in geographically structured populations: defining, estimating and interpreting FST

Nature Reviews Genetics volume 10, pages 639–650 (2009)Cite this article

Key Points

  • Wright's F-statistics, and especially FST, provide important insights into the evolutionary processes that influence the structure of genetic variation within and among populations, and they are among the most widely used descriptive statistics in population and evolutionary genetics.

  • FST is a property of the distribution of allele frequencies among populations. It reflects the joint effects of drift, migration, mutation and selection on the distribution of genetic variation among populations.

  • FST has a central role in population and evolutionary genetics and has wide applications in fields from disease association mapping to forensic science.

  • FST can be used to describe the distribution of genetic variation among any set of samples, but it is most usefully applied when the samples represent discrete units rather than arbitrary divisions along a continuous distribution.

  • Statistics related to FST can be useful for haplotype or microsatellite data if an appropriate measure of evolutionary distance among alleles is available.

  • Comparison of an estimate of FST from marker data with an estimate of QST from continuously varying trait data can be used to detect selection, but the estimate of FST may depend on the choice of marker and the estimate of QST may differ from neutral expectations if there is a non-additive component of genetic variance.

  • Although the simple relationship between FST and migration rates in Wright's island model makes it tempting to infer migration rates from FST, caution is needed if such an approach is to be used.

  • If estimates of FST from many loci are available, it may be possible to identify certain loci as 'outliers' that may have been subject to different patterns of selection or to different demographic processes.

  • Case–control studies for association-mapping studies must account for the possibility that population substructure accounts for an observed association between a marker and a disease. The genomic control method uses background estimates of FST to control for such substructure.

  • In forensic applications, the probabilities of obtaining a match are sometimes calculated for subpopulations that lack specific allele frequency data. A θ correction, in which θ is FST, is used to calculate the probability of a match using allele frequency information from a broader population that the subpopulation is part of.

  • The massive amount of data that is being generated by population genomics projects can be understood fundamentally as allelic variation at individual loci. We therefore expect F-statistics to be at least as useful in understanding these data sets as they have been in population and evolutionary genetics for most of the last century.

Abstract

Wright's F-statistics, and especially FST, provide important insights into the evolutionary processes that influence the structure of genetic variation within and among populations, and they are among the most widely used descriptive statistics in population and evolutionary genetics. Estimates of FST can identify regions of the genome that have been the target of selection, and comparisons of FST from different parts of the genome can provide insights into the demographic history of populations. For these reasons and others, FST has a central role in population and evolutionary genetics and has wide applications in fields that range from disease association mapping to forensic science. This Review clarifies how FST is defined, how it should be estimated, how it is related to similar statistics and how estimates of FST should be interpreted.

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Figure 1: Locus-specific estimates of FST on human chromosome 7.

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Acknowledgements

We thank R. Prunier and K. Theiss for their helpful comments on earlier versions of this Review. The work in the laboratories of the authors was supported in part by grants from the US National Institutes of Health (1 R01 GM 068449-01A1 to K.E.H; 1 R01 GM 075091 to B.S.W).

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Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, U-3043, University of Connecticut, Storrs, 06269-3043, Connecticut, USA

    Kent E. Holsinger

  2. Department of Biostatistics, University of Washington, Box 357232, Seattle, 98195, Washington, USA

    Bruce S. Weir

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FURTHER INFORMATION

Kent E. Holsinger's homepage

1,000 Genomes project

ABC4F (approximate Bayesian computation for F-statistics)

Arlequin (an integrated software application for population genetics data analysis)

BayeScan (BAYEsian genome SCAN for outliers)

Bayesian population genetic data analysis

GDA (Genetic Data Analysis)

GenAlEx (integrated software for analysis of genetic data with an interface to Excel)

Genepop

GESTE (GEnetic STructure inference based on genetic and Environmental data)

Hickory (software for the analysis of geographic structure in genetic data)

Hierfstat (Weir & Cockerham F-statistics for any number of levels in a hierarchy)

International HapMap Project

Nature Reviews Genetics series on Fundamental Concepts in Genetics

The genetic structure of populations

The genetic structure of populations: a Bayesian approach

The Wahlund effect and Wright's F-statistics

Glossary

Genetic drift

The random fluctuations in allele frequencies over time that are due to chance alone.

Short tandem repeat loci

Loci consisting of short sequences (2–6 nucleotides) that are repeated multiple times. Alleles at short tandem repeat loci differ from one another in their number of repeats.

Variance

A measure of the amount of variation around a mean value.

Diversifying selection

Selection in which different alleles are favoured in different populations. It is often a consequence of local adaptation (in which genotypes from different populations have higher fitness in their home environments owing to historical natural selection).

Hardy–Weinberg proportions

When the frequency of each diploid genotype at a locus equals that expected from the random union of alleles. That is, the genotypes AA, Aa and aa will be at frequencies p2, 2pq and q2, respectively.

Heterozygote advantage

A pattern of natural selection in which heterozygotes are more likely to survive than homozygotes.

Likelihood

A mathematical function that describes the relationship between the unknown parameters of a statistical distribution — for example, the mean and variance of the allele frequency distribution among populations or the allele frequency in a particular population — and the data. It is directly proportional to the probability of the data given the unknown parameters.

Prior distribution

A statistical distribution used in Bayesian analysis to describe the probability that parameters take on a particular value before examining any data. It expresses the level of uncertainty about those parameters before the data have been analysed.

Posterior distribution

A statistical distribution used in Bayesian analysis to describe the probability that parameters take a particular value after the data have been analysed. It reflects both the likelihood of the data given particular parameters and the prior probability that parameters take particular values.

Markov chain Monte Carlo methods

Methods that implement a computational technique that is widely used for approximating complex integrals and other functions. In this context, these methods are used to approximate the posterior distribution of a Bayesian model.

Multinomial distribution

A statistical distribution that describes the probability of obtaining a sample with a specified number of objects in each of several categories. The probability is determined by the total sample size and the probability of drawing an object from each category. The binomial distribution is a special case of the multinomial distribution in which there are two categories.

Additive genetic variance

The part of the total genetic variation that is due to the main (or additive) effects of alleles on a phenotype. The additive variance determines the degree of resemblance between relatives and therefore the response to selection.

Stabilizing selection

Selection in which either the same allele or the same genotype is favoured in different populations.

Effective population size

Formulated by Wright in 1931, the effective population size reflects the size of an idealized population that would experience drift in the same way as the actual (census) population. The effective population size can be lower than the census population size owing to various factors, including a history of population bottlenecks and reduced recombination.

Coalescent-based approaches

Approaches that use statistical properties of the genealogical relationship among alleles under particular demographic and mutational models to make inferences about the effective size of populations and about rates of mutation and migration.

Conditional autoregressive scheme

A statistical approach developed for analysis of data in which a random effect is associated with the spatial location of each observation. The magnitude of the random effect is determined by a weighted average of the random effects of nearby positions. In most applications, the weights of the averages are inversely related to the spatial distance between two sample points.

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Holsinger, K., Weir, B. Genetics in geographically structured populations: defining, estimating and interpreting FST. Nat Rev Genet 10, 639–650 (2009). https://doi.org/10.1038/nrg2611

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