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F-statistics (FST)

Also known as: FST, Wright's F-statistics, Population differentiation index

OriginatorSewall WrightYear1951Sources3Related methods16

F-statistics are a family of measures developed by Sewall Wright to quantify population genetic structure and the degree of genetic differentiation between populations. FST, the most widely used F-statistic, measures the proportion of total genetic variation attributable to differences between populations versus within populations. FST ranges from zero (no differentiation) to one (complete differentiation). These statistics have become fundamental tools for understanding population structure, detecting population admixture, and analyzing the evolutionary forces shaping genetic variation.

Key highlights

  • Simple, interpretable measure of population differentiation
  • Computationally efficient and widely implemented
  • Accounts for both allele frequency differences and polymorphism levels
  • Can identify loci under selection by detecting exceptionally high FST
  • Works across diverse genetic data types (allozymes, SNPs, microsatellites)

Intuition

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How it works

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When to use it

FST is applicable whenever you have genetic data from two or more populations and wish to quantify genetic differentiation. Use FST to assess population structure, test for population subdivision, detect gene flow, and identify loci under selection (those with unusually high FST). It is particularly useful as part of population genetic surveys and genome-wide association study quality control. Avoid interpreting FST from single loci as evidence of selection without accounting for genome-wide variation.

Strengths & limitations

Strengths
  • Simple, interpretable measure of population differentiation
  • Computationally efficient and widely implemented
  • Accounts for both allele frequency differences and polymorphism levels
  • Can identify loci under selection by detecting exceptionally high FST
  • Works across diverse genetic data types (allozymes, SNPs, microsatellites)
Limitations
  • FST values depend on the level of within-population polymorphism; very polymorphic loci tend to have lower FST
  • Different estimators produce different values; assumptions about sampling must be stated
  • Low FST does not always indicate high gene flow; similar allele frequencies can arise from shared ancestry
  • Interpretation of FST varies by organism and system; standards differ across taxa
  • May not capture population structure well when many closely related populations exist

Common pitfalls

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Applications

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Frequently asked

What is the difference between FST and other F-statistics?

FST measures population differentiation. FIS measures inbreeding within populations. FIT measures overall inbreeding including population substructure. The relationship is FIT = FIS + FST(1 - FIS). FST is most commonly used for studying population structure.

Why do some loci have much higher FST than others?

Loci with exceptionally high FST may be under localized selection, where different populations have been selected for different alleles. Alternatively, high FST can result from drift in small, isolated populations or from demographic history creating founder effects.

Can FST be calculated from whole-genome sequence data?

Yes, FST can be calculated from any genetic markers, including SNPs and genome-wide sequence data. Large-scale FST surveys across the genome have become routine, identifying selection signatures and quantifying population structure at unprecedented resolution.

How many markers are needed for a reliable FST estimate?

Hundreds to thousands of independent markers improve reliability. With fewer markers, FST estimates have larger sampling variance. Genome-wide studies typically use tens of thousands of SNPs, enabling precise FST estimation even for fine-scale population comparisons.

Sources

  1. 1.
    Wright, S. (1951). The genetical structure of populations. Annals of Eugenics, 15(4), 323–354.
    DOI 10.1111/j.1469-1809.1949.tb02451.x
  2. 2.
    Weir, B. S., & Cockerham, C. C. (1984). Estimating F-statistics for the analysis of population structure. Evolution, 38(6), 1358–1370.
    DOI 10.1111/j.1558-5646.1984.tb05657.x
  3. 3.
    Hudson, R. R., Boos, D. D., & Kaplan, N. L. (1992). A statistical test for detecting geographic subdivision in nucleotide sequences. Molecular Biology and Evolution, 9(3), 405–418.
    https://scholar.google.com/scholar?q=A+statistical+test+for+detecting+geographic+subdivision+in+nucleotide+sequences+Hudson

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ScholarGate. (2026, June 3). F-statistics (FST). ScholarGate. https://scholargate.app/genetics/f-statistics

F-statistics (FST) | ScholarGate